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Angle-based localization of a source at unique coordinates in the three-dimensional (3-D) space utilizes traditionally the two-dimensional (2-D) angle of arrival (AOA) measurements from planar arrays. This paper investigates the positioning performance and develops an optimal solution for the 3-D localization using one-dimensional (1-D) space angle (SA) measurements that has the appeal of involving linear arrays only. The localization performance by SA is less understood and the positioning algorithms from the literature are suboptimal and have restrictions on the altitudes and orientations of the linear array receivers. This paper establishes the basic concept of using SA for localization, and conducts analytical comparison of SA and AOA positionings by cross arrays, where the contrasts in the angle observation accuracy, the geometric dilution effect and overall localization performance are elaborated. A solution that can reach the Cramér-Rao Lower Bound performance under Gaussian noise is also proposed that does not have restriction on the placement of the linear array receivers. It consists of an initial solution by semidefinite relaxation and a refined solution by an algebraic estimator. Simulations validate the theoretical investigation and the algorithm performance. [ABSTRACT FROM AUTHOR]

This paper considers a low-complexity iterative linear minimum mean square error (LMMSE) multiuser detector for the multiple-input and multiple-output system with nonorthogonal multiple access (MIMO-NOMA), where multiple single-antenna users simultaneously communicate with a multiple-antenna base station (BS). While LMMSE being a linear detector has a low complexity, it has suboptimal performance in multiuser detection scenario due to the mismatch between LMMSE detection and multiuser decoding. Therefore, in this paper, we provide the matching conditions between the detector and decoders for MIMO-NOMA, which are then used to derive the achievable rate of the iterative detection. We prove that a matched iterative LMMSE detector can achieve the optimal capacity of symmetric MIMO-NOMA with any number of users, the optimal sum capacity of asymmetric MIMO-NOMA with any number of users, all the maximal extreme points in the capacity region of asymmetric MIMO-NOMA with any number of users, and all points in the capacity region of two-user and three-user asymmetric MIMO-NOMA systems. In addition, a kind of practical low-complexity error-correcting multiuser code, called irregular repeat-accumulate code, is designed to match the LMMSE detector. Numerical results shows that the bit error rate performance of the proposed iterative LMMSE detection outperforms the state-of-art methods and is within 0.8 dB from the associated capacity limit. [ABSTRACT FROM AUTHOR]

Orthogonal Time Frequency Space (OTFS) modulation is a recently proposed scheme for time-varying narrowband channels in terrestrial radio-frequency communications. Underwater acoustic (UWA) and ultra-wideband (UWB) communication systems, on the other hand, confront wideband time-varying channels. Unlike narrowband channels, for which time contractions or dilations due to Doppler effect can be approximated by frequency-shifts, the Doppler effect in wideband channels results in frequency-dependent non-uniform shift of signal frequencies across the band. In this paper, we develop an OTFS-like modulation scheme – Orthogonal Delay Scale Space (ODSS) modulation – for handling wideband time-varying channels. We derive the ODSS transmission and reception schemes from first principles. In the process, we introduce the notion of $\omega$ -convolution in the delay-scale space that parallels the twisted convolution used in the time-frequency space. The preprocessing 2D transformation from the Fourier-Mellin domain to the delay-scale space in ODSS, which plays the role of inverse symplectic Fourier transform (ISFFT) in OTFS, improves the bit error rate performance compared to OTFS and Orthogonal Frequency Division Multiplexing (OFDM) in wideband time-varying channels. Furthermore, since the channel matrix is rendered near-diagonal, ODSS retains the advantage of OFDM in terms of its low-complexity receiver structure. [ABSTRACT FROM AUTHOR]

A reconfigurable intelligent surface (RIS) is a nearly-passive flat layer made of inexpensive elements that can add a tunable phase shift to the impinging electromagnetic wave and are controlled by a low-power electronic circuit. This paper considers the fundamental problem of target detection in a RIS-aided multiple-input multiple-output (MIMO) radar. At first, a general signal model is introduced, which includes the possibility of using up to two RISs (one close to the radar transmitter and one close to the radar receiver) and subsumes both a monostatic and a bistatic radar configuration with or without a line-of-sight view of the prospective target. Upon resorting to a generalized likelihood ratio test (GLRT), the design of the phase shifts introduced by the RIS elements is formulated as the maximization of the probability of detection in the location under inspection for a fixed probability of false alarm, and suitable optimization algorithms are proposed. The performance analysis shows the benefits granted by the presence of the RISs and shed light on the interplay among the key system parameters, such as the radar-RIS distance, the RIS size, and the location of the prospective target. A major finding is that the RISs should be better deployed in the near-field of the radar arrays at both the transmit and the receive side. The paper is concluded by discussing some open problems and foreseen applications. [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]

