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In this paper, we consider the radio resource allocation problem for uplink orthogonal frequency-division multiple-access (OFDMA) systems. The existing algorithms have been derived under the assumption of Gaussian inputs due to its closed-form expression of mutual information. For the sake of practicality, we consider the system with finite symbol alphabet (FSA) inputs and solve the problem by capitalizing on the recently revealed relationship between mutual information and minimum mean square error (MMSE). We first relax the problem to formulate it as a convex optimization problem, and then, we derive the optimal solution via decomposition methods. The optimal solution serves as an upper bound on the system performance. Due to the complexity of the optimal solution, a low-complexity suboptimal algorithm is proposed. Numerical results show that the presented suboptimal algorithm can achieve performance very close to the optimal solution and that it outperforms the existing suboptimal algorithms. Furthermore, using our proposed algorithm, significant power saving can be achieved in comparison to the case when a Gaussian input is assumed. [ABSTRACT FROM PUBLISHER]

In this paper, system design for a multiple-input–multiple-output (MIMO) relay broadcast channel with individual signal-to-interference-plus-noise ratio (SINR) constraints at mobile stations (MSs) is considered. By exploring the structure of downlink–uplink duality at either the base station (BS) or the relay station (RS), we propose two schemes of joint power allocation and beamforming design at the BS and the RS. The problem of the existence of feasible solutions under practical power constraints at the BS and the RS with given SINR targets is considered first. Then, the problem of sum power minimization is considered. Each design problem can be efficiently solved using optimal joint power allocation and beamforming under the framework of convex optimization. We also show that with subchannel pairing at the RS, the transmission power can be further reduced. Finally, a generalization to the multihop scenario is provided to further improve power efficiency. [ABSTRACT FROM PUBLISHER]

This paper studies downlink coverage maximization for cellular networks in which base station (BS) locations are modeled using a spatial Poisson point process, considering three different coverage models, and under constraints on transmit power, BS density and transmit power density. Firstly, the coverage optimization problem is solved analytically for the first coverage model that focuses on noise-limited communication by ignoring interference and random fading effects. This model provides useful insights into the significance of bounded path loss models to obtain meaningful solutions for this problem. The other two coverage models are based on the users' received signal-to-interference-plus-noise-ratio (\sinr) from their associated BSs. For these models, it is shown that the coverage optimization problem can be reduced to a constrained single dimensional optimization problem without any loss of optimality. The related solutions can be obtained with limited computational complexity by resorting to a numerical search over a compact subset of candidate values. Bounds on the optimum BS density are also provided to further truncate the search space. All results are derived for general bounded path loss models. Specific applications are also illustrated to provide further design insights and to highlight the importance of using bounded path loss models for coverage analysis. [ABSTRACT FROM PUBLISHER]

Radar signal design in a spectrally crowded environment is a very challenging and topical problem due to the increasing demand for both military surveillance/remote-sensing capabilities and civilian wireless services. This paper deals with the synthesis of optimized radar waveforms ensuring spectral compatibility with the overlayed licensed electromagnetic radiators. A priori information, for instance, provided by a radio environmental map (REM), is exploited to force a spectral constraint on the radar waveform, which is thus the result of a constrained optimization process aimed at improving some radar performances (such as detection, sidelobes, resolution, tracking). The feasibility of the waveform optimization problem is extensively studied, and a solution technique leading to an optimal waveform is proposed. The procedure requires the relaxation of the original problem into a convex optimization problem and involves a polynomial computational complexity. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable signal to interference plus noise ratio (SINR), spectral shape, and the resulting autocorrelation function (ACF). [ABSTRACT FROM AUTHOR]

This paper considers a conditional decode-and-forward (DF) based cooperative system where a source (S) with multiple (M) antennas transmits information to a single-antenna destination (D) with the help of multiple (L ≤ M-1) single-antenna relays (R_i). The optimal transmit weighting vector at the source is not available in the literature due to the non-linear conditional DF operation, which renders the problem non-convex. To solve this problem, we first show that the optimal transmit vector for the single-relay system can be determined by comparing the S-D beamformer (maximum-ratio-transmit beamforming vector for the S-D link) with the one that utilizes "just sufficient" energy to activate the relay-link. However, it is difficult to directly apply the above idea to the multi-relay system, due to the fact that the S-R and S-D links are normally not orthogonal. To tackle this issue, we propose to utilize basis functions that are orthogonal to the S-D and S-R links, respectively, which enables the activating of one S-R link without considering the S-D link and the other S-R links. We then apply the new basis functions to the multi-relay system and propose a selective relay-activation algorithm, where the optimal solution is obtained by comparing the S-D beamformer with schemes that selectively activate different combinations of the relay-links. The selective relay-activation algorithm is different from the conventional water-filling in the sense that the energy is filled to discrete levels to activate the S-R links, a unique feature arising from the conditional DF operation. [ABSTRACT FROM PUBLISHER]

The interference to the primary receiver (PR) is a critical issue in the resource allocation of cognitive radio (CR) networks. For instance, the nonlinearity of the power amplifier (PA) causes nonlinear interference to the PRs. This paper studies the power allocation in cognitive radio networks by considering the nonlinear effects of the PA on the received signal-to-noise ratio (SNR) at the secondary receiver (SR) and the adjacent channel interference (ACI) to the PRs. A nonlinear PA with limited dynamic range and a lower limit on the transmit power is assumed for the secondary transmitter (ST). To control the resulting ACI from the ST to the PRs, the PA needs to be turned off in some fading blocks. To investigate the throughput, an analytical expression for the probability of data transmission between the secondary users is derived as a function of the interference temperature limits of the PRs. All analyses are performed for both peak and average ACI power constraints. Through theoretical analysis and simulation studies, maximum achievable average SNR at the SR is investigated. Moreover, the throughput degradation is studied and it is observed that the average ACI power constraints result in the better performance than the peak ones. [ABSTRACT FROM PUBLISHER]

