A hard-deadline, opportunistic scheduling problem in whichBbits must be transmitted withinTtime slots over a time-varying channel is studied: at the beginning of each slot the transmitter must decide how many bits to serve, or equivalently how much power to transmit with, based on causal channel knowledge where the channel varies from slot to slot (i.e., the channel in the current slot is known, but the channels in future slots are unknown). The objective of the opportunistic scheduling problem is to minimize expected transmission energy. It is assumed that no other packets are concurrently scheduled and that the transmission rate is equal to the capacity of the underlying additive white Gaussian noise channel within each slot, where the channel quality is fixed within a slot but varies in from slot to slot. Thus, the scheduler should be opportunistic, in the sense of transmitting more bits in slot(s) with good channel quality, while also being aware of the deadline so that not too many bits are left to the final slot. No closed-form solution for the optimal policy is known for this problem, which is naturally formulated as a finite-horizon dynamic program, but three different policies are shown to be optimal in the limiting regimes whereTis fixed andBis large,Tis fixed andBis small, and whereBandTare simultaneously taken to infinity. In addition, the advantage of optimal scheduling is quantified relative to a nonopportunistic (i.e., channel blind) equal-bit policy.

We present a method to compute, quickly and efficiently, the mutual information achieved by an independent identically distributed (IID) complex Gaussian signal on a block Rayleigh-faded channel without side information at the receiver. The method accommodates both scalar and multiple-input multiple-output (MIMO) settings. Operationally, this mutual information represents the highest spectral efficiency that can be attained using Gaussian codebooks. Examples are provided that illustrate the loss in spectral efficiency caused by fast fading and how that loss is amplified when multiple transmit antennas are used. These examples are further enriched by comparisons with the channel capacity under perfect channel-state information at the receiver, and with the spectral efficiency attained by pilot-based transmission.

In this paper, we do a cost-benefit analysis of multiuser diversity in single antenna broadcast channels. It is well known that the multiuser diversity can be beneficial but there is a significant cost in terms of system resources, bandwidth and power associated with acquiring instantaneous CSI. We work out a cost-benefit analysis of multiuser diversity for 2 types of CSI feedback methods, dedicated feedback and SNR dependent feedback, quantifying how many users should feedback CSI i.e the amount of available multiuser diversity that should be used from a net throughput perspective. Dedicated feedback, in which orthogonal resources are allocated to each user, has significant feedback cost and this limits the amount of available multiuser diversity that can be used. SNR dependent feedback method, in which only users with SNR above a threshold attempt to feedback, has relatively much smaller feedback cost and this allows for all of the available multiuser diversity to be used. Next, we study the effect of single user multiantenna techniques, which reduce the SNR variation, on the number of feedback users. It is seen that a broadcast channel using single user multiantenna techniques should reduce the number of feedback users with the spatial dimension.

In a multiple transmit antenna, single antenna per receiver downlink channel with limited channel state feedback, we consider the following question: given a constraint on the total system-wide feedback load, is it preferable to get low-rate/coarse channel feedback from a large number of receivers or high-rate/high-quality feedback from a smaller number of receivers? Acquiring feedback from many receivers allows multi-user diversity to be exploited, while high-rate feedback allows for very precise selection of beamforming directions. We show that there is a strong preference for obtaining high-quality feedback, and that obtaining near-perfect channel information from as many receivers as possible provides a significantly larger sum rate than collecting a few feedback bits from a large number of users. In terms of system design, this corresponds to a preference for acquiring high-quality feedback from a few users on each time-frequency resource block, as opposed to coarse feedback from many users on each block.

The performance of spatial multiplexing systems with linear minimum-mean-squared-error receivers is investigated in ad hoc networks. We present new exact closed-form expressions for the outage probability and transmission capacity. These expressions reveal that from a transmission capacity perspective, single-stream transmission is preferable over multi-stream transmission.

Multi-user MIMO (MU-MIMO) is seen as one of the key technologies for 4G communications systems. A critical factor affecting the throughput gains provided by MU-MIMO is the feedback mechanism that allows the transmitting basestation to periodically receive channel state information (CSI) from mobiles. Sufficient feedback is necessary for efficient MU-MIMO operation. At the same time feedback itself is an overhead using resources that could otherwise be used for data transmission. It is therefore important to carefully consider how feedback is designed and used to support MU-MIMO. This work studies key design parameters in such a feedback process: how often in time and in frequency should mobiles perform feedback (given coherence in the channel), the number of bits of feedback (accuracy of CSI quantization), and the number of mobiles to provide feedback. Within a bound on feedback resources these parameters can determine how best to use such resources to optimize MU-MIMO spectral efficiency. The results show that spectral efficiency is often maximized if mobiles quantize their channels very accurately but perform feedback at moderate intervals in time, such that CSI inaccuracy is primarily determined by the variation of the channel in time and frequency rather than being dominated by quantization errors.

The benefit of multi-antenna receivers is investigated in wireless ad hoc networks, and the main finding is that network throughput can be made to scale linearly with the number of receive antennas N_r even if each transmitting node uses only a single antenna. This is in contrast to a large body of prior work in single-user, multiuser, and ad hoc wireless networks that have shown linear scaling is achievable when multiple receive and transmit antennas (i.e., MIMO transmission) are employed, but that throughput increases logarithmically or sublinearly with N_r when only a single transmit antenna (i.e., SIMO transmission) is used. The linear gain is achieved by using the receive degrees of freedom to simultaneously suppress interference and increase the power of the desired signal, and exploiting the subsequent performance benefit to increase the density of simultaneous transmissions instead of the transmission rate. This result is proven in the transmission capacity framework, which presumes single-hop transmissions in the presence of randomly located interferers, but it is also illustrated that the result holds under several relaxations of the model, including imperfect channel knowledge, multihop transmission, and regular networks (i.e., interferers are deterministically located on a grid).

We consider a MIMO fading broadcast channel where the fading channel coefficients are constant over time-frequency blocks that span a coherent time x a coherence bandwidth. In closed-loop systems, channel state information at transmitter (CSIT) is acquired by the downlink training sent by the base station and an explicit feedback from each user terminal. In open-loop systems, CSIT is obtained by exploiting uplink training and channel reciprocity. We use closed-form lower bounds and tight approximations of the ergodic achievable rate in the presence of CSIT errors in order to optimize the overall system throughput, by taking explicitly into account the overhead due to channel estimation and channel state feedback. Based on three time-frequency block models inspired by actual systems, we provide useful guidelines for the overall system optimization. In particular, digital (quantized) feedback is found to offer a substantial advantage over analog (unquantized) feedback.