This paper investigates a novel intelligent reflecting surface (IRS)-based symbiotic radio (SR) system architecture consisting of a transmitter, an IRS, and an information receiver (IR). The primary transmitter communicates with the IR and at the same time assists the IRS in forwarding information to the IR. Based on the IRS’s symbol period, we distinguish two scenarios, namely, commensal SR (CSR) and parasitic SR (PSR), where two different techniques for decoding the IRS signals at the IR are employed. We formulate bit error rate (BER) minimization problems for both scenarios by jointly optimizing the active beamformer at the base station and the phase shifts at the IRS, subject to a minimum primary rate requirement. Specifically, for the CSR scenario, a penalty-based algorithm is proposed to obtain a high-quality solution, where semi-closed-form solutions for the active beamformer and the IRS phase shifts are derived based on Lagrange duality and Majorization-Minimization methods, respectively. For the PSR scenario, we apply a bisection search-based method, successive convex approximation, and difference of convex programming to develop a computationally efficient algorithm, which converges to a locally optimal solution. Simulation results demonstrate the effectiveness of the proposed algorithms and show that the proposed SR techniques are able to achieve a lower BER than benchmark schemes. [ABSTRACT FROM AUTHOR]

Integrating the wireless power transfer (WPT) technology into the wireless communication system has been important for operational cost saving and power-hungry problem solving of electronic devices. In this paper, we propose a resonant beam simultaneous wireless information and power transfer (RB-SWIPT) system, which utilizes a gain medium and two retro-reflecting surfaces to enhance and retro-reflect energy, and allows devices to recharge their batteries and exchange information from the resonant beam wirelessly. To reveal the SWIPT mechanism and evaluate the SWIPT performance, we establish an analytical end-to-end (E $\mathscr{2}$ E) transmission model based on a modular approach and the electromagnetic field propagation. Then, the intra-cavity power intensity distribution, transmission loss, output power, and E $\mathscr{2}$ E efficiency can be obtained. The numerical evaluation illustrates that the exemplary RB-SWIPT system can provide about 4.20 W electric power and 12.41 bps/Hz spectral efficiency, and shorter transmission distance or larger retro-reflecting surface size can lead to higher E $\mathscr{2}$ E efficiency. The RB-SWIPT presents a new way for high-power, long-range WPT, and high-rate communication. [ABSTRACT FROM AUTHOR]

Linear, time-varying (LTV) systems composed of time shifts, frequency shifts, and complex amplitude scalings are operators that act on continuous finite-energy waveforms. This paper presents a novel, resource-efficient method for identifying the parametric description of such systems, i.e., the time shifts, frequency shifts, and scalings, from the sampled response to linear frequency modulated (LFM) waveforms, with emphasis on the application to radar processing. If the LTV operator is probed with a sufficiently diverse set of LFM waveforms, then the system can be identified with high accuracy. In the case of noiseless measurements, the identification is perfect, while in the case of noisy measurements, the accuracy is inversely proportional to the noise level. The use of parametric estimation techniques with recently proposed denoising algorithms allows the estimation of the parameters with high accuracy. [ABSTRACT FROM PUBLISHER]

We consider a distributed resource allocation problem in networks where each transmitter–receiver pair aims at maximizing its local utility function by adjusting its action matrix, which belongs to a given feasible set. This problem has been addressed recently by applying a matrix exponential learning (MXL) algorithm, which has a very appealing convergence rate. In this learning algorithm, however, each transmitter must know an estimate of the gradient matrix of the local utility. The knowledge of the gradient matrix at the transmitters incurs a high signaling overhead, especially that the matrix size increases with the dimension of the action matrix. In this paper, we therefore, investigate two strategies in order to decrease the informational exchange per iteration of the algorithm. In the first strategy, each receiver sends at each iteration part of the elements of the gradient matrix with respect to a certain probability. In the second strategy, each receiver feeds back “sporadically” the whole gradient matrix. We focus on the analysis of the convergence of the MXL algorithm to optimum under these two strategies. We prove that the algorithm can still converge to optimum almost surely. Upper bounds of the average convergence rate are also derived in both situations with general step-size setting, from which we can clearly see the impact of the incompleteness of the feedback information. The proposed algorithms are applied to solve the energy efficiency maximization problem in a multicarrier multiuser multiple-input and multiple-output (MIMO) network. Simulation results further corroborate our claim. [ABSTRACT FROM AUTHOR]

This paper considers the joint fronthaul compression and transmit beamforming design for the uplink cloud radio access network (C-RAN), in which multi-antenna users communicate with a cloud-computing based centralized processor (CP) through multi-antenna base-stations (BSs) serving as relay nodes. A compress-and-forward relaying strategy, named the virtual multiple-access channel (VMAC) scheme, is employed, in which the BSs can either perform single-user compression or Wyner-Ziv coding to quantize the received signals and send the quantization bits to the CP via capacity-limited fronthaul links; the CP performs successive decoding with either successive interference cancellation (SIC) receiver or linear minimum-mean square-error (MMSE) receiver. Under this setup, this paper investigates the joint optimization of the transmit beamformers and the quantization noise covariance matrices for maximizing the network utility. A novel weighted minimum-mean-square-error successive convex approximation (WMMSE-SCA) algorithm is first proposed for maximizing the weighted sum rate under the user transmit power and fronthaul capacity constraints with single-user compression. Assuming a heuristic decompression order, the proposed algorithm is then adapted for optimizing the transmit beamforming and fronthaul compression under Wyner-Ziv coding. This paper also proposes a low-complexity separate design consisting of optimizing transmit beamformers for the Gaussian vector multiple-access channel along with per-antenna quantizers with uniform quantization noise levels across the antennas at each BS. Numerical results show that majority of the performance gain brought by C-RAN comes from the implementation of SIC at the CP. Furthermore, the low complexity separate design already performs very close to the optimized joint design in regime of practical interest. [ABSTRACT FROM PUBLISHER]

