This work develops a learning-based framework that directly exploits noisy pilots to optimize reconfigurable intelligent surface (RIS) systems while accommodating different service priorities and fairness via user weights. First, an adaptive beamforming configuration problem is formulated to generate the base station active beamforming vectors and RIS passive beamforming reflection coefficients that optimize the weighted sum-rate. Under mild regularity conditions, this problem is shown to attain a maximum. To learn approximate solutions, a novel hypernetwork-based beamforming (HNB) framework is proposed. Particularly, a beamforming network (BFN) exploits available information, including noisy pilots, to generate optimized beamforming configurations. Rather than learning one BFN, a hypernetwork is trained to dynamically generate BFN learning parameters from an input conditioning vector. When the conditioning vector is chosen as the user weights, the trained HNB can tune the BFN to the user weights without the need for retraining. Numerical experiments demonstrate that tuning allows the proposed HNB to perform close to an optimistic block-coordinate descent with perfect CSI benchmark and significantly outperform static learning where a BFN is directly trained to optimize beamforming configurations. Additionally, employing the HNB to also tune the BFN to location information considerably reduces the pilots needed to generate optimized beamforming configurations.

While multi-user (MU) massive MIMO is a critical technology for next generation wireless systems, its complexity poses significant operational challenges as it entails several processes. These include user selection, precoding, power distribution among the users, and Modulation and Coding Scheme (MCS) selection. While many studies have been conducted on MU-MIMO, most have made invalid assumptions (e.g., every non-zero signal received at a user yields a non-zero rate) or excluded some essential steps (e.g., MCS selection). We revisit the problem of operating a single-cell massive MIMO network with zero-forcing precoding, and develop real-time network operation algorithms. First, we relax the real-time constraint and perform an offline study to obtain a target performance for online algorithms. The joint problem can be solved exactly offline for small to medium sized settings using branch-reduce-and-bound. For larger settings, we note that, given a choice of user selection, the problem reduces to a power distribution problem that can be solved exactly. Thus, the joint problem reduces to a search over user-sets where for each considered user-set, a power distribution problem is solved. We propose various search methods and evaluate their performance. For online operation, we leverage the problem structure to propose an algorithm based on three ideas: i) grouping, 2) MCS-aware power distribution, and 3) an iterative process to remove users that see a zero rate. The algorithm achieves 94% of the performance target set by the offline study results.

Flexible resource allocation in intelligent reflecting surface (IRS)-assisted systems is necessary to account for fairness as well as time-varying channel behavior and user service priorities. An IRS-assisted system can achieve this flexibility by assigning different weights to each user when optimizing resources and the IRS configuration. In this paper, for the first time, we propose a hypernetwork-based beamforming (HNB) framework to dynamically leverage pilot information and user weights to generate the beamforming vectors and IRS configuration that maximize the weighted sum-rate (WSR) in a multi-user IRS-assisted system. As opposed to a traditional learning approach where a beamforming network (BFN) is trained once to optimize the WSR for every possible set of user weights, in a hypernetwork approach, a hypernetwork is trained to generate the learning parameters of the BFN conditioned on the input user weights, i.e., the BFN parameters are now adapted to the user weights without the need for any retraining. Numerical experiments corroborate the effectiveness of the proposed HNB framework to provide performance close to (within approximately 15−17% of) the optimistic benchmark produced by a numerical block-coordinate descent (BCD) algorithm that assumes perfect channel state information (CSI) knowledge. Moreover, the HNB trained with only a few epochs outperforms traditional fully-trained deep learning methods such as fully connected neural networks (FCNN) and graph neural networks (GNN). For example, in one considered scenario, the HNB nearly halves the gap to the BCD-with-CSI performance to 16% compared to gaps of 31% and 28% associated with FCNN and GNN schemes, respectively.

This paper revisits polar code design for a binary-input additive white Gaussian noise (BI-AWGN) channel when successive cancellation (SC) decoding is applied at the receiver. We focus on the so-called reciprocal channel approximation (RCA), which is often adopted in the design of low-density parity-check (LDPC) codes. Implementation of RCA requires the computation of the mutual information of BPSK signaling as well as a corresponding function known as the reciprocal channel mapping, and thus we develop rigorous closed-form approximations of these that are easy to calculate numerically and also valid over a wide range of SNR. Through numerical evaluation we find that, compared to approaches based on the popular Gaussian approximation (GA) as well as the so-called improved GA (IGA), the proposed RCA approach offers better estimates of the bit error rate of polarized channels with no additional computational cost. As a result, polar codes designed by the proposed RCA can achieve further improvement in terms of block error rate (BLER) performance. The gain achieved by the new approach becomes significant as the codeword length increases.

We consider the downlink of a multi-cell massive MIMO system suffering from asymptotic rate saturation due to pilot contamination. As opposed to treating pilot contamination interference as noise (TIN), we study the performance of decoding the pilot contamination interference. We model pilot-sharing users as an interference channel (IC) and study the performance of schemes that decode this interferencepartiallybased on rate-splitting (RS), and compare the performance to schemes that decode the interference in itsentiretybased on simultaneous unique decoding (SD) or non-unique decoding (SND). For RS, we non-uniquely decode each layer of the pilot contamination interference and use one common power splitting coefficient per IC. Additionally, we establish an achievable region for this RS scheme. Solving a maximum symmetric rate allocation problem based on linear programming (LP), we show that for zero-forcing (ZF) with spatially correlated/uncorrelated channels and with a practical number of BS antennas, RS achieves significantly higher spectral efficiencies than TIN, SD and SND. Furthermore, we numerically examine the impact of increasing the correlation of the channel across antennas, the number of users as well as the degree of shadow fading. In all cases, we show that RS maintains significant gain over TIN, SD and SND.

We investigate the performance of a hybrid non-orthogonal multiple access (NOMA) multi-cell downlink system (called hybrid as different users can have different successive interference cancellation (SIC) capabilities) by first formulating and solving a centralized proportional fair scheduling genie-assisted problem that jointly performs user selection, power allocation, power distribution, and modulation and coding scheme (MCS) selection. While such a genie is practically infeasible, it upper bounds the achievable performance. The results indicate that hybrid NOMA with a maximum of 2 multiplexed users can bring significant gains over a traditional OMA system (as long as enough users have the maximum SIC capability). Additionally, results show that the simple equal power allocation scheme (often used in the literature) yields performance lower than half the upper bound. Thus, we propose a simple static coordinated power allocation scheme across all cells for NOMA using a simple power map that is easily calibrated offline and show that with the calibrated power map, performance improves by 80%. Finally, we focus on the online scenario and propose a family of practical scheduling algorithms, each of them exhibiting a different trade-off between complexity (i.e., run-time) and performance.

We investigate the joint uplink-downlink configuration of an intelligent reflecting surface (IRS) for multi-user frequency-division-duplexing (FDD) and time-division-duplexing (TDD) systems. This is motivated in FDD since uplink and downlink transmissions occur simultaneously and hence an IRS must be jointly configured for both transmissions. In TDD, while a joint design is not strictly necessary, it can significantly reduce feedback overhead, power consumption, and configuration periods associated with updating the IRS. To compute the trade-off between uplink and downlink rates achieved by a joint design, a weighted-sum problem is formulated and optimized using a developed block-coordinate descent algorithm. The resulting uplink-downlink trade-off regions are investigated by numerical simulation to gain insights into different scenarios. In all FDD scenarios and some TDD scenarios, the jointly optimized design significantly outperforms the fixed-uplink (fixed-downlink) heuristic of using the IRS configuration optimized for uplink (downlink) to assist downlink (uplink) transmissions. Moreover, the joint design substantially bridges the gap to the individual design upper bound of allowing different IRS configurations in uplink and downlink. Otherwise, in the remaining TDD scenarios, the fixed-uplink and fixed-downlink designs nearly achieve the individual design performance and substantially reduce overhead and/or complexity compared to the optimized joint design and individual design.

