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This paper considers a multipair amplify-and-forward massive multipair multiple-input multiple-output relaying system with one-bit analog-to-digital converters and one-bit digital-to-analog converters at the relay. The channel state information is estimated via pilot training, and then utilized by the relay to perform simple maximum-ratio combining/maximum-ratio transmission processing. Leveraging on the Bussgang decomposition, an exact achievable rate is derived for the system with correlated quantization noise. Based on this, a closed-form asymptotic approximation for the achievable rate is presented, thereby enabling efficient evaluation of the impact of key parameters on the system performance. Furthermore, power scaling laws are characterized to study the potential energy efficiency associated with deploying massive one-bit antenna arrays at the relay. In addition, a power allocation strategy is designed to compensate for the rate degradation caused by the coarse quantization. Our results suggest that the quality of the channel estimates depends on the specific orthogonal pilot sequences that are used, contrary to unquantized systems where any set of orthogonal pilot sequences gives the same result. Moreover, the sum rate gap between the double-quantized relay system and an ideal nonquantized system is a moderate factor of ${\text{4}}/\pi ^2$ in the low power regime. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of analog–digital transmit beamforming under spectrum sharing constraints for backhaul systems. In contrast to fully digital designs, where the spatial processing is done at baseband unit with all the flexible computational resources of digital processors, analog–digital beamforming schemes require that certain processing is done through analog components, such as phase-shifters or switches. These analog components do not have the same processing flexibility as the digital processor, but on the other hand, they can substantially reduce the cost and complexity of the beamforming solution. This paper presents the joint optimization of the analog and digital parts, which results in a nonconvex, NP-hard, and coupled problem. In order to solve it, an alternating optimization with a penalized convex–concave method is proposed. According to the simulation results, this novel iterative procedure is able to find a solution that behaves close to the fully digital beamforming upper bound scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider the multiuser Gaussian broadcast channel with multiple transmit antennas at the base station and multiple receive antennas at each user. Assuming full knowledge of the channel state information at the transmitter and the different receivers, a new transmission scheme that employs partial interference cancellation at the transmitter with dirty-paper encoding and decoding is proposed. The maximal achievable throughput of this system is characterized, and it is shown that given any ordered set of users the proposed scheme is asymptotically optimal in the high signal-to-noise ratio (SNR) regime. In addition, with optimal user ordering, the proposed scheme is shown to be optimal in the low-SNR regime. We also consider a linear transmission scheme which employs only partial interuser interference cancellation at the base station without dirty-paper coding. Given a transmit power constraint at the base station, the sum-rate capacity of this scheme is characterized and a suboptimal precoding algorithm is proposed. In several cases, it is shown that, for all values of the SNR, the achievable throughput of this scheme is strictly larger than a system which employs full interference cancellation at the base station [21]. In addition, it is shown that, in some cases, the linear transmission scheme can support simultaneously an increased number of users while achieving a larger system throughput. [ABSTRACT FROM AUTHOR]

In recent years, considerable attention has been paid to the joint optimal transceiver design for multi-input multi-output (MIMO) communication systems. In this paper, we propose a joint transceiver design that combines the geometric mean decomposition (GMD) with either the conventional zero-forcing VBLAST decoder or the more recent zero-forcing dirty paper precoder (ZFDP). Our scheme decomposes a MIMO channel into multiple identical parallel subchannels, which can make it rather convenient to design modulation/demodulation and coding/decoding schemes. Moreover, we prove that our scheme is asymptotically for (moderately) high SNR in terms of both channel throughput and bit error rate (BER) performance. This desirable property is not shared by any other conventional schemes. We also consider the subchannel selection issues when some of the subchannels are too poor to be useful. Our scheme can also be combined with orthogonal frequency division multiplexing (OFDM) for intersymbol interference (ISI) suppression. The effectiveness of our approaches has been validated by both theoretical analyses and numerical simulations. [ABSTRACT FROM AUTHOR]

Detecting periodicity in a short sequence is an important problem, with many applications across science and engineering. Several efficient algorithms have been proposed for this over the years. There is a wide choice available today in terms of the tradeoff between algorithmic complexity and estimation accuracy. In spite of such a rich history, one particular aspect of period estimation has received very little attention from a fundamental perspective. Namely, given a discrete time periodic signal and a list of candidate integer periods, what is the absolute minimum datalength required to estimate its integer period? Notice that the answer we seek must be a fundamental bound, i.e., independent of any particular period estimation technique. Common intuition suggests the minimum datalength as twice the largest expected period. However, this is true only under some special contexts. This paper derives the exact necessary and sufficient bounds to this problem. The above-mentioned question is also extended to the case of mixtures of periodic signals. First, a careful mathematical formulation discussing the unique identifiability of the component periods (hidden integer periods) is presented. Once again, a rigorous theoretical framework in this regard is missing in the existing literature but is a necessary platform to derive precise bounds on the minimum necessary datalength. The bounds given here are generic, that is, independent of the algorithms used. Specific algorithm-dependent bounds are also presented in the end for the case of dictionary-based integer period estimation reported in recent years. [ABSTRACT FROM AUTHOR]

In this paper, wepropose a novel two-stage transmission framework for an intelligent reflecting surface (IRS) assisted millimeter wave (mmWave) system in the presence of phase error caused by hardware impairments. Specifically, in Stage I, we introduce a pilot-based phase calibration task and reformulate it as an alternating optimization program. Building on this formulation, an efficient iterative algorithm is developed to jointly estimate the phase error, which needs to be calibrated, and the channels corresponding to the first user. Stage II consists of two blocks, channel estimation and data transmission. During the channel estimation block, by leveraging on the sparsity of mmWave channels, we propose a fast Fourier transform based scheme to estimate the IRS-BS channel and user-IRS channels. During the data transmission block, based on the estimated channel state information, base station precoders and IRS phase shifts are jointly optimized to maximize the sum signal-to-leakage-plus-noise ratio of all users. To handle this non-convex problem, we further develop a low-complexity joint beamforming algorithm by exploiting fractional programming techniques. Moreover, to examine the performance of the proposed phase calibration scheme, we derive the Cramer-Rao lower bound for the intended phase error estimation. Simulation results validate the effectiveness of our proposed transmission framework and the necessity of IRS phase calibration. [ABSTRACT FROM AUTHOR]