This paper considers joint linear processing at multi-antenna sources and one multiple-input multiple-output (MIMO) relay station for both one-way and two-way relay-assisted wireless communications. The one-way relaying is applicable in the scenario of downlink transmission by a multi-antenna base station to multiple single-antenna users with the help of one MIMO relay. In such a scenario, the objective of join linear processing is to maximize the information throughput to users. The design problem is equivalently formulated as the maximization of the worst signal-to-interference-plus-noise ratio (SINR) among all users subject to various transmission power constraints. Such a program of nonconvex objective minimization under nonconvex constraints is transformed to a canonical d.c. (difference of convex functions/sets) program of d.c. function optimization under convex constraints through nonconvex duality with zero duality gap. An efficient iterative algorithm is then applied to solve this canonical d.c program. For the scenario of using one MIMO relay to assist two sources exchanging their information in two-way relying manner, the joint linear processing aims at either minimizing the maximum mean square error (MSE) or maximizing the total information throughput of the two sources. By applying tractable optimization for the linear minimum MSE estimator and d.c. programming, an iterative algorithm is developed to solve these two optimization problems. Extensive simulation results demonstrate that the proposed methods substantially outperform previously-known joint optimization methods. [ABSTRACT FROM PUBLISHER]

Cooperative communications via multiple relay nodes is known to provide the benefits of increase diversity and coverage. Simultaneous transmission via multiple relays, however, requires strong coordination between nodes either in terms of slot-based transmission or distributed space–time (ST) code implementation. Dynamically selecting a single best relay out of multiple relays and then using it alone for cooperative transmission alleviates the need for this strong coordination while still reaping the benefits of increased diversity and coverage. In this paper, we consider the design of relay selection (RS) under an imperfect knowledge of long-term channel state information (CSI) at the relay nodes, and we pursue minimax optimization to arrive at a robust RS approach that promises the best guarantee on the worst-case end-to-end signal-to-noise ratio (SNR). We provide some intuitive examples and extensive simulation results, not only in terms of worst-case SNR performance but also in terms of average bit-error-rate (BER) performance, to demonstrate the benefits of the proposed minimax robust RS scheme. [ABSTRACT FROM PUBLISHER]

This paper considers a downlink beamforming problem in a cognitive radio network where multiple primary and secondary cells coexist. Each multiantenna primary and secondary transmitter serves its own set of single antenna users. The optimization objective is to minimize the sum transmission power over secondary transmitters while guaranteeing the minimum SINR for each secondary user and satisfying the maximum aggregate interference power constraint for each primary user. We propose a decentralized beamforming algorithm where the original centralized problem is decomposed via primal decomposition method into two levels, i.e., transmitter-level subproblems managed by a network-level master problem. The master problem is solved independently at each secondary transmitter using a projected subgradient method requiring limited backhaul signaling among secondary transmitters. To solve the independent transmitter-level subproblems, we propose three alternative approaches which are based on second order cone programming, semidefinite programming and uplink–downlink duality. Special emphasis is put on the last approach, which is also considered in a multi-cell MISO cellular network. Numerical results show that the proposed algorithm achieves close to optimal solution even after a few iterations in quasi-static channel conditions. Moreover, near centralized performance is demonstrated in time-correlated channels. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of optimizing signal constellations for strong phase noise. The problem is investigated by considering three optimization formulations, which provide an analytical framework for constellation design. In the first formulation, we seek to design constellations that minimize the symbol error probability (SEP) for an approximate ML detector in the presence of phase noise. In the second formulation, we optimize constellations in terms of mutual information (MI) for the effective discrete channel consisting of phase noise, additive white Gaussian noise, and the approximate ML detector. To this end, we derive the MI of this discrete channel. Finally, we optimize constellations in terms of the MI for the phase noise channel. We give two analytical characterizations of the MI of this channel, which are shown to be accurate for a wide range of signal-to-noise ratios and phase noise variances. For each formulation, we present a detailed analysis of the optimal constellations and their performance in the presence of strong phase noise. We show that the optimal constellations significantly outperform conventional constellations and those proposed in the literature in terms of SEP, error floors, and MI. [ABSTRACT FROM PUBLISHER]

The outage probability limit is a fundamental and achievable lower bound on the word error rate of coded communication systems affected by fading. This limit is mainly determined by two parameters: the diversity order and the coding gain. With linear precoding, full diversity on a block fading channel can be achieved without error-correcting code. However, the effect of precoding on the coding gain is not well known, mainly due to the complicated expression of the outage probability. Using a geometric approach, this paper establishes simple upper bounds on the outage probability, the minimization of which yields to precoding matrices that achieve very good performance. For discrete alphabets, it is shown that the combination of constellation expansion and precoding is sufficient to closely approach the minimum possible outage achieved by an i.i.d. Gaussian input distribution, thus essentially maximizing the coding gain. [ABSTRACT FROM PUBLISHER]

In cellular networks, proximity users may communicate directly without going through the base station, which is called Device-to-device (D2D) communications and it can improve spectral efficiency. However, D2D communications may generate interference to the existing cellular networks if not designed properly. In this paper, we study a resource allocation problem to maximize the overall network throughput while guaranteeing the quality-of-service (QoS) requirements for both D2D users and regular cellular users (CUs). A three-step scheme is proposed. It first performs admission control and then allocates powers for each admissible D2D pair and its potential CU partners. Next, a maximum weight bipartite matching based scheme is developed to select a suitable CU partner for each admissible D2D pair to maximize the overall network throughput. Numerical results show that the proposed scheme can significantly improve the performance of the hybrid system in terms of D2D access rate and the overall network throughput. The performance of D2D communications depends on D2D user locations, cell radius, the numbers of active CUs and D2D pairs, and the maximum power constraint for the D2D pairs. [ABSTRACT FROM AUTHOR]