This paper focuses on an improved Gaussian approximation (GA) based construction of polar codes with successive cancellation (SC) decoding over an additive white Gaussian noise (AWGN) channel. Arıkan proved that polar codes with low-complexity SC decoding can approach the channel capacity of an arbitrary symmetric binary-input discrete memoryless channel, provided that the code length is chosen large enough. Nevertheless, how to construct such codes over an AWGN channel with low computational effort has been an open problem. Compared to density evolution, the GA is known as a low complexity yet powerful technique that traces the evolution of the mean log likelihood ratio (LLR) value by iterating a nonlinear function. Therefore, its high-precision numerical evaluation is critical as the code length increases. In this work, by analyzing the asymptotic behavior of this nonlinear function, we propose an improved GA approach that makes an accurate trace of mean LLR evolution feasible. With this improved GA, through numerical analysis and simulations with code lengths up to N = 218, we explicitly demonstrate that various code-rate polar codes with long codeword and capacity approaching behavior can be easily designed.

We study joint proportional-fair (PF) resource allocation (RA), including user selection, linear precoding design, power optimization, and modulation and coding scheme selection, in a single-cell downlink massive MIMO (m-MIMO) system over consecutive time-slots when taking per-antenna power constraints (PAPCs) into account. We formulate the general PF joint RA optimization problem as a weighted sum-rate maximization problem at each time-slot and develop a solution technique to obtain a quasi-optimal feasible solution via the introduction of auxiliary variables and a carefully chosen approximation of the spectral-efficiency function. To obtain results for larger settings (i.e., larger number of antennas and users), we propose an approximation to the general problem that yields quasi-optimal feasible solutions. Moreover, we consider state-of-the-art linear precoding techniques and propose a general heuristic RA scheme that takes PAPCs into account. Numerical results show that PAPCs have significant impact on performance even for a very large number of antennas, and that the best existing linear precoding technique, RZFT (regularized zero-forcing transmission) performs very well when RA is performed carefully as long as the PAPCs are not tight. However, RZFT is far from optimal under tight PAPCs, which highlights the need for practical PAPC-aware precoding techniques in this regime.

We consider a multi-cell massive MIMO system where a time-division duplex protocol is used to estimate the channel state information via uplink pilots. When maximum ratio combining (MRC) is used at the BSs, the re-use of pilots across cells causes the pilot contamination effect which yields interference components that do not vanish as the number of base-station (BS) antennas M → ∞. When treating interference as noise (TIN), this phenomenon limits the performance of multicell massive MIMO systems. In this paper, we analyze more advanced schemes based on simultaneous unique decoding (SD) as well as simultaneous non-unique decoding (SND) of the interference that can provide unbounded rate as M → ∞. We also establish a worst-case uncorrelated noise technique for multiple-access channels to derive achievable rate expressions for finite M. Furthermore, we study a much simpler subset of SND (called S-SND) which provides a lower bound to SND and achieves unbounded rate as M → ∞, and also outperforms SD for finite M. For the special cases of two-cell and threecell systems, using a maximum symmetric rate allocation policy we compare the performance of different interference decoding schemes with that of TIN. Finally, we numerically illustrate the improved performance of the proposed schemes.

We study the multiple-access interference channel (MAIC) where two two-user multiple-access channels (MAC) operate over a shared medium, thus mutually interfering. We first characterize an achievable region for the discrete memoryless MAIC based on the Han-Kobayashi scheme. For the Gaussian MAIC, a computable achievable region is provided by limiting the time-sharing variable to be binary, and depending on its binary state, the transmitters use a fixed private-common message power splitting. Focusing on the weak Gaussian MAIC, where the magnitude of the coefficient (gain) of the cross-channels are smaller than that of the direct-channels, two genie-aided outer bounds to the sum-rate are derived by providing noisy interfering signals as side-information. Numerical examples in the weak symmetric Gaussian MAIC shows that (i) the outer bounds improve upon previous bounds in many cases, and (ii) for a wide range of cross-channel gains, the achievable sum-rate and one of the outer bounds differ by a small numerical gap, thus providing good approximations to the sum-capacity in these cases.

Relay cooperation and integrated microwave and millimeter-wave (mm-wave) dual-band communication are likely to play key roles in 5G. In this paper, we study a two-user uplink scenario in such dual-bands, modeled as a multiple-access relay channel (MARC), where two sources communicate to a destination assisted by a relay. However, unlike the microwave band, transmitters in the mm-wave band must employ highly directional antenna arrays to combat the ill effects of severe path-loss and small wavelength. The resulting mm-wave links are point-to-point and highly directional, and thus used to complement the microwave band by transmitting to a specific receiver. For such MARCs, the capacity is partially characterized for sources that are near the relay in a joint sense over both bands. We then study the impact of the mm-wave spectrum on the performance of such MARCs by characterizing the transmit power allocation scheme for phase faded mm-wave links that maximizes the sum-rate under a total power budget. The resulting scheme adapts the link transmission powers to channel conditions by transmitting in different modes, and all such modes and corresponding conditions are characterized. Finally, we study the properties of the optimal link powers and derive practical insights.

We study a continuous-time power policy to maximize the ergodic channel throughput of an energy harvesting transmitter over a Markov fading channel. In particular, we consider transmission power policies that are adapted to the fading process of the channel as well as the storage process of the battery. We obtain a set of equations that determine the probability density of the energy in the battery at each channel state. Specifically, for an ergodic battery storage process, these equations describe the relation between the probability density of stored energy and the transmission power at each channel state. From these equations, we derive an upper bound on the average transmission power and an upper bound on the average transmission rate. To compute a lower bound on the average transmission rate, we apply a calculus of variations technique to a non-linear throughput maximization problem. As a result, we obtain a system of coupled ordinary differential equations for locally optimal power policies. We then focus on the Gilbert-Elliot channel as a special case and derive some structural results for specific classes of fast and slow fading channels. Furthermore, we numerically find a locally optimal transmission power policy for the two channel state scenario.

We consider a two-transmitter two-receiver dual-band Gaussian interference channel (GIC) which is motivated by the simultaneous use of both the conventional microwave band and the unconventional millimeter wave (mm-wave) band in future wireless networks where the traditional microwave band is complemented by additional spectrum in the mm-wave band. A key modeling feature of the mm-wave band is that due to severe path loss and relatively small wavelength, it must be used with highly directional antennas, and thus the transmitter is able to transmit to its intended receiver with negligible to no interference to other receivers. For this model, we derive some sufficient conditions on the channel gains under which the capacity of this type of dual-band GIC is determined. Specifically, these conditions are classified as when microwave band channel gains have (a) weak interference, i.e., both the cross channel gains are less than 1 and (b) mixed interference, i.e., only one of the cross channel gains is less than 1, while the channel gains in the dual-band GIC satisfy certain additional conditions in each case.

