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Terahertz (THz) band communication is envisioned as a key technology to satisfy the increasing demand for ultrabroadband wireless systems, thanks to its ultrabroad bandwidth. Tailored for the unique properties of pulse-based communications in the THz band, two timing acquisition algorithms are proposed and analyzed thoroughly in this paper. First, a low-sampling-rate (LSR) synchronization algorithm is proposed, by extending the theory of sampling signals with finite rate of innovation in the communication context and exploiting the properties of the annihilating filter. The simulation results show that the timing accuracy at an order of ten picoseconds is achievable. In particular, the LSR algorithm has high performance with uniform sampling at $1/20$ of the Nyquist rate when the signal-to-noise ratio (SNR) is high (i.e., greater than 18 dB). Complementary to this, a maximum likelihood (ML) approach for timing acquisition is developed, which searches for the timing offsets by adopting a two-step acquisition procedure based on the ML criterion. The simulation results show that the ML-based algorithm is well suitable in the low SNR case with a half-reduced search space. For further evaluation, the error performance and the resulting bit-error-rate sensitivity to the timing errors in the LSR and the ML algorithms are both analytically and numerically studied. This paper provides very different and promising angles to efficiently and reliably solve the timing acquisition problem for pulse-based THz band wireless systems. [ABSTRACT FROM PUBLISHER]

Due to signal fading and user mobility in wireless networks, quality-of-service provisioning for wireless services has become more challenging. As a promising technique, cooperative communications make use of the broadcasting nature of the wireless medium to facilitate data transmission and, thereby, reduce energy consumption. However, in many studies on wireless cooperative diversity, it is often assumed that the number of relays or the relay distribution intensity is known a priori. In this paper, we relax this assumption and propose an algorithm to estimate the relay intensity for a backoff-based cooperative scheme, where the relays are distributed as a homogeneous Poisson point process. It is proved that the algorithm can converge to an optimal solution with the minimum estimation error. Based on the estimated relay intensity, we further investigate a distributed energy saving strategy, which selectively turns off some relays to reduce energy consumption while maintaining the required transmission success probability. The performance of the proposed cooperative scheme is analytically evaluated with respect to the collision probability. The numerical and simulation results demonstrate the high accuracy and efficiency of the intensity estimation algorithm and validate the theoretical analysis. Moreover, the proposed cooperative scheme exhibits significant energy savings and satisfactory transmission performance, which offers a good match to accommodate green communications in wireless networks. [ABSTRACT FROM PUBLISHER]

This paper considers a method for improving physical layer security of wireless networks with full-duplex radio. In particular, fast algorithms are developed to compute power allocations in subcarriers, subject to power and rate constraints, to maximize the secrecy capacity of a three-node network consisting of a source, a full-duplex destination, and an eavesdropper. A residual level of radio self-interference channel is considered. The optimal power allocation at the destination is found to be significant especially when its power budget is high. Also studied in this paper are a network with multiple full-duplex destinations and another network with multiple sources. Using the algorithms developed in this paper, we are able to show that a multiuser strategy that optimizes the power distributions among the users (in terms of either the sources or the destinations) can yield a substantial gain of secrecy capacity over a single-user strategy. [ABSTRACT FROM PUBLISHER]

In this paper, we study the power allocation problem in a multiuser cooperative network, where users help each other's transmissions to an access point. We use iterative double auction (IDA) game to model the interaction among the users and the access point. In each iteration of this game, the users first submit bids for buying other users' power and asks for selling its own power, and then, the access point determines the power allocation based on users' bids and asks. We propose a distributed algorithm for the implementation of the IDA-based power allocation. We also show that the proposed algorithm achieves a weighted sum signal-to-noise ratio (sum-SNR)-optimal solution. Simulation results are conducted to verify the performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a self-directed adaptive phased array control method using the low-frequency part of the signal for a millimeter-wave wireless personal area network, which can reduce the hardware requirement and power consumption compared with those of the conventional digital beamforming method. The signal from each element antenna of the antenna array is phase shifted and down converted to the baseband and then divided into two paths. The first paths are low-pass filtered to extract the low-frequency part of the signals for beamforming control. The second paths from all antennas are combined in-phase in an analog domain and then sampled by two high-speed A/D converters for demodulation. A beamforming algorithm using the sampled low-frequency part of the signal is also proposed. The beamforming calculation time is established as a function of the signal-to-noise ratio, the bandwidth of the low-frequency part, and the required phase control accuracy. The calculated values match the measured results. Using the IEEE 802.15.3c specifications with an eight-element array antenna, the calculation time is less than \5~\mu\s for initial beam establishment and less than \30~\mu\s for beam tracking. Therefore, high-speed beamforming is possible while reducing the power consumption. [ABSTRACT FROM AUTHOR]