This paper studies the tradeoff between channel coding and ARQ (automatic repeat request) in Rayleigh block-fading channels. A heavily coded system corresponds to a low transmission rate with few ARQ re-transmissions, whereas lighter coding corresponds to a higher transmitted rate but more retransmissions. The optimum packet error probability, where optimum refers to the maximization of the average successful throughput, is derived and is shown to be a decreasing function of the average signal-to-noise ratio and of the channel diversity order. A general conclusion of the work is that the optimum error probability is quite large (e.g., 10% or larger) for reasonable channel parameters, and that operating at a very small error probability can lead to a significantly reduced throughput. This conclusion holds even when a number of practical ARQ considerations, such as delay constraints and acknowledgement feedback errors, are taken into account.

In this paper, the performance of the ALOHA and CSMA MAC protocols are analyzed in spatially distributed wireless networks. The main system objective is correct reception of packets, and thus the analysis is performed in terms of outage probability. In our network model, packets belonging to specific transmitters arrive randomly in space and time according to a 3-D Poisson point process, and are then transmitted to their intended destinations using a fully-distributed MAC protocol. A packet transmission is considered successful if the received SINR is above a predefined threshold for the duration of the packet. Accurate bounds on the outage probabilities are derived as a function of the transmitter density, the number of backoffs and retransmissions, and in the case of CSMA, also the sensing threshold. The analytical expressions are validated with simulation results. For continuous-time transmissions, CSMA with receiver sensing (which involves adding a feedback channel to the conventional CSMA protocol) is shown to yield the best performance. Moreover, the sensing threshold of CSMA is optimized. It is shown that introducing sensing for lower densities (i.e., in sparse networks) is not beneficial, while for higher densities (i.e., in dense networks), using an optimized sensing threshold provides significant gain.

Opportunistic routing is a multi-hop routing scheme which allows for selection of the best immediately available relay. In blind opportunistic routing protocols, where transmitters blindly broadcast without knowledge of the surrounding nodes, two fundamental design parameters are the node transmission probability and the transmission spectral efficiency. In this paper these parameters are selected to maximize end-to-end performance, characterized by the product of transmitter density, hop distance and rate. Due to the intractability of the problem as stated, an approximation function is examined which proves reasonably accurate. Our results show how the above design parameters should be selected based on inherent system parameters such as the path loss exponent and the noise level.

We present a method to compute, quickly and efficiently, the mutual information achieved by an IID (independent identically distributed) complex Gaussian input on a block Rayleigh-faded channel without side information at the receiver. The method accommodates both scalar and MIMO (multiple-input multiple-output) settings. Operationally, the mutual information thus computed represents the highest spectral efficiency that can be attained using standard Gaussian codebooks. Examples are provided that illustrate the loss in spectral efficiency caused by fast fading and how that loss is amplified by the use of multiple transmit antennas. These examples are further enriched by comparisons with the channel capacity under perfect channel-state information at the receiver, and with the spectral efficiency attained by pilot-based transmission.

The optimization of the pilot overhead in single-user wireless fading channels is investigated, and the dependence of this overhead on various system parameters of interest (e.g., fading rate, signal-to-noise ratio) is quantified. The achievable pilot-based spectral efficiency is expanded with respect to the fading rate about the no-fading point, which leads to an accurate order expansion for the pilot overhead. This expansion identifies that the pilot overhead, as well as the spectral efficiency penalty with respect to a reference system with genie-aided CSI (channel state information) at the receiver, depend on the square root of the normalized Doppler frequency. It is also shown that the widely-used block fading model is a special case of more accurate continuous fading models in terms of the achievable pilot-based spectral efficiency. Furthermore, it is established that the overhead optimization for multiantenna systems is effectively the same as for single-antenna systems with the normalized Doppler frequency multiplied by the number of transmit antennas.

This paper surveys and unifies a number of recent contributions that have collectively developed a metric for decentralized wireless network analysis known as transmission capacity. Although it is notoriously difficult to derive general end-to-end capacity results for multi-terminal or adhoc networks, the transmission capacity (TC) framework allows for quantification of achievable single-hop rates by focusing on a simplified physical/MAC-layer model. By using stochastic geometry to quantify the multi-user interference in the network, the relationship between the optimal spatial density and success probability of transmissions in the network can be determined, and expressed-often fairly simply-in terms of the key network parameters. The basic model and analytical tools are first discussed and applied to a simple network with path loss only and we present tight upper and lower bounds on transmission capacity (via lower and upper bounds on outage probability). We then introduce random channels (fading/shadowing) and give TC and outage approximations for an arbitrary channel distribution, as well as exact results for the special cases of Rayleigh and Nakagami fading. We then apply these results to show how TC can be used to better understand scheduling, power control, and the deployment of multiple antennas in a decentralized network. The paper closes by discussing shortcomings in the model as well as future research directions.

This paper studies the performance of hybrid-ARQ (automatic repeat request) in Rayleigh block-fading channels. The long-term average transmitted rate is analyzed in a fast-fading scenario where the transmitter only has knowledge of channel statistics, and, consistent with contemporary wireless systems, rate adaptation is performed such that a target outage probability (after a maximum number of H-ARQ rounds) is maintained. H-ARQ allows for early termination once decoding is possible, and thus is a coarse, and implicit, mechanism for rate adaptation to the instantaneous channel quality. Although the rate with H-ARQ is not as large as the ergodic capacity, which is achievable with rate adaptation to the instantaneous channel conditions, even a few rounds of H-ARQ make the gap to ergodic capacity reasonably small for operating points of interest. Furthermore, the rate with H-ARQ provides a significant advantage compared to systems that do not use H-ARQ and only adapt rate based on the channel statistics.

In this paper, we consider a multiple-input-multiple-output (MIMO) fading broadcast channel and compute achievable ergodic rates when channel state information (CSI) is acquired at the receivers via downlink training and it is provided to the transmitter by channel state feedback. Unquantized (analog) and quantized (digital) channel state feedback schemes are analyzed and compared under various assumptions. Digital feedback is shown to be potentially superior when the feedback channel uses per channel state coefficient is larger than 1. Also, we show that by proper design of the digital feedback link, errors in the feedback have a minor effect even if simple uncoded modulation is used on the feedback channel. We discuss first the case of an unfaded additive white Gaussian noise (AWGN) feedback channel with orthogonal access and then the case of fading MIMO multiple access (MIMO-MAC). We show that by exploiting the MIMO-MAC nature of the uplink channel, a much better scaling of the feedback channel resource with the number of base station (BS) antennas can be achieved. Finally, for the case of delayed feedback, we show that in the realistic case where the fading process has (normalized) maximum Doppler frequency shift 0 ¿ F < 1/2, a fraction 1 - 2F of the optimal multiplexing gain is achievable. The general conclusion of this work is that very significant downlink throughput is achievable with simple and efficient channel state feedback, provided that the feedback link is properly designed.