In this paper, we present a generalized framework for active eavesdropping in a frequency hopping spread spectrum passive radio frequency identification system. In our model, there exists an adversarial reader who is able to transmit its own continuous wave signal outside the frequency band of the legitimate reader. Due to the fact that under backscatter modulation, the tag cannot distinguish different frequencies and simply sets the impedance in its circuitry to either low or high to reflect a bit of 1 or 0, and the adversarial reader's received signal is a weighted sum of the response to both its own signal and the legitimate reader's signal. Using this model, we provide a theoretical analysis of the capability of the adversarial reader in terms of the decoding error probability for slow frequency and fast frequency hopping systems. We derive analytic formulas and conduct experiments using software defined radios that act as the legitimate reader, the adversarial reader, and Intel Wireless Identification Sensing Platform tags with parameters as specified in EPC Gen2. Simulations are also used to validate our findings. We find from the theoretical analysis as well the experimental results that the active eavesdropper can achieve a better decoding error rate than a conventional passive eavesdropper, even in the case that the eavesdropper's signal is a low power signal.

We study the transmission of a set of correlated sources (U1, . . . , UK) over a Gaussian multiple access relay channel with time asynchronism between the encoders. We assume that the maximum possible offset dmax(n) between the transmitters grows without bound as the block length n → ∞, while the relative ratio dmax(n)/n of the maximum possible offset to the block length asymptotically vanishes. For such a joint source-channel coding problem and under specific gain conditions, we derive necessary and sufficient conditions for reliable communications and show that separate source and channel coding achieves optimal performance. In particular, we first derive a general outer bound on the source entropy content for all channel gains as our main result. Then, using Slepian-Wolf source coding combined with the channel coding scheme on top of block Markov coding, we show that the thus achieved inner bound matches the outer bound. As a corollary, we also address the problem of sending a pair of correlated sources over a two-user interference channel in the same context.

Timing jitter in OFDM systems results in non-ideal sampling and pulse-shaped transmission times and thus, is an important limiting factor for practical OFDM systems. In this paper, the effect of sampling jitter on the spectrum of a transmitted OFDM signal is considered. This effect in particular can lead to a significant degradation in the performance of spectral shaping techniques in OFDM, e.g., out-of-band radiation reduction techniques, that are mostly based on the assumption of ideal sampling times and accurate synchronization. In this work, we first evaluate the effect of random sampling time jitter on the performance of the active interference cancellation (AIC) technique, a well-known out-of-band radiation reduction technique for OFDM. Then, using a minimax approach, we propose a robust scheme that takes the jitter effect into consideration and therefore, makes the sidelobe suppression technique robust against worst-case random jitter. Numerical simulations show a performance improvement of almost 3 dB in interference reduction for our proposed robust scheme in the presence of timing jitter.

Convolutional lattice codes, also known as signal codes, have been proposed as a technique to generate structured codes that have good performance. While in principle optimal decoding can be achieved using the Viterbi Algorithm, in practice due to Tomlinson-Harashima precoding, the size of the state space is too large, and one must resort to suboptimal techniques such as sequential decoding. In this paper, we take an alternate approach. By employing a judicious selection of tap coefficients and in combination with precoding, we show that the state space can be constrained to a relatively small set such that Viterbi decoding is practical. The performance of such codes still exhibits a large gap to capacity, and we further propose a parallel concatenation similar to that of turbo codes, resulting in a “turbo signal code.” Due to the relatively small state space, iterative decoding based on the BCJR algorithm is now possible. The gaps between the SNR for a frame error rate of 1% and the optimal performance theoretically achievable for a code of the same rate over an AWGN channel are found by simulation to be within 0.75-0.85 dB with a block length of 8192.

We study the problem of lossy joint source-channel coding in a single-user energy harvesting communication system with causal energy arrivals and the energy storage unit may have leakage. In particular, we investigate the achievable distortion in the transmission of a single source via an energy harvesting transmitter over a point-to-point channel. We consider an adaptive joint source-channel coding system, where the length of channel codewords varies based on the available battery charge. We first establish a lower bound on the achievable distortion. Then, as necessary conditions for local optimality, we obtain two coupled equations that determine the mismatch ratio between channel symbols and source symbols as well as the transmission power, both as functions of the battery charge. As examples of continuous and discrete sources, we consider Gaussian and binary sources respectively. For the Gaussian case, we obtain a closed-form expression for the mismatch factor in terms of the LambertW function, and show that an increasing transmission power policy results in a decreasing mismatch factor policy and vice versa. Finally, we numerically compare the performance of the adaptive mismatch factor scheme to the case of a constant mismatch factor.

In this paper, we consider a new definition of typicality based on the weak* topology that is applicable to Polish alphabets (which includes ℝn). This notion is a generalization of strong typicality in the sense that it degenerates to strong typicality in the finite alphabet case, and can also be applied to mixed and continuous distributions. Furthermore, it is strong enough to prove a Markov lemma, and thus can be used to directly prove a more general class of results than entropy (or weak) typicality. We provide two example applications of this technique. First, using the Markov Lemma, we directly prove a coding result for Gel'fand-Pinsker channels with an average input constraint for a large class of alphabets and channels without first proving a finite alphabet result and then resorting to delicate quantization arguments. This class of alphabets includes, for example, real and complex inputs subject to a peak amplitude restriction. While this large class does not directly allow for Gaussian distributions with average power constraints, it is shown to be straightforward to recover this case by considering a sequence of truncated Gaussian distributions. As a second example, we consider a problem of coordinated actions (i.e., empirical distributions) for a two node network, where we derive necessary and sufficient conditions for a given desired coordination.

Continuous-time lossy source transmission in an Energy Harvesting (EH) communication system is studied. In particular, the distortion performance for the transmission of a Gaussian source and compound Poisson energy arrivals is considered. Based on the convexity of the distortion function and Jensen's inequality, a lower bound on the average distortion is established in terms of the capacity of the battery and parameters such as the distribution of energy arrivals and the variance of the source. Moreover, a calculus of variations technique is used to derive an adaptive transmission power policy as well as an adaptive mismatch factor. More specifically, the structure of a locally optimal achievable power policy is characterized as the solution to a first order non-linear ordinary differential equation (ODE). Also for the mismatch factor, a closed-form formula as a function of the battery charge via the LambertW function is derived.

Most of the literature on heterogeneous cellular networks is focused on analyzing them as a single macro cell embedded with small cells. In this paper, we take a global perspective and analyze the effect of deploying small cells on the performance of a network comprising several macro cells. We identify potential locations for low-power base-stations based on the coverage patterns of the macro cells and propose three schemes for placing the small cells. Using the model recommended by 3GPP, we show that by judiciously installing just two small cells for every macro base-station at these locations and allocating separate resources to all the small cells on a global level, we can increase the performance of the network significantly (∼ 45%). An added benefit of our schemes is that we can switch off the macro base-stations at night (when the number of active users is low) and significantly reduce their operation cost.

We study the transmission of two correlated and memoryless sources (U1, U2) over several multiuser phase asynchronous channels. Namely, we consider a multiple access relay channel (MARC) with causal, and a MARC with non-causal unidirectional cooperation between encoders, referred to as phase-incoherent causal (respectively, non-causal) cognitive MARCs. We also consider phase-incoherent interference channel models with and without relay, in the same context. In all cases, the input signals are assumed to undergo non-ergodic phase shifts, which are unknown to the transmitters and known to the receivers as a realistic assumption. Both necessary and sufficient conditions in order to reliably send the correlated sources to the destinations are derived. In particular, for all of the channel models, by using a key lemma, we first derive an outer bound for reliable communication. Then, using separate source and channel coding and under specific gain conditions, we establish the same region as the inner bound. We thus conclude that without the knowledge of the phase shifts at transmitters, and under specific gain conditions, separation is optimal.

