Simulation issues for future wireless modems - IEEE...

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Simulation Issues for Future Wireless Modems Mobile communication systems present several design challenges that stem from the mobility of users throughout the system and the time-varying multipath channel and interference. To address these challenges, future wireless modems will rely on performance enhancing techniques such as adaptive antennas and real-time signal processing for capacity improvement.

Brian D. Woerner, Jeffrey H. Reed, and Theodore 5. Rappaport

BRIAN D. WOERNER is an assistant professor with !he Bradley Department of Elec- trical Engineering at Virginia Tech and a member of the

Mobile and Portable Radio Research Group (MPRG).

JEFFREY H. REED is a member of MPRG at Krginra Tech.

THEODORE S. RAPPA- PORT i.s an associate prof& sor at ViTiniu Tech and director of MPRG.

imulation tools that enable researchers and designers to accurately predict the performance of wireless systems under a wide range of conditions will become increasingly important as personal communications and wire-

less data services evolve. Mobile communications systems present several design challenges that stem from the mobility of users throughout the system and the time-varying multipath channel and interference. To address these challenges, future wireless modems will rely on performance enhancing techniques such as adaptive antennas and real-t ime signal processing for capacity improvement. In this article, we identify key simulation issues for wireless systems. We describe promising techniques for improving the capacity of future wireless modems, and discuss simulation strategies and research issues for evaluating these new techniques.

The ability to accurately design and predict the performance of wireless communications systems is becoming increasingly important as personal communications services proliferate throughout the world. Designers of such systems need to consider both link-level and system-wide performance issues. The desired link-level performance of a wireless system impacts the choice of data transmission format, modulation and coding technique, and dictates the requirements of digital signal processing (DSP) techniques, such as equalization and voice coding. The overall system performance dependson the interaction of the many mobile users within a system, and it influences the choice of multiple access scheme, power control technique, and the physical layout of the entire system.

To illustrate the importance of simulation at link and system levels, consider the development of the U.S. digital cellular (USDC) telephone standard in the late '80s [l]. The USDC standards body included engineers from more than a dozen cellular manufacturers and made heavy use of simulation to select system parameters. One goal

of this standards body was to create a digital cellular phone standard suitable for seamless integration with the analog Advanced Mobile Phone System (AMPS) standard, without degrading speech quality. A second goal was to increase the number of users by a factor of three and eventually by a factor of six, while occupying the same 30-kHz AMPS channel. The USDC phones in operation rely on a wide range of proprietary implementations for voice coding and equalization, with widely varying degrees of success. Manufacturers have relied on mobile communications system simulations to develop their specific implementations before committing to hardware.

There are a number of specific metrics that are used to evaluate the performance of mobile communications systems. Linkmetricsprovide an objec- tive measure of performance for a particular user within a system and are directly dependent on the radio channel characteristics, as well as the specific transmitter and receiver implementations. Such metrics include average and instantaneous bit error rate (BER), average frame error rate (FER), and outage probability. In both the USDC standard and the U.S. cellular Code Division Multiple Access (CDMA) transmission standard IS-95 [2], an outage describes the condition when a received frame contains more than a specific number of bit errors. The instantaneous bit error patternis important for evaluating the performance ofvoice coding algorithms and error correction techniques. In packet radio systems, the average and worst case transmission delays are important link metrics.

While link-level parameters describe the performance of a particular user, system-level performance characteristics determine the capacity of awirelesssystem. Typically, capacity forvoice systems is defined as the number of simultaneous users that may be supported without exceeding a specified average or maximum error rate. For packet data systems, capacity is measured by the data rate that can be successfully supported within a specified delay.

42 0163-6804/94!$04.00 1994 0 IEEE IEEE Communications Magazine July 1994

All of these metrics are affected by the multipath and fading conditions within the channel, the cochannel and adjacent channel interference levels, the modulation and coding, and the receiver implementation. To properly simulate a mobile radio communications system, accurate models must be used for all portions of the system. The complexity and time-varying nature of mobile radio channels and systems often cannot be fullyrep- resented by analytic techniques. Thus, the design- er must rely on computer simulation as a tool to evaluate the performance of alternative system designs.

Accurate simulation of wireless systems requires consideration of several key issues. One issue is the choice of performance metric, as discussed above. A second major aspect of simulating wireless communications systems is accurate modeling of the time-varying channel which char- acterizes most wireless systems. The modeling and simulation of thewireless channel isconsidered in the next section of this article. A final crucial facet of wireless communications system simulation is the efficient simulation of the signal processing operations in the transmitter and receiver. The third section considers link level simulation of wireless transceivers. Because of the complexity of modern wireless systems and channel models, straightforward Monte Carlo simulation of wireless systems may be inappropriate, particularly for today’s wideband systems. Several techniques that may be employed in order to improve simulation speed are also discussed. The final section presentstwo examplesillustrating theuse of simulation for evaluating advanced techniques for improving the spectral efficiency of wireless systems: steerable antenna arrays and DSP-based methods for interference rejection. Simulation issues relevant to these techniques for designing future wireless modems are also discussed .

Wireless Channel Modeling he goal of channel simulation is to accurately T represent a signal environment for the desired

frequency band and geographic location. There are four approaches to the channel modeling problem: 1) to use classical analytically-based channel models, 2) to use statistical channel models based on experimental observations, 3) to use experimentally measured channel impulse responses, and 4) to create a deterministic channel model based on knowing the geometry of the environment.