A contemporary perspective on transmit antenna diversity and spatial multiplexing is provided. It is argued that, in the context of most modern wireless systems and for the operating points of interest, transmission techniques that utilize all available spatial degrees of freedom for multiplexing outperform techniques that explicitly sacrifice spatial multiplexing for diversity. Reaching this conclusion, however, requires that the channel and some key system features be adequately modeled and that suitable performance metrics be adopted; failure to do so may bring about starkly different conclusions. As a specific example, this contrast is illustrated using the 3GPP long-term evolution system design.

A contemporary perspective on the tradeoff between transmit antenna diversity and spatial multiplexing is provided. It is argued that, in the context of modern cellular systems and for the operating points of interest, transmission techniques that utilize all available spatial degrees of freedom for multiplexing outperform techniques that explicitly sacrifice spatial multiplexing for diversity. Reaching this conclusion, however, requires that the channel and some key system features be adequately modeled; failure to do so may bring about starkly different conclusions. As a specific example, this contrast is illustrated using the 3GPP Long-Term Evolution system design.

This paper studies the tradeoff between channel coding and ARQ (automatic repeat request) in Rayleigh block-fading channels. A heavily coded system corresponds to a low transmission rate with few ARQ retransmissions, whereas lighter coding corresponds to a higher transmitted rate but more retransmissions. The optimum error probability, where optimum refers to the operating point that maximizes the average successful throughput, is derived and is shown to be a decreasing function of the average signal-to-noise ratio and of the channel diversity order. A general conclusion of the work is that the optimum error probability is quite large (e.g., 10% or larger) for reasonable channel parameters, and that operating at a very small error probability can lead to a significantly reduced throughput.

The performance benefits of two interference cancellation methods, successive interference cancellation (SIC) and joint detection (JD), in wireless ad hoc networks are compared within the transmission capacity framework. SIC involves successively decoding and subtracting out strong interfering signals until the desired signal can be decoded, while higher-complexity JD refers to simultaneously decoding the desired signal and the signals of a few strong interferers. Tools from stochastic geometry are used to develop bounds on the outage probability as a function of the spatial density of interferers. These bounds show that SIC performs nearly as well as JD when the signal-to-interference ratio (SIR) threshold is less than one, but that SIC is essentially useless for SIR thresholds larger than one whereas JD provides a significant outage benefit regardless of the SIR threshold.

The benefit of multiple antenna communication is investigated in wireless ad hoc networks, and the primary finding is that throughput can be made to scale linearly with the number of receive antennas even if each transmitting node uses only a single antenna. The linear throughput gain is achieved by (i) using the receive antennas to cancel the signals of nearby interferers as well as to increase signal power (i.e., for array gain), and (ii) maintaining a constant per-link rate and increasing the spatial density of simultaneous transmissions linearly with the number of antennas at each receiver. Numerical results show that even a few receive antennas provide substantial throughput gains, thereby illustrating that the asymptotic linear scaling result is also indicative of performance for reasonable numbers of antennas.

A point-to-point discrete-time scheduling problem of transmitting B information bits within T hard delay deadline slots is considered assuming that the underlying energy-bit cost function is a convex monomial. The scheduling objective is to minimize the expected energy expenditure while satisfying the deadline constraint based on information about the unserved bits, channel state/statistics, and the remaining time slots to the deadline. At each time slot, the scheduling decision is made without knowledge of future channel state, and thus there is a tension between serving many bits when the current channel is good versus leaving too many bits for the deadline. Under the assumption that no other packet is scheduled concurrently and no outage is allowed, we derive the optimal scheduling policy. Furthermore, we also investigate the dual problem of maximizing the number of transmitted bits over T time slots when subject to an energy constraint.

The spectral efficiency achievable with joint processing of pilot and data symbol observations is compared with that achievable through the conventional (separate) approach of first estimating the channel on the basis of the pilot symbols alone, and subsequently detecting the data symbols. Studied on the basis of a mutual information lower bound, joint processing is found to provide a non-negligible advantage relative to separate processing, particularly for fast fading. It is shown that, regardless of the fading rate, only a very small number of pilot symbols (at most one per transmit antenna and per channel coherence interval) should be transmitted if joint processing is allowed.

We consider a MIMO fading broadcast channel where channel state information is acquired at user terminals via downlink training and channel feedback is used to provide transmitter channel state information (CSIT) to the base station. The feedback channel (the corresponding uplink) is modeled as an AWGN channel, orthogonal across users. The total bandwidth consumed is the sum of the bandwidth/resources used for downlink training, channel feedback, and data transmission. Assuming that the channel follows a block fading model and that zero-forcing beamforming is used, we optimize the net achievable rate for unquantized (analog) and quantized (digital) channel feedback. The optimal number of downlink training pilots is seen to be essentially the same for both feedback techniques, but digital feedback is shown to provide a larger net rate than analog feedback.

A delay-constrained scheduling problem for point-to-point communication is considered: a packet of B bits must be transmitted by a hard deadline of T slots over a time-varying channel. The transmitter/scheduler must determine how many bits to transmit, or equivalently how much energy to transmit with, during each time slot based on the current channel quality and the number of unserved bits, with the objective of minimizing expected total energy. In order to focus on the fundamental scheduling problem, it is assumed that no other packets are scheduled during this time period and no outage is allowed. Assuming transmission at capacity of the underlying Gaussian noise channel, a closed-form expression for the optimal scheduling policy is obtained for the case T = 2 via dynamic programming; for T > 2, the optimal policy can only be numerically determined. Thus, the focus of the work is on derivation of simple, near-optimal policies based on intuition from the T = 2 solution and the structure of the general problem. The proposed bit-allocation policies consist of a linear combination of a delay-associated term and an opportunistic (channel-aware) term. In addition, a variation of the problem in which the entire packet must be transmitted in a single slot is studied, and a channel-threshold policy is shown to be optimal.

The multihop spatial reuse Aloha (MSR-Aloha) protocol was recently introduced by Baccelli et aL, where each transmitter selects the receiver among its feasible next hops that maximizes the forward progress of the head of line packet towards its final destination. They identify the optimal medium access probability (MAP) that maximizes the spatial density of progress, defined as the product of the spatial intensity of attempted transmissions times the average per-hop progress of each packet towards its destination. We propose a variant called longest edge routing where each transmitter selects its longest feasible edge, and then identifies a packet in its backlog whose next hop is the associated receiver. The main contribution of this work (and of Baccelli et aL) is the use of stochastic geometry to identify the optimal MAP and the corresponding optimal spatial density of progress.