We consider the uplink transmission in multiuser wireless systems with multiple single-antenna transmitting users and a multiantenna receiver. We address two problems in this paper. The first problem is user admission, i.e., given a large number of users, how to admit the maximum number of users that can simultaneously satisfy quality of service (QoS) and power constraints. The second problem is how to distribute the data among the users when they are allowed to share data before transmission. The aim is to minimize the total data exchange cost. Such a problem is called user clustering. We formulate those problems into sparsity-maximization problems, which are NP-hard. Inspired by compressive sensing techniques, we propose a common framework to tackle those problems by first applying the \ell1 -norm relaxation and then solving them with convex optimization methods. Simulations show that the proposed algorithms achieve excellent performance. For user admission, the numbers of admitted users by the proposed algorithms are close to the optimum numbers of admitted users obtained by exhaustive search (ES) . For user clustering, the total data exchange cost is reduced by more than 10% after only a few iterations. When the QoS requirement is low, the user data exchange can be avoided using the proposed method, which achieves the optimum result obtained by ES. [ABSTRACT FROM PUBLISHER]

One of the key challenges in wireless networking is allocating the available radio resources to maximize key service delivery parameters such as the aggregate throughput and the multimedia quality of experience (QoE). We propose a novel and effective scheduling policy that allocates resource blocks, such that interference power is shifted toward capacity-saturated users, while improving the throughput of unsaturated users. The highlight of this paper is that the proposed scheme can dramatically improve the performance of cells that have a high discrepancy in its signal-to-noise ratio (SNR) distribution, which is typical in urban areas. The results show that a free-lunch (FL) solution is possible, whereby for negligible performance degradation in the saturated users, a large improvement in the nonsaturated users can be obtained. However, on average, the number of FL user pairings is low. By relaxing the degradation constraints, the non-FL (NFL) solution can yield a greater multiuser throughput gain. Motivated by a surge in mobile multimedia traffic, we further demonstrate that the proposed scheduling may have a profound impact on both energy efficiency and QoE of multimedia service delivery. [ABSTRACT FROM PUBLISHER]

Consider the single-group multicast beamforming problem, where multiple users receive the same data stream simultaneously from a single transmitter. The problem is NP-hard and all existing algorithms for the problem either find suboptimal approximate or local stationary solutions. In this paper, we propose an efficient branch-and-bound algorithm for the problem that is guaranteed to find its global solution. To the best of our knowledge, our proposed algorithm is the first tailored global algorithm for the single-group multicast beamforming problem. Simulation results show that our proposed algorithm is computationally efficient (albeit its theoretical worst-case iteration complexity is exponential with respect to the number of receivers) and it significantly outperforms a state-of-the-art general-purpose global optimization solver called Baron. Our proposed algorithm provides an important benchmark for performance evaluation of existing algorithms for the same problem. By using it as the benchmark, we show that two state-of-the-art algorithms, semidefinite relaxation algorithm and successive linear approximation algorithm, work well when the problem dimension (i.e., the number of antennas at the transmitter and the number of receivers) is small but their performance deteriorates quickly as the problem dimension increases. [ABSTRACT FROM PUBLISHER]

This paper considers the transmission of scheduling information in orthogonal frequency-division multiple-access (OFDMA)-based cellular communication systems such as the Third-Generation Partnership Project (3GPP) long-term evolution (LTE). These systems provide efficient usage of radio resources by allowing users to be dynamically scheduled in both frequency and time. This requires considerable amounts of scheduling information to be sent to the users. This paper compares two basic transmission strategies: transmitting a separate scheduling message to each user versus broadcasting a joint scheduling message to all users. Different scheduling granularities are considered, as are different scheduling algorithms. The schemes are evaluated in the context of the LTE downlink using multiuser system simulations, assuming a full-buffer situation. The results show that separate transmission of the scheduling information requires a slightly lower overhead than joint broadcasting when proportional fair scheduling is employed and the users are spread out over the cell area. The results also indicate that the scheduling granularity standardized for LTE provides a good tradeoff between scheduling granularity and overhead. [ABSTRACT FROM PUBLISHER]

We present in this paper a novel proactive interference avoidance scheme for mobile-to-mobile (M2M) communication as an underlay in Long-Term Evolution (LTE) downlink networks. The proposed scheme introduces an intelligent user-selective resource allocation algorithm that aims to proactively avoid interference between M2M pairs and LTE regular users while achieving a satisfactory link budget for both M2M and LTE regular links. The proposed resource allocation algorithm is based on a novel low-complexity interference estimation approach that requires only channel quality indicator (CQI) information (CSI). Since the CQI reporting mechanism has been already implemented in current LTE systems, the proposed algorithm is indeed practical and standard compatible, compared with the existing works that require perfect knowledge of the channel state information (CSI). In this research, achievable data throughput with LTE-compliant adaptive modulation and coding has been adopted as the optimization measurement, rather than the information-theoretic capacity. Therefore, the performance of the proposed scheme is practically realizable in LTE communication systems. Since the performance of the proposed scheme highly relies on the accuracy of CQI feedback, we also carry out performance analysis to evaluate the impact of CQI estimation error and delay. The simulation results show that the total achievable data throughput for LTE networks is dramatically enhanced by M2M communication with this proposed interference avoidance resource allocation scheme. [ABSTRACT FROM PUBLISHER]