Selection of an appropriate channel model poses a challenging problem to the wireless system researcher. Wireless transmissions undergo multipath-induced fading as the radiated energy interacts with objects within the channel. Radio frequency (RF) signals travel at a speed of 300m/ps, or about 1 ftins, and are subject to reflection, scattering, and diffraction within the channel. In outdoor environments, buildings tend to shadow mobile users at street level, while mountains scat- ter energy throughout a coverage region of several tens of kilometers, thereby inducing time delays of several microseconds. Within a building, hallways channel RF energy with less loss than in free space propagation. Walls, metalized tint- ed windows, and office cubicles can attenuate sig-

nals while inducing multiple reflections that arrive hundreds of nanoseconds after the first signal component reaches the receiver.

For link level simulations, the small scale fading effects due to Doppler fading, impulsive noise, and the short term variation of the multipath channel impulse response must be simulated to determine realistic bit error patterns and transient modem behavior. Inorder to study system level performance issues such as multiple-access capacity, trunking efficiency, and capacity enhancement from adaptive antennas, it is necessarytomodel the signal levels from numerous transmitters throughout a wide coverage area.

General Multipath Channel Model Radio channels distort the transmitted signal with amplitude attenuation and fading, t ime delay, and phase shifts. In its most general form, the RF channel may be regarded as a time-varying linear filter, with baseband complex envelope impulse response h(t,t) given by

- The goal of channel simulation is to accurately represent a signal environment for the desired

where L ( t ) is the number of arriving multipath components,A,(t) is the magnitude of the Ith arriving multipath component, q ( t ) is the relative multipath delay of the lth arriving component compared with the first arriving component that arrives at t = 0, and @ ( t ) is the relative phase of the Ith arriving component [3]. The variable z represents the excess time delay of the multipath in the channel at a specific time t . Each of the channel parameters will vary as a function of time t , depending on the physical movement of the transmitter, receiver, or objects in the channel. This explicitly demonstrates the time-varying nature of the impulse response structure.

Given a channel impulse response h(t , t) , the received signal r ( t ) may be related to the transmitted signal s ( t ) by the time-varying convolution operation

wheren(t) represents the noise and interference present in the system. Accurate link level simulation of wideband communications systems requires the use of detailed models for h(t,z). In many sit- uations, however, it is possible to obtain useful results with less detailed models. Below, we consider channel models in order of increasing complexity, beginning with simple path loss models, and pro- ceeding to flat fading, frequency-selective fading, two-ray models, and concludingwith measurement- based statistical models for the mobile channel impulse response.

Path Loss The path loss describes the large-scale attenuation of signal strength over distance. Path loss generally increases logarithmically with increasing distance between the transmitter and receiver. Measurements in many different environments show that a general model for average path loss may be described by

band and geographic location.

IEEE Communications Magazine July 1994 43

- Fading may be classijied as either flat or frequency- selective, depending on the impulse response of the mobile channel and bandwidth of the transmitted signal.

Path Loss (d) = k - k, I; (3)

where do is a reference distance that is close to the transmitting antenna but in the far-field, and the path loss exponent n is a function of the antenna height h, and environment as shown in Table 1 . Assuming R F power radiates perfectly in a sphere from the antenna, the surface area of that sphere is proportional to the square of the radius. As aresult, in free-space the received power will decay in proportion to the square of the distance between the transmitter and receiver corresponding to avalue of n = 2 in Eq . (3). In practice, n can vary from slightly less than 2 for hallways within buildings to larger than 5 for dense urban environments and hard partitioned office buildings [4-61. During the past decade, the intense interest in wireless communications has motivated literally hundreds of propagation studies for both outdoor and indoor mobile channels 14, 7, 8, 18-20]. This body of work has inspired improvements and alternative modeling techniques built on the basic path loss model given in Eq. (3). New path loss and coverage prediction techniques use terrain maps and building blueprints to accurately predict coverage in specific locations [9, 101.

Fading Multipath channels cause signals to experience fading due to the constructive and destructive interference between replicas of the transmitted signal. Because the wavelength of RFsignals above 900 MHz is less than 0.33 meters, small movements can produce phase shifts of individual multipath components that result in large variations in their coherent sum. Thus, fading describes small scale variations that occur over small distances (on the order of meters) and small time intervals (on the orderofsecondsormilliseconds). Asaresult therate offading dependsupon the rateof motion.Thisrate of motion is frequently characterized by the Doppler spread, which is the maximum frequency shift in the received signal due to the relative motion of the transmitter, receiver, and objects in the channel. Amplitude fading models are often grouped into two classes. Fast fading implies that the channel is changing rapidly compared to one symbol interval. Physically, this corresponds to a Doppler spread that is larger than the baseband bandwidth of the transmitted symbol. Slow fading implies that the channel does not significantly change during one symbol interval. Physically, this corresponds to a Doppler spread that is much smaller than the baseband bandwidth ofthe transmittedsymbol [ 111.

FIatFading-fading may be classified aseitherflat orfrequency-selective, depending on the impulse response of the mobile channel and bandwidth of the transmitted signal. Equation (1) shows how the multipath channel delays the signal. If the multipath delay spread is significantly less than the symbol duration, then the time dispersion of the channel has a negligible effect on the shape of the transmitted signal, and only the gain of the chan- nelvaries with time. In the frequency domain, all frequency components of the transmitted signal are affected in the same fashion, rewlting i n frequen-

W Table 1. Typical values ofpath loss exponent in mobile radio channels.

cy-nonselective or flat fading. The amplitudes of flat fading signals are often modeled by one of several common probability distributions. Signals received byseverelyshadowed mobileswhichconsistofmany multipath components are often modeled by a Rayleigh distribution. Less severe shadowing, in which both diffuse and specular components are present, is better modeled by a Ricean distribution. Recent work has shown that individual multipath components do not fade according to a Rayleigh distribution, but rather a log-normal or Ricean distribution 1181.