Signal processing applications have high instruction level parallelism (ILP) and real-time performance requirements. Embedded and application specific multicluster architecture is desirable to provide the large computation power that these applications need. As technology moves to deep submicron level, it becomes more important and challenging to design an efficient intercluster connection network to satisfy the rapid growing intercluster data transfer needs under the power and cost constraints. This paper addresses the automatic generation of intercluster connection network with partially connected buses. An application specific approach is proposed in this paper to determine the minimum number of required partially connected buses without performance degradation for a given schedule in polynomial time. The intercluster connection topology is then generated with the determined minimum number of partially connected buses to minimize the connection bus segments. Further, a scheduling algorithm is presented in this paper to minimize the intercluster communication needs for the given application and to reduce the minimum number of partially connected buses required in the interciuster connection network under schedule length constraint. Experimental results indicate that an average reduction up to 50.6% in the number of minimum required buses and an average reduction of 64.5% in bus segments can be achieved compared to commonly used intercluster communication aware scheduling techniques and as soon as possible (ASAP) data transfer scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a novel linear estimator named stochastic MV-PURE estimator, developed for the stochastic linear model, and designed to provide improved performance over the linear minimum mean square error (MMSE) Wiener estimator in cases prevailing in practical, real-world settings, where at least some of the second-order statistics of the random vectors under consideration are only imperfectly known. The proposed estimator shares its main mathematical idea and terminology with the recently introduced minimum-variance pseudo-unbiased reduced-rank estimator (MV-PURE), developed for the linear regression model. The proposed stochastic MV-PURE estimator minimizes the mean square error (MSE) of its estimates subject to rank constraint and inducing minimium distortion to the target random vector. Therefore, the stochastic MV-PURE combines the techniques of the reduced rank Wiener filter (named in this paper RR-MMSE) and the distortionless-constrained estimator (named in this paper C-MMSE), in order to achieve greater robustness against noise or model errors than RR-MMSE and C-MMSE. Furthermore, to ensure that the stochastic MV-PURE estimator combines the reduced-rank and minimum-distortion approaches in the MSE-optimal way, we propose a rank selection criterion which minimizes the MSE of the estimates obtained by the stochastic MV-PURE. As a numerical example, we employ the stochastic MV-PURE, RR-MMSE, C-MMSE, and MMSE estimators as linear receivers in a MIMO wireless communication system. This example is chosen as a typical signal processing scenario, where the statistical information on the data, on which the estimates are built, is only imperfectly known. We verify that the stochastic MV-PURE achieves the lowest MSE and symbol error rate (SER) in such settings by employing the proposed rank selection criterion. [ABSTRACT FROM AUTHOR]

The normal offset decomposition is an adaptive (nonlinear) multiscale data transform, constructed in a way similar to the lifting scheme for wavelet transforms. The main difference lies in the fact that the detail coefficients are not wavelet coefficients corresponding to a wavelet basis function. Wavelet coefficients carry detail information situated at the location of the basis function. Besides detail information, normal offset coefficients also contain directional/geometrical information, i.e., information on where to locate the details. The normal offset decomposition is a jump-adaptive transform leading to a sparse representation of edges in images and sharp reconstructions of these edges. A major issue in this paper is the design of a new class of wavelet transforms that can be extended to normal offset decompositions. The combination of normal offsets and a stable lifting scheme is nontrivial. A necessary condition for an -stable lifted wavelet transform is that the wavelet basis functions have a vanishing integral (i.e., at least one vanishing moment). Although the normal offset decomposition is not a basis transform in the strict sense, a similar criterion for stability can be established and instability is equally a problem in a naive implementation. This paper constructs an ldquoupdatedrdquo normal offset decomposition that is stable and therefore appropriate for use in applications that sensitive to the numerical condition, such as applications involving (heavy) noise. The new scheme combines numerical stability, fast computation, and sharp edge representations. [ABSTRACT FROM PUBLISHER]

In this paper, the maximum achievable data rate performance is addressed for MIMO systems that employ transmission of known training sequences for channel estimation. In Part I of this paper, it was found that systems based on superimposed pilots (SIP) offer an increased performance over conventional time-multiplexed pilots for fast fading channels. In other cases, the performance gain is lost due the poor channel estimates that are attained when there is interfering data power present during the channel estimation process. By allowing channel reestimation using a decision-directed approach, this interfering data power can instead be utilized as additional training power, which significantly improves the quality of the channel estimates. As the channel estimates improve within the coherence interval, a higher coding rate can successfully be applied. These multiple coding rate schemes can also be applied to conventional time-multiplexed pilot schemes. However, our numerical examples show that a larger performance increase in maximum achievable data rate is attained if the decision-directed approach is applied to a SIP scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a time-domain as well as a frequency-domain per-tone equalization for orthogonal frequency-division multiplexing (OFDM) over doubly selective channels. We consider the most general case, where the channel delay spread is larger than the cyclic prefix (CP), which results in interblock interference (IBI). IBI in conjunction with the Doppler effect destroys the orthogonality between subcarriers and, hence, results in severe intercarrier interference (Id). In this paper, we propose a time-varying finite-impulse-response (TV-FIR) time-do-main equalizer (TEQ) to restore the orthogonality between subcarriers, and hence to eliminate ICIIIBI. Due to the fact that the TEQ optimizes the performance over all subcarriers in a joint fashion, it has a poor performance. An optimal frequency-domain per-tone equalizer (PTEQ) is then obtained by transferring the TEQ operation to the frequency domain. Through computer simulations, we demonstrate the performance of the proposed equalization techniques. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, due to the bandwidth constraint, the distributed nodes (DNs) might only provide binary representatives of the source signal, and then transmit them to the central node (CN). In this paper, we consider the joint estimation of signal amplitude and background noise variance from binary observations over decentralized channels. We first analyze the Cramér–Rao lower bounds (CRLBs) of the parameters of interest and develop a quasilinear estimator (QLE), in which the desirable estimates can be obtained from several intermediate parameters linearly. Next, we consider a more realistic situation where the decentralized channel is noisy during the data transmission. Based on the error propagation model, the asymptotic analysis shows that the performance of the proposed QLE is mainly dominated by the thresholds of the quantizers, which encourages us to adopt a correlated quantization (CQ) scheme by exploiting the spatial correlation among background noises/channel noises. To ease the implementation of QLE in practice, an adaptive quantization (AQ) scheme is also proposed so as to obtain reasonable selections of the required thresholds. Finally, numerical simulations are provided to validate our theoretical findings. [ABSTRACT FROM AUTHOR]