This paper presents an optimization framework for a wireless sensor network whereby, in a given route, the optimal relay selection and power allocation are performed subject to signal-to-noise ratio constraints. The proposed approach determines whether a direct transmission is preferred for a given configuration of nodes, or a cooperative transmission. In the latter case, for each node, data transmission to the destination node is performed in two consecutive phases: broadcasting and relaying. The proposed strategy provides the best set of relays, the optimal broadcasting power and the optimal power values for the cooperative transmission phase. Once the minimum-energy transmission policy is obtained, the optimal routes from every node to a sink node are built-up using cooperative transmission blocks. We also present a low-complexity implementation approach of the proposed framework and provide an explicit solution to the optimization problem at hand by invoking the theory of multi-parametric programming. This technique provides the optimal solution as a function of measurable parameters in an off-line manner, and hence the on-line computational tasks are reduced to finding the parameters and evaluating simple functions. The proposed efficient approach has many potential applications in real-world problems and, to the best of the authors' knowledge, it has not been applied to communication problems before. Simulations are presented to demonstrate the efficacy of the approach. [ABSTRACT FROM AUTHOR]

Spectrum sensing is an important aspect of cognitive radio networks (CRNs). Secondary users (SUs) should periodically sense the channels to ensure primary-user (PU) protection. Sensing with cooperation among several SUs is more robust and less error prone. However, cooperation also increases the energy spent for sensing. Considering the periodic nature of sensing, even a small amount of savings in each sensing period leads to considerable improvement in the long run. In this paper, we consider the problem of energy-efficient (EE) spectrum sensing scheduling with satisfactory PU protection. Our model exploits the diversity of SUs in their received signal-to-noise ratio (SNR) of the primary signal to determine the sensing duration for each user/channel pair for higher energy efficiency. We model the given problem as an optimization problem with two different objectives. The first objective is to minimize the energy consumption, and the second objective is to minimize the spectrum sensing duration to maximize the remaining time for data transmission. We solve both problems using the outer linearization method. In addition, we present two suboptimal but efficient heuristic methods. We provide an extensive performance analysis of our proposed methods under various numbers of SUs, average channel SNR, and channel sampling frequency. Our analysis reveals that all proposals with an energy minimization perspective provide significant energy savings compared with a pure transmission-time maximization (TXT) technique. [ABSTRACT FROM PUBLISHER]

We consider hybrid automatic repeat request with Chase combining (HARQ-CC) in a Rayleigh block-fading channel, where rate adaptation is based on long-term channel statistics instead of instantaneous channel information because the latter may be outdated. In the HARQ-CC with long-term rate adaptation, optimal selection of the transmission rate for each HARQ round requires a two-step numerical search procedure with heavy computational burden. In this paper, we propose two suboptimal rate-selection algorithms that substantially reduce the computational burden: In the first scheme, the solution of the second-step numerical search is approximated by using a closed-form Lambert W function, and in the second scheme, additionally, the solution of the first-step numerical search is approximated by a closed-form lower bound. Analytical and numerical results are presented to show that the proposed algorithms yield the performance of the long-term average transmission rate tightly approaching that of the optimal numerical search algorithm. [ABSTRACT FROM PUBLISHER]

A generalized version of orthogonal frequency division multiplexing interleave division multiple access (OFDM-IDMA) referred to as grouped OFDM-IDMA (G-OFDM-IDMA) is introduced in this paper. By dividing users into groups and transmitting each group's data only on some (as opposed to all) subcarriers, G-OFDM-IDMA can have much lower decoding complexity compared with conventional OFDM-IDMA while preserving the bit error probability (BEP) performance and the bandwidth efficiency. The user grouping problem is formulated into an integer linear programming problem whose suboptimal solution is proposed and compared with the lower bound. The optimization complexity issue is also addressed. Simulations are carried out to test the performance of G-OFDM-IDMA under various system configurations. It is observed that up to 80% complexity could be saved when the conventional OFDM-IDMA is substituted by G-OFDM-IDMA configured according to the suboptimal grouping solution. [ABSTRACT FROM AUTHOR]

Beamforming design for multi-user wireless relay networks under the criterion of maximin information throughput is an important but also very hard optimization problem due to its nonconvex nature. The existing approach to reformulate the design as a matrix rank-one constrained optimization problem is highly inefficient. This paper exploits the d.c. difference of two convex functions) structure of the objective function and the convex structure of the constraints in such a global optimization problem to develop efficient iterative algorithms of very low complexity to find the solutions. Both cases of concurrent and orthogonal transmissions from sources to relays are considered. Numerical results indicate that the proposed algorithms provide solutions that are very close to the upper bound on the solution of the non-orthogonal source transmissions case and are almost equal to the optimal solution of the orthogonal source transmissions case. This demonstrates the ability of the developed algorithms to locate approximations close to the global optimal solutions in a few iterations. Moreover, the proposed methods are superior to other methods in both performance and computation complexity. [ABSTRACT FROM AUTHOR]

Energy-aware power allocation in cooperative communication systems depends on the availability of channel state information (CSI), which is often imperfect in practice. This paper proposes energy-aware, optimal power allocation schemes for an amplify-and-forward cooperative communication system in the presence of imperfect CSI, to i) minimize the total transmit power; ii) maximize the end-to-end signal-to-noise ratio (SNR). Energy awareness is incorporated in the schemes either by avoiding over-provisioning of quality-of-service (QoS) or, by preventing unsuccessful transmissions, depending on the application. In both the schemes, the source and relay nodes share a fixed total transmit power budget, and transmission is allowed only if the minimum required end-to-end SNR can be achieved with the available power budget. Closed-form expressions for the source and relay optimal transmit powers are derived for both these schemes. Performance of the systems under the proposed schemes are investigated through simulations. [ABSTRACT FROM AUTHOR]

This paper considers a multiple-input–multiple-output (MIMO) cognitive radio network, in which a single primary user (PU) coexists with multiple secondary users (SUs). Standard cognitive radio techniques require perfect channel state information (CSI) and are sensitive to the errors in channel estimation. In practice, such errors are inevitable due to finite feedback resources, quantization errors, and other physical constraints. As a result, robustness has become a crucial issue. Under the assumption of norm-bounded channel imperfections, we develop a robust energy-aware design that strives to admit the maximum number of served SUs and enhance the transmit rate of the secondary links while using the least transmit power while imposing the upper limit of the interference temperature to the PU. A utility function is introduced to achieve the multi-objective function by dealing with the tradeoff between maximizing the transmit rate and minimizing the transmit power. Instead of using the deterministic target, the signal-to-interference-and-noise ratio (SINR) target becomes one of the ongoing optimization variables, which is dynamically adapted with the channel condition under the pre-specified bit-error-rate (BER) requirement, and acts as admission control implicitly, i.e., the cognitive link is not allowed to transmit as long as its SINR falls to zero. Due to its nonconvexity and NP-hardness, the underlying problem is reformulated in a semidefinite programming (SDP) form and solved via an iterative bisection search approach. Simulation results are provided to validate the robustness and efficiency of the proposed scheme. [ABSTRACT FROM PUBLISHER]