In this paper the performance of unslotted ALOHA and CSMA are analyzed in spatially distributed wireless networks. Users/packets arrive randomly in space and time according to a Poisson process, and are thereby transmitted to their intended destinations using a fully-distributed MAC protocol (either ALOHA or CSMA). An SINR-based model is considered, and a packet transmission is successful if the received SINR is above a threshold value for the duration of the packet. Accurate bounds to the probability of outage, which is a function of the density of transmissions, are developed for both MAC protocols. These bounds are used to evaluate the performances of ALOHA and CSMA, and to gain insight into the design of general MAC protocols for ad hoc networks. Moreover, CSMA with receiver- sensing is proposed to improve the performance of CSMA.

The cooperative dirty paper coding (DPC) rate region is investigated in a two-transmitter two-receiver network with full channel state information available at all terminals. The transmitters cooperate by first exchanging messages over an orthogonal cooperation channel, then they mimic a broadcast channel (BC) and jointly perform DPC to send to the two independent receivers. The allocation of network power and bandwidth between the data and the cooperation channel is studied to characterize the cooperative DPC rate region. First, the optimal sum power allocation for a multiple access channel (MAC) is presented. Then through an application of the MAC-BC capacity duality, the cooperative DPC rate region is evaluated under different bandwidth allocation assumptions. Cooperative DPC outperforms non-cooperative time-division (TD) only when the cooperation channel is strong, since the joint-encoding capacity gain is negated by the overhead of message exchanges in a weak cooperation channel. Moreover, the cooperative capacity advantage over TD is more pronounced at the maximum sum rate point than when the rate vector is skewed toward one of the users.

A multiple transmit antenna, single receive antenna (per receiver) downlink channel with limited channel feedback is considered. Given a constraint on the total system-wide channel feedback, the following question is considered: is it preferable to get low-rate feedback from a large number of receivers or to receive high-rate/high-quality feedback from a smaller number of (randomly selected) receivers? Acquiring feedback from many users allows multi-user diversity to be exploited, while high- rate feedback allows for very precise selection of beamforming directions. It is shown that systems in which a limited number of users feedback high-rate channel information significantly outperform low-rate/many user systems. While capacity increases only double logarithmically with the number of users, the marginal benefit of channel feedback is very significant up to the point where the CSI is essentially perfect.

This paper studies the performance of transmission schemes that have rate that increases with average SNR while maintaining a fixed outage probability. This is in contrast to the classical Zheng-Tse diversity-multiplexing tradeoff (DMT) that focuses on increasing rate and decreasing outage probability. Three different systems are explored: antenna diversity systems, time/frequency diversity systems, and automatic repeat request (ARQ) systems. In order to accurately study performance in the fixed outage setting, it is necessary to go beyond the coarse, asymptotic multiplexing gain metric. In the case of antenna diversity and time/frequency diversity, an affine approximation to high SNR outage capacity (i.e., multiplexing gain plus a power/rate offset) accurately describes performance and illustrates the very significant benefits of diversity. ARQ is also seen to provide a significant performance advantage, but even an affine approximation to outage capacity is sometimes unable to capture this advantage and outage capacity must be directly studied in the non-asymptotic regime.

We consider a MIMO fading broadcast channel where channel state information is acquired at user terminals via downlink training and explicit analog feedback is used to provide transmitter channel state information (CSIT) to the base station. The feedback channel (the corresponding uplink) is modeled as a MIMO multiple access channel. Under the assumption that data transmission, downlink training, and feedback are performed within the same channel coherence interval of length T symbols, the optimization of a lower bound on the achievable ergodic rate sum yields a non-trivial resource allocation tradeoff.We solve this tradeoff and provide the optimal training and feedback resource allocation for the case of zero-forcing beamforming. When the same power level is used during all stages, it is found that the optimal length of the training + feedback phases increases as O(radicT) for large T. On the other hand, when different power levels can be used for different stages, for sufficiently large T it is optimal to use the minimum number of symbols for training + feedback but to use power of order O(radicT).

A delay-constrained scheduling problem for point-to-point communication is considered: a packet of B bits must be transmitted by a hard deadline of T slots over a time-varying channel. The transmitter/scheduler determines how many bits to transmit, or equivalently how much energy to transmit with, during each time slot based on the current channel quality and the number of unserved bits, with the objective of minimizing expected total energy. Assuming transmission at capacity of the underlying Gaussian noise channel, a closed-form expression for the optimal scheduling policy is obtained for the case T = 2 via dynamic programming; for T > 2, the optimal policy can only be numerically determined. Thus, the focus of the work is on derivation of simple, near-optimal policies. The proposed bit-allocation policies consist of a linear combination of a delay-associated term and an opportunistic (channel-aware) term. In addition, a variation of the problem in which the entire packet must be transmitted in a single slot is studied.

This paper considers the performance of the MAC protocols ALOHA and CSMA in wireless ad hoc networks, where the total system bandwidth may be divided into smaller subbands. In the network model used, the arrival of users/packets follows a Poisson point process, communication between nodes is continuous in time, selection of a subband to transmit across is made randomly at each transmitter, and the outage assessments made in the network are based on SINR measurements. Accurate bounds on the probability of outage for the MAC protocols are derived, and evaluated with respect to the number of subbands. It is observed that there exists an optimal number of subbands for each protocol, for which the probability of outage is minimized. For ALOHA, we obtain an analytical expression for this optimal value, while in CSMA, the optimal value is observed through simulations. Furthermore, we improve the performance of CSMA by introducing channel sensing across all subbands, in order to decrease the probability that a packet is in outage upon arrival. The obtained results are used to compare the performance of the two MAC protocols. Finally, we also evaluate the performance of our network in terms of sum capacity.

In this paper, we propose a new joint optimization of linear transmit beamforming and receive combining vectors for the multiple-input multiple-output (MIMO) broadcast channel. We consider the transmission of a single information stream to two users with two or more receive antennas. Unlike past work in which iterative computation is required to design the beamformers, we derive specific formulations for the transmit beamformers for two active users via a power iteration and a generalized eigen analysis. To enable practical implementation, a new limited feedback algorithm is proposed that exploits the structure of the algorithm to avoid full channel quantization. The feedback overhead of the proposed algorithm is independent of the number of receive antennas. Monte Carlo simulations are used to evaluate the bit error rate and the sum rate performances of the proposed algorithm. Simulation results show that the proposed method performs close to the sum capacity of the MIMO broadcast channel even with limited feedback.