In this paper, direction-of-arrival (DOA) estimation using non-coherent processing for partly calibrated arrays composed of multiple subarrays is considered. The subarrays are assumed to compute locally the sample covariance matrices of their measurements and communicate them to the processing center. A sufficient condition for the unique identifiability of the sources in the aforementioned non-coherent processing scheme is presented. We prove that, under mild conditions, with the non-coherent system of subarrays, it is possible to identify more sources than identifiable by each individual subarray. This property of non-coherent processing has not been investigated before. We derive the maximum likelihood estimator for DOA estimation at the processing center using the sample covariance matrices received from the subarrays. Moreover, the Cramér–Rao bound (CRB) for our measurement model is derived and is used to assess the presented DOA estimators. The behavior of the CRB at high signal-to-noise ratio (SNR) is analyzed. In contrast to coherent processing, we prove that the CRB approaches zero at high SNR only if at least one subarray can identify the sources individually. [ABSTRACT FROM AUTHOR]

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled radio access network (RAN) with the UAV acting as an aerial platform to communicate with a set of ground users (GUs) in a variety of modes of practical interest, including data collection in the uplink, data transmission in the downlink, and data relaying between GUs involving both the uplink and downlink. Under this general framework, two UAV operation scenarios are considered: periodic operation, where the UAV serves the GUs in a periodic manner by following a certain trajectory repeatedly, and one-time operation where the UAV serves the GUs with one single fly and then leaves for another mission. In each scenario, we aim to minimize the UAV periodic flight duration or mission completion time, while satisfying the target data requirement of each GU via a joint UAV trajectory and communication resource allocation design approach. Iterative algorithms are proposed to find efficient locally optimal solutions by utilizing successive convex optimization and block coordinate descent techniques. Moreover, as the quality of the solutions obtained by the proposed algorithms critically depends on the initial UAV trajectory adopted, we propose new methods to design the initial trajectories for both operation scenarios by leveraging the existing results for solving the classic Traveling Salesman Problem and Pickup-and-Delivery Problem. Numerical results show that the proposed trajectory initialization designs lead to significant performance gains compared to the benchmark initialization based on circular trajectory. [ABSTRACT FROM AUTHOR]

Both localization and synchronization of mobile nodes are important for wireless networks. In this paper, we propose new methods for network localization and synchronization (NLS) using full-duplex radios through only two frames of transmission. Specifically, all nodes simultaneously transmit their signature signals in the first frame, while receiving others’ signals via full-duplex radios. In the second frame, nodes transmit either scrambled versions of their received signals in the first frame or a digital packet of the channel parameter estimates of the received signals. We develop distributed algorithms to estimate the arrival times of different components in the received signals. These arrival times are then used to determine the local network geometry and clock offsets. The Cramér-Rao lower bounds for internode distances and clock offsets are derived, and the former can be translated into error bounds of the node positions. Compared with conventional frequency division duplex or time-division duplex, we demonstrate the high efficiency of NLS using full-duplex radios, revealing its potential beyond data communications in future wireless networks. [ABSTRACT FROM AUTHOR]

In this paper, we study the target tracking problem in a wireless sensor network. A sensor receives a measurement from an energy emitting target and employs binary quantization to the received measurement to generate its decision. A sinusoidal waveform with a certain duration is then used to transmit the sensor decision to the fusion center (FC). All sensor decisions are transmitted to the FC over erroneous wireless channels based on a time division multiple access scheme. We introduce the proportional time allocation (PTA) algorithm where at each time step of tracking, PTA jointly determines the sensors binary quantization thresholds and their time allocations devoted for the transmissions of binary sensor decisions. Simulation results show that, PTA optimally and dynamically distributes the available transmission time among the sensors near the target so that the decisions of such sensors become less subject to channel errors, and turns off the non-informative sensors located far away from the target. Hence, PTA both saves from the number of sensors transmitting to the FC and provides better estimation performance as compared to ad hoc equal time allocation approaches. [ABSTRACT FROM PUBLISHER]

This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian systems under the dynamic event-triggered protocol. In order to avert frequent data transmission and reduce the communication overhead, a dynamic event-triggered transmission mechanism is adopted to decide whether the data should be transmitted or not. We first consider a scenario where all sensor nodes selectively transmit their newly obtained measurements to a central node, and a full likelihood function at the central node is derived by fusing the transmitted measurements and the information embodied in the non-triggered measurements. Based on the derived full likelihood function, a centralized auxiliary particle filtering algorithm is proposed to select those particles that are more likely to match the current measurement information. Next, based on the diffusion strategy, a distributed auxiliary particle filtering algorithm is further developed, where the local measurements and the local posteriors (approximated by the Gaussian mixture models) are exchanged among neighboring nodes under the dynamic event-triggered communication strategy. Finally, the effectiveness of the proposed filtering schemes is demonstrated via Monte Carlo simulations in a target tracking problem with received-signal-strength sensors. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of an orthogonal frequency-division multiplexing (OFDM)-based cognitive radio (CR) spectrum sharing communication system that assumes random allocation and absence of the primary user's (PU) channel occupation information, i.e., no spectrum sensing is employed to acquire information about the availability of unused subcarriers. In case of a single secondary user (SU) in the secondary network, due to the lack of information of PUs' activities, the SU randomly allocates the subcarriers of the primary network and collide with the PUs' subcarriers with a certain probability. To maintain the quality of service (QoS) requirement of PUs, the interference that SU causes onto PUs is controlled by adjusting SU's transmit power below a predefined threshold, referred to as interference temperature. In this paper, the average capacity of SU with subcarrier collisions is employed as performance measure to investigate the proposed random allocation scheme for both general and Rayleigh channel fading models. Bounds and scaling laws of average capacity with respect to the number of SUs, PUs, and available subcarriers are derived. In addition, in the presence of multiple SUs, the multiuser diversity gain of SUs assuming an opportunistic scheduling is also investigated. To avoid the interference at the SUs that might be caused by the random allocation scheme and obtain the maximum sum rate for SUs based on the available subcarriers, an efficient centralized sequential algorithm based on the opportunistic scheduling and random allocation (utilization) methods is proposed to ensure the orthogonality of assigned subcarriers. [ABSTRACT FROM PUBLISHER]

An amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay network composed of a source, a relay, and a destination is considered, where transmit beamforming is employed both at the source and at the relay. The relay is a user who is willing to help the communication from the source to the destination. In our paper, however, the relay is untrusted in the sense that it may make a passive security attack; that is, it may decode messages of the source. We consider two ways to transmit confidential information of the source to the destination: noncooperative secure beamforming and cooperative secure beamforming. In the noncooperative scheme, the relay is simply treated as an eavesdropper, and does not participate in communication. In the cooperative scheme, the relay is asked to relay signals from the source to the destination. In this paper, the source and relay beamforming is jointly designed to maximize the secrecy rate in the cooperative scheme. The conditions under which the cooperative scheme achieves a higher secrecy rate than the noncooperative scheme are derived in the low and high signal-to-noise ratio (SNR) regimes of the source-relay and relay-destination links. The performance of the secure beamforming schemes is compared through extensive numerical simulations. [ABSTRACT FROM PUBLISHER]

Constant modulus transforms like discrete Fourier transform (DFT), Walsh transform, and Gold codes have been successfully used over several decades in several engineering applications, including discrete multi-tone (DMT), orthogonal frequency division multiplexing (OFDM) and code division multiple access (CDMA) communications systems. In this paper, we present a generalized framework for DFT called Generalized DFT (GDFT) with nonlinear phase by exploiting the phase space. We show that GDFT offers sizable correlation improvements over DFT, Walsh, Oppermann and Gold codes, leading to better performance in all multi-carrier communications scenarios investigated. We also highlight how known constant modulus orthogonal transforms are special solutions of the proposed GDFT framework. Moreover, we introduce practical design methods offering computationally efficient implementations of GDFT as enhancements to DFT. We conclude the paper with examples of communications applications where GDFT is shown to outperform DFT and other known constant modulus bases. [ABSTRACT FROM PUBLISHER]

This paper addresses cooperative time division duplex (TDD) relaying in the multiple-antenna case with full channel state information (CS!), i.e., assuming perfect knowledge of all channels. The main focus of the paper is on the compress-and-forward (CF) strategy, for which an achievable rate on the Gaussian MIMO relay channel can be derived by applying distributed vector com- pression techniques. The processing at the CF relay consists in a conditional Karhunen-Loëve transform (CKLT) followed by a separate Wyner-Ziv (WZ) coding of each output stream at a different rate. The paper provides a simple analytical expression for the optimum WZ coding rates, and also proposes an iterative procedure to perform this optimization jointly with that of the transmit covariance matrices at the source and relay. The multiple access channel (MAC) formed by the source and relay transmitting simultaneously to the destination is considered, and it is shown that an optimal decoding order exists at least in the single-antenna case. We discuss the extension to MIMO-OFDM, as well as practical source coding implementation. The CF achievable rates are bench-marked with other upper and lower bounds on capacity. Simulation results show that CF can outperform decode-and-forward (DF) and approach capacity for realistic SNR values, which validates the performance of the proposed optimization procedure. [ABSTRACT FROM AUTHOR]

This paper is focused on the cross-layer design problem of joint multiuser detection and power control for energy-efficiency optimization in wireless data networks through a game-theoretic approach. Unlike previous studies in this area, which have considered the application of game theory to achieve energy-efficiency in idealized synchronous code division multiple access systems, more realistic assumptions including system asynchronism, the use of band-limited chip-pulses, and the multipath distortion induced by the wireless channel are explicitly incorporated in the development of this paper. Several noncooperative games are proposed wherein each user may vary its transmit power and uplink receiver in order to maximize its utility, which is defined here as the ratio of data throughput to transmit power. In particular, the case in which a linear multiuser detector is adopted at the receiver is considered first, and then, the more challenging case in which a nonlinear decision feedback multiuser detector is employed is considered. Via large system analysis (LSA), a decentralized implementation of the power allocation game requiring very little prior information on the interference background is proposed. LSA is also used to compare the energy efficiency of several linear multiuser detectors, and to obtain the optimal (i.e., utility-maximizing) length of the training sequence of each data frame. Numerical results show the effectiveness of the proposed solutions, as well as a very satisfactory agreement of the LSA-based analysis with simulation results obtained for systems with finite (and not so large) numbers of users. [ABSTRACT FROM AUTHOR]

Grant-free random access is a promising protocol to support massive access in beyond fifth-generation (B5G) cellular Internet-of-Things (IoT) with sporadic traffic. Specifically, in each coherence interval, the base station (BS) performs joint activity detection and channel estimation (JADCE) before data transmission. Due to the deployment of a large-scale antennas array and the existence of a huge number of IoT devices, JADCE usually has high computational complexity and needs long pilot sequences. To solve these challenges, this paper proposes a dimension reduction method, which projects the original device state matrix to a low-dimensional space by exploiting its sparse and low-rank structure. Then, we develop an optimized design framework with a coupled full column rank constraint for JADCE to reduce the size of the search space. However, the resulting problem is non-convex and highly intractable, for which the conventional convex relaxation approaches are inapplicable. To this end, we propose a logarithmic smoothing method for the non-smoothed objective function and transform the interested matrix to a positive semidefinite matrix, followed by giving a Riemannian trust-region algorithm to solve the problem in complex field. Simulation results show that the proposed algorithm is efficient to a large-scale JADCE problem and requires shorter pilot sequences than the state-of-art algorithms which only exploit the sparsity of device state matrix. [ABSTRACT FROM AUTHOR]

High peak-to-average-power ratio (PAPR) is a particular disadvantage of orthogonal frequency-division multiplexing (OFDM) systems and can significantly degrade the power efficiency at the transmitter. The aim of this paper is to propose a post-inverse fast Fourier transform (IFFT) PAPR reduction method based on intelligent linear symbol combining with the objective to minimize the PAPR at the transmitter. Unlike the methods so far reported in the literature, the proposed method has a relatively low complexity and does not require external randomization sequences. This paper will also investigate the impact of the proposed method on the system's bit error rate (BER) performance and provide a closed-form analytical expression that takes into account the BER dependency on the side information bits and multiblock combination. It will be shown that the proposed technique can provide significant PAPR reductions at manageable levels of signal processing requirements while the BER degradation is relatively small. [ABSTRACT FROM AUTHOR]