This paper describes a highly sensitive spectrum sensing system based on a cross-correlation technique that employs low-cost software-defined radio receivers. By multiplying two data streams received by duplicated receivers and averaging the outputs, a cross-correlation system is able to suppress uncorrelated noise at the cost of measurement time. In this paper, phase-domain detection is proposed to determine whether or not a radio signal is present without any knowledge of signal features. The robustness of our method to noise uncertainty has been verified in both theoretical analyses and simulations. The sensing system has been implemented with low-cost USB tuners and a PC-based digital signal processor. TV-band spectrum sensing has been performed as a demonstration of the system. [ABSTRACT FROM PUBLISHER]

This paper presents two secure transmission algorithms for millimeter-wave wireless communication, which are computationally attractive and have analytical solutions. In the proposed algorithms, we consider phased-array transmission structure and focus on phase shift keying (PSK) modulation. It is found that the traditional constellation synthesis problem can be solved with the aid of polygon construction in the complex plane. A detailed analysis is then carried out and an analytical procedure is developed to obtain a qualified phase solution. For a given synthesis task, it is derived that there exist infinite weight vector solutions under a mild condition. Based on this result, we propose the first secure transmission algorithm by varying the transmitting weight vector at symbol rate, thus resulting exact phases at the intended receiver and producing randomnesses at the undesired eavesdroppers. To improve the security without significantly degrading the symbol detection reliability for target receiver, the second secure transmission algorithm is devised by allowing a relaxed symbol region for the intended receiver. Compared to the first algorithm, the second one incorporates an additional random phase rotation operation to the transmitting weight vector and brings extra disturbance for the undesired eavesdroppers. Different from the existing works that are only feasible for the case of single-path mmWave channels, our proposed algorithms are applicable to more general multi-path channels. Moreover, all the antennas are active in the proposed algorithms and the on-off switching circuit is not needed. Simulations are presented to demonstrate the effectivenesses of the proposed algorithms under various situations. [ABSTRACT FROM AUTHOR]

This paper focuses on finding the optimal sensor placement strategies for circular time-of-arrival (TOA) localization in the three-dimensional (3D) space. The A-optimality criterion, minimizing the trace of the inverse Fisher information matrix (FIM), is applied to determine optimal sensor placements under the Gaussian measurement noise. A comprehensive analysis of optimal sensor-target geometries is provided in the general circumstance with no restriction on the number of sensors, sensor elevation angle and sensor-target range. The analysis is divided into two detailed sub-cases with uniform sensor measurement noise variance. The reachable minimum trace of Cramér-Rao lower bound (CRLB) $=\frac{9\sigma ^2}{4N}$ is derived. Two new closed-form solutions, i.e., the resistor network and special solution methods, are developed to quickly determine the optimal geometries. Furthermore, the theoretical smallest tr(CRLB) $=\frac{9}{4} (\sum _{i=1}^N\frac{1}{\sigma _{i}^{2}})^{-1}$ of using different noise variances is also presented. The analytical results are verified by simulation examples. [ABSTRACT FROM AUTHOR]

Considering the increasing demand for large-scale MapReduce distributed computing, coded distributed computing (CDC) has become a promising technology to reduce the significant bottleneck of MapReduce computation. Despite its importance, most of the works on CDC have assumed homogeneous environments, where the nodes have the same storage, computing, and communication capacities. However, since many practical scenarios involve heterogeneous environments, heterogeneous CDC design is highly demanded to reduce communication load. Under consideration of full heterogeneity among workers, this paper addresses the joint design of function assignment and coded shuffling to reduce communication load effectively. We first devise a new embedded index coding (EIC) solution for the device-to-device (D2D) environment and prove its optimality. By extending our novel approach, we propose the optimal shuffling design for the MapReduce framework. Then, we figure out the optimal function assignment with the optimal shuffling design. On account of the prohibitive complexity, we propose an efficient greedy search algorithm and analyze how much performance the proposed scheme can guarantee. Numerical simulations show that our proposed algorithm provides the near-optimal communication load with a considerable complexity reduction, while we observe that the function assignment design is vital for heterogeneous CDC by comparing with the well-designed file allocation design. [ABSTRACT FROM AUTHOR]

A carrier phase reconstruction technique is presented as an enabler for low-power centimeter-accurate mobile positioning. Reliable carrier phase reconstruction permits the duty cycling of a Global Navigation Satellite System (GNSS) receiver whose outputs are used for precise carrier-phase differential GNSS (CDGNSS) positioning. Existing CDGNSS techniques are power intensive because they require continuous tracking of each GNSS signal's carrier phase. By contrast, the less-precise code-ranging technique that is commonly used in mobile devices for 3-to-10-meter-accurate positioning allows for aggressive measurement duty-cycling, which enables low-power implementations. The technique proposed in this paper relaxes the CDGNSS continuous phase tracking requirement by solving a mixed real and integer estimation problem to reconstruct a continuous carrier phase time history from intermittent phase measurement intervals each having an ambiguous initial phase. Theoretical bounds on the probability of successful phase reconstruction, corroborated by Monte-Carlo-type simulation, are used to investigate the sensitivity of the proposed technique to various system parameters, including the time period between successive phase measurement intervals, the duration of each interval, the carrier-to-noise ratio, and the line-of-sight acceleration uncertainty. A demonstration on real data indicates that coupling a GNSS receiver with a consumer-grade inertial measurement unit enables reliable phase reconstruction with phase measurement duty cycles as low as 5%. [ABSTRACT FROM PUBLISHER]