Block diagonalization is a linear preceding technique for the multiple antenna broadcast (downlink) channel that involves transmission of multiple data streams to each receiver such that no multi-user interference is experienced at any of the receivers. This low-complexity scheme operates only a few dB away from capacity but requires very accurate channel knowledge at the transmitter. We consider a limited feedback system where each receiver knows its channel perfectly, but the transmitter is only provided with a finite number of channel feedback bits from each receiver. Using a random quantization argument, we quantify the throughput loss due to imperfect channel knowledge as a function of the feedback level. The quality of channel knowledge must improve proportional to the SNR in order to prevent interference-limitations, and we show that scaling the number of feedback bits linearly with the system SNR is sufficient to maintain a bounded rate loss. Finally, we compare our quantization strategy to an analog feedback scheme and show the superiority of quantized feedback.

A multiple antenna downlink channel where limited channel feedback is available to the transmitter is considered. In a vector downlink channel (single antenna at each receiver), the transmit antenna array can be used to transmit separate data streams to multiple receivers only if the transmitter has very accurate channel knowledge, i.e., if there is high-rate channel feedback from each receiver. In this work it is shown that channel feedback requirements can be significantly reduced if each receiver has a small number of antennas and appropriately combines its antenna outputs. A combining method that minimizes channel quantization error at each receiver, and thereby minimizes multi-user interference, is proposed and analyzed. This technique is shown to outperform traditional techniques such as maximum-ratio combining because minimization of interference power is more critical than maximization of signal power in the multiple antenna downlink. Analysis is provided to quantify the feedback savings, and the technique is seen to work well with user selection and is also robust to receiver estimation error.

This paper addresses the following question, which is of interest in the design of a multiuser decentralized network. Given a total system bandwidth of W Hz and a fixed data rate constraint of R bps for each transmission, how many frequency slots N of size W/N should the band be partitioned into in order to maximize the number of simultaneous links in the network? Dividing the available spectrum results in two competing effects. On the positive side, a larger N allows for more parallel, non- interfering communications to take place in the same area. On the negative side, a larger N increases the SINR requirement for each link because the same information rate must be achieved over less bandwidth. Exploring this tradeoff and determining the optimum value of N in terms of the system parameters is the focus of the paper. Using stochastic geometry, the optimal SINR threshold - which directly corresponds to the optimal spectral efficiency - is derived for both the low SNR (power-limited) and high SNR (interference-limited) regimes. This leads to the optimum choice of the number of frequency bands N in terms of the path loss exponent, power and noise spectral density, desired rate, and total bandwidth.

We consider a new approach to power control in decentralized wireless networks, termed fractional power control (FPC). Transmission power is chosen as the current channel quality raised to an exponent -s, where s is a constant between 0 and 1. The choices s = 1 and s = 0 correspond to the familiar cases of channel inversion and constant power transmission, respectively. Choosing s isin (0,1) allows all intermediate policies between these two extremes to be evaluated, and we see that usually neither extreme is ideal. We derive closed-form approximations for the outage probability relative to a target SINR in a decentralized (ad hoc or unlicensed) network as well as for the resulting transmission capacity, which is the number of users/m2that can achieve this SINR on average. Using these approximations, which are quite accurate over typical system parameter values, we prove that using an exponent of s* = 1/2 minimizes the outage probability, meaning that the inverse square root of the channel strength is a sensible transmit power scaling for networks with a relatively low density of interferers. We also show numerically that this choice of s is robust to a wide range of variations in the network parameters. Intuitively, s* = 1/2 balances between helping disadvantaged users while making sure they do not flood the network with interference.

We consider a MIMO line of sight (LOS) broadcast channel where each user has perfect knowledge of its own channel and feeds back 'quantized' channel information to the transmitter with finite number of bits. The rate loss incurred due to imperfect channel information at the transmitter is quantified in terms of the number of feedback bits, assuming that low-complexity zero-forcing beamforming is used by the transmitter. Moreover, in order to maintain a constant rate loss relative to a perfect CSIT system, it is shown that the number of feedback bits per user should grow linearly with the system SNR in dB but only logarithmitcally with the number of transmit antennas. Simulation results comparing zero-forcing, MMSE and conventional beamforming with greedy user selection and finite rate feedback are also presented.

Block diagonalization is a linear precoding technique for the multiple antenna broadcast (downlink) channel that involves transmission of multiple data streams to each receiver such that no multi-user interference is experienced at any of the receivers. This low-complexity scheme operates only a few dB away from capacity but does require very accurate channel knowledge at the transmitter, which can be very difficult to obtain in fading scenarios. We consider a limited feedback system where each receiver knows its channel perfectly, but the transmitter is only provided with a finite number of channel feedback bits from each receiver. Using a random vector quantization argument, we quantify the throughput loss due to imperfect channel knowledge as a function of the feedback level. The quality of channel knowledge must improve proportional to the SNR in order to prevent interference-limitations, and we show that scaling the number of feedback bits linearly with the system SNR is sufficient to maintain a bounded rate loss. Finally, we investigate a simple scalar quantization scheme that is seen to achieve the same scaling behavior as vector quantization.

We consider a MIMO fading broadcast channel and compare the achievable ergodic rates when the channel state information at the transmitter is provided by "analog" noisy feedback or by quantized (digital) feedback. The superiority of digital feedback is shown, with perfect or imperfect CSIR, whenever the number of feedback channel uses per channel coefficient is larger than 1. Also, we show that by proper design of the digital feedback link, errors in the feedback have a minor effect even by using very simple uncoded modulation. Finally, we show that analog feedback achieves a fraction 1 - 2F of the optimal multiplexing gain even in the presence of a feedback delay, when the fading belongs to the class of "Doppler processes" with normalized maximum Doppler frequency shift 0 les F les 1/2.

This paper addresses the following question, which is of interest in the design of a multiuser decentralized network: given a total system bandwidth of W Hz and a fixed data rate constraint of R bps for each transmission, how many frequency slots N of size W/N should the band be partitioned into to maximize the number of simultaneous transmissions in the network? Dividing the available spectrum reduces the number of users on each band and therefore decreases multiuser interference level, but also increases the SINR requirement for each transmission because the same information rate must be achieved over a smaller bandwidth. Exploring this tradeoff between bandwidth and SINR and determining the optimum value of N in terms of the system parameters is the focus of the paper. Using stochastic geometry, we analytically derive the optimal SINR threshold on this tradeoff curve and show that it is a function of only the path loss exponent. Furthermore, the optimal SINR point lies between the low-SINR (power-limited) and high-SINR (bandwidth-limited) regimes.