In heterogeneous networks (HetNets), the load between macrocell base stations (MBSs) and small-cell base stations (SBSs) is imbalanced due to their different transmission powers and locations. This load imbalance significantly impacts system performance and affects the experience of mobile users (MUs) with different priorities. In this paper, we aim to distributively optimize the user association in HetNets with various user priorities to solve the load balancing problem. Since the user association is a binary matching problem, which is NP-hard, we propose a distributed belief propagation (BP) algorithm to approach the optimal solution. We first develop a factor graph model, using the network topology, to represent this user association problem. With this factor graph, we propose a novel distributed BP algorithm by adopting the proportional fairness as the objective. Next, we theoretically prove the existence of the fixed point in our BP algorithm. To be more practical, we develop an approximation method to significantly reduce the computational and communication complexity of the BP algorithm. Furthermore, we analyze some properties of the factor graph relevant to the performance of the BP algorithm using the stochastic geometry. Simulation results show that 1) the proposed BP algorithm well approaches the optimal system performance and achieves a much better performance compared with other association schemes and that 2) the analytical results on the average degree distribution and sparsity of the factor graph match with those obtained from the Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

Estimation of signal-to-noise ratio (SNR) plays an important role in many fields of communications. This paper addresses the SNR estimation of multiple phase-shift keying (MPSK) signals in Nakagami- $m$ fading channels. A family of SNR estimators with closed-form expressions is developed based on the relationship among different orders of moments. Compared with the existing algorithms, the estimator, which exploits the minimum order of moments, exhibits the best performance in general. We also propose a joint estimation algorithm for SNR and the fading factor when it is unknown. Simulation results verify the feasibility and superiority of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In an interference limited network, joint power and admission control (JPAC) aims at supporting a maximum number of links at their specified signal-to-interference-plus-noise ratio (SINR) targets while using minimum total transmission power. Various convex approximation deflation approaches have been developed for the JPAC problem. In this paper, we propose an effective polynomial time non-convex approximation deflation approach for solving the problem. The approach is based on the non-convex \ell q~(0< q< 1) approximation of an equivalent sparse \ell 0 reformulation of the JPAC problem. We show that, for any instance of the JPAC problem, there exists a \bar q\in (0,1) such that it can be exactly solved by solving its \ell q approximation problem with any q\in (0, \bar q]. We also show that finding the global solution of the \ell q approximation problem is NP-hard. Then, we propose a potential reduction interior-point algorithm, which can return an \epsilon -KKT solution of the NP-hard \ell q approximation problem in polynomial time. The returned solution can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure, resulting in the polynomial time non-convex approximation deflation approach for the JPAC problem. Numerical simulations show that the proposed approach outperforms the existing convex approximation approaches in terms of the number of supported links and the total transmission power, particularly exhibiting a quite good performance in selecting which subset of links to support. [ABSTRACT FROM PUBLISHER]

Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to tackle the capacity crunch in cellular networks. However, imbalanced load among small and macrocells and poor resource utilization as a consequence in HetNets remains a long-standing problem. This paper addresses this problem by presenting a solution for maximization of coverage and capacity while minimizing load imbalance among macro and small cells. Most recent studies on the topic focus on either optimization of coverage, capacity or load, or a combination of two of these three intertwined objectives. We formulate the optimization problem as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity, and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization (CCO) and load balancing (LB) self-organizing network (SON) functions. In the presented joint CCO-LB solution, a conflict free operation of CCO and LB is ensured by designing a novel load aware user association methodology and resolving the effects of shadowing on coverage probability using stochastic approximation. The problem is proven to be nonconvex and is solved using genetic algorithm, sequential quadratic programming, and pattern search algorithms. The proposed CCO-LB solution is compared against two recently proposed CCO and CCO-LB solutions in the literature. Results show that the proposed solution can yield significant gain in terms of throughput, spectral efficiency, and load distribution. [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]