This paper studies the structure of downlink sum-rate maximizing selective decentralized feedback policies for opportunistic beamforming under finite feedback constraints on the average number of mobile users feeding back. First, it is shown that any sum-rate maximizing selective decentralized feedback policy must be a threshold feedback policy. This result holds for all fading channel models with continuous distribution functions. Second, the resulting optimum threshold selection problem is analyzed in detail. This is a nonconvex optimization problem over finite-dimensional Euclidean spaces. By utilizing the theory of majorization, an underlying Schur-concave structure in the sum-rate function is identified, and the sufficient conditions for the optimality of homogenous threshold feedback policies are obtained. Applications of these results are illustrated for well-known fading channel models such as Rayleigh, Nakagami, and Rician fading channels. Rather surprisingly, it is shown that using the same threshold value at all mobile users is not always a rate-wise optimal feedback strategy, even for a network in which mobile users experience statistically the same channel conditions. For the Rayleigh fading channel model, on the other hand, homogenous threshold feedback policies are proven to be rate-wise optimal if multiple orthonormal data carrying beams are used to communicate with multiple mobile users simultaneously. [ABSTRACT FROM PUBLISHER]

In communication theory, the conventional role of precoding is to reshape a spectrally flat sequence of data symbols in favor of a particular inter-symbol interference (ISI) channel such that signal-to-noise power ratio (SNR) is maximized. In contrast, in this paper a novel precoding technique is devised to exploit a particular property of the information source rather than the channel. Specifically, we focus on the scenario where the source sequence has an inherent correlation that is known a priori, and design a precoding filter that attempts to reduce the average power of the resulting transmit symbol sequences. The proposed precoding technique is thus termed power-reductive precoding (PRP). This reduction of average transmit power accrues without changing the amplitude of the original sequence and thus can be treated as a system gain. An apparent drawback of this approach is the intentional ISI introduced by the precoding filter, which may lessen the minimum distance among legitimate sequences. Nevertheless, we show that even if the loss associated with the minimum distance penalty is properly taken into account, the achievable error rate versus SNR can be made superior to the case without precoding. [ABSTRACT FROM AUTHOR]

The maximization of the ergodic capacity for single-stream beamforming, which is a (constrained) transmission scheme referred to as “optimum beamforming,” has been extensively addressed in the open literature for multiple-input–single-output (MISO) Rayleigh fading channels and spatially uncorrelated MISO Rician fading channels with a unit transmit covariance matrix, and closed-form solutions have been derived for these cases. However, optimum beamforming for spatially correlated or uncorrelated MISO Rician fading channels with a nonunit transmit covariance matrix has received less attention and remains a complex multidimensional optimization problem. This paper first proves that this convex constrained optimization problem can be reduced to only one dimension; hence, it can be solved very fast using standard 1-D search algorithms. Then, simulations mainly performed for linear equispaced antenna arrays demonstrate that: 1) the proposed method for the calculation of the optimum beamformer has significantly lower computational complexity compared with other currently used multidimensional algorithms; and 2) the optimum beamformer further improves capacity compared with the (single-stream) beamforming transmission that maximizes the signal-to-noise ratio (SNR) at the receiver, whereas in some operational environments, it achieves ergodic capacity that is very close or equal to the maximum ergodic capacity. [ABSTRACT FROM PUBLISHER]

A fundamental aspect in performance engineering of wireless networks is optimizing the set of links that can be concurrently activated to meet given signal-to-interference-plus-noise ratio (SINR) thresholds. The solution of this combinatorial problem is the key element in scheduling and cross-layer resource management. In this paper, we assume multiuser decoding (MUD) receivers, which can cancel strongly interfering signals. As a result, in contrast to classical spatial reuse, links being close to each other are more likely to be active concurrently. Our focus is to gauge the gain of successive interference cancelation (SIC) and the simpler, yet instructive, case of parallel interference cancelation (PIC) in the context of optimal link activation (LA). We show that both problems are NP-hard, and we develop compact integer linear programming (ILP) formulations to approach global optimality. We provide an extensive numerical performance evaluation, indicating that, for low to medium SINR thresholds, the improvement is quite substantial, particularly with SIC, whereas for high SINR thresholds, the improvement diminishes, and both schemes perform equally well. [ABSTRACT FROM PUBLISHER]

Joint channel and rate allocation with power minimization in orthogonal frequency-division multiple access (OFDMA) has attracted extensive attention. Most of the research has dealt with the development of suboptimal but low-complexity algorithms. In this paper, the contributions comprise new insights from revisiting tractability aspects of computing the optimum solution. Previous complexity analyses have been limited by assumptions of fixed power on each subcarrier or power-rate functions that locally grow arbitrarily fast. The analysis under the former assumption does not generalize to problem tractability with variable power, whereas the latter assumption prohibits the result from being applicable to well-behaved power-rate functions. As the first contribution, we overcome the previous limitations by rigorously proving the problem's NP-hardness for the representative logarithmic rate function. Next, we extend the proof to reach a much stronger result, namely, that the problem remains NP-hard, even if the channels allocated to each user are restricted to be a consecutive block with given size. We also prove that, under these restrictions, there is a special case with polynomial-time tractability. Then, we treat the problem class where the channels can be partitioned into an arbitrarily large but constant number of groups, each having uniform gain for every individual user. For this problem class, we present a polynomial-time algorithm and provide its optimality guarantee. In addition, we prove that the recognition of this class is polynomial-time solvable. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a simple power-allocation technique between the source and the relay node and between the data and pilot symbols in a dual-hop amplify-and-forward (AF) relay channel, which is subject to unknown but correlated channel variations. For this purpose, we prove tight lower and upper bounds for the channel-estimation-error variance and use it in an effective signal-to-noise ratio (SNR) to optimize the power-allocation coefficients in the system. The power-allocation solution depends on long-term channel statistics and system parameters and hence can be used in practice. We demonstrate the efficacy of the proposed power-allocation technique by comparing it with equal power allocation (EPA) between nodes and symbols and with well-known and recently derived capacity performance benchmarks. Numerical results indicate that, by proper power allocation, we are able to get closer to harvesting the inherent information rate of correlated dual-hop relay channels. [ABSTRACT FROM PUBLISHER]