We investigate performance limits of a multiple access communication system with energy harvesting nodes where the utility function is taken to be the long-term average sum-throughput. We assume a causal structure for energy arrivals and study the problem in the continuous time regime. For this setting, we first characterize a storage dam model that captures the dynamics of a battery with energy harvesting and variable transmission power. Using this model, we next establish an upper bound on the throughput problem as a function of battery capacity. We also formulate a nonlinear optimization problem to determine optimal achievable power policies for transmitters. Applying a calculus of variations technique, we then derive Euler-Lagrange equations as necessary conditions for optimum power policies in terms of a system of coupled partial integro-differential equations. Based on a Gauss-Seidel algorithm, we devise an iterative algorithm to solve these equations. We also propose a fixed-point algorithm for the symmetric multiple access setting in which the statistical descriptions of energy harvesters are identical. To further support our iterative algorithms, along with the analysis, comprehensive numerical results are also obtained.

We study cross-layer design in random-access-based fixed wireless multihop networks under a physical interference model. Due to the complexity of the problem, we consider a simple slotted ALOHA medium access control (MAC) protocol for link-layer operation. We formulate a joint routing, access probability, and rate allocation optimization problem to determine the optimal max-min throughput of the flows and the optimal configuration of the routing, access probability, and transmission rate parameters in a slotted ALOHA system. We then also adapt this problem to include an XOR-like network coding without opportunistic listening. Both problems are complex nonlinear and nonconvex. We provide extensive numerical results for both problems for medium-size mesh networks using an iterated optimal search technique. Via numerical and simulation results, we show that: 1) joint design provides a significant throughput gain over a default configuration in slotted-ALOHA-based wireless networks; and 2) the throughput gain obtained by the simple network coding is significant, especially at low transmission power. We also propose simple heuristics to configure slotted-ALOHA-based wireless mesh networks. These heuristics are extensively evaluated via simulation and found to be very efficient.

This work is an offline study to characterize the performance of cooperative relaying in interference-limited multihop networks, where nodes are equipped with multi-rate and continuous power control capabilities. We formulate a cross-layer flow-based framework to obtain the achievable throughput rates by jointly optimizing the parameters for multi-path routing, scheduling, rates, transmit powers, and selection of cooperative nodes. This framework is generic in that it is not restricted to any particular cooperative combining technique or type of network architecture. To take continuous power control into account, we introduce a non-trivial power allocation subproblem while keeping the main cross-layer framework as a linear program. We solve the problem optimally to obtain the max-min throughput for the case when cooperation is based on the distributed Alamouti code and networks have a mesh-like topology. We derive a number of practical engineering insights based on our numerical optimal results obtained for small-to-medium-sized random networks. In particular, we establish that the use of cooperative relaying in a small-to-medium-sized random mesh network often does not yield significant performance gains in throughput and connectivity even when multi-rate and continuous power control capabilities are available at the nodes.

We investigate fundamental limits of lossy communication in a bi-directional (two-way), half-duplex relay channel, where users wish to exchange correlated Gaussian sources and do so by sending their data according to a two-phase transmission protocol. We first establish a general distortion inner bound using the data processing inequality. Based on uncoded transmission schemes, separate source and channel coding schemes, and a lattice coding scheme, we then develop several cooperative joint source-channel coding (JSCC) approaches. Furthermore, we compare the corresponding achievable distortion performances in terms of the correlation coefficient between Gaussian sources. This comparison particularly shows that separate source and channel coding in combination with decode-and-forward relaying achieves the best distortion values of any joint source-channel coding scheme in this paper.

We investigate the problem of continuous-time energy harvesting in communication systems operating over fading wireless channels. We model the fading as a finite-state continuous-time Markov process, and the battery dynamics as a storage dam process with reflecting boundary conditions. We describe a set of necessary conditions for the ergodicity of the dam process. Followed by these conditions, we establish an upper bound on the ergodic channel throughput. We further determine some structure for good transmission power policies based on a throughput maximization problem. Specifically, using calculus of variations techniques, we derive Euler-Lagrange equations as a necessary condition for optimal power policies. In the case of a Markov channel with two channel states (i.e. Gilbert-Elliot channel), we characterize power policies by solving these equations numerically.

We study the transmission of a pair of correlated sources over a Gaussian multiple access channel with weak time asynchronism between the encoders. In particular, we assume that the maximum possible offset dmax(n) between the transmitters grows without bound as the block length n → ∞ while the ratio dmax(n)/n of the maximum possible offset to the block length asymptotically vanishes. For such a joint source-channel coding problem, we derive the capacity region and also show that separate source and channel coding achieves optimal performance. Specifically, we first derive an outer bound on the source entropy content as our main result. Then, using Slepian-Wolf source coding combined with the channel coding introduced in [1], we show that the thus achieved inner bound matches the outer bound.

We study simultaneous channel sharing of collocated primary and secondary networks at three different levels of coexistence: pure interference, asymmetric, and symmetric. At the pure interference level, both networks operate simultaneously in the same frequency band regardless of their interference to each other. At the asymmetric level, only the secondary network performs user scheduling based on various degrees of interference and channel gain knowledge while at the symmetric level both networks do so. Using a lemma on the asymptotic behavior of the largest order statistic and a proposition on the asymptotic sum of lower order statistics, we derive asymptotic primary and secondary sum-rates under simultaneous channel sharing at each coexistence level. As a baseline comparison, time-division (TD) channel sharing is considered. While maintaining the same asymptotic primary sum-rate, the asymptotic secondary sum-rate under TD is compared with that achievable by simultaneous channel sharing. The results indicate that simultaneous channel sharing at both asymmetric and symmetric co-existence levels can outperform TD. Furthermore, this enhancement is achievable asymptotically when user scheduling in uplink mode is based only on the interference gains to the opposite network and not on a network's own channel gains.

The problem of cross-band interference in single-antenna and multi-antenna OFDM cognitive transmitters is considered. Cross-band interference, which is caused by large OFDM signal sidelobes, is a major drawback of OFDM, especially in cognitive radio applications where it is crucial to protect primary licensed users from the secondary user's interference. In this paper, we propose a novel low complexity technique, referred to as a phase adjustment technique, to tackle this problem in single-antenna and multi-antenna OFDM cognitive transmitters. In this technique, the phase of each OFDM symbol is adjusted in an attempt to minimize the interference caused by the secondary user to the primary. Unlike prior methods, this technique does not decrease data throughput and has no impact on the bit-error-rate and peak-to-average power ratio of the OFDM symbols. Furthermore, to calculate the adjustment phases, three heuristics, one of which is very low complexity and achieves near optimal performance in numerical simulations, are also proposed. In addition, the performance of the proposed technique is evaluated analytically in some special cases in single and multi-antenna cognitive transmitters, and is verified by numerical simulations.

We investigate channel-coded physical-layer network coding in a two-way relaying scenario, where the end nodes A and B choose their symbols, S_A and S_B, from a small non-binary field, \mathbb{F}, and adopt a non-binary PSK modulation. The relay then directly decodes the network-coded combination {aS_A+bS_B} over \mathbb{F} from the noisy received superimposed channel-encoded packets. The advantage of working over non-binary fields is that it offers the opportunity to decode according to multiple decoding coefficients (a,b). As only one of the network-coded combinations needs to be successfully decoded, a key advantage is then a reduction in error probability by attempting to decode against all choices of (a,b). In this paper, we compare different mappings between \mathbb{F} and the PSK constellation, and prove that many have identical performance in terms of frame error rate (FER). Moreover, we derive a lower bound on the performance of decoding the network-coded combinations. Simulation results show that if we adopt either i) concatenated Reed-Solomon and convolutional coding or ii) low-density parity check codes, our non-binary constellations can outperform the binary case significantly in the sense of minimizing the FER and, in particular, the ternary constellation has the best FER performance among all considered cases.