The cross-layer utility maximization problem, which is subject to stability constraints for a multicommodity wireless network where all links share the same number of orthogonal channels, is considered in this paper. We assume a time-slotted network, where the channel gains randomly change from one slot to another. The optimal cross-layer network control policy can be decomposed into the folloing three subproblems: 1) flow control; 2) next-hop routing and in -node scheduling; and 3) power and rate control, which is also known as resource allocation (RA). These subproblems span the layers from the physical layer to the transport layer. In every time slot, a network controller decides the amount of each commodity data admitted to the network layer, schedules different commodities over the network's links, and controls the power and rate allocated to every link in every channel. To fully exploit the available multichannel diversity, we consider the general case, where multiple links can be activated in the same channel during the same time slot, and the interference is controlled solely through power and rate control. Unfortunately, the RA subproblem is not yet amendable to a convex formulation, and in fact, it is NP-hard. The main contribution of this paper is to develop efficient RA algorithms for multicommodity multichannel wireless networks by applying complementary geometric programming and homotopy methods to analyze the quantitative impact of gains that can be achieved at the network layer in terms of end-to-end rates and network congestion by incorporating different RA algorithms. Although the global optimality of the solution cannot be guaranteed, the numerical results show that the proposed algorithms perform close to the (exponentially complex) optimal solution methods. Moreover, they efficiently exploit the available multichannel diversity, which provides significant gains at the network layer in terms of end-to-end rates and network congestion. In addition, the assessment of the improvement in performance due to the use of multiuser detectors at the receivers is provided. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a wireless amplify-and-forward relay network with one relay node and multiple source-destination pairs/users and propose a pricing framework that enables the relay to set prices to maximize either its revenue or any desirable system utility. Specifically, depending on the quality of the received signals, the relay sets prices and correspondingly charges the users utilizing its resources for their transmissions. The price is determined in such a way that the relay's revenue or system utility is maximized. Given the specified price, the users competitively employ the relay node to forward their signals. We model each user as a rational player, which aims at maximizing its own net utility through power allocation, and analyze the competition among the users within the framework of noncooperative game theory. It is shown that, in the game played by the users, there always exists a unique pure Nash equilibrium point that can be achieved through distributed iterations. Next, subject to the availability of complete information about the users at the relay, we propose a low-complexity uniform pricing algorithm and an optimal differentiated pricing algorithm, in which the relay either charges the users at a suboptimal uniform price or charges different users at different prices. We also show that, by applying the differentiated pricing algorithm that enforces the users to transmit at certain power levels, any system utility can be maximized. Extensive simulations are conducted to quantify the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Interference alignment (IA) is a transmission technique for exploiting all available degrees of freedom in the frequency- or time-selective interference channel with an arbitrary number of users. Most prior work on IA, however, neglects interference from other nodes in the network that are not participating in the alignment operation. This paper proposes three generalizations of IA for the multiple-antenna interference channel with multiple users that account for colored noise, which models uncoordinated interference. First, a minimum interference-plus-noise leakage (INL) algorithm is presented and shown to be equivalent to previous subspace methods when noise is spatially white or negligible. This algorithm results in orthonormal precoders that are desirable for practical implementation with limited feedback. A joint minimum mean square error design that jointly optimizes the transmit precoders and receive spatial filters is then proposed, whereas previous designs neglect the receive spatial filter. Finally, a maximum signal-to-interference-plus-noise ratio (SINR) algorithm is developed and proven to converge, unlike previous maximum SINR algorithms. The sum throughput of these algorithms is simulated in the context of a network with uncoordinated cochannel interferers that are not participating in the alignment protocol. It is found that a network with cochannel interference can benefit from employing precoders that are designed to consider that interference, but in extreme cases, such as when only one receiver has a large amount of interference, ignoring that the cochannel interference is advantageous. [ABSTRACT FROM PUBLISHER]

The multiple-input multiple-output (MIMO) system makes efficient use of spectrum and increases the transmission throughput in wireless communications. The sphere decoding (SD) is an efficient algorithm that enables the maximum-likelihood (ML) detection for the MIMO system. However, the SD algorithm has variable complexity, and its complexity increases rapidly with decreasing signal-to-noise ratio (SNR). In this paper, we propose a novel ML detection algorithm for the MIMO system based on differential metrics. We define the differential metrics and derive the associated recursive calculation. We then give the indicative functions, which can be used to possibly find some ML-detected bits of the initial sequence. The indicative functions are further applied to implement an efficient tree search for ML detection. The proposed algorithm does not need QR decomposition and matrix inversion. The tree search process needs only the additive operation, while the number of multiplications before the tree search is constant. Our algorithm can achieve the exact ML detection as the SD algorithm. Unlike the SD algorithm, the complexity of our algorithm reduces with decreasing SNR, whereas at high SNR, the complexity is nearly constant. We also give the convergence analysis for the SD and proposed algorithms, and the simulation verifies our analysis. For the proposed algorithm, the number of necessary memory is constant during the tree search, and the implementation by parallel processing is possible. The soft output of ML-detected bits can also be generated in our algorithm. [ABSTRACT FROM PUBLISHER]

Optimal signaling for secrecy rate maximization in Gaussian MIMO wiretap channels is considered. While this channel has attracted a significant attention recently and a number of results have been obtained, including the proof of the optimality of Gaussian signalling, an optimal transmit covariance matrix is known for some special cases only and the general case remains an open problem. An iterative custom-made algorithm to find a globally-optimal transmit covariance matrix in the general case is developed in this paper, with guaranteed convergence to a global optimum. While the original optimization problem is not convex and hence difficult to solve, its minimax reformulation can be solved via the convex optimization tools, which is exploited here. The proposed algorithm is based on the barrier method extended to deal with a minimax problem at hand. Its convergence to a global optimum is proved for the general case (degraded or not) and a bound for the optimality gap is given for each step of the barrier method. The performance of the algorithm is demonstrated via numerical examples. In particular, 20 to 40 Newton steps are already sufficient to solve the sufficient optimality conditions with very high precision (up to the machine precision level), even for large systems. Even fewer steps are required if the secrecy capacity is the only quantity of interest. The algorithm can be significantly simplified for the degraded channel case and can also be adopted to include the per-antenna power constraints (instead or in addition to the total power constraint). It also solves the dual problem of minimizing the total power subject to the secrecy rate constraint. [ABSTRACT FROM PUBLISHER]