We consider a MIMO fading broadcast channel and study the achievable throughput of zero-forcing downlink beamforming when the channel state information (CSI) available to the transmitter and/or receivers is imperfect. Each receiver (mobile) acquires imperfect CSI via downlink training pilots, and the transmitter acquires CSI through explicit feedback from each mobile. We analyze both analog and digital (i.e., quantized) channel feedback techniques. Our analysis quantifies the throughput degradation due to limited training, limited feedback resources (measured in feedback channel symbols rather than bits), and errors on the feedback channel. Using these results we are able to quantify scenarios in which digital feedback outperforms analog and also provide guidelines for the optimal allocation of resources to training and channel feedback.

We analyze the sum-rate performance of a multi- antenna downlink system carrying more users than transmit antennas, with partial channel knowledge at the transmitter due to finite rate feedback. In order to exploit multiuser diversity, we show that the transmitter must have, in addition to directional information, information regarding the quality of each channel. Such information should reflect both the channel magnitude and the quantization error. Expressions for the SINR distribution and the sum-rate are derived, and tradeoffs between the number of feedback bits, the number of users, and the SNR are observed. In particular, for a target performance, having more users reduces feedback load.

In this correspondence, we compare the achievable throughput for the optimal strategy of dirty paper coding (DPC) to that achieved with suboptimal and lower complexity linear precoding techniques (zero-forcing and block diagonalization). Both strategies utilize all available spatial dimensions and therefore have the same multiplexing gain, but an absolute difference in terms of throughput does exist. The sum rate difference between the two strategies is analytically computed at asymptotically high SNR. Furthermore, the difference is not affected by asymmetric channel behavior when each user has a different average SNR. Weighted sum rate maximization is also considered. In the process, it is shown that allocating user powers in direct proportion to user weights asymptotically maximizes weighted sum rate.

Capacity improvement from transmitter and receiver cooperation is investigated in a two-transmitter, two-receiver network with phase fading and full channel state information (CSI) available at all terminals. The transmitters cooperate by first exchanging messages over an orthogonal transmitter cooperation channel, then encoding jointly with dirty-paper coding. The receivers cooperate by using Wyner-Ziv compress-and-forward over an analogous orthogonal receiver cooperation channel. To account for the cost of cooperation, the allocation of network power and bandwidth among the data and cooperation channels is studied. It is shown that transmitter cooperation outperforms receiver cooperation and improves capacity over noncooperative transmission under most operating conditions when the cooperation channel is strong. However, a weak cooperation channel limits the transmitter cooperation rate; in this case, receiver cooperation is more advantageous. Transmitter-and-receiver cooperation offers sizable additional capacity gain over transmitter-only cooperation at low signal-to-noise ratio (SNR), whereas at high SNR transmitter cooperation alone captures most of the cooperative capacity improvement.

This paper addresses three issues in the field ofad hocnetwork capacity: the impact of (i) channel fading, (ii) channel inversion power control, and (iii) threshold–based scheduling on capacity. Channel inversion and threshold scheduling may be viewed as simple ways to exploit channel state information (CSI) without requiring cooperation across transmitters. We use thetransmission capacity(TC) as our metric, defined as the maximum spatial intensity of successful simultaneous transmissions subject to a constraint on the outage probability (OP). By assuming the nodes are located on the infinite plane according to a Poisson process, we are able to employ tools from stochastic geometry to obtain asymptotically tight bounds on the distribution of the signal-to-interference (SIR) level, yielding in turn tight bounds on the OP (relative to a given SIR threshold) and the TC. We demonstrate that in the absence of CSI, fading can significantly reduce the TC and somewhat surprisingly, channel inversion only makes matters worse. We develop a threshold-based transmission rule where transmitters are active only if the channel to their receiver is acceptably strong, obtain expressions for the optimal threshold, and show that this simple, fully distributed scheme can significantly reduce the effect of fading.

In this correspondence, we deal with noncoherent communications over multiple-input-multiple-output (MIMO) wireless links. For a Rayleigh flat block-fading channel with M transmit- and N receive-antennas and a channel coherence interval of length T, it is well known that for TGtM, or, at high signal-to-noise-ratio (SNR) rhoGt1 and Mlesmin{N,lfloorT/2rfloor}, unitary space-time modulation (USTM) is capacity-achieving, but incurs exponential demodulation complexity in T. On the other hand, conventional training-based schemes that rely on known pilot symbols for channel estimation simplify the receiver design, but they induce certain SNR loss. To achieve desirable tradeoffs between performance and complexity, we propose a novel training approach where USTM symbols over a short length Ttau(tauis a small fraction of T, and recovers part of the SNR loss experienced by the conventional training-based schemes. When rhorarrinfin and TgesTtauges2M=2Nrarrinfin, but the ratios alpha=M/T, alpha1=Ttau/T are fixed, we obtain analytical expressions of the asymptotic SNR loss for both the conventional and new training-based approaches, serving as a guideline for practical designs

We consider the linear coding of a discrete memoryless Gaussian source transmitted through a discrete memoryless fading channel with additive white Gaussian noise (AWGN). The goal is to minimize the mean squared error (MSE) of the source reconstruction at the destination subject to an average power constraint imposed on the channel input symbols. We show that among all single-letter (or symbol-by-symbol) codes, linear coding achieves the smallest MSE, and is thus optimal. But when block length increases, the linear coding still shares the same performance with the single-letter coding, and thus can not approach the Shannon's bound. In spite of the suboptimality, the performance loss of linear coding compared to the optimal coding can be quantitively bounded in terms of the variance of the fading gain and the average transmit power. We also show that for linear coding, when there is no transmitter channel state information (CSI), uniform power allocation is optimal, and in the presence of transmitter CSI, the optimal power allocation can be analytically solved in terms of the channel fading gains and the average power budget.