In this paper, we study amplify-and-forward (AF) schemes in two-hop one-way relay networks. In particular, we consider multigroup multicast transmission between long-distance users. Assuming that perfect channel state information is perceived, our goal is to design the AF process so that the max-min-fair signal-to-interference-plus-noise ratio (SINR) is optimized while generalized power constraints are satisfied. We propose a beamformed Alamouti (BFA) AF scheme and formulate the corresponding AF design problem as a two-block fractional quadratically constrained quadratic program (QCQP). We then tackle the two-block fractional QCQP using the semidefinite relaxation (SDR) technique and analyze the approximation accuracy of the proposed SDR. From a theoretical perspective, our results are fundamentally new and reveal that the proposed BFA AF scheme can outperform the traditional BF AF scheme, especially when there are many users in the system or many generalized power constraints in the problem formulation. From a practical perspective, our proposed BFA AF scheme improves the receivers’ SINR by offering two degrees of freedom (DoFs) in beamformer design, as opposed to only one DoF offered by the BF AF scheme. In the latter part of this paper, we demonstrate how this extra DoF leads to provable performance gain by considering two special relay scenarios, in which the AF process is shown to possess a special structure. Numerical simulations further confirm that the proposed BFA AF scheme outperforms the BF AF scheme and works well for large-scale relay systems. [ABSTRACT FROM PUBLISHER]

In this paper, precoder designs are proposed for the wireless X networks aided by a relay and with no or very limited channel state information at the transmitters (CSIT). First, we derive the general feasibility conditions for interference alignment (IA) for a relay-aided symmetric multi-input-multi-output X channel (also called the X relay channel or XRC) without any CSI at the source transmitters. Next, we propose an IA-based joint beamforming approach with very limited feedback to the transmitters (referred to as “IA-LFB” in this paper), which can attain the maximum degrees of freedom (DoF) of the network under arbitrarily low relay power. Finally, we propose an iterative minimum-sum mean-square-error (MMSE) precoder design for the single-input-single-output XRC for throughput improvement in the finite signal-to-noise ratio (SNR) region. The proposed MMSE precoder also shows better bit error rate results than the IA precoder over a wide SNR range. Since the MMSE precoder can achieve only locally optimal solutions, we propose to initialize the iterative MMSE algorithm with the IA-LFB precoder which, unlike random initializations, not only improves the throughput in the finite SNR regions but also preserves the network DoF even under limited relay power. [ABSTRACT FROM AUTHOR]

Time-of-arrival (TOA) based localization plays a central role in current and future localization systems. Such systems, exploiting the fine delay resolution properties of wideband and ultra-wideband (UWB) signals, are particularly attractive for ranging under harsh propagation conditions in which significant multipath may be present. While multipath has been traditionally considered detrimental in the design of TOA estimators, it can be exploited to benefit ranging. This paper investigates the impact of a priori multipath information on TOA estimation. To this end, bounds on the performance of TOA estimation in dense multipath channels are provided and discussed under different operating conditions. The effects of channel dispersion and transmission bandwidth on ranging systems are investigated showing that, contrary to communication systems, diversity behavior is only exhibited in the medium signal-to-noise ratio regime. [ABSTRACT FROM AUTHOR]

Target detection is studied for a cloud multiple-input multiple-output (MIMO) radar using quantized measurements. According to the local sensor quantization strategies and fusion strategies, this paper discusses three methods: quantize local test statistics which are linearly fused (QTLF), quantize local test statistics which are optimally fused (QTOF), and quantize local received signals which are optimally fused (QROF). We first directly analyze the detection performance of each method when the quantizer output is represented as a discrete random variable, where it is difficult to obtain a closed-form expression for the detection probability. Then, an approximate description for the quantization is analyzed for the case of a Gaussian signal and a closed-form expression for the detection probability is obtained. We prove that the QTOF method outperforms the QTLF method in general, and for small SCNR the QROF method has the best detection performance among the three methods, while for large SCNR the QROF method performs the worst. The correctness of theoretical analysis is verified by simulations. [ABSTRACT FROM AUTHOR]