Radar signal design in a spectrally crowded environment is currently a challenge due to the increasing requests for spectrum from both military sensing applications and civilian wireless services. The goal of this paper is to improve a previously devised algorithm for the synthesis of optimized radar waveforms fulfilling spectral compatibility with overlaid licensed radiators. The new technique achieves an enhanced spectral coexistence with the surrounding electromagnetic environment through a suitable modulation of the transmitted waveform energy, which was kept fixed at the maximum level in the previously devised algorithm. 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), also in situations where the previous technique cannot be applied. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is currently one of the most promising solutions for designing spectrum-efficient wireless communication systems. In this paper, we present a greedy cognitive access policy that handles spectrum sensing with the tools of image processing. By means of the Wigner–Ville transform (WVT), we are able to analyze the time and frequency features of the received interference, and represent them on a time–frequency grid as an image in which brightness reveals the spectrum occupancy. According to sensed interference, each secondary user autonomously tries to access the grid following a multiple-division principle, i.e., by properly shaping the signal in the code, time, and frequency domains. In detail, this is realized by processing the WVT to derive the shaping mask (and the matching algorithm) that allows the filling up of the “time spectrum holes.” Numerical simulations and a test-bed implementation show how the proposed scheme outperforms selected state-of-the-art approaches while still presenting an affordable computational cost. [ABSTRACT FROM AUTHOR]

We propose a marginal-power-efficiency-based adaptation (MPEA) scheme for distributed power control in wireless communication systems. Nodes autonomously adapt their transmit power by keeping a differential measure of power efficiency, which is defined as the additional capacity obtained per unit of transmit power, at a desirable threshold. We combine the MPEA with the Foschini–Miljanic (FM) algorithm into a power control algorithm for a multihop cellular network where nodes can dynamically adapt to both local and global conditions. We derive a fixed point to which the power allocations converge under the scheme and illustrate a significant improvement in the system throughput. [ABSTRACT FROM AUTHOR]

A multiple-relay assisted cooperative automatic-repeat-request (CARQ) system is proposed in interference-limited scenario, where multiple-user detection (MUD) receivers using successive interference cancellation (SIC) are employed at the relay nodes (RNs) and the destination node (DN) to combat cochannel interference (CCI). We analyze the outage performance and propose a novel best effort-detection-based opportunistic relay selection (BED-ORS) scheme, which achieves significantly better outage performance than the traditional highest SNR-based ORS scheme offered in the literature. Moreover, considering the effects of the outdated channel state information (CSI), an improved-BED-ORS (I-BED-ORS) scheme is proposed to enhance the robustness against CSI imperfections. [ABSTRACT FROM PUBLISHER]

In this paper, altruistic coalition formation in cooperative relay networks is studied. The communication is performed over two phases, the broadcasting phase and the cooperation phase. In the broadcasting phase, each node broadcasts its signal in its time-slot, while in the cooperation phase, all the nodes within their coalitions simultaneously relay each others' signals. A distributed merge-and-split algorithm is proposed to allow nodes to form coalitions and improve their total achievable rate. Moreover, the impact of different power allocation criteria is studied, where the sum-of-rates maximizing power allocation is shown to promote altruistic coalition formation and results in the largest coalitions among the different power allocation criteria. Finally, the proposed algorithm is compared with centralized power allocation and coalition formation, and shown to yield a good tradeoff between network sum-rate and computational complexity. [ABSTRACT FROM AUTHOR]

Channel estimation error results in severe performance deterioration in wireless networks. In this paper, we study the impact of imperfect channel estimation (ICE) on the outage performance of bidirectional relaying where the two sources have asymmetric traffic requirements (ATRs). In particular, we focus on the amplify-and-forward (AF) relay-assisted multiple-access broadcast (MABC) protocol, i.e., MABC-AF, which has received much attention due to its high spectrum efficiency and low complexity. In the single-relay scenario, we derive exact and generalized closed-form expressions for system outage probability, which indicate that the system outage is determined by the unidirectional outage in the case of highly asymmetric traffic patterns. For more insights into our approach, the closed-form asymptotic expressions are also evaluated, manifesting the interesting error floor (EF) phenomenon due to ICE. Using these analytical results, we further develop a robust and practical optimum power-allocation (OPA) algorithm that minimizes the system outage probability under aggregate and individual node power constraints. In the multirelay scenario, by taking into account the traffic knowledge, a novel relay selection criterion is proposed for asymmetric MABC-AF, followed by its impact on the system outage probability in the presence of ICE. Numerical results validate the accuracy of our analytical results and highlight the effect of the proposed OPA algorithm under various traffic requirements and channel estimation errors. Furthermore, the results also show that the proposed relay selection criterion is superior to the classical max–min criterion with ATRs, and the performance improvement becomes remarkable as channel estimation error increases. [ABSTRACT FROM PUBLISHER]