Frequency Selective Fading - The time varying- impulse response function h(t,z) can be viewed as the channel response at a timet to an impulse applied to the channel z seconds in the past. An alternative channel description is given by the Doppler spread and scattering functions. The output Doppler spread function H(f ,v ) relates the output spectrum to the input spectrum of a channel and can be viewed as representing the output spectrum at some frequency f due to an amplitude change and Doppler frequency shift of v Hz 112-141. These two functions completely characterize a time- varying multipath channel and can be related to the scattering function S(z,v) that describes the channel in terms of both time-delay and Doppler shift. The inverse Fourier transform of S(z,v) with respect tov is simplyh(t,z) and the Fourier transform of S(z,v) with respect to z is H(f,v). Often these system functions are implemented in simulation as a tapped delay line (TDL) in the time domain, or as complex exponential weights in the frequency domain [15]. By convolving the baseband complex envelope representation of an RF signal with the impulse response function, the baseband complex envelope of the output of the channel is readily obtained in the time domain. For interference analysis and nonlinear modulation, frequency domain simulations often provide computational savings over time-domain techniques.

By discretizing the impulse response function of Eq. (l), itbecomespossible to use a timevarying tapped delay channel model, which is generic in form and whichmayembodya large number ofgeneraland specific models. Depending on the system being simu-

lated, the tapped delay line channel model may be used in a static or dynamic manner. For example, in high data rate indoor wireless systems operating in a slow fading environment, the channel appears static for hundreds or thousands of consecutive bifs, due to the fact that human motion inside buildings produces Doppler shifts of less than 10 Hz. Simulations for such systems typically rely on a quasi-static approach, whereby the channel is considered static for a specific period of time, and a specific conditional performance measure (e.g., BER) is found either by simulationor analysisfor the specifiedthannel state. Then, a new simulation is conducted for a completely different channel state, and a new conditional performance measure is found, and so on. The overall system performance is then evaluated as the average of all conditional performance measures. This Monte Carlo technique is described in Chap- ter 7 of [15] and lends itself well to the performance evaluation of modulation and coding techniques, antenna diversity techniques, and capacity analysis for mobile radio systems. Many researcherschoose to use avery simple tapped delay linemodel, oftenconsistingofonlytwoorthreemul- tipath components, in order to determine order of magnitude estimates of system performance in a short amount of time.

On the other hand, accurate simulation of fast fading for rapidly moving transceivers necessitates the modeling of slight variations in the channel over time and frequency. This is especially true for systems employing dynamic algorithms such as adaptive equalizers or interference rejection devices, since the correlation over small time and frequency shifts is often critical for determining algorithm performance. Here, channel models with sufficient time resolution are required, since a simulation event needs to model channel variations within a fraction of a bit duration. As a rule of thumb, transient simulations rely on the dis- cretization of the channel and the bit level signals to at least an order of magnitude resolution greater than the bit level signals themselves. For such transient simulation runs, the Monte Carlo technique is employed, where the overall performance is determined as the average of specific performance metrics from many transient events. Often, the distribution of the performance metrics is as important as the average value of the metric, with a small deviation about the average value being the indication of the best possible algorithm.

The dispersive characteristics of the channel play a crucial role in determining whether the flat fading models described above are appropriate. Many parameters have been defined which attempt to measure the multipath delay spread, but the most widely used of these is RMS delay spread, which describes the time dispersion of a mobile channel as measured over a local average. The reciprocal of the RMS delay spread is a rough upper limit on the symbol rate. The coherence bandwidth is the maximum frequency difference for which received signal amplitudes are strongly correlated. The coherence bandwidth is inversely proportional to the RMS delay spread of the system. Based on the coherence bandwidth of the channel, a flat or frequency selective fading model may be appropriate. Frequency selective fading should be chosen when the RF signal bandwidth

is larger than the coherence bandwidth of the channel. To properly model frequency selective fading, knowledge of the time-varying nature of the impulse response h(t,z) is required.

An often used model for frequency selective fading is a two-ray channel model, which represents the time-dispersive effects of the channel with only two multipath components (L( t ) = 2 ) . Ampli- tudes of the two multipath components are typically chosen to have Rayleigh or log-normal distributions. The relative power of the first component with respect t o the second component is referred to as the C/Dratioofthechannelin[16].Thisratioisanimpor- tant parameter since it impacts the RMS delay spread of the channel model. The relative timedelayischo- sen to reflect the worst case time dispersion of the channel. Phases are updated in accordance with the appropriate Doppler spread determined by the velocity of the mobile. The two ray model was used heavily in simulations for deriving the USDC standard. Subsequent field trials, however , have shown that the two-ray model led to optimistic BER results in many cases, forcing fur- ther development workon the USDC equalizer and illustrating the need for realistic frequency selective mobile channel models.

Statistical Channel Impulse Simulation Although simplified channel models are appropriate in many instances, accurate link level simulation of wideband systems in wireless environments requires realistic multipath channel models, using Eq. (1). Over the last decade, numerous researchers have undertaken detailed statistical channel impulse response measurement campaigns for both indoor and outdoor environments [3,17,18,19,20]. Those measurements may be used directly in simulation or, more commonly, the measurement statistics are embedded intosoftware channel simulators that are developed from the empirical database.

Two issues need to be considered when simulating channel impulse responses based on channel measurement data. The first is that unlike the impulse response model of Eq. (l), actual channel measurements have finite resolution. This is not an important issue provided that the bandwidth of the simulated communications system is significantly less than the bandwidth of the measurements. For measurement systems with reso- lutions of better than Ions, communications systems with bandwidths up to 20 MHz can be simulated accurately. Secondly, in order to simulate a wireless channel, continuous time delays must be made discrete in time. This is conveniently implemented using a complex envelope representation for the channel impulse response, eliminating the dependency of a specific carrier frequency [21]. Work in (221 details how the time-varying impulse response of Eq. (1) can be used with the complex envelope representation of any digital modulation in order to simulate link level performance.