Active interference cancellation (AIC) is known to be a very effective technique for reducing the interference of OFDM sidelobes to primary licensed users in cognitive OFDM systems. However, AIC has some shortcomings such as high computational complexity and spectrum overshoot on the cancellation subcarriers. Spectrum overshoot is mainly caused due to unconstrained or unbalanced power allocation to the cancellation tones used in AIC. In this paper, we propose an improved AIC technique in which the problem of spectrum overshoot is tackled. We show that by a modification to the solution of the optimization problem involved in AIC, we can obtain a trade-off between the amount of spectrum overshoot and sidelobe suppression without increasing the system complexity. In particular, spectrum overshoot can be completely eliminated. Furthermore, simulations prove that with this modification, the peak spectral interference at the primary band is less than that of the AIC technique with a single power constraint.

Sufficient and necessary conditions for reliable lossless communication of two correlated sources over classes of phase asynchronous cognitive interference channels are derived. Namely, we consider interference channels in which one of the encoders, i.e., the secondary or cognitive user, is causally or non-causally aware of the other's message. Moreover, as a practical constraint, we assume the transmitters are not aware of the phase shifts introduced by channels. We show that, for both classes of causal and noncausal cooperation, under strong interference conditions, separate source and channel coding is optimal for reliable communication of both users. Also, we derive necessary and sufficient conditions for reliable communication of the cognitive radio transmission while the primary is able to maintain the same information rate it could reliably send in the absence of the secondary. To the best of our knowledge, this is the first work to find fundamental limits of lossless reliable communication for cognitive interference channels.

We investigate the impact of cooperative relaying used to create virtual multipoint-to-point links (as opposed to conventional multihop relaying) on the throughput optimal configuration of a wireless network. We achieve this by formulating a cross-layer framework for a joint routing and scheduling problem with cooperative relaying. We consider a general case, where cooperation is allowed between any pair of nodes in a given network. We optimally solve this formulation for max-min throughput in mesh-like networks of medium size and quantify gains for key performance metrics. We establish that, contrary to popular belief, cooperative relaying provides performance gains in a mid-size network surprisingly rarely. Moreover, if gains can be obtained, these gains are typically only marginal. We quantify these gains and provide engineering insights based on numerical results for 200 random realizations of a network with 16 nodes.

We consider the problem of optimal transmission power for a continuous-time energy harvesting system where energy arrivals occur at random times in random amounts. We do not assume that the energy arrivals are known non-causally and consider the online setting. Here, there is a tradeoff between increasing instantaneous transmission power, which increases instantaneous transmission rate and can reduce battery overflow, and decreasing transmission power, which increases battery life and energy efficiency. We formulate the problem as that of maximizing the average transmission rate or throughput. We first find the non-linear relationship between the transmission power (which is a function of remaining battery energy) and the stationary distribution on the remaining battery energy. We then show that the resulting maximization problem is concave in the distribution of the remaining battery energy. This is non-trivial due to the nonlinear relationship with the transmission power. We then use a calculus of variations approach to derive necessary conditions on the optimal transmission power. Specifically, we find that it must satisfy a first order non-linear autonomous ordinary differential equation (ODE) that has two degree of freedom for optimization purposes, one of which is the initial condition of the ODE. Solving the ODE numerically we compute achieved throughputs as a function of the battery capacity.

We study the lossy communication of a bivariate Gaussian source over an interference channel with the presence of a helping relay. We assume that the wireless links introduce random phase shifts which are unknown to the transmitters (including the relay) and call the channel a phase incoherent interference relay channel (PI-IRC). We derive inner and outer bounds for the achievable distortion region, where the inner bound is derived under specific strong interference conditions as well as strong gain conditions between transmitters and the relay. When the sources are correlated, we find an approximate achievable distortion region in the high SNR regime. In case of independent sources, the bounds are tight and by explicitly providing the achievable distortion region, we show that a separation theorem results for the PI-IRC under strong interference conditions. By removing the relay, the result also specializes to an interference channel.

In this paper, we propose a new technique to reduce the interference caused by high OFDM sidelobes to the primary users in MIMO-OFDM cognitive systems. The proposed technique does not affect the rate of the system and has very low complexity. We also analyze the performance of the technique for a class of flat fading channels and show that the analytical results agree with simulations. Furthermore, for frequency selective fading channels, numerical results show that the performance is almost the same as that obtained analytically for flat fading channels.

Current point-to-multipoint systems suffer significant performance losses due to greater attenuation along the signal propagation path at higher frequencies, transmit power constraints of mobile users and base stations, and interference from neighboring cells. Fractional Frequency Reuse (FFR) is a technique to counteract these effects. Typically, the proposed FFR technique partitions a cell into a reuse 1 area, centered near the base-station and a reuse 3 area, located near the edges of the cell, with reuse 3 regions scheduled to minimize interference from neighboring cells. Unfortunately, virtually all analysis of FFR has been done under a perfect hexagonal lattice cellular grid, while no practical deployment has this degree of symmetry. In this paper we revisit the analysis of FFR for non-ideal cellular grids for cases with fading. We find that while for some non-ideal grids, a combination of reuse 1 and 3 is indeed optimal, for many others a combination of reuse 1 and 4 provide better performance. Thus, we conclude that for practical cellular layouts, the optimal re-use pattern for the edge of the cells is not necessarily 3 as commonly assumed, but is topology dependent.

Cognitive radio is a promising approach for efficient utilization of radio spectrum. Due to its high spectral efficiency and flexibility, OFDM is considered as a good signaling scheme for cognitive radios. In this paper, we investigate the problem of cross-band interference minimization in OFDM-based cognitive systems. Cross-band interference is mainly caused by high OFDM sidelobes. In the first part of our work, we propose a framework to study the trade-off between two recently proposed techniques, adaptive symbol transition which is performed in the time domain, and active interference cancellation which is performed in the frequency domain. We use the trade-off study results to maximize the useful data rate for a desired level of interference. Simulation results show that the best trade-off depends on the configuration of spectral opportunities. In the second part, a new method for interference reduction in multiple-antenna cognitive systems is developed. We show that with knowledge of the channel, the secondary transmitted sequences can be jointly optimized over multiple antennas such that the interference at the primary receiver location is better minimized. Computer simulations demonstrate an improvement of almost 10 dB compared to separate-antenna optimization.

This paper studies the design of beamforming weights for a multi-antenna secondary transmitter in an underlay cognitive setting that simultaneously maximizes the secondary received-power while limiting the primary interference to some threshold ∈. With perfect channel state information (CSI), a closed-form expression for the maximum secondary received-power is found. Under imperfect CSI and when the beamforming weights are computed using the channel estimates, the actual secondary received-power, G, and the actual primary interference-power, I, are derived. We show that the mean E[G] has a term that grows linearly with the number of secondary antennas, N, and additional terms dependent on ∈. Consequently, we obtain tradeoffs between E[G] and c. Under perfect CSI, we show that small increases in c from zero lead to moderate enhancements in E[G] for small N. However, increasing N reduces the enhancements. Under imperfect CSI, the gain in E[G] is less compared to the perfect CSI case. Furthermore, we show that the dominant parts of E[I] are independent of N. Thus, we conclude that there is no significant loss for the secondary to perform null-steering beamforming instead. Moreover, it can employ additional antennas to improve E[G] without generating significant extra interference on the primary.