This paper investigates transmit antenna selection for linearly precoded multiuser multiple-input–multiple-output (MU-MIMO) systems. First, in some precoded single-user MIMO systems, using all transmit antennas does not always lead to the best performance due to ill-conditioned channel matrices. This condition motivates us to investigate whether a similar result can be obtained in MU-MIMO systems. Based on the derived analytical results, we found that, for a given number of transmit antennas, decreasing the number of active transmit antennas [number of radio frequency (RF) units] always degrades system performance in the linearly precoded MU-MIMO systems. However, in practical systems, RF units are expensive. To reduce the hardware cost, antenna selection is usually used to reduce the number of RF units. Thus, we further analyze the performance loss due to transmit antenna selection (TAS). These analytical results provide good design references for using TAS in practical systems. Moreover, based on the analytical results, we proposed several simple TAS algorithms for linearly precoded MU-MIMO systems. Complexity analysis and simulation results show that the computational complexity of the proposed algorithms can significantly be reduced, whereas the performance is still comparable with the optimal selection scheme. As a result, the analyzed results enable us to better understand how TAS affects the MU-MIMO systems. In addition, the proposed algorithms make TAS more feasible to be used in practical systems. [ABSTRACT FROM PUBLISHER]

In wireless communication systems with multiple users, the near–far effect and the cochannel interferences decrease the spectral efficiency and system performance. An efficient known method to combat these destructive effects is to properly adjust the transmit powers. A practical and efficient way for power control (PC) in cellular communication systems is the autonomous distributed PC (DPC). In this method, the transmit power of each link is independently modified based on the received signal-to-interference-plus-noise ratio (SINR) reported from the other side of that link. It is known that in a perfect scenario where the reported SINRs are exact, this algorithm converges to the optimum transmit powers. However, in the case of erroneous SINR reports, the transmit powers deviate from their optimum values. This paper addresses the destructive effect of inaccurate SINR reports on the performance of a DPC algorithm. Then, independent variable step-sizes that update the transmit powers are proposed, and convergence of the proposed DPC algorithm to the optimum solution of the original problem is analytically shown. Additionally, a protocol to adaptively tune the step-sizes in our algorithm, which improves the performance of PC in the presence of inaccurate SINR reports, is proposed. Computer simulations are used to study the performance of the proposed method in terms of both the speed of converges and the steady-state accuracy of link qualities. [ABSTRACT FROM PUBLISHER]

A method for superresolution direction-of-arrival (DoA) estimation of correlated signals using a circular array is presented. This is accomplished in two steps: 1) the directions of arrival and amplitudes of signals with angular spacing larger than the array beamwidth are detected using the array signal segregation using an iterative approach (ASSIA) and 2) signals with angular proximity smaller than the array beamwidth are separated using a novel closely spaced nulls-synthesis method (CNSM). It is demonstrated that the proposed ASSIA-CNSM is superior to the other superresolution DoA estimation techniques such as maximum-likelihood method. The experimental results indicate that ASSIA-CNSM can be effectively used to distinguish the DoAs of the signals with angular spacing as small as 0.15 BW (where BW is the array half-power beamwidth) and with signal-to-noise ratio as low as 5 dB. [ABSTRACT FROM AUTHOR]

In this paper, a bidirectional Fano algorithm (BFA) is proposed, in which a forward decoder (FD) and a backward decoder (BD) search in the opposite direction in the code tree simultaneously. It is shown that the proposed BFA can achieve more than twice the decoding throughput compared to the conventional unidirectional Fano algorithm (UFA) and there is higher throughput improvement at low signal-to-noise ratio (SNR). This new BFA decoding technique is applied in the parallel convolutional decoding architecture in very high throughput systems, such as the WirelessHD system. Due to the variability in the decoding delays of the parallel codewords, a scheduler is introduced in the parallel Fano decoding architecture which can dynamically allocate the idle decoders to assist with decoding the other parallel codewords in a bidirectional manner. It is shown that the proposed parallel Fano decoding with scheduling can dramatically increase the decoding throughput compared to the parallel Fano decoding without scheduling, and its computational complexity is much lower than that of parallel Viterbi decoding, especially at high SNR. The performance of the parallel Fano decoding with different scheduling schemes is also compared and analyzed in detail in the paper. [ABSTRACT FROM AUTHOR]

This paper considers how to estimate the average signal-to-noise ratio (SNR) for a communication system employing orthogonal non-coherent M-ARY frequency shift keying (NCMFSK), in white Gaussian noise (AWGN) and over both symbol-by-symbol fading channels and block fading channels. The proposed algorithm finds its application in a variety of applications including a cooperative transmission system, which is the main motivation behind this study. The maximum likelihood estimator and one using data statistics have been derived and simulated for various scenarios including data-aided, non-data aided and joint estimation using both the data and pilot sequences. We also derive the Cramer-Rao bound for the estimators in the case of Rayleigh fading channels. The results show that for a particular region of interest (e.g. high SNR or low SNR) and depending upon the availability of pilot sequence, a particular SNR estimation scheme is suitable. [ABSTRACT FROM AUTHOR]