Link Level Modeling nce a channel model is established, link level 0 simulation of the communications system is pos-

sible. Factorsinvolved in linklevel simulation include signal generation, channel distortion (including nonlinearities imposed by the communications system), carrier recovery and tracking, symbol synchro-

- An often used model for frequency selective fading is a two-ray channel model, which represents the time- dispersive effects of the channel with only two multipath components.


, .

t

Figure 1. Example of baseband signal source for BERSIM. Binary image before corruption by mobile channel impairments.

nization, equalization, and performance assessment. Furthermore, each of the various multiple access techniques presents particular simulation challenges.

Overview of a Simulation Approach Binary source data for a simulation may be gen- erated randomly o r may be imported directly from actual speech or data files. When the data has been compressed through voice or image coding, it is imperative that the compression procedure be included in the simulation. Perceptual quality can be diminished by burst error patterns, so implementation of speech and image coding is necessary to fully evaluate the quality of the received data. The processed data is used to produce a simulated modulated signal. Although a communications system operates at R F frequencies, simulation is greatly simplified when it is carried out using a baseband complex envelope representation [15, 221. Any bandpass signal s ( t ) may be written in complex envelope form:

s(t) = Re{g(t)ejW4, (4)

where g(t) is a complex baseband signal and o, is the carrier frequency. All frequency-dependent aspects of the simulation may be incorporated through selection of an appropriate channel impulse response. The sampling theorem implies that g(t) may be completely represented by samples taken at rate W complex samplesisecond where W is the bandwidth of the signal s(t). As discussed above, accurate channel modeling may require oversampling the signalg(t). For practical pulse shapes, alias- ing may also be reduced by oversampling.

As described in the previous section, the channel may be modeled by a time-varying linear impulse response h(t , t) , additive noise n( t ) . and by cochan- ne1 interference c ( t ) . As a result, the received signal r ( t ) may be described using Eq. (2) . T h e channel impulse response h(t,z) may assume any of the forms described in Section I1 including the TDL. Channel impulse response models that can be used can incorporate a standard two-ray model, statistical channel impulse response data, and actual channel impulse responses obtainedfrom

measurements. New values are periodically sub- stituted into the TDL to reflect the time-varying nature of the channel impulse response h(t,z). The frequency of substitution corresponds to the speed of the mobile unit. Care must be taken to maintain phase continuity when the substitution is performed. The complex envelopey(t) of the received signal r ( t ) may be expressed as:

where * denotes the convolution operation andcb(t) and nb(t) are baseband equivalents.

Thermal noise effects may be simulated by adding a Gaussian random variable to each sample of the simulated waveform and by passing this value through the receiver filter. In order to maintain a constant signal-to-noise density ratio, the variance of each noise sample must increase in proportion t o the number of samples p e r bit [23]. In an indoor environment, wireless communications systems will be subject to impulsive noise from avari- ety of manmade sources including elevators, copying machines, and microwave ovens. Impulsive noise may also be modeled as additive with random duration and intensity using appropriate statistical distributions. These distributions may be derived from actual indoor noise measurements [24].

Nonlinearities can also be present in the channel due to the amplifier, limiter, or AGC [15, 25- 271. T h e two most common ways to model a nonlinearity are to use Volterra series modeling or AMiPM and AMiAM lookup tables. Once the nonlinear model is constructed, a one-tone test can be used to measure the frequency response and harmonic content and a two-tone test can be used to quantify the intermodulation distortion. Understanding the nature of the nonlinearities is important since nonlinearities constrain the dynamic range of a communications system.

Carrier recovery, equalization, and baud timing are important to the demodulation process, and all involve both acquisition and tracking. The performance of these operations in both the acquisition and tracking mode is an important parameter of the system and it is useful to know the conditions underwhich these processes fail. The evaluation of these processes is complicated when the processes are integrated together, i.e., joint carrier recover, equalization, and baud timing. Simulation is the only viable alternative for exam- ining such systems. Other synchronization parameters may include the frame, word, code, and packet structure of the signal. However, often there are symbols that are multiplexed into the data stream to help synchronize these parameters, and thus the impact of this synchronization process is usually not a major factor. A number of different timing recovery models may be implemented at the receiver to model the effects of receiver timingjitter by sampling the matched filter receiver output at a randomly distributed offset from the optimal sampling time [28]. This can require high oversampling of the signal which results in an increased computational burden.

Multiple Access Techniques Multiple access communications can be classified as e i ther frequency division multiple access

46 IEEE Communications Magazine July 1994

(FDMA), time-division multiple access (FDMA), or code division multiple access (CDMA). Each of these techniques presents its own advantages and disadvantages for operation, and each is susceptible to different system imperfections. For instance, in FDMA the intermodulation distortion is important since harmonic distortion from one channel can interfere with another channel. Likewise, frequency drift of oscillators can cause adjacent channel interference. Very tight filter specifications designed to prevent adjacent channel interference can lead to distortion. TDMA suffers from the effects of nonlinearities and timing error and needs rapid and robust acquisition. Equalization becomes very difficult with TDMA systems because the gaps in time can lead to very different channel conditions from burst to burst . CDMA systems can, if not implemented correctly, lead to poor spectral efficiency. AMiPM distortion is also manifested as additional noise. Timing errors lead to reduced performance because of partial cross correlation between user codes. The effect of imperfect power control can also play a major role in limiting capacity of these systems.

