Error Exponent Regions for Gaussian Broadcast and Multiple-Access Channels

In modern communication systems, different users have different requirements for quality of service (QoS). In this work, QoS refers to the average codeword error probability experienced by the users in the network. Although several practical schemes (collectively referred to as unequal error protection schemes) have been studied in the literature and are implemented in existing systems, the corresponding performance limits have not been studied in an information-theoretic framework. In this paper, an information-theoretic framework is considered to study communication systems which provide heterogeneous reliabilities for the users. This is done by defining individual probabilities of error for the users in the network and obtaining the fundamental tradeoffs of the corresponding error exponents. In particular, we quantify the reliability tradeoff by introducing the notion of error exponent region (EER), which specifies the set of error exponent vectors that are simultaneously achievable by the users for a fixed vector of users' rates. We show the existence of a tradeoff among the users' error exponents by deriving inner and outer bounds for the EER. Using this framework, a system can be realized, which can provide a tradeoff of reliabilities among the users for a fixed vector of users' rates. This adds a completely new dimension to the performance tradeoff in such networks, which is unique to multiterminal communication systems, and is beyond what is given by the conventional performance-versus-rate tradeoff in single-user systems. Although this is a very general concept and can be applied to any multiterminal communication system, in this paper we consider Gaussian broadcast and multiple-access channels (MACs).     

Computation Over Multiple-Access Channels

The problem of reliably reconstructing a function of sources over a multiple-access channel (MAC) is considered. It is shown that there is no source-channel separation theorem even when the individual sources are independent. Joint source-channel strategies are developed that are optimal when the structure of the channel probability transition matrix and the function are appropriately matched. Even when the channel and function are mismatched, these computation codes often outperform separation-based strategies. Achievable distortions are given for the distributed refinement of the sum of Gaussian sources over a Gaussian multiple-access channel with a joint source-channel lattice code. Finally, computation codes are used to determine the multicast capacity of finite-field multiple-access networks, thus linking them to network coding.     

Multiple-Access Strategies for Frequency-Selective MIMO Channels

In this paper, we consider frequency-selective coherent multiple-input multiple-output (MIMO) multiple-access fading channels. Assuming that each of the users employs orthogonal frequency-division multiplexing (OFDM), we introduce a multiple-access scheme that gradually varies the amount of user collision in signal space by assigning different subsets of the available OFDM tones to different users. The corresponding multiple-access schemes range from frequency-division multiple access (FDMA) (each OFDM tone is assigned to at most one user) to CDMA (each OFDM tone is assigned to all the users). We quantify the effect of signal space collision between the users by computing the ergodic capacity region for the entire family of multiple-access schemes. It is shown that the ergodic capacity region obtained by a fully collision-based scheme (CDMA) is an outer bound to that corresponding to any other multiple-access strategy. In practice, however, minimizing the amount of user collision in frequency is desirable as this minimizes the receiver complexity incurred by having to separate the interfering (colliding) signals. Our analysis shows that the impact of collision on spectral efficiency depends critically on the channel's spatial fading statistics and the number of antennas     

Linear-Codes-Based Lossless Joint Source-Channel Coding for Multiple-Access Channels

A general lossless joint source-channel coding (JSCC) scheme based on linear codes and random interleavers for multiple-access channels (MACs) is presented and then analyzed in this paper. By the information-spectrum approach and the code-spectrum approach, it is shown that a linear code with a good joint spectrum can be used to establish limit-approaching lossless JSCC schemes for correlated general sources and general MACs, where the joint spectrum is a generalization of the input–output weight distribution. Some properties of linear codes with good joint spectra are investigated. A formula on the “distance” property of linear codes with good joint spectra is derived, based on which, it is further proved that, the rate of any systematic codes with good joint spectra cannot be larger than the reciprocal of the corresponding alphabet cardinality, and any sparse generator matrices cannot yield linear codes with good joint spectra. The problem of designing arbitrary rate coding schemes is also discussed. A novel idea called “generalized puncturing” is proposed, which makes it possible that one good low-rate linear code is enough for the design of coding schemes with multiple rates. Finally, various coding problems of MACs are reviewed in a unified framework established by the code-spectrum approach, under which, criteria and candidates of good linear codes in terms of spectrum requirements for such problems are clearly presented.      