Recent advances in interference cancellation and signal-processing techniques can enable full-duplex radios and multipacket reception (MPR), which will have significant impact on medium access control (MAC) schemes. In this paper, we propose a MAC scheme in unmanned aerial vehicle (UAV) ad-hoc networks with full-duplex radios and MPR. To efficiently handle the highly mobile environment of a UAV ad-hoc network, a token-based technique is used to update information in the network as well. The MAC schemes in the presence of perfect and imperfect channel state information (CSI) are formulated as a combinatorial optimization problem and a discrete stochastic optimization problem, respectively. Simulation results show the effectiveness of the proposed MAC. [ABSTRACT FROM PUBLISHER]

In a multi-user wireless network equipped with multiple relay nodes, some relays are more intelligent than other relay nodes. The intelligent relays are able to gather channel state information, perform linear processing and forward signals whereas the dumb relays are only able to serve as amplifiers. As the dumb relays are oblivious to the source and destination nodes, the wireless network can be modeled as a relay network with smart instantaneous relay only: the signals of the source-destination link arrive at the same time as the source-relay-destination link. Recently, instantaneous relaying is shown to improve the degrees-of-freedom of the network as compared to the classical cut-set bound. In this paper, we study an achievable rate region and its boundary of the instantaneous interference relay channel in the scenario of (a) uninformed non-cooperative source-destination nodes (source and destination nodes are not aware of the existence of the relay and are non-cooperative) and (b) informed and cooperative source-destination nodes. Further, we examine the performance of interference neutralization: a relay strategy which is able to cancel interference signals at each destination node in the air. We observe that interference neutralization, although promised to achieve desired degrees-of-freedom, may not be feasible if the relay has limited power. Simulation results show that the optimal relay strategies improve the achievable rate region and provide better user-fairness in both uninformed non-cooperative and informed cooperative scenarios. [ABSTRACT FROM PUBLISHER]

Large-scale propagation effect plays an important role in multiple-input multiple-output (MIMO) relay systems. In particular, the position of the relay (between the source and destination) determines the path-loss effects of adjacent hops, which will further affect the performance of each hop and thus the relay system. In this paper, by minimizing the outage probability of a decode-and-forward (DF) MIMO relay system with orthogonal space-time block coding, we show that relay position optimization improves the finite-SNR (signal-to-noise ratio) diversity gain of a relay system whose adjacent hops have different diversity orders (unbalanced system). Specifically, with relay position optimization, the diversity gain is no longer bounded by that of the weaker hop, i.e., the hop with a lower diversity order, but approaches the diversity order of the stronger hop. This diversity improvement provides a dramatic improvement for the end-to-end outage probability. It will also be shown that although power allocation has no effects on the achievable diversity order, it provides some SNR gains. [ABSTRACT FROM AUTHOR]

In this paper we consider orthogonal frequency division multiplexing (OFDM) based cognitive radio systems that operate as secondary users (SUs) in a spectrum overlay approach and study joint spectral shaping and power control subject to specified target signal-to-interference+noise ratio (SINR) as a constrained optimization problem. We discuss necessary and sufficient conditions for the optimal solution of this problem where the spectrum overlay constraints are satisfied and the specified target SINR value is achieved with minimum transmit power. We also present an algorithm that incrementally adapts the OFDM transmitter to reach the optimal solution and study the convergence speed of the proposed algorithm through simulations. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of robotic router formation, where two nodes need to maintain their connectivity over a large area by using a number of mobile routers. We are interested in the robust operation of such networks in realistic communication environments that naturally experience path loss, shadowing, and multipath fading. We propose a probabilistic router formation and motion-planning approach by integrating our previously proposed stochastic channel learning framework with robotic router optimization. We furthermore consider power constraints of the network, including both communication and motion costs, and characterize the underlying tradeoffs. Instead of taking the common approach of formation optimization through maximization of the Fiedler eigenvalue, we take a different approach and use the end-to-end bit error rate (BER) as our performance metric. We show that the proposed framework results in a different robotic configuration, with a considerably better performance, as compared with only considering disk models for communication and/or maximizing the Fielder eigenvalue. Finally, we show the performance with a simple preliminary experiment, with an emphasis on the impact of localization errors. Along this line, we show interesting interplays between the localization quality and the channel correlation/learning quality. [ABSTRACT FROM AUTHOR]

The focus of the paper is on the design of space-time codes for a general multiple-input, multiple-output detection problem, when multiple observations are available at the receiver. The figure of merit used for optimization purposes is the convex combination of the Kullback-Leibler divergences between the densities of the observations under the two hypotheses, and different system constraints are considered. This approach permits to control the average sample number (i.e., the time for taking a decision) in a sequential probability ratio test and to asymptotically minimize the probability of miss in a likelihood ratio test: the solutions offer an interesting insight in the optimal transmit policies, encapsulated in the rank of the code matrix, which rules the amount of diversity to be generated, as well as in the power allocation policy along the active eigenmodes. A study of the region of achievable divergence pairs, whose availability permits optimization of a wide range of merit figures, is also undertaken. A set of numerical results is finally given, in order to analyze and discuss the performance and validate the theoretical results. [ABSTRACT FROM PUBLISHER]