Future personal communications systems will operate with high levels of multiple access interference from densely packed cells. As a result, these systems will tend to be interference-limited rather than noise-limited. Interference models can range from simple to complex. Feher has found that for narrowband TDMA systems, cochannel interference from adjacent cells may be modeled in many cases by a Gaussian distribution [ 161.

Simulation of multiple access interference within CDMA systems may be considerably more complex. Although a Gaussian approximation will produce accurate results for systemswith a large number of equal power users [29], this approximation is overly optimistic in systems with low bit error probability or systems in which a near/far problem is present [30]. The Gaussian approximation is based on the application of the central limit theorem, which may not hold for the case of a single dominant interference source or at the extreme tails of the interference probability distribution. As a result , C D M A simulators may need to include full simulation of multiple access interference to assure accurate results. For example, consider the cellular CDMA system proposed in IS-95. Since all signals on the forward channel from base to mobile share a common path, CDMA signals may be combined before convolution with the time-varying impulse response. However, for the reverse channel from mobile to base, each CDMA signal travels over a distinct multipath channel. As aresult, complete simulation of the reverse channel CDMA link may become quite computation- ally intensive.

The quality of a transmission is usually a sub- jectiveevaluation; that is, the important qualitymea- sure is how well a transmitted voice sounds or how well a transmitted picture looks. Simple bit error rate or frame error rate is insufficient for evaluating the impact on perceived quality of a transmission for a complex systems. Link level simulations can generate bit-by-bit error patterns which may be used in subsequent real-time bit error simulation to evaluate the quality of the transmission and thus supplement bit error rate and frame error

W Figure 2 . Example of BERSIM output for input signal shown in Figure 1. Binary image after corruption by mobile channel impairments ( ~ 1 4 DQPSK, velocity = 40 kmlhr, f, = 850 MHz, r = 0.2, E O 0 = 20 dB, CII =30 de, C/D = OdB).

rate for system evaluation. One implementation of this technique, BERSIM, is described in [22], in which a binary one is written to an external file for each bit error encountered and a binary zero is written if a bit is received correctly. This bit error pattern is used to characterize the effects of fading and burst errors on qualitative system performance. Once the off-line simulation is done, real- timesimulation is performed by alteringdatastreams from a source (e.g., fax machine, speech files, or video camera) according to the recorded bit error pattern as the data streams pass through the hardware simulator. The hardware simulator reads the bit error record in real-time from the external file and inserts errors into the data stream. Corrupted data is received at the data sink. In this manner, digital communications in mobile radio channels are efficiently simulated at baseband and subjective evaluation of data links are performed easily in the laboratory. As an example, a test video image (shown in Fig. 1) was transmitted through the hardware simulator [22]. This simulation was for the USDC standard operating at 24.3 k symbols per second, and the video image was represented by binary pixels. The simulated digital cellular radio channel was a frequency-selective two rayfad- ing channel of 6 p RMS delay spread, EbINo of 20 dB, and C/I of 30 dB. The corrupted image received at the data sink is shown in Fig. 2. The bursty nature of bit errors in mobile fading channels is seen clearly seen in the image. Subjective quality evaluation of data links can be performed easily with the BERSIM simulator, and it may be used to evaluate speech and image coding for awide range of channels and systems.

Reduced Computational Complexity Simulation Techniques One significant drawback of link-level simulation techniques is their computational complexity, which is particularly evident in wideband systems. The minimum sample rate for baseband simulationis directly proportional to the bandwidth of the simulated system [31]. Furthermore, since there are numerous


Figure 3. Comparison offrame error rate for importunce wmpling andfull simulation of IS-95 as a filnction of inteference k i d (Eb/No = 10 dB, Sirhirr- ban Environment, f C = 1.92 GHz).

signals comprising a CDMA system, many different channel models would need to be created, trans- lating into a heavy computational burden. Often it is useful to employ a combination of analytic techniques and Monte Carlo simulations. For instance, an analytic error function can be determined, and simulation can be used to identify the parametersof the analytic error function. This error function will be of the form of an expected value of a random variable, typically having the form of a Gaussian pdf. This pdf can be expressed as a series expansion involving the moments of the distribution. Recursive procedures are available for calculating these moments [15].

Multirate filtering techniques can also be employed to greatly reduce the computation. For instance, for a BER measure based on slicing the signal in the middle of the baud, the channel distortion process can be formulated as a multirate or poly-phase filter. Since the only output sample of interest is the center of the baud, the unneces- sary computations can be easily recognized and eliminated. Performing the filtering in the frequency domain can also significantly reduce the number of computations [32 ] .

Alternatively, the expected value can be approximated when the pdf is not known explicitly or when closed form integration is not possible using a technique referred to as the quadrature approximation. This approximation consist of a sum formed from weights wi and abscissasxi which can be found in [15] for a number of pdfs. In the event tha t the distribution is not known, the weights and abscissascan be calculated from the first 2N-1 moments using efficient recursions. Using the quadrature approximation, the expected value of a function g(x> can be expressed as

and for a Gaussian distribution

where px and ox are the mean and variance of the distribution. The accuracy of the approximations depends on N , and this approximation error can be found in [15]. Thus the e r ror ra te , which depends on the expected value of some g ( X ) ( g ( X ) is commonly a variation of the erfc func- t ion) , can be de te rmined by measuring the momentsofasimulated signal, estimating the expect- edvalueof some function using these moments, and then calculating the BER based on this expected value.