The objective of this paper is to study the impact of advanced physical layer techniques on the maximum achievable throughput of wireless multihop mesh networks. We formulate a cross-layer optimization framework for the routing and scheduling problem jointly with the following physical layer techniques: successive interference cancellation, superposition coding, dirty-paper coding and their combinations. In the case when each node is enabled with superposition coding, we need to formulate a power allocation subproblem for the optimal power partition of the superimposed signals. We solve these joint problems exactly to compute the maximum achievable throughput in realistic size networks. This allows us to quantify the performance gains obtained by using these techniques (and their combinations). Specifically, we find that the use of dirty-paper coding (only at the gateway) is not justified in networks with mixed uplink and downlink flows. On the other hand, the combination of superposition coding with successive interference outperforms significantly other techniques across all transmission power range for both uplink and downlink flows. We also provide a number of interesting practical insights on throughput improvement by comparing different combinations of these techniques.

We evaluate the performance of a secondary system which is equipped with a multi-antenna transmitter and a single-antenna receiver. The secondary system coexists with a primary system in an underlay cognitive setting where both systems share the same frequency bands simultaneously. A secondary beamforming vector is characterized such that the interference power at each primary receiver is nullified. While constraining the secondary transmitted power, we aim to achieve the maximum received power at the secondary receiver. With perfect channel state information (CSI), we show that the secondary system can achieve a mean received power that grows linearly in the number of secondary transmitting antennas and is directly proportional to the power of the line of sight (LOS) component between the secondary transmitter and the secondary receiver. Furthermore, in the case of imperfect CSI, it is shown that a moderate secondary LOS component can significantly reduce the effect of estimation error on the performance.

We consider simultaneous operation of primary and secondary point-to-multipoint networks in the same frequency band when interference in treated as noise. The variations in channel and interference gains are characterized by path loss and multipath fading. Scaling laws for the primary and secondary sum-rates are derived when the scaling is in terms of the number of primary users. In order to maintain a quality of service requirement for the primary system and simultaneously obtain positive sum-rate for the secondary, a scheduling strategy can be applied to activate users in each network based on their interference gains only. Interestingly, using this strategy, the scaling laws for sum-rates are independent of the path loss consideration of the channel, i.e., the primary and secondary networks can achieve the same asymptotic sum-rates as shown in when only multipath fading is considered. Consequently, while the primary network is protected, we can obtain significant improvement in the asymptotic secondary sum-rate compared to that achievable under channel sharing via time division (TD).

A half-duplex wireless network with 6 lateral nodes, 3 transmitters and 3 receivers, and a central relay is considered. The transmitters wish to send information to their corresponding receivers via a two phase communication protocol. The receivers decode their desired messages by using side information and the signals received from the relay. We derive an outer bound on the capacity region of any two phase protocol as well as 3 achievable regions by employing different relaying strategies. In particular, we combine physical and network layer coding to take advantage of the interference at the relay, using, for example, lattice-based codes. We then specialize our results to the exchange rate. It is shown that for any snr, we can achieve within 0.5 bit of the upper bound by lattice coding and within 0.15 bit, if we take the best of the 3 strategies. Also, lattice coding asymptotically achieves the upper bound as snr -> \infty.

This paper presents a study on achievable throughput in wireless multihop networks with unicast flows that use XOR-like network coding. A joint routing, scheduling, and network coding problem is formulated under a realistic signal to interference plus noise ratio interference model. This formulation provides a mathematical framework to study the achievable throughput of a given wireless network for a given utility function. We optimally solve it for max-min throughput in small to medium size networks by developing an efficient computation tool. Our numerical results show that throughput gains can be obtained at low transmission powers by using simple XOR-like network coding in a mesh-like network provided it is optimally configured in terms of routing, scheduling, and network coding but that they are only significant (i.e., greater than 15%) for some special cases. We also compute max-min throughput by restricting network coding to some key nodes or flows to quantify key conditions that provide a significant portion of gains.

In a bi-directional relay channel, two nodes wish to exchange independent messages over a shared wireless half-duplex channel with the help of a relay. In this paper, we derive achievable rate regions for four new half-duplex protocols and compare these to four existing half-duplex protocols and outer bounds. In time, our protocols consist of either two or three phases. In the two phase protocols, both users simultaneously transmit during the first phase and the relay alone transmits during the second phase, while in the three phase protocol the two users sequentially transmit followed by a transmission from the relay. The relay may forward information in one of four manners; we outline existing amplify and forward (AF), decode and forward (DF), lattice based, and compress and forward (CF) relaying schemes and introduce the novel mixed forward scheme. The latter is a combination of CF in one direction and DF in the other. We derive achievable rate regions for the CF and Mixed relaying schemes for the two and three phase protocols. We provide a comprehensive treatment of eight possible half-duplex bi-directional relaying protocols in Gaussian noise, obtaining their relative performance under different SNR and relay geometries.

In this paper we consider resource allocation for an OFDMA-based cognitive radio point-to-multipoint network with fixed users. Specifically, we assume that secondary users are allowed to transmit on any subchannel provided that the interference that is created to any primary users is below a critical threshold. We focus on the downlink. We formulate the joint subchannel, power and rate allocation problem in the context of finite queue backlogs with a total power constraint at the base station. Thus, users with small backlogs are only allocated sufficient resources to support their backlogs while users with large backlogs share the remaining resources in a fair and efficient fashion. Specifically, we formulate the problem as a max-min problem that is queue-aware, i.e., on a frame basis. We maximize the smallest rate of any user whose backlog cannot be fully transmitted. While the problem is a large non-linear integer program, we propose an iterative method that can solve it exactly as a sequence of linear integer programs, which provides a benchmark against which to compare fast heuristics. We consider two classes of heuristics. The first is an adaptation of a class of multi-step heuristics that decouples the power and rate allocation problem from the subchannel allocation and is commonly found in the literature. To make this class of heuristics more efficient we propose an additional (final) step. The second is a novel approach, called selective greedy, that does not perform any decoupling. We find that while the multi-step heuristic does well in the non-cognitive setting, this is not always the case in the cognitive setting and the second heuristic shows significant improvement at reduced complexity compared to the multi-step approach. Finally, we also study the influence of system parameters such as number of primary users and critical interference threshold on secondary network performance and provide some valuable insights on the operation of such systems.

We consider the coexistence of collocated primary and secondary point-to-multipoint systems such as cellular systems that simultaneously access the same spectrum. In particular, we analyze the number of users and sum-rate of the secondary system as a function of the maximum impact it is allowed to have on the primary system when simultaneous transmissions are treated as interference. As a base reference, we compare to channel sharing via time division (TD).We find that provided the secondary system is given sufficient channel state information, the impact of the interference to the primary system on its sum-rate can be mitigated by judicious choice of the active secondary nodes and quantify the improvement that is realized. This quantification is achieved by a key lemma on the sum of low order statistics of an exponential random variable. We also analyze the more symmetric case when both primary and secondary networks are given sufficient channel state information to selectively activate nodes.