In this paper, a new energy-efficiency performance metric is proposed for multiple-input multiple-output (MIMO) point-to-point systems. In contrast with related works on energy-efficiency, this metric translates the effects of using finite blocks for transmitting, using channel estimates at the transmitter and receiver, and considering the total power consumed by the transmitter instead of the radiated power only. The main objective pursued is to choose the best precoding matrix used at the transmitter in the following two scenarios : 1) the one where imperfect channel state information (CSI) is available at the transmitter and receiver and 2) the one where no CSI is available at the transmitter. In both scenarios, the problem of optimally tuning the total used power is shown to be nontrivial. In scenario 2), the optimal fraction of training time can be characterized by a simple equation. These results and others provided in the paper, along with the provided numerical analysis, show that the present work can therefore be used as a good basis for studying power control and resource allocation in energy-efficient multiuser networks. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of beam alignment for millimeter wave (mmWave) communications, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station), and an omni-directional antenna or an antenna array is used at the receiver (i.e., user). By exploiting the sparse scattering nature of mmWave channels, the beam alignment problem is formulated as a sparse encoding and phaseless decoding problem. More specifically, the problem of interest involves finding a sparse sensing matrix and an efficient recovery algorithm to recover the support and magnitude of a sparse signal from compressive phaseless measurements. A sparse bipartite graph code algorithm is developed for sparse encoding and phaseless decoding. Our theoretical analysis shows that, in the noiseless case, our proposed algorithm can perfectly recover the support and magnitude of the sparse signal with a probability exceeding a pre-specified value from $\mathcal {O}(K^2)$ measurements, where $K$ is the number of nonzero entries of the sparse signal. The proposed algorithm has a simple decoding procedure which is computationally efficient and noise robust. Simulation results show that our proposed method renders reliable beam alignment even in the low signal-to-noise ratio regime and presents a clear performance advantage over existing methods. [ABSTRACT FROM AUTHOR]

This paper examines the Rayleigh fading multiple-input single-output interference channel with single-user decoding and limited channel state information (CSI) feedback. Its contribution is twofold. First, the achievable rate-region with imperfect CSI at the transmitters due to channel quantization errors is analyzed for the two-user case. An analytically tractable inner bound on that region is derived, which provides insights into the problem. Similarly to the case of perfect CSI at the transmitters, it can be shown that beamforming constitutes an optimal strategy for achieving the boundary points of the inner bound. Second, we present a novel CSI feedback scheme that reduces the cross-link CSI feedback overhead, and enhances throughput for any rate-limited feedback scheme. This feedback reduction is crucial when control channels for sharing information between transmitters and unintended receivers are rate limited. A distinguishing feature of the proposed scheme is that feedback is taken into account already at the channel estimation stage. While maintaining a high array gain in the direct channel towards the intended user, the scheme induces an effective low-dimensional cross-link channel, which can be quantized more accurately than the full-dimensional channel. This eventually leads to enhanced performance while maintaining a low CSI feedback overhead. Finally, the proposed feedback scheme is extended to the case of $N>2$ users and shown to provide a significant reduction in CSI feedback overhead in a practically oriented setting. [ABSTRACT FROM AUTHOR]

In this paper, we consider a coordinated beamforming design for a multiple-input single-output bursty interference channel. In practical scenarios, the distributed medium access control mechanisms or the decentralized networking protocols across different users contribute to the bursty nature of network traffic. The potential gains provided by such burstiness can be exploited by feeding back the interference state information to the transmitters. Errors may occur during such feedback, thus causing uncertainties in the user traffic and resulting in complicated beamforming design problems. In this paper, we model such traffic uncertainties by using a hidden Markov model (HMM). Assuming that perfect channel state information (CSI) is available at the transmitters, our goal is to maximize the average system utility subjected to average power constraints under traffic uncertainties. We also extend the optimization problem to the imperfect CSI case. However, the resulting problem is highly nonconvex. Hence, we apply a series of convex approximation techniques. We further improve the approximation accuracy by using our proposed successive convex approximation (SCA) algorithms (HMM-SCA). In our simulation results, we observe that our proposed HMM-SCA provides a $46.3\%$ improvement in weighted sum rate over the SCA algorithm, in which the interference is assumed to be always present (NB-SCA). For the average weighted geometric mean rate, we observe that our proposed HMM-SCA also provides a $41.3\%$ improvement over the NB-SCA when user fairness is considered. Therefore, it is crucial to exploit bursty traffic for a practical wireless communication system when channel estimation errors are considered. [ABSTRACT FROM AUTHOR]

Orthogonal signal-division multiplexing (OSDM) is a generalized modulation scheme to bridge the gap between orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization. It allows significantly more flexibility in system design; however, over doubly-selective channels, it suffers from a special signal distortion structure, namely inter-vector interference, which is analogous to inter-carrier interference in conventional OFDM. To analyze its effect, in this paper, the complex exponential basis expansion model (CE-BEM) is used to approximate the doubly-selective channel. We show that the composite channel matrix of OSDM systems is cyclically block banded in this case, and the blocks in its main band can be further diagonalized. By exploiting this unique matrix structure, low-complexity block and serial OSDM equalization algorithms are then proposed. These two equalization algorithms are based on block LDL $^H$ factorization and block iterative matrix inversion, respectively, both of which are implemented in a transformed domain to avoid direct inversion of large matrices. In addition, a CE-BEM channel estimation method is designed for OSDM systems, which uses frequency-shifted Chu sequences as pilots to ease the computation. Numerical simulations are finally provided to justify the validity of our channel equalization and estimation algorithms. [ABSTRACT FROM AUTHOR]