One more alternative is the use of importance sampling techniques [ 15,331. A variety of importance sampling techniques have been proposed. However, all such techniques share the common idea that they bias the probability distribution of the received signal so as to produce errors more frequently than they would occur in the unbiased distribution. After simulation is completed, the computed error rate is adjusted according to a known analytic formula. In order to produce valid results, importance sampling must be used to evaluate independent error events. In most CDMA systems of interest, adjacent bit errors are highly dependent because of error correction coding. As a result, importance sampling is most appropriately used to simulate frame error rates rather than bit error rates. Furthermore, importance sampling does not producevalid bit error patterns. However, given these limitations, simulations show that importance sampling can duplicate the frame e r r o r ra te results of full simulation, while decreasing simulation times by a factor of 10 [34, 351. Results for importance sampling and full simulation are compared in Fig. 3 for the case of an IS-95 system operating in a suburban environment with Eb/NO = 10 dB and the number of users ranging from 1 to 20.

Simulation Issues for Future Modems

he rapidly increasing demand for wireless ser- T vices has necessitated the design of spectrally efficient systems that make use of novel signal processing and frequency reuse techniques. One impor tan t trend in modem simulation is the migration toward multiprocessor hardware. The advantage of multiprocessor techniques is that computation rate can be greatly increased. The drawback is that the algorithm being modeled must lend itself to amultiprocessor implementation. Cre- ating and applying channels, DSP algorithms, and performance evaluation mechanisms compatible with amultiprocessor computingplatform are important simulation issues.

Another evolving trend is the re-allocation of new wireless services to existing bands. For instance, the new U.S. PCS band will share the spectrum with existing microwave users. In the cellular band, new digital signal formats will share the spectrumwith existing AMPS users. Both single and

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multichannel interference rejection techniques will become increasingly important as these bands begin to support multiple (potentially) interfering communications systems.

As modems evolve and increase in complexity, so too must the.simulation strategies for modeling these systems. For instance, adaptive arrays appear to be an emerging technique for increasing spectral efficiency. However, the ability to model the performance of such systems lags behind the ability to realize these systems. The simulation issues associated with modeling adaptive antennas and DSP- based interference rejection techniques are discussed below, as examples of the application of simulation techniques to emerging wireless systems.

Antenna Arrays and CDMA Because of the limited availability of spectrum, current wireless systems are under pressure to achieve higher spectral efficiency. CDMA techniques represent one approach to this problem [36]. Future wireless modems will almost certainly employ CDMA in combination with adaptive antenna arrays. Efficient and accurate methods for evaluating the system level performance of these systems using computer simulation are essential because straightforward Monte Carlo simulation requires excessive computations. Here we show one approach to estimating the performance gains provided by adaptive arrays located at the base station.

The reverse link presents the most difficulty in CDMAcellular systems for several reasons. The base station has complete control over the relative power of transmitted signals on the forward link; however, because of different radio propagation paths between each user and the base s ta t ion, the receivedpower from each portable unit within acell must be dynamically controlled to avoid the nearifar problem. Cochannel users from neighboring cells may radiate interference on the reverse link to apar- ticular base station.

Adaptive antennas at the base station and at the portable unit may mitigate some of these problems. With sufficient directionality, adaptive antennas can provide a unique spatial channel for each user that is f ree from interference from other users. Alluserswithinthesystemwould be able to communicate at the same time using the same frequency channel, in effect providing Space Divi- sion Multiple Access (SDMA) [37]. In addition, an adaptive antenna system could track individual multipath components and combine them in an optimal manner to collect all of the available signal energy [38,39]. Although ideal SDMA may be unrealizable at the present time, even modest antenna directionality can serve to reduce the level of CDMAinterference on the reverse channel.

Adaptive antennas can be implemented using intelligent sectorized antenna elements o r by using phased arrays. An intelligent sectorized antenna works by monitoring the signal strength of each user at the base station and then selecting the best receivingsector. For instance,with 60-degree sectorization and a uniform distribution of users throughout the cell, the system detects only one-sixth of the interference compared to an omnidirectional antenna.

Adaptive phased arrays are more flexible and provided greater performance improvements. Adap- tive phased arrays work by combining an appro-

W Figure 4. Example of users distributed throughout a cellular system. Each user within a cell is served by the base station at the center of the cell.

priately weighted sum of received signals from an array of antenna elements. Both fading and noise can be reduced by exploiting the uncorrelated distortion from each antenna. Furthermore, an adaptive array can provide spatial separation of interfering signals and thus can greatly increase the capacity of a single cell. Strong multipath components can be coherently combined by the adaptive phased array to improve signal quality.

The system performance advantages of simple adaptive arrays have been demonstrated through computer simulation [38,40,41]. Consider a CDMA cellular radio system in which the portable unit has an omnidirectional antenna and one where each base station tracks users in the cell with an adaptive array antenna [38]. Figure 4 illustrates a seven-cell system with many users distributed throughout the coverage area. Assume a CDMA system in which all users in all cells share the same carrier frequency and are perfectly power controlledso that each userwithin acellprovides the same power incident to the base station antenna. Out-of-cell mobile transmitters will interfere with the desired base station receiver depending on the particular power controlsettings of adjacent base stations. Simulations can be conducted under the assumption that the users are uniformly distributed over a flat earth and that each user’s spreading code is uncorrelated with the others. The traditional hexagonal cell geometry shown in Fig. 4 is awkward for analysis and simulation. Anoth- e r cell geometry, proposed in [42], provides comparable results in simulations by using the convenient concentric circular geometry shown in Fig. 5. This geometry provides a central circular cell and eight surrounding wedges of equal area in the first ring of adjacent cells. The second ring of adjacent cells contains 16 cells, also of equal area. This geometry is more convenient to simulate. Recursion can be used to evaluate the capac-


I 3 I 0.082 I 1

0.055 - . ____

4 - -.