This paper proposes a min-max design of noise-shaping delta-sigma modulators. We first characterize the all stabilizing loop-filters for a linearized modulator model. By this characterization, we formulate the design problem of lowpass, bandpass, and multi-band modulators as minimization of the maximum magnitude of the noise transfer function (NTF) in fixed frequency band(s). We show that this optimization minimizes the worst-case reconstruction error, and hence improves the SNR (signal-to-noise ratio) of the modulator. The optimization is reduced to an optimization with a linear matrix inequality (LMI) via the generalized KYP (Kalman–Yakubovich–Popov) lemma. The obtained NTF is an FIR (finite-impulse-response) filter, which is favorable in view of implementation. We also derive a stability condition for the nonlinear model of delta-sigma modulators with general quantizers including uniform ones. This condition is described as an H^\infty norm condition, which is reduced to an LMI via the KYP lemma. Design examples show advantages of our design. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of relay assignment in cooperative wireless networks having multiple sources, multiple relays, and a single destination. With the objective of maximizing the minimum capacity among all sources in the network, a mixed-integer linear programming (MILP) problem is formulated, which can be solved by standard branch-and-bound algorithms. To reduce computational complexity, a greedy solution in the form of a lexicographic bottleneck assignment algorithm is proposed. Simulation results obtained for the IEEE 802.16j uplink scenarios show that the greedy algorithm performs very close to the optimal solution but at a much lower computational cost. The proposed greedy solution can also be tailored to provide further improvements on other network performance criteria. [ABSTRACT FROM PUBLISHER]

Intercell interference is the major limitation to the performance of future cellular systems. Despite the joint detection and joint transmission techniques aiming at interference cancelation which suffer from the limited possible cooperation among the nodes, power allocation is a promising approach for optimizing the system performance. If the interference is treated as noise, the power allocation optimization problem aiming at maximizing the sum rate with a total power constraint is nonconvex and up to now an open problem. In the present paper, the solution is found by reformulating the nonconvex objective function of the sum rate as a difference of two concave functions. A globally optimum power allocation is found by applying a branch-and-bound algorithm to the new formulation. In principle, the algorithm partitions the feasible region recursively into subregions where for every subregion the objective function is upper and lower bounded. For each subregion, a linear program is applied for estimating the upper bound of the sum rate which is derived from a convex maximization formulation of the problem with piecewise linearly approximated constraints. The performance is investigated by system-level simulations. The results show that the proposed algorithm outperforms the known conventional suboptimum schemes. Furthermore, it is shown that the algorithm asymptotically converges to a globally optimum power allocation. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of fair resource allocation based on the Nash bargaining solution (NBS) over wireless amplify-and-forward (AF) relay networks in which variable-rate users and constant-rate users coexist. Via the dual decomposition method, a distributed algorithm, including relay selection, relay power allocation, and rate adaptation, is proposed. When the number of users is large and/or the rate constraints are stringent, the problem becomes infeasible, and admission control is necessary. We formulate the admission control and fair resource allocation problem as a two-stage optimization problem, aiming to first find the maximal number of users the network can support with guaranteed rates and then allocate resources based on NBS for the admitted users. Since the problem is combinatorially hard, we transform it into an equivalent one-stage optimization problem, which can be solved by existing methods but with a higher computational complexity. To further reduce the computational complexity, a suboptimal algorithm is then developed. Simulations verify the convergence and fairness of the distributed algorithm. Simulations also show that the suboptimal algorithm achieves an approximate optimal performance but with a significant running time reduction. [ABSTRACT FROM PUBLISHER]

The capacity region of the N-sender Gaussian multiple access channel with feedback is not known in general. This paper studies the class of linear-feedback codes that includes (nonlinear) nonfeedback codes at one extreme and the linear-feedback codes by Schalkwijk and Kailath, Ozarow, and Kramer at the other extreme. The linear-feedback sum-capacity CL(N,P) under symmetric power constraints P is characterized, the maximum sum-rate achieved by linear-feedback codes when each sender has the equal block power constraint P. In particular, it is shown that Kramer's code achieves this linear-feedback sum-capacity. The proof involves the dependence balance condition introduced by Hekstra and Willems and extended by Kramer and Gastpar, and the analysis of the resulting nonconvex optimization problem via a Lagrange dual formulation. Finally, an observation is presented based on the properties of the conditional maximal correlation—an extension of the Hirschfeld-Gebelein-Rényi maximal correlation—which reinforces the conjecture that Kramer's code achieves not only the linear-feedback sum-capacity, but also the sum-capacity itself (the maximum sum-rate achieved by arbitrary feedback codes). [ABSTRACT FROM PUBLISHER]

In this paper, the average rate maximization problem given transmission power constraints in a decode-and-forward (DF) relay network over slow fading channels is studied. For the problem, either a long- or short-term power constraint is imposed on the source and relays. In each combination of the power constraints at the source and relays, the original optimization problem is decomposed into subproblems, each corresponding to a specific channel gain realization. For each subproblem, a fast algorithm with closed-form solutions is provided. The case with additional peak power spectrum density constraints is also investigated. Numerical results are presented to demonstrate the effectiveness of the algorithms. [ABSTRACT FROM PUBLISHER]

This paper aims at maximizing the average spectral efficiency of a secondary link in a cognitive radio context, where both the primary and secondary users are equipped with the adaptive modulation and coding (AMC) capability. In the training phase, the fixed training power of the secondary user is selected such that the prescribed average spectral efficiency of the primary link is guaranteed. In the data transmission phase, the transmit power and the AMC mode of the secondary user are selected under the maximum packet error rate (PER) constraint of the primary user, as well as the average power and PER constraints of the secondary user. According to different kinds of primary-link information available to the secondary user, i.e., the transmission mode, the signal-to-interference-plus-noise ratio (SINR), and no information at all, three optimization problems that maximize the average spectral efficiency of the secondary user are formulated, respectively. Based on their optimal solutions, three AMC-based adaptive transmission strategies that are composed of both online and offline algorithms are proposed. Their performances are then evaluated by extensive simulation. [ABSTRACT FROM PUBLISHER]