Adaptive signal detection algorithms in unknown interference are generally formulated under the assumption that training sets are available for the estimation of the test cell interference characteristics. A significant problem occurs in applications such as radar surveillance when the interference covariance matrices of vectors from the training cells and that of the test cell vector are mismatched. False alarm rates may increase beyond acceptable levels and thus overwhelm a receiver if the interference in the test cell is not adequately cancelled. A number of adaptive detection algorithms with the constant false alarm rate (CFAR) property have been designed assuming the availability of training data that have the same covariance matrix as the interference in the test cell (although the adaptive coherence estimator algorithm requires the two covariance matrices to be related by a positive multiplicative constant that is not required to be known). The detection threshold in these CFAR detectors can be selected without knowledge of the interference-plus-noise covariance matrix. To the best of our knowledge, an analytical characterization of the performance effects of a receiver using a mismatched training set (perhaps inadvertently) in adaptive detection is currently not available. This paper addresses the problem analytically. An exact analysis of the effects of interference covariance matrix mismatch on the performance of the adaptive matched filter test is carried out. Results provide insights to the specific aspects of covariance matrix mismatch that cause the increase in false alarms when compared to the matched training set case. Analytical results are illustrated with an example and verified independently with simulations. These results are useful in developing CFAR algorithms under conditions of mismatched training. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the multiuser diversity gain in a cognitive radio (CR) system where the secondary transmitters opportunistically utilize the spectrum licensed to primary users only when it is not occupied by the primary users. To protect the primary users from the interference caused by missed detection of primary transmissions at the secondary receivers, minimum average throughput of the primary network is guaranteed by transmit power control at the secondary transmitters. The traffic dynamics of a primary network are also considered in our analysis. We derive the average system throughput of the secondary network and analyze its asymptotic behaviors to characterize the multiuser diversity gain in the CR system. It is shown that the throughput gain of the CR system depends on the levels of the peak power constraints on the secondary transmitters and the reliability of spectrum sensing. The asymptotic scaling law of the secondary network throughput is also shown to be different according to maximum allowable transmit power at the secondary transmitters. The derived scaling law is interestingly similar to that in underlay CR systems even though the spectrum utilization mechanism of the underlay CR systems is totally different from the CR model considered in this paper. We also extend our results to non-independent and identically distributed (non-i.i.d.) channels by employing fairness scheduling and show that fairness scheduling in non-i.i.d. channels has the same asymptotic throughput characteristics as the best user selection in i.i.d. channels. [ABSTRACT FROM PUBLISHER]

One main drawback of using time difference of arrival (TDOA) methods for source localization and navigation is that they require centralization of multiple copies of a signal. This paper considers blindly estimating the location of a cyclic prefix (CP) in an orthogonal frequency division multiplexing (OFDM) signal, enabling distributed TDOA computation up to an integer ambiguity. This ambiguity can be resolved using integer least-squares methods, if enough TDOAs are available, requiring only minimal cooperation between receivers. The contributions of this paper are development of an algorithm for simultaneously resolving the integer ambiguities and obtaining a position estimate; and derivation of the Cramér–Rao lower bound (CRLB) on locating the CP, and hence, on the underlying source localization and navigation problems. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of joint wideband spectrum sensing and direction-of-arrival (DoA) estimation in a sub-Nyquist sampling framework. Specifically, considering a scenario where a number of uncorrelated narrow-band signals spread over a wide (say, several GHz) frequency band, our objective is to estimate the carrier frequencies and the DoAs associated with the narrow-band sources, as well as reconstruct the power spectra of these narrow-band signals. To overcome the sampling rate bottleneck for wideband spectrum sensing, we propose a new phased-array-based sub-Nyquist sampling architecture with flexible time delays, where a uniform linear array is employed and the received signal at each antenna is delayed by a flexible amount of time and then sampled by a synchronized low-rate analog–digital converter. Based on the collected sub-Nyquist samples, we calculate a set of cross-correlation matrices with different time lags, and develop a CANDECOMP/PARAFAC decomposition-based method for joint DoA, carrier frequency, and power spectrum recovery. Conditions for perfect recovery of the associated parameters and the power spectrum are analyzed. Simulation results show that our proposed method presents a clear performance advantage over existing methods, and achieves an estimation accuracy close to the associated Cramér–Rao bounds using only a small number of data samples. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the robust beamforming for the magnetic-based wireless power transfer (WPT) with multiple transmitters and multiple receivers to improve the power efficiency and reliability. By taking into account the imperfection of the magnetic mutual inductances information (MII), we firstly formulate a beamforming optimization problem to minimize the total transmit power while guaranteeing the reliable power delivery for each receiver simultaneously. Then, observing that the optimization problem is highly nonconvex with infinite number of constraints, we utilize the norm-bounded MII error models to reformulate the original problem into two worst-case optimization problems for the cases of the single receiver and multiple receivers, respectively. Finally, the optimal beamforming is obtained for the case of single receiver by applying the semidefinite programming relaxation (SDR) and the approximately optimal solution is obtained for the case of multiple receivers by adopting the SDR with randomization. Extensive simulation results verify the power transfer reliability and efficiency of the proposed beamforming schemes for the magnetic-based WPT systems. [ABSTRACT FROM AUTHOR]