A multiple antenna broadcast channel with perfect channel state information at the receivers is considered. If each receiver quantizes its channel knowledge to a finite number of bits which are fed back to the transmitter, the large capacity benefits of the downlink channel can be realized. However, the required number of feedback bits per mobile must be scaled with both the number of transmit antennas and the system SNR, and thus can be quite large in even moderately sized systems. It is shown that a small number of antennas can be used at each receiver to improve the quality of the channel estimate provided to the transmitter. As a result, the required feedback rate per mobile can be significantly decreased

The multiple antenna multicast channel is considered, in which the transmitter, equipped with an antenna array, sends a common message to multiple receivers, each of which are assumed to have only a single antenna. The information theoretic capacity of this channel is studied, along with the rates achievable using lower complexity transmission schemes. The primary focus of the paper is on the scaling of the capacity and achievable rates as the number of antennas and/or users is taken to infinity

This paper studies the symmetric capacity of the MIMO downlink channel, which is defined to be the maximum rate that can be allocated to every receiver in the system. The symmetric capacity represents absolute fairness and is an important metric for slowly fading channels in which users have symmetric rate demands. An efficient and provably convergent algorithm for computing the symmetric capacity is proposed, and it is shown that a simple modification of the algorithm can be used to compute the minimum power required to meet given downlink rate demands. In addition, the difference between the symmetric and sum capacity, termed the fairness penalty, is studied. Exact analytical results for the fairness penalty at high SNR are provided for the 2 user downlink channel, and numerical results are given for channels with more users

We explore the capacity of opportunistic secondary (cognitive) communication over a spectral pool of two independent channels. Due to the distributed nature of the primary user's spectral activity, the cognitive receiver does not have full knowledge of the channel used at the transmitter for secondary communication. Tracking the transmitter state at the receiver is therefore a primary issue in such channels. The problem is further complicated as the channel availability changes with time. The tracking uncertainty also makes decoding at the receiver non-trivial. Using genie based outer bounds and training based lower bounds, we estimate the capacity of the secondary link. The capacity analysis shows that the benefits of spectral pooling are lost in dynamic spectral environments

We analyze the sum-rate performance of a multi-antenna downlink system carrying more users than transmit antennas, with partial channel knowledge at the transmitter due to finite rate feedback. In order to exploit multiuser diversity, we show that the transmitter must have, in addition to directional information, information regarding the quality of each channel. Such information should reflect both the channel magnitude and the quantization error. Expressions for the SINR distribution and the sum-rate are derived, and tradeoffs between the number of feedback bits, the number of users, and the SNR are observed. In particular, for a target performance, having more users reduces feedback load

Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this correspondence, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The well-known zero-forcing transmission technique is considered, and simple expressions for the throughput degradation due to finite-rate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the signal-to-noise ratio (SNR) (in decibels) in order to achieve the full multiplexing gain. This is in sharp contrast to point-to-point multiple-input multiple-output (MIMO) systems, in which it is not necessary to increase the feedback rate as a function of the SNR

Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e. multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this paper, a system where the receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. Simple expressions for the capacity degradation due to finite rate feedback as well as the required increases in feedback load per mobile as a function of the number of access point antennas and the system SNR are provided.

The behavior of the multiple antenna broadcast channel at high SNR is investigated. The multiple antenna broadcast channel achieves the same multiplexing gain as the system in which all receivers are allowed to perfectly cooperate (i.e. transforming the system into a point-to-point MIMO system). However, the multiplexing gain alone is not sufficient to accurately characterize the behavior of sum rate capacity at high SNR. An affine approximation to capacity which incorporates the multiplexing gain as well as a power offset (i.e. a zero-order term) is a more accurate representation of high SNR behavior. The power offset of the sum rate capacity is shown to equal the power offset of the cooperative MIMO system when there are less receivers than transmit antennas. In addition, the power offset of using the sub-optimal strategy of beamforming is calculated. These calculations show that beamforming can perform quite well when the number of antennas is sufficiently larger than the number of receivers, but performs very poorly when there are nearly as many receivers as transmit antennas

We compare the capacity of dirty-paper coding (DPC) to that of time-division multiple access (TDMA) for a multiple-antenna (multiple-input multiple-output (MIMO)) Gaussian broadcast channel (BC). We find that the sum-rate capacity (achievable using DPC) of the multiple-antenna BC is at most min(M,K) times the largest single-user capacity (i.e., the TDMA sum-rate) in the system, where M is the number of transmit antennas and K is the number of receivers. This result is independent of the number of receive antennas and the channel gain matrix, and is valid at all signal-to-noise ratios (SNRs). We investigate the tightness of this bound in a time-varying channel (assuming perfect channel knowledge at receivers and transmitters) where the channel experiences uncorrelated Rayleigh fading and in some situations we find that the dirty paper gain is upper-bounded by the ratio of transmit-to-receive antennas. We also show that min(M,K) upper-bounds the sum-rate gain of successive decoding over TDMA for the uplink channel, where M is the number of receive antennas at the base station and K is the number of transmitters.

In this correspondence, we consider the problem of maximizing sum rate of a multiple-antenna Gaussian broadcast channel (BC). It was recently found that dirty-paper coding is capacity achieving for this channel. In order to achieve capacity, the optimal transmission policy (i.e., the optimal transmit covariance structure) given the channel conditions and power constraint must be found. However, obtaining the optimal transmission policy when employing dirty-paper coding is a computationally complex nonconvex problem. We use duality to transform this problem into a well-structured convex multiple-access channel (MAC) problem. We exploit the structure of this problem and derive simple and fast iterative algorithms that provide the optimum transmission policies for the MAC, which can easily be mapped to the optimal BC policies.

We derive the outage capacity region of an M-user fading multiple-access channel (MAC) under the assumption that both the transmitters and the receiver have perfect channel side information (CSI). The outage capacity region is implicitly obtained by deriving the outage probability region for a given rate vector. Given a required rate and average power constraint for each user, we find a successive decoding strategy and a power allocation policy that achieves points on the boundary of the outage probability region. We discuss the scenario where an outage must be declared simultaneously for all users (common outage) and when outages can be declared individually (individual outage) for each user.

In this paper we derive an upper bound on the sum-rate gain that dirty-paper coding provides over TDMA for MIMO broadcast channels. We find that the sum-rate capacity (achievable using dirty-paper coding) of the multiple-antenna broadcast channel is at most min(M;K) times the largest single-user capacity (i.e. the TDMA sum-rate) in the system, where M is the number of transmit antennas and K is the number of receivers. This result is independent of the number of receive antennas. We investigate the tightness of this bound in a time-varying channel (assuming perfect channel knowledge at receivers and transmitters) where the channel experiences uncorrelated Rayleigh fading and in some situations we find that the dirty paper gain is upper bounded by the ratio of transmit to receive antennas. We also show that min(M,K) upper bounds the sum rate gain of successive decoding over TDMA for the uplink, where M is the number of receive antennas at the base station and K is the number of transmitters.

We consider a set of parallel, two-user scalar Gaussian broadcast channels, where the transmitter wishes to send independent information to each of the receivers and common information to both receivers. The capacity region of this channel is implicitly characterized in [A. El Gamal, (1980)]. Here, we provide an explicit characterization of the power and rate allocation schemes that achieve the boundary of the three-dimensional rate region. We also propose a dirty-paper coding achievable region for MIMO broadcast channels with common information.