In this two-part paper, we propose a decentralized strategy, based on a game-theoretic formulation, to find out the optimal precoding/multiplexing matrices for a multipoint-to-multipoint communication system composed of a set of wideband links sharing the same physical resources, i.e., time and bandwidth. We assume, as optimality criterion, the achievement of a Nash equilibrium and consider two alternative optimization problems: 1) the competitive maximization of mutual information on each link, given constraints on the transmit power and on the spectral mask imposedby the radio spectrum regulatory bodies; and 2) the competitive maximization of the transmission rate, using finite order constellations, under the same constraints as above, plus a constraint on the average error probability. In this first part of the paper, we start by showing that the solution set of both noncooperative games is always nonempty and contains only pure strategies. Then, we prove that the optimal precoding/multiplexing scheme for both games leads to a channel diagonalizing structure, so that both matrix-valued problems. can be recast in a simpler unified vector power control game; with no performance penalty. Thus, we study this simpler game and derive sufficient conditions ensuring the uniqueness of the Nash equilibrium. Interestingly, although derived under stronger constraints, incorporating for example spectral mask constraints, our uniqueness conditions have broader validity than previously known conditions. Finally, we assess the goodness of the proposed decentralized strategy by comparing its performance with the performance of a Pareto-optimal centralized scheme. To reach the Nash equilibria of the game, in Part II, we propose alternative distributed algorithms, along with their convergence conditions. [ABSTRACT FROM AUTHOR]

In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on space-time adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we make a performance comparison between two different training-based schemes for multiple-input multiple-output (MIMO) channel estimation. The two schemes are the conventional time-multiplexed pilot (CP) scheme and the more recently suggested superimposed pilot (SIP) scheme. Unlike previous comparisons found in the literature, which are mostly based on estimation error performances, the performance comparison in this paper is made by deriving and comparing the maximum data rate (or rather a tight lower bound on the maximum mutual information) achieved by each scheme. By using the maximum mutual information criterion, for each training scheme, we can optimally allocate the time and power spent on transmission of training and data sequences. Once the system parameters (time and power) are tuned to give optimal performance, we can compare their respective maximum data rate. The theory is applied to a blockwise flat-fading MIMO channel, and it is found that in certain scenarios (such as many receive antennas and/or short channel coherence times), it is beneficial to use the SIP. In other scenarios, the SIP scheme suffers from a higher estimation error, and its gain over the CP scheme is often lost. [ABSTRACT FROM AUTHOR]

Networked sensors are increasingly being used to perform tasks such as detection, source localization, and tracking. It is intuitive to expect a performance increase by completing these tasks with networked arrays. Quantifying the increase in performance requires a generalized analysis, which is typically done using the Cramér-Rao bounds (CRB). Previously, the CRB for multisource-single-array and multiarray-single-source models were derived, but not the general multiarray-multisource case. In this paper, the general case is modeled and is shown to reduce to previously published cases. The model and CRB derived in this paper serve as a benchmark to which current and future research in multiarray, multisource parameter estimation algorithms can be compared. It is proven that the CRB of a scalar parameter in a model containing K + 1 sources is always higher than the CRB of K sources. In addition, using numerical analysis, it is shown that adding sources to a multiarray model has unique effects that cannot be predicted from less general models. The number of sources and sensors, the geometry of the model (i.e., source and sensor locations), and the source and noise power levels all affect the CRB. The effect of changing model parameters is shown for several multiarray-multisource examples when the estimated parameter is source location. [ABSTRACT FROM AUTHOR]

A spectrally efficient strategy is proposed for cooperative multiple access (CMA) channels in a centralized communication environment with N users. By applying super- position coding, each user will transmit a mixture containing its own information as well as the other users', which means that each user shares parts of its power with the others. The use of superposition coding in cooperative networks was first proposed in [11], which will be generalized to a multiple-user scenario in this paper. Since the proposed CMA system can be seen as a precoded point-to-point multiple-antenna system, its performance can be best evaluated using the diversity-multiplexing tradeoff. By carefully categorizing the outage events, the diversity-multiplexing tradeoff can be obtained, which shows that the proposed cooperative strategy can achieve larger diversity/multiplexing gain than the compared transmission schemes at any diversity/multiplexing gain. Furthermore, it is demonstrated that the proposed strategy can achieve optimal tradeoff for multiplexing gains 0 ≤ r ≤ 1 whereas the compared cooperative scheme is only optimal for 0 ≤ r ≤ (1/N). As discussed in the paper, such superiority of the proposed CMA system is due to the fact that the relaying transmission does not consume extra channel use and, hence, the deteriorating effect of cooperative communication on the data rate is effectively limited. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of signal transmission and Doppler compensation in multipath/Doppler channels. Based on a wavelet-based broadband Doppler compensation structure, this paper presents the design and performance characterization of optimum receivers for this class of communication systems. The wavelet-based Doppler compensation structure takes account of the coexistence of multiple Doppler scales in a multipath/Doppler channel and captures the information carried by multiple scaled replicas of the transmitted signal rather than an estimation of an average Doppler as in conventional Doppler compensation schemes. The transmitted signal is recovered by the perfect reconstruction (PR) wavelet analysis filter bank (FB). We demonstrate that with rational orthogonal wavelet signaling, the proposed communication structure corresponds to a Lth-order diversity system, where L is the number of dominant transmission paths. Two receiver designs for pulse amplitude modulation (PAM) signal transmission are presented. Both receiver designs are optimal under the maximum-likelihood (ML) criterion for diversity combination and symbol detection. Good performance is achieved for both receivers in combating the Doppler effect and inter-symbol interference (ISI) caused by multipath while mitigating the channel noise. In particular, the second receiver design overcomes symbol timing sensitivities present in the first design at reasonable cost to performance. [ABSTRACT FROM AUTHOR]