We investigate downlink resource allocation for OFDM-based cognitive radio networks. It is assumed that secondary users are allowed to transmit on all subchannels as long as the interference they create for primary users remains below a critical threshold. We consider a practical setting where secondary users have finite queue backlogs and a total power constraint at the base station and we perform resource allocation either over one or multiple time slots. Specifically, secondary users with small queue backlogs are only allocated sufficient rates to support their traffic demands and the remaining radio resources are shared among highly backlogged users. Under this setting, we formulate the joint subchannel and power problem with max-min fairness for highly backlogged users. Then, we propose an iterative procedure to find an optimal resource allocation solution using an integer program solver. For online implementation, we develop several heuristics of increasing complexity and performance. Numerical results show that the proposed heuristics achieve very good performance compared to the optimal solutions and that taking queue backlogs into account does not make the heuristics much slower while making the system more responsive to users' need.

For a multiple-antenna system, we find a simple and accurate expression for the asymptotic distribution of the antenna selection gain when the transmitter selects the transmit antenna with the strongest channel. We use this to estimate the underlying channel capacity distributions and obtain the approximate ergodic capacity. This estimate is compared with upper and lower bounds. This analysis demonstrates that unlike multiple-input/multiple-output (MIMO) systems, the channel for antenna selection systems hardens at a slower rate, and thus a significant multiuser scheduling gain can exist-Theta (1/logm) for channel selection as opposed to Theta (1/radicm) for MIMO, where m is the number of transmit antennas.

In this letter, we first define a cognitive network to be useful if at least one node can be scheduled to transmit without causing significant simultaneous interference to any primary user and then investigate the interaction between secondary network size and the probability of the secondary network being useful. First the size of the primary network is fixed, and we analyze how quickly the interference threshold limit of the primary network can be reduced as a function of secondary network size. Here there is a tradeoff between the rate of interference threshold reduction and the probability that the secondary network is useful which is completely characterized for Rician fading. We then allow both networks to grow simultaneously. Here the tradeoff is determined in the regime that the interference decreases sufficiently fast for Rayleigh fading. We also investigate the effect of primary channel correlation. Finally, we say that the secondary network is -useful provided at least one of any secondary nodes can be scheduled. We show that in the asymptotic regime, the probabilities of the secondary network being-useful are uniquely related and do not depend on the asymptotic behavior of the interference threshold, the rates at which the networks grow or even the distribution of the fading.

The diversity-multiplexing tradeoff has become a powerful tool to analyze MIMO fading channels. In this paper, we first derive the diversity-multiplexing tradeoff for the case that the transmitter and receiver are connected by multiple parallel and independent MIMO channels, each of which may be utilized for some limiting fraction of the total time. Our main result in this respect is an elegant geometric characterization of the tradeoff curve in terms of the Zheng-Tse tradeoff for a single MIMO channel. We then apply this result to compute bounds on the DMT for a three phase bi-directional cooperation (or two-way relay) protocol. These bounds are shown to be tight in the case of single antenna nodes and the optimal relative time duration of each phase that maximizes the diversity of the end-users is determined. The DMT derived here may also be of importance to problems where a node can be seen to receive information over channels that can be decomposed into parallel independent components, which is often the case in protocols for half-duplex cooperative networks.

We consider a resource allocation problem for spectrum sharing in cognitive radio networks. Specifically, we investigate the joint subchannel, rate and power allocation for secondary users which share, in a non-disruptive manner, some frequency bands with primary users using OFDM technology. We consider the resource allocation problem for downlink and take into account the maximum total power constraints of the base station and the power constraints determined by distributed spectrum sensing and scanning. We formulate a resource allocation problem as an optimization problem which achieves max-min rate sharing among users. We propose both integer program based optimal and suboptimal fast and low complexity approaches for the spectrum sharing problem. Numerical results are then presented for the proposed heuristics and compared with the optimal solution.

In coded bidirectional cooperation, two nodes wish to exchange messages over a shared half-duplex channel with the help of a relay. In this correspondence, we derive performance bounds for this problem for each of three decode-and-forward protocols. The first protocol is a two phase protocol where both users simultaneously transmit during the first phase and the relay alone transmits during the second. In this protocol, our bounds are tight. The second protocol considers sequential transmissions from the two users followed by a transmission from the relay while the third protocol is a hybrid of the first two protocols and has four phases. In the latter two protocols the bounds are not identical. Numerical evaluation shows that in some cases of interest our bounds do not differ significantly. Finally, in the Gaussian case with path loss, we derive achievable rates and compare the relative merits of each protocol. This case is of interest in cellular systems. Surprisingly, we find that in some cases, the achievable rate region of the four phase protocol contains points that are outside the outer bounds of the other two protocols.

For a multiple antenna system, we compute the asymptotic distribution of antenna selection gain when the transmitter selects the transmit antenna with the strongest channel. We use this to asymptotically estimate the underlying channel capacity distributions, and demonstrate that unlike multiple- input/multiple-output (MIMO) systems,the channel for antenna selection systems hardens at a slower rate, and thus a significant multiuser scheduling gain can exist. Additionally, even without this scheduling gain, it is demonstrated that transmit antenna selection systems outperform open loop MIMO systems at low signal-to-interference-plus-noise ratio (SINR) regimes, particularly for small number of receive antennas. This may have some implications on wireless system design, because most of the users in modern wireless systems have low SINRs.

In this paper, we consider the class of channels corrupted by timing errors and intersymbol interference (ISI). The timing error is modeled as a discrete-time, discrete-valued random walk process. We prove that this class of channels are information stable for Markov input process of any finite order, and thereby, show achievable transmission rates in terms of asymptotic information rates. The proof we provide is extended from the proof by Dobrushin for the class of memoryless channels with independent timing errors.

Cognitive radio promises a low-cost, highly flexible alternative to the classic single-frequency band, single-protocol wireless device. By sensing and adapting to its environment, such a device is able to fill voids in the wireless spectrum and can dramatically increase spectral efficiency. In this paper, the cognitive radio channel is defined as a two-sender, two-receiver interference channel in which sender 2 obtains the encoded message sender 1 plans to transmit. We consider two cases: in the genie-aided cognitive radio channel, sender 2 is noncausally presented the data to be transmitted by sender 1 while in the causal cognitive radio channel, the data is obtained causally. The cognitive radio at sender 2 may then choose to transmit simultaneously over the same channel, as opposed to waiting for an idle channel as is traditional for a cognitive radio. Our main result is the development of an achievable region which combines Gel'fand-Pinkser coding with an achievable region construction for the interference channel. In the additive Gaussian noise case, this resembles dirty-paper coding, a technique used in the computation of the capacity of the Gaussian multiple-input multiple-output (MIMO) broadcast channel. Numerical evaluation of the region in the Gaussian noise case is performed, and compared to an inner bound, the interference channel, and an outer bound, a modified Gaussian MIMO broadcast channel. Results are also extended to the case in which the message is causally obtained.

Costa has proved that for noncausally known Gaussian interference at a power constrained transmitter communicating over an additive white Gaussian noise channel there is no capacity loss when compared to a scenario where interference is not present. For the case of a transmitter communicating over a quasistatic (i.e., nonergodic) fading channel, his method does not apply. In this correspondence, we derive upper and lower bounds on the capacity of compound channels with side information at the transmitter, first for finite alphabet channels and then, based on this result, for channels on standard alphabets (this includes real alphabets). For the special case of a degenerate compound channel with only one possible realization, our bounds are equivalent to the well-known capacity with side-information formula of Gel'fand and Pinsker. For the quasistatic fading channel, when fading is Ricean, we suggest a scheme based on our lower bound for which the performance is found to be relatively good even for moderate K-factor. As K/spl rarr//spl infin/, the uncertainty on the channel vanishes and our scheme obtains the performance of dirty paper coding, namely that the interference is perfectly mitigated. As K/spl rarr/0, the proposed scheme treats the interferer as additional noise. These results may be of importance for the emerging field of cognitive radios where one user may be aware of another user's intended message to a common receiver, but is unaware of the channel path gains.