Modern communication systems have frequency bands that are shared with multiple channels. A typical front-end transceiver is responsible for processing multiple channels simultaneously within the band. Although it is simpler to design a dedicated sub-transceiver for each channel, the overall cost of implementation is prohibitive. It is more desirable to design a single system that can process many channels at the same time. However, it is difficult to realize such a transceiver in wireless communication systems, since it needs to accommodate different standards, data rates, sampling rates, etc. In this paper, we address the challenges of flexible, cost-effective, multi-channel implementation of wideband receiver systems. In particular, we develop a novel implementation by applying graphics processing unit (GPU) technology to a wideband receiver that processes multiple channels at the same time, including channelization and arbitrary resampling. Our proposed new receiver architecture is flexible and reconfigurable via software, while providing high throughput and low latency. [ABSTRACT FROM AUTHOR]

In this paper, we develop frequency-domain phase noise estimation schemes for an orthogonal frequency division multiplexing (OFDM) system that utilize the geometrical structure associated with the spectral components of the complex exponential of the phase noise process. This geometry can be expressed as a set of non-convex quadratic equality constraints that involve permutation matrices. Since the oscillator power spectral density is typically a low-pass process, we can reduce the dimensionality of the phase noise spectral vector. In particular, we propose a new dimensionality reduction model that preserves the phase noise spectral geometry when moving from smaller to larger dimensional spaces. Using this new model and building upon existing works, the phase noise estimation problem is posed as an optimization problem. We show, using the S-procedure, that the optimization problem has an optimal solution that is attained and can be solved using semidefinite programming. In order to reduce the computational complexity of the aforementioned optimization problem, we consider sub-optimal schemes that provide an estimate satisfying this geometry. Through numerical simulations, we demonstrate superior bit-error-rate performance when the proposed model used in conjunction with the phase noise spectral geometry is incorporated in the estimation process. [ABSTRACT FROM PUBLISHER]

This paper considers a power splitting based multiuser multiple-input-single-output (MISO) downlink system with simultaneous wireless information and power transfer, where each single antenna receiver splits the received signal into two streams of different power for decoding information and harvesting energy separately. Assuming that the most common zero-forcing (ZF) beamforming scheme is employed by the base station, we aim to maximize the system energy efficiency in bits per Joule by joint beamforming and power splitting under both the signal-to-interference-plus-noise ratio constraints and energy harvesting constraints. The energy efficiency optimization (EEO) problem is nonconvex and very hard to solve. In this paper, by exploiting the problem structure, we first simplify the EEO problem to a joint transmit power allocation and receive power splitting problem. Then, with a tactful reformulation, we propose a Lagrangian relaxation (LR) method coupled with Dinkelbach method to address the simplified EEO problem, whilst devising a nearly closed-form solution for the subproblems involved in the Dinkelbach method. It is proven that the proposed LR method is optimum under some condition and can guarantee at least a feasible solution, which is a notable advantage over the existing methods. Besides, we develop a low complexity EEO algorithm by proportionally distributing the total power to users. Finally, numerical results validate the excellent efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present methods and strategies for robust detection and slowness estimation of weak compressional (P) and shear (S) waves in borehole sonic logging in noisy environments such as while logging while drilling, by proposing methods for enhancing the semblance used in the slowness time coherence method used in the logging. In this direction our contributions are two fold. First, we propose a novel class of shrinkage estimators in the discrete Radon transform domain derived from data semblance, for waveform de-noising to combat random additive noise and reflections. In this context we also identify necessary and sufficient conditions for the optimality of the proposed estimator. Our second contribution lies in developing a novel method to cancel energy propagating at the slowness of borehole modes, chiefly Stoneley, and to a lesser extent, shear, that significantly lowers the semblance of the weaker head wave arrivals. This approach is based on representing the data using time-frequency compact space-time propagators. Finally, we present an algorithm that combines the space time shrinkage in the discrete Radon transform domain and the proposed interference cancelation strategies for enhanced compressional and shear detection via semblance processing. The algorithm is validated on synthetic data and demonstrated to enhance performance on real logging while drilling field data sets. [ABSTRACT FROM PUBLISHER]

Compressive sensing (CS) allows for acquisition of sparse signals at sampling rates significantly lower than the Nyquist rate required for bandlimited signals. Recovery guarantees for CS are generally derived based on the assumption that measurement projections are selected independently at random. However, for many practical signal acquisition applications, including medical imaging and remote sensing, this assumption is violated as the projections must be taken in groups. In this paper, we consider such applications and derive requirements on the number of measurements needed for successful recovery of signals when groups of dependent projections are taken at random. We find a penalty factor on the number of required measurements with respect to the standard CS scheme that employs conventional independent measurement selection and evaluate the accuracy of the predicted penalty through simulations. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of interference among the simultaneous multiuser transmissions in the downlink of multiple-antenna systems. Using symbol-level precoding, a new approach to exploit the multiuser interference is discussed. The concept of exploiting the interference between spatial multiuser transmissions by jointly utilizing data information (DI) and channel state information (CSI), in order to design symbol-level precoders, is proposed. To this end, the interference between data streams is transformed under certain conditions into useful signal that can improve the signal to interference noise ratio (SINR) of the downlink transmissions. We propose a maximum ratio transmission (MRT) based algorithm that jointly exploits DI and CSI to glean the benefits from constructive multiuser interference. Subsequently, a relation between the constructive interference downlink transmission and physical layer multicasting is established. In this context, novel constructive interference precoding techniques that tackle the transmit power minimization (min-power) with individual SINR constraints at each user's receivers is proposed. Furthermore, fairness through maximizing the weighted minimum SINR (max-min SINR) of the users is addressed by finding the link between the min power and max min SINR problems. Moreover, heuristic precoding techniques are proposed to tackle the weighted sum rate problem. Finally, extensive numerical results show that the proposed schemes outperform other state of the art techniques. [ABSTRACT FROM PUBLISHER]