Walsh codes are perfectly orthogonal binary (antipodal) block codes that found their use in many popular applications over several decades including synchronous multiuser communications. It is well known in the literature that they perform poorly for the case of asynchronous multiuser communications. Therefore, the near-orthogonal and nonlinear phase Gold codes with better performance are the preferred user codes in asynchronous direct sequence code division multiple access (DS-CDMA) communications standards. In this paper, we relaxed linear phase requirements and new sets of Walsh-like nonlinear phase binary orthogonal user codes (transforms) are obtained for asynchronous and synchronous spread spectrum multiuser communications. It is shown that the proposed binary user code family outperforms the Walsh codes significantly and they match in performance with the popular, nearly orthogonal Gold codes closely for asynchronous multiuser communications in additive white Gaussian noise (AWGN) channels. It is also shown that all of the binary code families considered in this paper performed comparable for Rayleigh flat-fading channels. We present in this paper that there are a good number of such desirable code sets available in the binary sample space with different transform sizes. These new binary sets with good performance and flexible code lengths might help us to improve the spread spectrum multiplexing capabilities of future wireline and wireless CDMA communications systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a soft-iterative approach for the estimation of block-fading channels based on multiblock (MB) processing. Coded transmission over fading channels is usually performed by interleaving the code word over a large time interval and arranging it into data packets (or blocks) that include also pilot symbols to allow the estimation of the time-varying channel. In this paper, we propose to exploit this block-based structure, and also the inherent latency of soft-iterative receivers due to data interleaving, to improve the channel estimate accuracy. Within the observed frame of blocks, the MB approach uses the invariance of some multipath features, such as delays and mean powers, to estimate the covariance matrix of the channel vector and to identify the corresponding channel subspace. Here, the MB method is extended to incorporate soft information, which is available in iterative receiver structures. The performance of this soft-iterative method is derived in terms of mean square error of the estimate by using a general framework that applies also to other soft-based methods in the literature. A performance comparison is carried out among these estimation methods, both analytically and by numerical simulations, showing the benefits of the proposed approach on the turbo equalizer convergence. [ABSTRACT FROM AUTHOR]

This paper proposes several novel decimation filtering schemes tailored to the design of ΣΔ analog-to-digital (A/D) converters. On the one hand, it extends the class of modified-sinc filters introduced by Lo Presti, in order to obtain decimation filters capable of decoding high-order ΣΔ modulators, say up to the fifth order; on the other hand, it introduces the class of sharpened—modified comb filters (SMCF) aimed at increasing the rejection of the ΣΔ quantization noise around the folding bands and reducing the passband drops of the designed decimation filters with respect to the classic comb structures. Design criteria leading to optimized structures are described. The paper compares classic comb filters with modified and SMCF in terms of performance metrics widely adopted for comb filters. Finally, it derives the maximum ΣΔ modulator order decodable by any proposed filter cell in the class of the SMCFs. [ABSTRACT FROM AUTHOR]

This paper proposes a simple semi-analytical method with reduced simulation time for predicting at any iteration the performance of a turbo-equalization/demapping scheme using the Wang and Poor soft-in/soft-out (SISO) minimum mean-square error (MMSE)/interference cancellation (IC) equalizer and a SISO decoder. The proposed method may be applied to multilevel/phase data modulations as well as multiuser context. This paper shows that the equalizer behavior may be very reliably predicted to- tally by calculations (no simulations are needed) whereas that of the decoder still requires simulations. A comparison between the proposed prediction method and plain simulations of the overall turbo equalization scheme demonstrates that our method accurately determines the system performance at any iteration. Static channels, long frames, and perfect channel knowledge are assumed throughout the paper. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the problem of fusing decisions transmitted over fading channels in a wireless sensor network. Previous development relies on instantaneous channel state information (CSI). However, acquiring channel information may be too costly for resource constrained sensor networks. In this paper, we propose a new likelihood ratio (LR)-based fusion rule which requires only the knowledge of channel statistics instead of instantaneous CSI. Based on the assumption that all the sensors have the same detection performance and the same channel signal-to-noise ratio (SNR), we show that when the channel SNR is low, this fusion rule reduces to a statistic in the form of an equal gain combiner (EGC), which explains why EGC is a very good choice with low or medium SNR; at high-channel SNR, it is equivalent to the Chair-Varshney fusion rule. Performance evaluation shows that the new fusion rule exhibits only slight performance degradation compared with the optimal LR-based fusion rule using instantaneous CSI. [ABSTRACT FROM AUTHOR]

This paper presents new developments of directional filterbanks (DFBs). The motivation for the paper is the existence of multiresolution and multidirection orthogonal transform for two-dimensional (2-D) discrete signals. Based on the frequency supports of the ideal transform, a new uniformly, maximally decimated DFB with six highpass directional subbands and two lowpass subands is introduced. The uniform DFB (uDFB) can be implemented by a binary tree structure of two-channel filterbanks. The filterbank employed in the tree is shown to be alias-free decimation and permissible. The uDFB is then extended to a nonuniform case (nuDFB), which is still maximally decimated, by combining the two lowpass subbands. The nuDFB yields nonuniform frequency division, which composes of one lowpass filter with a decimation factor of one fourth and six highpass directional filters with a decimation factor of one eighth. The new DFBs offer alternative image decompositions, which overcome the limited directional selectivity of the separable wavelets and the limited multiresolution of the conventional DFB. The lowpass subband of the nuDFB can be used to obtain a multiresolution representation by simply reiterating the same nuDFB decomposition. On the other hand, the directional subbands can also be further refined by simply applying a two-channel conventional DFB at each highpass component. A simple design method yielding near orthogonal uniform and nonuniform multidimensional filterbanks is presented. Finally, the performances of the newly proposed nuDFB are compared with other conventional transforms in non-linear approximation, image denoising, and texture classification to demonstrate its potential. [ABSTRACT FROM AUTHOR]