W Table 2. Path loss exponent n and corresponding p as used in Eq. (6). These values can he determined through simulation.

ity of a cellular network for an arbitrary number of surrounding cochannel cells. By using a path loss model of the form of Eq. (3), Monte Carlo simulation can be used to generate interference levels from a large population.

Liberti [40] has shown through simulation and analysis that the average probability of error for a single channel CDMA cellular system using directive antennas is given by

where Pf, is the average probability of error of a single user, D is the directionality of the adaptive antenna at the base, Nis theCDMAprocessinggain, K is the number of users per cell, and P relates the frequency reuse factor of the system to the propagation path loss exponent n , in Eq. (3) with values found in Table 2 [38]. The Gaussian approximation [29] has been used to couple BER withantenna directionality and path loss. More exact expressions for Pb may also be found when K is small or when the out-of-cell interference is large by using techniques described i n [43]. Equation (8) relates the antenna gain, propagation characteristics, and the number of U W I \ within a CDMA

system to the average performance of each user. Simulations were used to confirm the validity

of Eq. (8), and results are displayed in Fig. 6 for several adaptive sectorized antennas and a phased array antenna. Dramatic capacity improvements occur when directive beam antennas at the base station are used to improve the reverse link. Fig- ure 6 shows that for a 10-3average BER for each user, an omnidirectional antenna will support only 11 0 usersper cell where simple antenna patternswill support between 300 and 350 users for the same RER.

Interference Rejection Interference from cochannel and adjacent channel users is a major source of channel impairment in mobile communications. The availability of high-speed, low-power computing will support sophisticated signal processing techniques for enhancing the performance of future wireless systems, especially for CDMA communications. The designof newwireless deviceswith DSP-based interference rejection capability requires the availability of tools for accurate prediction of performance.

The single channel interference rejection techniques for wireless communications may be grouped into five major categories. Single channel techniques require only one RF front end as opposed to an adaptive array which requires multiple RF front ends. Simulating the performance of these single channel techniques poses some distinct and common problems. The five categories of interference rejection techniques are listed below.

Adaptive notch filters [44]. Adaptive equalizers. Multistage receivers [45-491. Time-dependent filters [50,51]. Neural network filters [52-541. The bursty nature of mobile radio interference

also influences the performance of the interference rejection technique, since all techniques are adaptive in nature and require some response time before optimal performance is obtained. As with most adaptive systems, there is a trade off in quick response time, adaptive algorithm stability, and error after convergence. Understanding the rela- tionship between these parameters is critical for assessing the performance of the interference rejection techniques, and simulation is the principal way to study these tradeoffs.

Another important consideration for all five interference rejection techniques is the dynamic range requirement. In many mobile communications systems, the dynamic range of desired and interfering signals often exceeds 60 dB. Unfortunately, 60 dB of dynamic range requires an AID converter with at least 10 to 12 b. In this case, fixed point arithmetic to implement the DSP interference rejection technique is not always practical and even floating point arithmeticcan be subject tosubstantial numer- ical errors. Thus, for emerging wideband systems, the impact of realistic AID converter resolution for the needed sampling rate and the arithmetic pre- cision must be simulated to assess the practical performance of an interference rejection algorithm.

Adaptive notch filters and adaptive equalization are two traditional techniques used to reject interference for single receiver 1441. Multistage receivers, time-dependent filters, andneural networksare emerging interference rejection techniques.

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50 BEST COPY AVAILABLE IEEE Communications Magazine July 1994

While adaptive notch filtering and most equalization techniques are appropriate for rejecting narrowband interference, they do not provide rejection of broadband interference on a broadband signal, as in the case with CDMA interference [55]. Since the mid-’%, new classes of wideband interference rejection algorithms have emerged. These techniques, which include multistage receivers, time dependent filters, and neural networks, show promise for use in future CDMA systems and are briefly described below along with associated simulation issues.

The key idea underlying multistage reception is to perform simultaneous demodulation ofmany CDMA signals.Thisapproach is particularly applicable at the base station, which must attain synchronization with each user’s spreading code and which has significant processing power available. Verdu [45] has shown the optimum multi-user detector for a CDMA system takes a form similar to the Viterbi algorithm. Unfortunately, this receiver is extremely complex. Ifthere areKusersin thesystemwithspreadingcodes oflengthh’, thentheresultingViterbialgorithmmus1 have 2KN states, making simulation or implementation of this optimal receiver impractical.

The current generation of CDMA systems employ single stage correlation receivers which cor- relate the received signal with a synchronized copy of the desired signal’s spreading code. In a single cell environment, CDMA systems employing simple correlation receivers cannot approach the spectral efficiency of time or frequency division multiple access [56] . Furthermore, correlation receivers are particularly susceptible to the nearifar problem when multiple access signals are received with different signal powers.

Multistage correlation receivers, proposed by Varanasi and Aazhang [46], are a promising sub- optimal approach for reducing interference and increasing channel capacity. At each stage, a bank of single-user receivers demodulates the received signal. After each stage, the estimated signals for all interference sources are subtracted from the received signal, and then demodulation is repeated. This procedure can be repeated for an arbitrary number of stages to obtain an iterative estimate of the interference. Preliminary simulation results for a simple channel model indicate that only a few stages are necessary to achieve most of the potential performance improvement.Thisreceiv- er configuration isalso highlyresistant to thenearifar problem. In Fig. 7, the BERcurves are presented for a CDMA system with seven cochannel users. Although the single stage correlation receiver performs poorly, a two-stage receiver has virtual- ly eliminated the effects of multiple access interference and provides performance which is almost identical to a system with no interference at all. This approach can be adapted to multipath channels [47-491.