Optimal Successive Group Decoders for MIMO Multiple-Access Channels

We consider a slow-fading narrowband multiple-input multiple-output (MIMO) multiple-access channel (MAC) in which multiple users, each equipped with multiple transmit antennas, communicate to a receiver equipped with multiple receive antennas. The users are unaware of the channel state information (CSI) whereas the receiver has perfect CSI and employs a successive group decoder (SGD). We obtain achievable outage probabilities for the case where an outage must be declared simultaneously for all users (common outage) as well as the case where outages can be declared individually for each user (individual outage). We then derive the optimum successive group decoder (OSGD) that simultaneously minimizes the common outage probability and the individual outage probability of each user, over all SGDs of permissible decoding complexity. For each channel realization, the OSGD is also shown to maximize the error exponent of the decodable set of users. An adaptive SGD is derived which not only retains the outage optimality of the OSGD but also minimizes the expected decoding complexity. Asymptotically tight (in the limit of high signal-to-noise ratio (SNR)) affine approximations are then obtained for the weighted sum common and individual outage capacities and the symmetric outage capacitiy yielded by the OSGD. Limiting expressions for the relevant capacities as the number of users and the number of receive antennas approach infinity are also obtained and it is shown that the OSGD yields symmetric capacity gains commensurate with the decoding complexity allowed. Simulation results with practical low-density parity-check (LDPC) outer codes show that the OSGD offers significantly improved performance at low decoding complexity.      

An Iterative Multiuser Detector for Overloaded LDPC Coded CDMA Systems

Maximum number of users in a Code Division Multiple Access (CDMA) system, disregarding the type of used signature sequences, is equal to the processing gain; but in overloaded CDMA systems, it is tried to use some special methods of applying signature sequences so that the number of users exceeds the processing gain of the system. This growth in capacity is gain at the cost of decrease in performance of the conventional systems; and usually it is tried to use channel coding methods or multi-user detectors to compensate this decrease. Because of advantages of using coding methods joined with multiuser detectors in achieving better performance and also because of some benefits of using (Low Density Parity Check) LDPC method in comparison with similar capacity achieving coding methods, in this article, an iterative multi-user detector for an overloaded LDPC Coded CDMA system is proposed. This receiver consists of a combination of matched filters in the first stage and a linear (Minimum Mean Square Error) MMSE detector and an Interference Cancellation (IC) scheme in the successive stages. In the suggested method, a bank of LDPC decoders gives the soft information to the IC blocks, which help for the better interference cancellation. Comparing the performance of the proposed system with that of Turbo coded system shows that the proposed system, in addition to advantages of using LDPC codes instead of Turbo codes, has better bit error rate performance.     

Outage Minimization and Rate Allocation for the Multiuser Gaussian Interference Channels With Successive Group Decoding

We consider a memoryless Gaussian interference channel (GIC) where $K$ single-antenna users communicate with their respective receivers using Gaussian codebooks. Each receiver employs a successive group decoder with a specified complexity constraint, to decode its designated user. It is aware of the coding schemes employed by all other users and may choose to decode some or all of them only if it deems that doing so will aid the decoding of its desired user. For a GIC with predetermined rates for all transmitters, we obtain the minimum outage probability decoding strategy at each receiver which satisfies the imposed complexity constraint and reveals the optimal subset of interferers that must be decoded along with the desired user. We then consider the rate allocation problem over the GIC under successive group decoding and design a sequential rate allocation algorithm which yields a pareto-optimal rate allocation, and two parallel rate allocation algorithms which yield the symmetric fair rate allocation and the max-min fair rate allocation, respectively. Remarkably, even though the proposed decoding and rate allocation algorithms use “greedy” or myopic subroutines, they achieve globally optimal solutions. Finally, we also propose rate allocation algorithms for a cognitive radio system.      

Compound Multiple-Access Channels With Partial Cooperation

A two-user discrete memoryless compound multiple-access channel (MAC) with a common message and conferencing decoders is considered. The capacity region is characterized in the special cases of physically degraded channels and unidirectional cooperation, and achievable rate regions are provided for the general case. The results are then extended to the corresponding Gaussian model. In the Gaussian setup, the provided achievable rates are shown to lie within some constant number of bits from the boundary of the capacity region in several special cases. An alternative model, in which the encoders are connected by conferencing links rather than having a common message, is studied as well, and the capacity region for this model is also determined for the cases of physically degraded channels and unidirectional cooperation. Numerical results are also provided to obtain insights about the potential gains of conferencing at the decoders and encoders.      

Entanglement-Assisted Capacity of Quantum Multiple-Access Channels

We find a regularized formula for the entanglement-assisted (EA) capacity region for quantum multiple-access channels (QMAC). We illustrate the capacity region calculation with the example of the collective phase-flip channel which admits a single-letter characterization. On the way, we provide a first-principles proof of the EA coding theorem based on a packing argument. We observe that the Holevo-Schumacher-Westmoreland theorem may be obtained from a modification of our EA protocol. We remark on the existence of a family hierarchy of protocols for multiparty scenarios with a single receiver, in analogy to the two-party case. In this way, we relate several previous results regarding QMACs.     