This paper considers the problem of simultaneous multiuser downlink beamforming. The idea is to employ a transmit antenna array to create multiple "beams" directed toward the individual users, and the aim is to increase throughput, measured by sum capacity. In particular, we are interested in the practically important case of more users than transmit antennas, which requires user selection. Optimal solutions to this problem can be prohibitively complex for online implementation at the base station and entail so-called Dirty Paper (DP) precoding for known interference. Suboptimal solutions capitalize on multiuser (selection) diversity to achieve a significant fraction of sum capacity at lower complexity cost. We analyze the throughput performance in Rayleigh fading of a suboptimal greedy DP-based scheme proposed by Tu and Blum. We also propose another user-selection method of he same computational complexity based on simple zero-forcing beamforming. Our results indicate that the proposed method attains a significant fraction of sum capacity and throughput of Tu and Blum's scheme and, thus, offers an attractive alternative to DP-based schemes. [ABSTRACT FROM AUTHOR]

This papers considers the design of a mutiple-input multiple-output (MIMO) communication system with channel knowledge at the transmitter and receiver. The design methods available in the literature have addressed the following two aspects of the problem: a) choice of the symbol constellations for a given transmission scheme or b) choice of the optimal (linear) precoder and equalizer for a given choice of the constellations. More specifically, the choice of the constellations has been made enforcing a diagonal, or parallel, transmission. However, in practice, the two problems of choosing the constellations and the linear precoder/equalizer are clearly coupled, and the diagonal structure may not be necessarily the best. This paper attempts to provide a global view of the problem) by bridging the gap between the existing results on the selection of the constellations and on the design of the signal processing in the form of a linear transceiver (i.e., precoding at the transmitter and equalization at the receiver). [ABSTRACT FROM AUTHOR]

The implementation of orthogonal frequency division multiplexing (OFDM)-based physical layers suffers from the effect of In-phase and Quadrature-phase (IQ) imbalances in the front-end analog processing. The IQ imbalances can severely limit the achievable operating signal-to-noise ratio (SNR) at the receiver and, consequently, the supported constellation sizes and data rates. In this paper, the effect of IQ imbalances on OFDM receivers is studied, and system-level algorithms to compensate for the distortions are proposed. The algorithms include post-fast Fourier transform (FFT) least-squares and least mean squares (LMS) equalization, as well as pre-FFT correction using adaptive channel/distortion estimation and special pilot tones to enable accurate and fast training. Bounds on the achievable performance of the compensation algorithms are derived and evaluated as a function of the physical distortion parameters. A motivation is included for the physical causes of IQ imbalances and for the implications of the approach presented in this paper on designing and implementing wireless transceivers. [ABSTRACT FROM AUTHOR]

Channel estimation and tracking pose real problems in broadband single-carrier wireless communication systems employing multiple transmit and receive antennas. An alternative to estimating the channel is to adaptively equalize the received symbols. Several adaptive equalization solutions have been researched for systems operating in the time domain. However, these solutions tend to be computationally intensive. A low-complexity alternative is to adaptively equalize the received message in the frequency domain. In this paper, we present an adaptive frequency-domain equalization (FDE) algorithm for implementation in single-carrier (SC) multiple-input multiple-output (MIMO) systems. Furthermore, we outline a novel method of reducing the overhead required to train the proposed equalizer. Finally, we address the issues of complexity and training sequence design. Other computationally efficient adaptive FDE algorithms for use in SC systems employing single transmit and receive antennas, receive diversity, or space-lime block codes (STBC) can be found in the literature. However, the algorithm detailed in this paper can be implemented in STBC systems as well as in broadband spatial multiplexing systems, making it suitable for use in high data rate MIMO applications. [ABSTRACT FROM AUTHOR]

This paper investigates, under the zero-forcing (ZF) condition, the design of optimal precoders which minimize the bit error rate (BER) for the multiple-input/multiple-output (MIMO) downlink communication channels. It is shown that the optimal full information control can be employed to design the optimal ZF precoder. The closed-form solutions are derived, and an efficient design algorithm is proposed. Numerical simulations are provided to illustrate the proposed design technique and to demonstrate the performance of the optimal precoders. [ABSTRACT FROM AUTHOR]

This paper considers the employment of linear transceivers for communication through multiple-input multiple-output (MIMO) channels with channel state information (CSI) at both sides of the link. The design of linear MIMO transceivers has been studied since the 1970s by optimizing simple measures of the quality of the system, such as the trace of the mean-square error matrix, subject to a power constraint. Recent results showed how to solve the problem in an optimal way for the family of Schur-concave and Schur-convex cost functions. In particular, when the constellations used on the different transmit dimensions are equal, the bit-error rate (BER) averaged over these dimensions happens to be a Schur-convex function, and therefore, it can be optimally solved. In a more general case, however, when different constellations are used, the average BER is not a Schur-convex function, and the optimal design in terms of minimum BER is an open problem. This paper solves the minimum BER problem with arbitrary constellations by first reformulating the problem in convex form and then proposing two solutions. One is a heuristic and suboptimal solution, which performs remarkably well in practice. The other one is the optimal solution obtained by decomposing the convex problem into several subproblems controlled by a master problem (a technique borrowed from optimization theory), for which extremely simple algorithms exist. Thus, the minimum BER problem can be optimally solved in practice with very simple algorithms. [ABSTRACT FROM AUTHOR]

The problem of finding the least-squares solution to a system of linear equations where the unknown vector is comprised of integers, but the matrix coefficient and given vector are comprised of real numbers, arises in many applications: communications, cryptography, GPS, to name a few. The problem is equivalent to finding the closest lattice point to a given point and is known to be NP-hard. In communications applications, however, the given vector is not arbitrary but rather is an unknown lattice point that has been perturbed by an additive noise vector whose statistical properties are known. Therefore, in this paper, rather than dwell on the worst-case complexity of the integer least-squares problem, we study its expected complexity, averaged over the noise and over the lattice. For the "sphere decoding" algorithm of Fincke and Pohst, we find a closed-form expression for the expected complexity, both for the infinite and finite lattice. It is demonstrated in the second part of this paper that, for a wide range of signal-to-noise ratios (SNRs) and numbers of antennas, the expected complexity is polynomial, in fact, often roughly cubic. Since many communications systems operate at noise levels for which the expected complexity turns out to be polynomial, this suggests that maximum-likelihood decoding, which was hitherto thought to be computationally intractable, can, in fact, be implemented in real time—a result with many practical implications. [ABSTRACT FROM AUTHOR]