In frequency-selective channels linear receivers enjoy significantly-reduced complexity compared with maximum likelihood receivers at the cost of performance degradation which can be in the form of a loss of the inherent frequency diversity order or reduced coding gain. This paper demonstrates that the minimum mean-square error symbol-by-symbol linear equalizer incurs no diversity loss compared to the maximum likelihood receivers. In particular, for a channel with memory ν, it achieves the full diversity order of (ν+1) while the zero-forcing symbol-by-symbol linear equalizer always achieves a diversity order of one. [ABSTRACT FROM AUTHOR]

Cooperative communications have received considerable attention for wireless multimedia applications as a technique to improve reliability and service coverage. To apply such cooperation over slow fading channels, we consider exploiting the spatial diversity in multiple relay networks. In this paper, we focus on amplify-and-forward relaying schemes, namely, orthogonal amplify-and-forward, selective relaying, and distributed beamforming, for the cooperative wireless multimedia transmission. Furthermore, the successive refinement source coding is exploited such that the multimedia signal is encoded into multiple layers and the quality of its reconstruction at the receiver is improved when more layers are received correctly. We study two strategies for the layered source transmission, namely, progressive transmission and superposition coding. With our developed framework, we propose suboptimal resource allocation algorithms to efficiently assign rate, power, and channel uses to different layers so as to maximize the quality of the multimedia signal reconstructed at the receiver. The proposed optimization methodology is simple and only requires the knowledge of the channel statistics compared to the existing algorithms. As a result, the receiver only requires to feedback the determined rate, power, and channel uses to the transmitter whenever the channel statistics or the layered source transmission strategy changes. [ABSTRACT FROM PUBLISHER]

A novel low-complexity decision-feedback detection algorithm with constellation constraints (DFCC) is proposed for multi-input–multi-output (MIMO) systems. An enhanced interference cancellation is achieved by introducing multiple constellation points as decision candidates. A complexity reduction strategy is also developed to avoid redundant processing with reliable decisions. For time-varying channels, the proposed receiver updates the filter weights using a recursive least-squares (RLS)-based algorithm. This efficient detector is also incorporated in a multiple-branch (MB) structure to achieve a higher detection diversity order. A soft-output DFCC detector is also proposed as a component of an iterative detection and decoding receiver scheme. Simulations show that the proposed DFCC technique has complexity as low as the adaptive decision-feedback (DF) detector while it significantly outperforms ordered successive interference cancellation (OSIC) processing. [ABSTRACT FROM PUBLISHER]

Parameter estimation of multiple sinusoids is one of the fundamental problems of signal processing having numerous application areas, including wireless communications and array signal processing. This article proposes an iterative, fast, and efficient algorithm to estimate the frequencies and amplitudes of multiple sinusoids in noisy environments. The proposed approach interpolates on the q-shifted DFT coefficients of multiple sinusoids. We theoretically demonstrate that the performance of the proposed algorithm depends on DFT-shift, number of iterations and minimum DFT frequency separation parameters. Further, mean square error of the proposed algorithm achieves the Cramér-Rao lower bound when these parameters are selected appropriately. We also propose solid bounds regarding how these parameters can be selected for optimum overall performance. The total computational cost of the proposed algorithm is in the order of O(K N log N), where K is the number of sinusoidal components and N is the signal length. We provide comprehensive numerical simulations that confirm the correctness of our theoretical deductions. [ABSTRACT FROM AUTHOR]

The challenge of the fifth generation of wireless systems is to overcome fundamental limits of existing cellular networks by means of advanced technologies such as network densification. Nevertheless, dense and random deployment of the network infrastructure poses new challenges in the network management. In particular, rethinking cell association policies and introducing new specific load balancing strategies will be mandatory to shift traffic from heavy-loaded cells to light-loaded neighbors. Toward this goal, cell biasing is considered one of the most effective way. This letter proposes a low-complexity distributed cell-specific bias algorithm suitable for an actual implementation on highly dense scenarios. This solution takes into account the users’ data rate requests and aims at minimizing the outage rate defined as the amount of unsatisfied data rate requests. Numerical results show that despite its low complexity, the solution achieves performance only slightly lower than the benchmark near-optimum solution. [ABSTRACT FROM AUTHOR]

Fifth generation new radio aims to facilitate new use cases in wireless communications. Some of these new use cases have highly demanding latency requirements; many of the powerful forward error correction codes deployed in current systems, such as the turbo and low-density parity-check codes, do not perform well when the low-latency requirement does not allow iterative decoding. As such, there is a rejuvenated interest in noniterative/one-shot decoding algorithms. Motivated by this, we propose a signal-to-noise ratio-adaptive convolutionally coded system with optimized constellations designed specifically for a particular set of convolutional code parameters. Numerical results show that significant performance improvements in terms of bit-error-rate and spectral efficiency can be obtained compared to the traditional adaptive modulation and coding systems in low-latency communications. [ABSTRACT FROM AUTHOR]