The Water-Filling Game in Fading Multiple-Access Channels

A game-theoretic framework is developed to design and analyze the resource allocation algorithms in fading multiple-access channels (MACs), where the users are assumed to be selfish, rational, and limited by average power constraints. The maximum sum-rate point on the boundary of the MAC capacity region is shown to be the unique Nash equilibrium of the corresponding water-filling game. This result sheds a new light on the opportunistic communication principle. The base station is then introduced as a player interested in maximizing a weighted sum of the individual rates. A Stackelberg formulation is proposed in which the base station is the designated game leader. In this setup, the base station announces first its strategy defined as the decoding order of the different users, in the successive cancellation receiver, as a function of the channel state. In the second stage, the users compete conditioned on this particular decoding strategy. This formulation is shown to be able to achieve all the corner points of the capacity region, in addition to the maximum sum-rate point. On the negative side, it is shown that there does not exist a base station strategy in this formulation that achieves the rest of the boundary points. To overcome this limitation, a repeated game approach, which achieves the capacity region of the fading MAC, is presented. Finally, the study is extended to vector channels highlighting interesting differences between this scenario and the scalar channel case.     

Outer Bounds for Multiple-Access Channels With Feedback Using Dependence Balance

We use the idea of dependence balance to obtain a new outer bound for the capacity region of the discrete memoryless multiple-access channel with noiseless feedback (MAC-FB). We consider a binary additive noisy MAC-FB whose feedback capacity is not known. The binary additive noisy MAC considered in this paper can be viewed as the discrete counterpart of the Gaussian MAC-FB. Ozarow established that the capacity region of the two-user Gaussian MAC-FB is given by the cut-set bound. Our result shows that for the discrete version of the channel considered by Ozarow, this is not the case. Direct evaluation of our outer bound is intractable due to an involved auxiliary random variable whose large cardinality prohibits an exhaustive search. We overcome this difficulty by using a composite function and its properties to explicitly evaluate our outer bound. Our outer bound is strictly less than the cut-set bound at all points on the capacity region where feedback increases capacity. In addition, we explicitly evaluate the Cover-Leung achievable rate region for the binary additive noisy MAC-FB in consideration. Furthermore, using the tools developed for the evaluation of our outer bound, we also explicitly characterize the boundary of the feedback capacity region of the binary erasure MAC, for which the Cover-Leung achievable rate region is known to be tight. This last result confirms that the feedback strategies developed by Kramer for the binary erasure MAC are capacity achieving.     

Coding Strategies for Multiple-Access Channels With Feedback and Correlated Sources

The multiple-access channel with feedback and correlated sources (MACFCS) models a sensor network in which sensors collect and transmit correlated data to a common sink. We present four achievable rate regions and a capacity outer bound for the MACFCS. For the first achievable region, we construct a decode-forward based coding strategy. The sources first exchange their data, and then cooperate to send full information to the destination. We term this strategy full decoding at sources with decode-forward (FDS-DF). For two of the other achievable regions, we first perform Slepian-Wolf coding to remove the correlation among the source data. This is followed by either (i) a compress-forward based coding strategy for the multiple-access channel with feedback, or (ii) an existing coding strategy for the multiple-access channel. We also find another achievable region using a multihop coding strategy, which only uses point-to-point coding (no cooperation). From numerical computations, we see that different strategies perform better under certain source correlation structures and network topologies. More specifically, FDS-DF approaches the capacity when (i) the inter-source distance decreases, or (ii) the correlation among the sources gets higher. Furthermore, the cooperative coding strategies considered support larger achievable rate regions than the noncooperative multihop strategy.     

Frame Synchronization for Gaussian Channels

The problem of locating a periodically inserted frame synchronization pattern in random data for aM-ary digital communication system operating over the additive white Gaussian noise channel is considered. The optimum maximum-likelihood decision rule, high signal-to-noise approximate maximum likelihood decision rule, and ordinary correlation decision rule for frame synchronization are derived for both coherent and noncoherent phase demodulation. A general lower bound on synchronization probability is derived for the coherent correlation rule. Monte Carlo computer simulations of all three decision rules, along with evaluations of the lower bound for the coherent correlation rule, were performed for the coherent MPSK, coherent, and noncoherentMary orthogonal, and 16 QAM signaling schemes. These results show that in each case the high signal-to-noise maximum-likelihood rules have a performance nearly equal to that of the maximum-likelihood rules over a wide range of practically interesting signal-to-noise ratios (SNR's). These high SNR decision rules also provide significant performance improvement over the simple correlation rules. Moreover, they are much simpler to implement than the maximum-likelihood decision rules and, in fact, are no more complex than the correlation rules.     