In this paper, we aim at developing a robust H2 filtering approach to the design of robust equalizers for communication systems. The contents of this paper mainly include two parts. First, we present a robust H2 filter design method for general multi-input multioutput linear systems with norm-bounded uncertainties in the system matrices based on the linear matrix inequality technique. The characteristics of the equalization problem are taken into account in the filtering model considered here. The advantage of the proposed method is that we can find the optimal solution to robust H2 filtering problem at a reasonable computational burden. Second, we apply the above method to the design of robust equalizers. Two generic examples are studied, one for a single transmit and receive antenna system and another for a multiple transmit and receive antenna system. Both analytical and simulation results show that robust H2 equalizer outperforms the zero-forcing equalizer in both bit-error rate and mean-square errors in the whole simulated range of the signal-to-noise ratio (SNR) when the channel is perturbed from its nominal value, while for the nominal channel, the robust H2 equalizer performs better than the zero-forcing equalizer when the SNR is lower, but it performs worse than the zero-forcing equalizer when the SNR is higher. [ABSTRACT FROM AUTHOR]

We consider the problem of the selection of channel sensing order in cognitive radio networks (CRNs) where the primary users (PUs) use multiple channels for data transmission. We assume that the occupancy state of each channel is correlated with that of the other channels, and the spectrum occupancy state for all the channels follows the discrete-time Markov model. In this paper, we propose to characterize the channel availability of the primary network using the idle probability of consecutive time slots (IPCOTS). Under the discrete-time Markov model, we derive the IPCOTS of each particular channel, under the condition that the states of all the channels in the primary network are identically distributed. Moreover, we propose a suboptimal state-dependent policy, called IPCOTS policy, for the selection of channel sensing order. Finally, we extend the policy to three typical cases. Simulation results show that, in an M/M/m/m-modeled primary network, more secondary user (SU) rewards can be obtained using the IPCOTS policy in comparison with the existing approaches. [ABSTRACT FROM PUBLISHER]

We consider stochastic approximations that arise from such applications as data communications and image processing. We demonstrate why constraints are needed in a stochastic approximation and how a constrained approximation can be incorporated into a preconditioning technique to derive the preconditioned stochastic gradient method (PSGM). We perform convergence analysis to show that the PSGM converges to the theoretical best approximation under some simple assumptions on the preconditioner and on the independence of samples drawn from a stochastic process. Simulation results are presented to demonstrate the effectiveness of the constrained and preconditioned stochastic gradient method. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a wireless-powered cooperative communication network consisting of one hybrid access-point (AP), one source, and one relay. In contrast to conventional cooperative networks, the source and relay in the considered network have no embedded energy supply. They need to rely on the energy harvested from the signals broadcasted by the AP for their cooperative information transmission. Based on this three-node reference model, we propose a harvest-then-cooperate (HTC) protocol, in which the source and relay harvest energy from the AP in the downlink and work cooperatively in the uplink for the source's information transmission. Considering a delay-limited transmission mode, the approximate closed-form expression for the average throughput of the proposed protocol is derived over Rayleigh fading channels. Subsequently, this analysis is extended to the multi-relay scenario, where the approximate throughput of the HTC protocol with two popular relay selection schemes is derived. The asymptotic analyses for the throughput performance of the considered schemes at high signal-to-noise radio are also provided. All theoretical results are validated by numerical simulations. The impacts of the system parameters, such as time allocation, relay number, and relay position, on the throughput performance are extensively investigated. [ABSTRACT FROM PUBLISHER]

In dynamic time-division-duplexing networks, the available resources per cell can be freely allocated to either uplink (UL) or downlink (DL) depending on the instantaneous traffic demand. Hence, complicated UL–DL and DL–UL interference scenarios arise due to simultaneous UL and DL data transmission in adjacent cells. In this paper, decentralized iterative beamformer designs are obtained for several traffic aware network optimization objectives such that only minimal information exchange is required among the coordinated base stations (BS) and user equipment (UE). Bi-directional forward–backward training via spatially precoded over-the-air pilot signaling is used to facilitate coordinated beamforming. This allows BSs and UEs to iteratively optimize their respective transmitters/receivers based on only locally measured reverse link pilot measurements. Novel bi-directional beamformer training strategies and methods for direct estimation (DE) of the stream specific beamformers are developed for each intermediate beamformer update in a limited and noisy pilot environment. The proposed signaling and DE schemes allow for non-orthogonal and overlapping pilots, which considerably reduces the resource coordination effort. Also, the decontamination ability of the proposed strategies are analyzed with limited pilot resources. The numerical examples illustrate the superior system performance of the proposed training and estimation framework in comparison to both the traditional stream-specific channel estimation method and an uncoordinated system. [ABSTRACT FROM AUTHOR]

In this paper, a new orthogonal-frequency-division multiplexing using the \bf C-transform (C-OFDM) is introduced. An exact closed-form bit-error rate (BER) is derived for the proposed C-OFDM system over multipath channels and different kinds of modulation formats. The BER performance of the proposed C-OFDM is evaluated by mathematics and simulation for different channel models, modulation formats, under zero-padding (ZP) and minimum mean-square-error (MMSE) detection. The results are then compared with those of the discrete cosine transform (DCT) and discrete Fourier transform (DFT)-based OFDM, showing that over multipath channel, the new C-OFDM has better BER performance than both the DCT-OFDM and the DFT-OFDM. The proposed scheme is also found to achieve some reduction in the peak-to-average power ratio (PAPR) in comparison with the aforementioned OFDM schemes as the block-diagonal-structure (BDS) property of the \bf C-transform minimizes the input signal superposition. The proposed system has the merits of being resilience to multipath channel dispersion and relatively has lower PAPR. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient user scheduling and power control problem in downlink multicell multiuser orthogonal frequency division multiple access networks, where only the channel distribution information is available. The resulting optimization problem is a combinatorial problem, which belongs to the class of cardinality constrained fractional programming problems subject to a maximum outage probability constraint. To solve the original problem, we derive a centralized joint user scheduling and power control solution satisfying the generalized Karush–Kuhn–Tucker conditions by resorting to the successive convex approximation (SCA). In particular, the proposed scheme can be used to solve the resource allocation problem subject to each user's minimum throughput constraint even if all the subcarriers have the same channel gain. A decentralized power control scheme is proposed to maximize the energy efficiency through combining SCA and alternating direction method of multipliers. Simulation results demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]