In this paper, we consider transmit beamforming (BF) design under per-antenna power constraint (PPC) for multiple-input–single-output (MISO) frequency-selective channels. Both cyclic-prefixed (CP) single carriers and orthogonal frequency-division-multiplexing (OFDM) systems are investigated. Since there is no closed-form expression for the optimal PPC BF coefficients, and the optimization tools employed to find the PPC solution are usually complicated, here, we propose three computationally more effective suboptimal solutions to minimize the arithmetic mean of the effective error probabilities. Moreover, we address the issue of limited-rate feedback, and a simple codebook design method for frequency-selective channels is proposed. Unlike conventional methods where the codebook is directly designed for BF vectors, here, we construct codebooks for the amplitude and phase of the time-domain channel state information (CSI) to reduce the rate of feedback. Further, the nonuniform feedback strategy is investigated. Simulation results show that the proposed suboptimal solutions have much lower complexities than the convex optimization-based PPC solutions. The effectiveness of the proposed uniform and nonuniform limited-rate feedback methods is also demonstrated via simulations. [ABSTRACT FROM PUBLISHER]

A space adaptive flexible block quantizer (SA-FBQ), suited for spaceborne synthetic aperture radar missions, is presented. This quantizer is actually an extension of the flexible dynamic block adaptive quantizer (FDBAQ), proposed in refid="ref1"/, which, in turn, is an extension of the block adaptive quantizer (BAQ). The BAQ is the optimal quantizer for a homogeneous target. The FDBAQ gets better performances on heterogeneous targets by adaptively selecting the best BAQ according to the local signal-to-thermal-noise ratio: The worst the SNTR, the lower the quantizer rate. The quantizer selection is precomputed in a lookup table (LUT), by assuming a fixed and known probability distribution function (pdf) of the reflectivity \sigma0. The SA-FBQ extends further this concept allowing the reflectivity pdf to vary, coping with this by exploiting many LUTs (i.e., quantizer set), each adapted to the local statistics. In this paper, we introduce an algorithm to adaptively find the best set of quantizers constrained on the mean bit rate; we discuss the implementation of the SA-FBQ, and we estimate its performances in comparison with the FDBAQ and the BAQ under different scenarios. Preliminary results are shown by exploiting the worldwide mosaic of C-band reflectivity derived from European Space Agency ENVISAT data and Sentinel-1 system parameters. [ABSTRACT FROM AUTHOR]

This paper studies the max-min weighted signal-to-interference-plus-noise ratio (SINR) problem in the multiple- input-multiple-output (MIMO) downlink, where multiple users are weighted according to priority and are subject to a weighted-sum-power constraint. First, we study the multiple-input-single-output (MISO) and single-input-multiple- output (SIMO) problems using nonlinear Perron–Frobenius theory. As a by-product, we solve the open problem of convergence for a previously proposed MISO algorithm by Wiesel, Eldar, and Shamai in 2006. Furthermore, we unify our analysis with respect to the previous alternate optimization algorithm proposed by Tan, Chiang, and Srikant in 2009, by showing that our MISO result can, in fact, be derived from their algorithm. Next, we combine our MISO and SIMO results into an algorithm for the MIMO problem. We show that our proposed algorithm is optimal when the channels are rank-one, or when the network is operating in the low signal-to-noise ratio (SNR) region. Finally, we prove the parametric continuity of the MIMO problem in the power constraint, and we use this insight to propose a heuristic initialization strategy for improving the performance of our (generally) suboptimal MIMO algorithm. The proposed initialization strategy exhibits improved performance over random initialization. [ABSTRACT FROM PUBLISHER]

This paper investigates the optimal design issue of general two-hop amplify-and-forward (AF) MIMO relay networks with one source, one destination and multiple relays, each having multiple antennas. Beginning with the study of the mean-square error (MSE) matrix of the system and the use of Wiener filter for the destination processing, a constrained optimization problem with respect to the unknown relay precoder matrix is formulated with a goal of minimizing the total relay transmit power subject to the MSE-based quality of service (QoS) requirement on each data stream. As the original optimization problem is very difficult to solve, a lower bound of the cost function is pursued to achieve an approximate optimization problem. The modified problem is then solved under the perfect channel state information (CSI) condition using the diagonalization method along with the majorization theory to obtain optimal relay precoder with either nonoptimal or optimal source precoding. It is shown that a convex relay precoding solution is available in the case of nonoptimal source precoding whereas a joint optimal source and relay precoder can be obtained through a multilevel waterfilling algorithm. The optimal design of the relay network under imperfect CSI condition is also considered by modifying the optimization problem with the channel mean and covariance matrix, leading to a robust optimization design of the relay precoder. The proposed technique is validated by Monto Carlo simulations and is also compared with one existing method. [ABSTRACT FROM AUTHOR]

In this paper, low-complexity joint power assignment algorithms are developed for multi-source multi-destination relay networks where multiple sources share a common relay that forwards all received signals simultaneously to destinations. In particular, we consider the following power optimization strategies: (i) Minimization of the total transmission power of the sources and the relay under the constraint that the signal-to-interference-plus-noise ratio (SINR) requirement of each source-destination pair is satisfied, and (ii) Maximization of the minimum SINR among all source-destination pairs subject to any given total power budget. Both optimization problems involve K power variables, where K is the number of source-destination pairs in the network, and an exhaustive search is prohibitive for large K. In this work, we develop a methodology that allows us to obtain an asymptotically tight approximation of the SINR and reformulate the original optimization problems to single-variable optimization problems, which can be easily solved by numerical search of the single variable. Then, the corresponding optimal transmission power at each source and relay can be calculated directly. The proposed optimization schemes are scalable and lead to power assignment algorithms that exhibit the same optimization complexity for any number (K) of source-destination pairs in the network. Moreover, we apply the methodology that we developed to solve a related max-min SINR based optimization problem in which we determine power assignment for the sources and the relay to maximize the minimum SINR among all source-destination pairs subject to any given total power budget. Extensive numerical studies illustrate and validate our theoretical developments. [ABSTRACT FROM AUTHOR]