We consider the problem of designing optimal probing signals for finite-hypothesis testing. Equivalently, we cast the problem as the design of optimal channel input sequences for identifying a discrete channel under observation from a finite set of known channels. The optimality criterion that we employ is the exponent of the Bayesian probability of error. In our study, we consider a feedforward scenario where there is no feedback from the channel output to the signal selector at the channel input and a feedback scenario where the past channel outputs are revealed to the signal selector. In the feedforward scenario, only the type of the input sequence matters and our main result is an expression for the error exponent in terms of the limiting distribution of the input sequence. In the feedback case, we show that when discriminating between two channels, the optimal scheme in the first scenario is simultaneously the optimal time-invariant Markov feedback policy of any order.

The performance of two-phase collaborative communication protocols is studied for wireless networks. All the communication nodes in the cluster are assumed to share the same channel and transmit or receive collaboratively in a quasi-static Rayleigh flat-fading environment. In addition to small-scale fading, the effect of large-scale path loss is also considered. Based on a decode-and-forward approach, we consider various variable-rate two-phase protocols that can achieve full diversity order and analyze the effect of node geometry on their performance in terms of the outage probability of mutual information. For the single-relay node case, it is shown that if the collaborator node is close to the source node, a protocol based on space-time coding (STC) can achieve good diversity gain. Otherwise, a protocol based on receiver diversity performs better. These protocols are also compared with one based on fixed-rate repetition coding and their performance tradeoffs with node geometry are studied. The second part deals with multiple relays. It is known that with N relays an asymptotic diversity order of N+1 is achievable with STC-based protocols in the two-phase framework. However, in the framework of collaborative STC, those relay nodes which fail to decode remain silent (this event is referred to as a node erasure). We show that this node erasure has the potential to considerably reduce the diversity order and point out the importance of designing the STC to be robust against such node erasure

The performance of collaborative beamforming is studied using the theory of random arrays within the framework of wireless sensor networks. With the application to ad hoc networks in mind, two scenarios, one denoted closed-loop and the other open-loop, are considered. Associated with these scenarios, the effects of phase jitter and location estimation errors on the average beam pattern are analyzed.

The use of the spatial dimension is known to greatly increase the reliability of quasi-static (non-ergodic) wireless channels. In this paper we demonstrate that most of this gain can also he achieved through collaborative communications with single-antenna/multiple-antenna nodes when there is one receiving agent. In particular, for the single antenna case, we consider communication to take place between clusters of nearby nodes. We show the existence of collaborative codes for communications for which the intra-cluster negotiation penalty is in principle small and almost all the diversity gain of traditional space-time codes may be realized. For example, for single transmitter/receiver nodes with two collaborators that have as little as 10 dB path loss advantage over the receiver, the penalty for collaboration over traditional space-time systems is negligible.

A cognitive radio can sense the transmission of other users in its environment and possibly extract the corresponding messages. It can use this information to transmit over the same channel while reducing interference from, and to other users. In this paper, we define inter/intra-cluster competitive, cooperative, and cognitive behavior in wireless networks. We define intercluster cognitive behavior as simultaneous transmissions by two or more clusters in which some clusters know the messages to be transmitted by other clusters, and so can act as relays or use a Gel'fand-Pinsker coding-like technique to mitigate interference. We construct an achievable region for the inter-cluster behavior of two multiple access channels. In the Gaussian case, we compare our achievable region to that of competitive behavior as well as that of cooperative behavior

The use of the spatial dimension is known to greatly increase the reliability of quasi-static (i.e., nonergodic) wireless channels. In this paper, it is demonstrated that most of this gain can also be achieved through collaborative communications with single-antenna/multiple-antenna nodes when there is one receiving agent. In particular, for the single-antenna case, communication is considered to take place between clusters of nearby nodes. The existence of collaborative codes for which the intra-cluster negotiation penalty is, in principle, small (and almost all the diversity gain of traditional space-time codes may be realized) is shown. For example, for a single transmitter node with two collaborators and one receiver node, if the collaborators have as little as a 10-dB path loss advantage over the receiver, the penalty for collaboration over traditional space-time systems is negligible.

We consider the issue of signal selection in hypothesis testing. In particular, we model each hypothesis as a discrete memoryless channel. We first derive the Bayesian error exponent as a function of the limiting empirical distribution of the input sequence. We show that in the case of discriminating between two hypotheses, the asymptotically optimal input sequence consists of always repeating the same input. Finally, we derive an efficient method to evaluate the error exponent as a function of the limiting distribution.

The performance of collaborative beamforming is analyzed using the theory of random arrays. The statistical average and distribution of the beam patterns of randomly generated phased arrays are derived in the framework of wireless ad hoc sensor networks. Each sensor node is assumed to have a single isotropic antenna and nodes in the cluster collaboratively transmit the signal such that the signal in the target direction is coherently added in the far-field region. The distribution of the maximum beam pattern sidelobe is also analyzed and a closed-form bound is derived based on the Gaussian approximation method.

We investigate the joint source-channel coding problem of transmitting nonuniform memoryless sources over binary phase-shift keying-modulated additive white Gaussian noise and Rayleigh fading channels via turbo codes. In contrast to previous work, recursive nonsystematic convolutional encoders are proposed as the constituent encoders for heavily biased sources. We prove that under certain conditions, and when the length of the input source sequence tends to infinity, the encoder state distribution and the marginal output distribution of each constituent recursive convolutional encoder become asymptotically uniform, regardless of the degree of source nonuniformity. We also give a conjecture (which is empirically validated) on the condition for the higher order distribution of the encoder output to be asymptotically uniform, irrespective of the source distribution. Consequently, these conditions serve as design criteria for the choice of good encoder structures. As a result, the outputs of our selected nonsystematic turbo codes are suitably matched to the channel input, since a uniformly distributed input maximizes the channel mutual information, and hence, achieves capacity. Simulation results show substantial gains by the nonsystematic codes over previously designed systematic turbo codes; furthermore, their performance is within 0.74-1.17 dB from the Shannon limit. Finally, we compare our joint source-channel coding system with two tandem schemes which employ a fourth-order Huffman code (performing near-optimal data compression) and a turbo code that either gives excellent waterfall bit-error rate (BER) performance or good error-floor performance. At the same overall transmission rate, our system offers robust and superior performance at low BERs (< 10/sup -4/), while its complexity is lower.

Summary form only given. All traditional data compression techniques, such as Huffman coding, the Lempel-Ziv algorithm, run-length limited coding, Tunstall coding and arithmetic coding are highly susceptible to residual channel errors and noise. We have previously proposed the use of parallel concatenated codes and iterative decoding for fixed-length to fixed-length source coding, i.e., turbo coding for data compression purposes. The work presented here extends these results and also considers the case when decompression must be done from compressed data corrupted by additive white Gaussian noise (AWGN).

This paper proposes a practical coding scheme for the Slepian-Wolf problem of separate encoding of correlated sources. Finite-state machine (FSM) encoders, concatenated in parallel, are used at the transmit side and an iterative turbo decoder is applied at the receiver. Simulation results of system performance are presented for binary sources with different amounts of correlation. Obtained results show that the proposed technique outperforms by far both an equivalent uncoded system and a system coded with traditional (non-concatenated) FSM coding.