Although multistage reception may be possible at the base station, computational demands and required knowledge of other users make this approach impractical for use at a mobile unit. An alternative receiver uses an adaptive time-dependent filter t o reject CDMA and narrowband cochannel interference. Adaptive time-dependent optimal filters exploit the cyclostationary characteristics of communications signals t o achieve reduction in cochannel wideband interference. For instance, spread spectrum signals exhibit a

Figure 6. Average BER in a CDMA system as a function of base station antenna pattem and number of users (N = 511 and n = 4).

Figure 7. BER vs. E@, for CDMA system with one and seven users with single stage detection, and seven users with multistage detection (481.

tremendous amount of spectral redundancy that can be used to enhance the signal-of-interest or t o estimate and remove the interfering spread spectrum signals. Simulations have shown reductions in the average BER of more than an order of magnitude for the case of a 32-user CDMA system using Gold-like codes and 1,2, and 3 dB of power variation [57]. The capacity of a conventional matched filter, shown in Fig. 8a, may be compared with the capacity provided by the time- dependent filter based receiver in Fig. 8b. The figures show the average BER among users as a function of the number of users.

Interpreting the simulation results can be as important as accurately modeling the system. For the results shown in Fig. 8b, even though the average BER for the t ime-dependent, filter- based receiver is better than the matched filter, some users acquire a substantially higher BER than before processing. Solutions to this problem of large variance of BER among users are provided

,


in [57]. In general the quality o f a communications system depends on the ability to handle all users with a minimal acceptable BER.

Another promising approach to interference rejection (which requires careful consideration in simulating) is based on using neural nets. Simu- lating the performance ofneural netsis theonlyalter- native since analytical techniques are not available for determining their performance. Unlike other common interference rejection techniques, neural networks are capable of nonlinear filtering. Recent research hasshown that neural networksare a promis- ingmeansof interference rejection. Demonstrations of the capability of neural nets to reject interfer- encearecontained in [52-541. In [54],Chen shows that an adaptive radial basis function neural net equalizer can implement the optimal Baycsian symbol- decision equalizer using a two-stage learning

Figure 8. Averuge RERfor (U) the conwnrionuf mutc h r 4 filter und (b) the time-dependent filter (nuniher of users twruc B1 I.' \ M 'i = 0, 10. 20, 30 dB)

algorithm. In one example, the neural net pro- videsan effective improvement in SINR by7dBover the transversal equalizer for a BER of The algorithm converges remarkably fast even compared to traditional equalization algorithms.

In assessing the performance of neural nets for interference rejection using simulation. xver- al issues must be considered. First, realistic training intervals need to be applied; typically, neural nets require a long training interval and often require trial-and-error adjustment of parameters. The sensitivity of the network to parameter variations is an important quality measure for assessing the practicalityofthe algorithm. The training procedure and sensitivity of the performance to channel and interference conditions not indicative of the origi- nal training data are also essential for assessing the performance. Finally, performing simulations with neural nets is necessary in order to ensure that the neural net is not presented with excessive training using the same linear recursive sequence generator, or else i t is possible for the neural net to predict the pseudo-random sequence used to create the simulated data rather than equalizc or f i l - ter interference from the corrupted signal 158,591.

Conclusions n thisarticle,we have presented anoverview of key I simulation issuesfor wireless communicationssys-

tems. First, the burst errorcharacteristicsof the mobile channel require the selection of appropriate performance measures. Second, accurate simulations require realistic channel models that include the effects of attenuation, multipath propagation. noise, and interference. Third, link-level simulation ofwireless systemsrequiresattention todetailsofsystcm implementation including the effects of nonlinearities. Finally. efficient simulation o f CDMA systems may require a combination o f analytic and simulation techniques. We have discussed the application of simulation techniques to evaluate the performance ofwireless systems that employ cmerg- ing technologies such as adaptive antenna arrays and DSP-based interference rejection techniques.

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- Eficient simulation of CDlMA systems may require a combination of ana2ytic and simulation techniques.

Biographies BRIAN D. WOERNER received his B.S. degree in computer and electrical engineeringfromPurdueUniversityin 1986,and hisM.S.and Ph.D.degrees from the University of Michigan, in 1987 and 1991 respectively, where he was a Unisys Fellow. He has also earned a Master's degree in pub- l ic pol icy f r o m t h e Universi ty o f M ich igan w i t h a n emphasis in telecommunications policy. Since 1991, he has worked as an assistant professor with the Bradley Department of Electrical Engineering at Vir- ginia Tech in Blacksburg, Virginia. His research interests lie in the field of wireless communications, particularly in the analysis of modulation, error correction and code division multiple access techniques.

JEFFREY H. REED is a member of the MPRG at Virginia Tech. His specialty is in applying digital signal processing t o communication systems, and he has a particular interest in DSP techniques for interference rejection. He received his B.S.E.E. in 1979, M.S.E.E. in 1980, and Ph.D. in 1987. all f rom the University of California, Davis. He received the American Electronics Teaching Fellowship Award while completing his Ph.D. at the University of California, Davis. From 1980 t o 1986. he worked for Signal Science, a consulting f i rm specializing in DSP and communication systems. After graduating, he worked as a private consultant and as a part-time faculty member at the University of Cali- fornia, Davis. In August, 1992, he joined the faculty o f the Bradley Department of Electrical Engineering at Virginia Tech.

THEODORE S . RAPPAPORT i s an associate professor at Virginia Tech and director of the Mobile and Portable Radio Research Group (MPRG). He teaches and conducts research in the areas of wireless personal communications and RF channel modeling, and i s involved w i th various IEEE activities and organizations.


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