We consider a multichannel cognitive radio network where multiple secondary users (SUs) share a single channel and where multiple channels are simultaneously used by a single SU to satisfy their rate requirements. We attempt to evaluate the optimal power and rate distribution choices that each SU has to make to maintain their quality of service (QoS). Our measures for QoS include bit error rate (BER) and minimum rate requirement. We design a QoS-constrained bi-objective optimization problem with the objective of minimizing transmit power and maximizing rate. Unlike prior efforts, we transform the BER constraint into a convex constraint to guarantee optimality of the resulting solution. Our interest in this paper is to develop a user-based distributed approach to solve the optimization problem and compare the solution with the centralized approach. We employ dual decomposition theory to derive three different formulations of the distributed problem. Simulation results demonstrate that optimal transmit power follows the “reverse water filling” process and that rate allocation follows signal-to-interference-plus-noise ratio (SINR). We show that the solutions from the distributed formulations follow the centralized solution. [ABSTRACT FROM PUBLISHER]

In this paper, we consider cooperative spectrum sensing when two secondary users (SUs) collaborate via the relaying scheme. We investigate two cooperative sensing strategies, i.e., SUs exchange data information locally, and SUs relay information to a central controller. The relaying scheme at each SU is optimized via functional analysis with either the average or peak power constraints. For the local cooperative sensing strategy, the optimal relaying schemes look like amplify-and-forward (AF) in the low-signal-to-noise-ratio (SNR) region and behave like decode-and-forward (DF) in the high-SNR region. The fundamental performance limit using local cooperative sensing is discussed. For the global cooperative sensing strategy, we propose both coherent and noncoherent sensing, depending on whether SUs are synchronized. In the coherent case, a decentralized approach is designed, and each SU optimizes its relaying function locally. In the noncoherent case, we use linear energy combination detector to decouple the relaying function from weight coefficient optimization. Simulation results demonstrate that the proposed protocols achieve much better performance over the existing protocols. [ABSTRACT FROM PUBLISHER]

Based on worst-case performance optimization, the recently developed adaptive beamformers utilize the uncertainty set of the desired array steering vector to achieve robustness against steering vector mismatches. In the presence of large steering vector mismatches, the uncertainty set has to expand to accommodate the increased error. This degrades the output signal-to-interference-plus-noise ratios (SINRs) of these beamformers since their interference-plus-noise suppression abilities are weakened. In this paper, an iterative robust minimum variance beamformer (IRMVB) is proposed which uses a small uncertainty sphere (and a small flat ellipsoid) to search for the desired array steering vector iteratively. This preserves the interference-plus-noise suppression ability of the proposed beamformer and results in a higher output SINR. Theoretical analysis and simulation results are presented to show the effectiveness of the proposed beamformer. [ABSTRACT FROM PUBLISHER]

Secure transmission techniques have been receiving growing attention in recent years, as a viable, powerful alternative to blocking eavesdropping attempts in an open wireless medium. This paper proposes a secret transmit beamforming approach using a quality-of-service (QoS)-based perspective. Specifically, we establish design formulations that: i) constrain the maximum allowable signal-to-interference-and-noise ratios (SINRs) of the eavesdroppers, and that ii) provide the intended receiver with a satisfactory SINR through either a guaranteed SINR constraint or SINR maximization. The proposed designs incorporate a relatively new idea called artificial noise (AN), where a suitable amount of AN is added in the transmitted signal to confuse the eavesdroppers. Our designs advocate joint optimization of the transmit weights and AN spatial distribution in accordance with the channel state information (CSI) of the intended receiver and eavesdroppers. Our formulated design problems are shown to be NP-hard in general. We deal with this difficulty by using semidefinite relaxation (SDR), an approximation technique based on convex optimization. Interestingly, we prove that SDR can exactly solve the design problems for a practically representative class of problem instances; e.g., when the intended receiver's instantaneous CSI is known. Extensions to the colluding-eavesdropper scenario and the multi-intended-receiver scenario are also examined. Extensive simulation results illustrate that the proposed AN-aided designs can yield significant power savings or SINR enhancement compared to some other methods. [ABSTRACT FROM AUTHOR]

In metro passenger information systems (PISs), frequent train handoffs can cause severe video distortion. In this paper, we take an integrated design approach to jointly optimize application-layer parameters and handoff decisions to improve video transmission quality over PISs. We present a train-ground video communication network based on fountain codes and IEEE 802.11p for metro PISs. The metro PIS channel is modeled as a finite-sate Markov chain, and the channel-state transition probability matrix is calculated from the data that were measured in real field tests. The handoff decision and application-layer parameters adaptation problem is formulated as a stochastic semi-Markov decision process (SMDP). Minimizing the end-to-end total video distortion is the objective in our model. Simulation results show that the proposed SMDP-based optimization algorithm can significantly improve the end-to-end video transmission quality in metro PISs. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problems of power allocation at relays to maximize the transmission rate in a nonorthogonal amplify-and-forward (AF) relay-assisted network, in which multiple relays simultaneously transmit the amplified signals in the same time and frequency. We first formulate a power allocation problem that aims to maximize the minimum of transmission rates in a multicast under a sum power constraint. Then, we prove that the optimal solution can be alternatively obtained by solving its inverse problem, which minimizes the sum power consumption at relays while achieving the target signal-to-noise ratio (SNR) with the same constraint. The inverse problem can be solved by using linear programming, and the inverse problem's complexity can be further reduced by a proposed suboptimal power allocation algorithm. As in the special case of multicast, the optimal solution is derived in unicast, and two suboptimal algorithms are proposed to reduce the feedback overhead and complexity, in which multirelay diversity is also investigated. [ABSTRACT FROM PUBLISHER]

This paper studies distributed optimization schemes for multicell joint beamforming and power allocation in time-division-duplex (TDD) multicell downlink systems where only limited-capacity intercell information exchange is permitted. With an aim to maximize the worst-user signal-to-interference-and-noise ratio (SINR), we devise a hierarchical iterative algorithm to optimize downlink beamforming and intercell power allocation jointly in a distributed manner. The proposed scheme is proved to converge to the global optimum. For fast convergence and to reduce the burden of intercell parameter exchange, we further propose to exploit previous iterations adaptively. Results illustrate that the proposed scheme can achieve near-optimal performance even with a few iterations, hence providing a good tradeoff between performance and backhaul consumption. The performance under quantized parameter exchange is also examined. [ABSTRACT FROM AUTHOR]