Future multi-input multi-output (MIMO) wireless communications systems will use beamforming as a first-step towards realizing the capacity requirements necessitated by the exponential increase in data demands. The focus of this work is on beam alignment for time-division duplexing (TDD) systems, for which we propose a number of novel algorithms. These algorithms seek to obtain good estimates of the optimal beamformer/combiner pair (which are the dominant singular vectors of the channel matrix). They are motivated by the power method, an iterative algorithm to determine eigenvalues and eigenvectors through repeated matrix multiplication. In contrast to the basic power method which considers only the most recent iteration and assumes noiseless links, the proposed techniques consider information from all the previous iterations of the algorithm and combine them in different ways. The first technique (Sequential Least-Squares method) sequentially constructs a least-squares estimate of the channel matrix, which is then used to calculate the beamformer/combiner pair estimate. The second technique (Summed Power method) aims to mitigate the effect of noise by using a linear combination of the previously tried beams to calculate the next beam, providing improved performance in the low-SNR regime (typical for mmWave systems) with minimal complexity/feedback overhead. A third technique (Least-Squares Initialized Summed Power method ) combines the good performance of the first technique at the high-SNR regime with the low-complexity advantage of the second technique by priming the summed power method with initial estimates from the sequential method. [ABSTRACT FROM PUBLISHER]

Due to the ever increasing data traffic demands, which are directly connected to increased energy consumption, it becomes challenging for operators to achieve capacity enhancement while limiting their electric bill. To that end, exploiting the context awareness of future cognitive networks is expected to play a key role. Next generation cellular networks are about to include a plethora of small cells, with users being able to communicate via multiple bands. Given that small cells are expected to be eventually as close as 50 m apart, not all of them will have a direct connection to the core network; thus, multihop communication through neighboring small cells may be required. In such architectures, the user association problem becomes challenging, with backhaul energy consumption being a definitive parameter. Thus, in this article, we study the user association problem in cognitive heterogeneous networks. We evaluate the existing approaches in terms of energy efficiency and show the potential of exploiting the available context-aware information (i.e., users? measurements and requirements, knowledge of the network architecture, and the available spectrum resources of each base station) to associate the users in an energyefficient way, while maintaining high spectrum efficiency. Our study considers both the access network and backhaul energy consumption, while the performance of the association algorithms is evaluated under two different case study scenarios [ABSTRACT FROM AUTHOR]

Efficient optimization techniques are important to manage interference in emerging dense wireless networks. Here, we address interference management through power control as a general utility maximization problem. For the class of utility functions that are concave in the logarithm of the optimization variables, we propose a power control algorithm based on fixed-point iterations. The iterations converge to the globally optimal power vector. One key benefit is that, for a network with N transmitters and a centralized implementation of the power control algorithm, the computational complexity per iteration of the algorithm is \cal O(N^2). When implemented in a distributed fashion and allowing for a signaling complexity of N messages per iteration, the computation complexity is reduced to \cal O(N). We show that the proposed centralized and distributed versions of the algorithm converge to the optimal power vector at a linear rate. Our numerical results suggest that in most instances, the algorithm takes fewer than ten iterations to converge, even fewer if the initialization is close to the optimal power vector. The proposed algorithm is, therefore, very efficient for power control in slowly fading channels. Furthermore, unlike previous works in the literature, the proposed algorithm does not require the objective function to be separable into a sum of individual utilities. As an example, we present results for power control in a two-hop decode-and-forward cooperative relay network and illustrate the performance gains due to interference management. [ABSTRACT FROM AUTHOR]

In many wireless sensor networks, local sensors adopt binary decisions because they can be transmitted to a fusion center using very low power. However, if a binary decision is wrong, the probability of the fusion center making a wrong final decision is dramatically increased. This study proposes a reliability-based adaptive method to resolve this problem with little extra computation. Before a sensor makes a binary local decision, its observation must be evaluated. Unreliable ranges are set for this evaluation. If the sensor's observation result does not fall within the unreliable range, the sensor makes a local decision. Otherwise, the sensor must make another observation. The optimal unreliable ranges are then derived. This study applies the proposed method to an existing distributed classification scheme using the binary decision. Performance analysis shows that this approach efficiently reduces the misclassification probability at the fusion center. Simulation results show that the transmission power is reduced by 7.5 dB to achieve a misclassification probability of 0.1 under some practical conditions. [ABSTRACT FROM PUBLISHER]

In wireless communications, knowledge of the channel coefficients is required for coherent demodulation. In this work, a blind channel estimation method based on the Lloyd-Max algorithm is proposed for single-tap fading channels. The algorithm estimates the constellation points for the received signal using the Lloyd-Max algorithm. The algorithm is investigated for frequency hopping systems with small hop durations and operating under partial-band jamming for both detecting the jammer and estimating the channel. The performance of the Lloyd-Max channel estimation algorithm is compared to the performance of pilot-based channel estimation algorithms and non-coherent demodulation and decoding. A special concatenated channel code is constructed to work with the proposed blind channel estimation due to the phase ambiguity in estimates. [ABSTRACT FROM AUTHOR]