Optimized Transmission for Fading Multiple-Access and Broadcast Channels With Multiple Antennas

In mobile wireless networks, dynamic allocation of resources such as transmit powers, bit-rates, and antenna beams based on the channel state information of mobile users is known to be the general strategy to explore the time-varying nature of the mobile environment. This paper looks at the problem of optimal resource allocation in wireless networks from different information-theoretic points of view and under the assumption that the channel state is completely known at the transmitter and the receiver. In particular, the fading multiple-access channel (MAC) and the fading broadcast channel (BC) with additive Gaussian noise and multiple transmit and receive antennas are focused. The fading MAC is considered first and a complete characterization of its capacity region and power region are provided under various power and rate constraints. The derived results can be considered as nontrivial extensions of the work done by Tse and Hanly from the case of single transmit and receive antenna to the more general scenario with multiple transmit and receive antennas. Efficient numerical algorithms are proposed, which demonstrate the usefulness of the convex optimization techniques in characterizing the capacity and power regions. Analogous results are also obtained for the fading BC thanks to the duality theory between the Gaussian MAC and the Gaussian BC.     

一种多径衰落信道中的多用户检测算法

针对多径衰落CDMA信道,提出了一种基于信号子空间估计的联合盲特征波形估计与多用户检测方案,分析了两处线性多用户检测器（解相关检测器和线性MMSE检测器）在信号子空间参数下的闭式解及其抗远近效应能力,并讨论了用于联合盲特征波形估计与多用户检测实现的PASTd算法。最后给出了仿真结果。     

On Minimum-BER Linear Multiuser Detection for DS-CDMA Channels

The minimum bit-error rate (MBER) linear multiuser detection of direct-sequence code-division multiple-access (DS-CDMA) communications is considered. It is shown that the problem of MBER linear multiuser detection is equivalent to a constrained optimization problem where the constrained space is the surface and interior of a hyperellipsoid. It is further shown that any solution of the constrained optimization problem must be a solution of a set of nonlinear equations. Based on these the MBER linear multiuser detector of the two-user case is obtained. For general cases, we propose a convex subspace of the constrained space and show that the BER cost function is convex on the subspace and therefore has a unique solution. Some sufficient conditions that guarantee the convex subspace always contains the global minimizer are provided, which show that if the signal-to-noise ratio (SNR) is sufficiently large, the convex subspace always contains the global minimizer. A Newton method is developed for the set of nonlinear equations and computer simulation gives some illustrative examples     

Constrained Adaptive Linear Multiuser Detection Schemes

By using a fair comparison method we show that contrary to the general belief the conventional LMS, when in training mode, does not necessarily outperform the popular blind LMS (BLMS). With the help of a constrained MMSE criterion we identify the correct trained version which is guaranteed to have uniformly superior performance over BLMS since it maximizes the SIR over an algorithmic class containing BLMS. Because the proposed optimum trained version requires knowledge of the amplitude of the user of interest we also present simple and efficient techniques that estimate the amplitude in question. The resulting algorithm in both modes, training and decision directed, is significantly superior to BLMS.     

Linear Differentially Coherent Multiuser Detection for Multipath Channels

By combining multipath processing, differential signal detection, and multiuser detection techniques, we develop a class of near-far resistant linear detectors for differentially coherent multipath signals. We derive and establish performance relationships among the following detectors: an optimally near-far resistant detector, a suboptimum detector which does not require knowledge of the signal coordinates, and a minimum mean square error (MMSE) detector which achieves near-optimum asymptotic efficiency. We present an adaptive multiuser detector which converges to the MMSE detector without training sequences and which requires less information than the conventional single user rake receiver.     

Multiplexing Video on Multiuser Broadcast Channels

We propose a framework to address the problem of broadcasting a multiplicity of video sequences over a multiuser broadcast channel. The approach is intended to be general, without assumptions about specific video-coding or modulation techniques. However, we do assume the channel is Gaussian and exhibits quasi-static Rayleigh fading. Under the proposed framework, the algorithms seek to minimize the total distortion of multiple sequences broadcast simultaneously. To suit different applications, both greedy and long-term distortion metrics are considered. A salient aspect of this work is support for real-time video transport, hence delay and buffer constraints need to be accounted for. Under these constraints, the algorithms compute a jointly optimal source-rate and transmit-power allocation for all users under a power constraint. It turns out that problem can be formulated efficiently as a geometric program, which can be solved in different ways. In particular, we investigate a class of primal-dual convex algorithms. The complexity of the optimization is seen to scale well with the number of users. For the purpose of comparison, an orthogonal multiplexing scheme is also considered. Numerical results with H.264-coded video show that significant coding gains can be obtained.