This paper examines communication between a cluster of closely-packed nodes with another cluster of closely-packed nodes. The nodes within each cluster are separated by small distances, relative to the distance between the two clusters. The effect on capacity of cooperation between nodes in the transmitting cluster and cooperation between nodes in the receiving cluster is investigated.

We define a duality between Gaussian multiple-access channels (MACs) and Gaussian broadcast channels (BCs). The dual channels we consider have the same channel gains and the same noise power at all receivers. We show that the capacity region of the BC (both constant and fading) can be written in terms of the capacity region of the dual MAC, and vice versa. We can use this result to find the capacity region of the MAC if the capacity region of only the BC is known, and vice versa. For fading channels we show duality under ergodic capacity, but duality also holds for different capacity definitions for fading channels such as outage capacity and minimum-rate capacity. Using duality, many results known for only one of the two channels can be extended to the dual channel as well.

A two transmitter two receiver channel where independent data is sent on each communication link of the system is considered. We consider a three-link system, termed the "Z" channel, in which one transmitter is connected to both receivers while the other transmitter is only connected to one of the receivers. Thus, the "Z" channel has a three dimensional capacity region. We characterize the capacity region of a special class of degraded "Z" channels and establish an achievable region for the Gaussian "Z" channels. Finally, we use genie-aided techniques previously used for the interference and broadcast channels to obtain an outer bound for general "Z" channels.

We provide an overview of the extensive results on the Shannon capacity of single-user and multiuser multiple-input multiple-output (MIMO) channels. Although enormous capacity gains have been predicted for such channels, these predictions are based on somewhat unrealistic assumptions about the underlying time-varying channel model and how well it can be tracked at the receiver, as well as at the transmitter. More realistic assumptions can dramatically impact the potential capacity gains of MIMO techniques. For time-varying MIMO channels there are multiple Shannon theoretic capacity definitions and, for each definition, different correlation models and channel information assumptions that we consider. We first provide a comprehensive summary of ergodic and capacity versus outage results for single-user MIMO channels. These results indicate that the capacity gain obtained from multiple antennas heavily depends on the available channel information at either the receiver or transmitter, the channel signal-to-noise ratio, and the correlation between the channel gains on each antenna element. We then focus attention on the capacity region of the multiple-access channels (MACs) and the largest known achievable rate region for the broadcast channel. In contrast to single-user MIMO channels, capacity results for these multiuser MIMO channels are quite difficult to obtain, even for constant channels. We summarize results for the MIMO broadcast and MAC for channels that are either constant or fading with perfect instantaneous knowledge of the antenna gains at both transmitter(s) and receiver(s). We show that the capacity region of the MIMO multiple access and the largest known achievable rate region (called the dirty-paper region) for the MIMO broadcast channel are intimately related via a duality transformation. This transformation facilitates finding the transmission strategies that achieve a point on the boundary of the MIMO MAC capacity region in terms of the transmission strategies of the MIMO broadcast dirty-paper region and vice-versa. Finally, we discuss capacity results for multicell MIMO channels with base station cooperation. The base stations then act as a spatially diverse antenna array and transmission strategies that exploit this structure exhibit significant capacity gains. This section also provides a brief discussion of system level issues associated with MIMO cellular. Open problems in this field abound and are discussed throughout the paper.

We derive the capacity region and optimal power allocation scheme for a slowly fading broadcast channel in which minimum rates must be maintained for each user in all fading states, assuming perfect channel state information at the transmitter and at all receivers. We show that the minimum-rate capacity region can be written in terms of the ergodic capacity region of a broadcast channel with an effective noise determined by the minimum rate requirements. This allows us to characterize the optimal power allocation schemes for minimum-rate capacity in terms of the optimal power allocations schemes that maximize ergodic capacity of the broadcast channel with effective noise. Numerical results are provided for different fading broadcast channel models.

We consider a multiuser multiple-input multiple- output (MIMO) Gaussian broadcast channel (BC), where the transmitter and receivers have multiple antennas. Since the MIMO BC is in general a nondegraded BC, its capacity region remains an unsolved problem. We establish a duality between what is termed the "dirty paper" achievable region (the Caire-Shamai (see Proc. IEEE Int. Symp. Information Theory, Washington, DC, June 2001, p.322) achievable region) for the MIMO BC and the capacity region of the MIMO multiple-access channel (MAC), which is easy to compute. Using this duality, we greatly reduce the computational complexity required for obtaining the dirty paper achievable region for the MIMO BC. We also show that the dirty paper achievable region achieves the sum-rate capacity of the MIMO BC by establishing that the maximum sum rate of this region equals an upper bound on the sum rate of the MIMO BC.

We consider a multiuser multiple input multiple output (MIMO) Gaussian broadcast channel (BC), where the transmitter and receivers have multiple antennas. Since the MIMO broadcast channel is in general a non-degraded broadcast channel, its capacity region remains an unsolved problem. We establish a duality between what is termed the "dirty paper" achievable region (the Caire-Shamai achievable region) for the MIMO broadcast channel and the capacity region of the MIMO multiple-access channel (MAC), which is easy to compute. Using this duality, we greatly reduce the computational complexity required for obtaining the dirty paper achievable region for the MIMO BC. The duality also enables us to translate previously known results for the MIMO MAC to the MIMO BC. We also show that the dirty paper achievable region achieves the sum-rate capacity of the MIMO BC by establishing that the maximum sum rate of this region equals an upper-bound on the sum rate of the MIMO BC.

The paper compares performance of finite impulse response (FIR) adaptive linear equalizers based on the recursive least-squares (RLS) and least mean square (LMS) algorithms in nonstationary uncorrelated scattering wireless channels. Simulation results, in terms of steady-state mean-square estimation error (MSE) and average bit-error rate (BER) metrics, are found for the frequency-selective Rayleigh fading wireless channel experienced in a mobile ad hoc network where nodes are lognormally shadowed from each other. For the nonstationary channel models considered, RLS is always found to outperform LMS.

We derive the capacity and optimal power allocation scheme for a multiuser fading broadcast channel in which minimum rates must be maintained for each user in all fading states, assuming perfect channel state information at the transmitter and at all receivers. We show that superposition coding can achieve the capacity of such channels and explicitly characterize the boundary of the capacity region. The optimal power allocation scheme is a two-step process: we first allocate the minimum power required to achieve the minimum rates in all fading states, and we then optimally allocate the excess power to maximize the ergodic rates averaged over all fading states in excess of the minimum rate requirements. The optimal allocation of the excess power is a multi-level water-filling relative to effective noise that incorporates the minimum rate constraints. Numerical results are provided for different fading broadcast channel models.