This paper considers a power splitting-based MISO interference channel for simultaneous wireless information and power transfer (SWIPT), where each single antenna receiver splits the received signal into two streams of different power for decoding information and harvesting energy separately. We aim to minimize the total transmission power by joint beamforming and power splitting (JBPS) under both the signal-to-interference-plus-noise ratio (SINR) constraints and energy harvesting (EH) constraints. The JBPS problem is nonconvex and has not yet been well addressed in the literature. Moreover, decentralized algorithm design for JBPS based on local channel state information (CSI) and limited information exchange remains open. In this paper, we first propose a novel relaxation method named second-order cone programming (SOCP) relaxation to address the JBPS problem. We formulate the relaxed problem as an SOCP and present two sufficient conditions under which the SOCP relaxation is tight. For the case when the SOCP solution is not necessarily optimal to the JBPS problem, a closed-form feasible-solution-recovery method is provided. Then, we develop a distributed algorithm for the JBPS problem based on primal-decomposition (PD) method. The PD-based distributed algorithm consists of a master problem and a set of subproblems. The former is solved by using subgradient method while the latter are solved using coordinate descent method. Finally, numerical results validates the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid analog–digital beamforming architecture with resolution-adaptive ADCs for millimeter wave (mmWave) receivers with large antenna arrays. We adopt array response vectors for the analog combiners and derive ADC bit-allocation (BA) solutions in closed form. The BA solutions reveal that the optimal number of ADC bits is logarithmically proportional to the RF chain's signal-to-noise ratio raised to the $1/3$ power. Using the solutions, two proposed BA algorithms minimize the mean square quantization error of received analog signals under a total ADC power constraint. Contributions of this paper include 1) ADC bit-allocation algorithms to improve communication performance of a hybrid MIMO receiver, 2) approximation of the capacity with the BA algorithm as a function of channels, and 3) a worst-case analysis of the ergodic rate of the proposed MIMO receiver that quantifies system tradeoffs and serves as the lower bound. Simulation results demonstrate that the BA algorithms outperform a fixed-ADC approach in both spectral and energy efficiency, and validate the capacity and ergodic rate formula. For a power constraint equivalent to that of fixed 4-bit ADCs, the revised BA algorithm makes the quantization error negligible while achieving 22% better energy efficiency. Having negligible quantization error allows existing state-of-the-art digital beamformers to be readily applied to the proposed system. [ABSTRACT FROM PUBLISHER]

We investigate the multiple-input multiple-output broadcast channel with statistical channel state information available at the transmitter. The so-called linear assignment operation is employed, and necessary conditions are derived for the optimal transmit design under general fading conditions. Based on this, we introduce an iterative algorithm to maximize the linear assignment weighted sum-rate by applying a gradient descent method. To reduce complexity, we derive an upper bound of the linear assignment achievable rate of each receiver, from which a simplified closed-form expression for a near-optimal linear assignment matrix is derived. This reveals an interesting construction analogous to that of dirty-paper coding. In light of this, a low-complexity transmission scheme is provided. Numerical examples illustrate the significant performance of the proposed low complexity scheme. [ABSTRACT FROM PUBLISHER]

Revived interest in physical layer security has led to a cascade of information theoretic results for various system topologies under different constraints. In the present paper, we provide practically oriented solutions to the problem of maximizing achievable secrecy rates in an environment consisting of multiple legitimate and eavesdropping radio nodes. By assuming “genie” aided perfect channel state information (CSI) feedback for both types of nodes, we first study two scenarios of interest. When independent messages are intended for all legitimate users (called “broadcast” mode), provably convergent second-order cone programming (SOCP)-based iterative procedure is used for designing secrecy rate maximizing beamformers. In the same manner, when a common message is intended only for legitimate nodes (dubbed “multicast” mode), SOCP-based design is proposed for obtaining linear precoders that maximize the achievable secrecy rate. Subsequently, we leverage the analysis to the more real-world scenario, where the CSI of the malicious nodes has to be somehow estimated and that of the legitimate users is corrupted with unavoidable errors. For this case, we devise provably convergent iterative semidefinite programming (SDP) procedures that maximize the achievable secrecy rates for both the beamforming-based broadcast and the linearly precoded multicast modes. Finally, numerical results are reported that evaluate the performance of the proposed solutions as a function of different system parameters. The results presented in the paper are demonstrated to outperform the ones based on interference alignment strategies. We also ascertain superior performance of the proposed schemes in the realms of real world. [ABSTRACT FROM AUTHOR]

This paper studies a multi-antenna wiretap channel with a passive eavesdropper and an external helper, where only quantized channel information regarding the legitimate receiver is available at the transmitter and helper due to finite-rate feedback. Given a fixed total bandwidth for the two feedback channels, the receiver must determine how to allocate its feedback bits to the transmitter and helper. Assuming zero-forcing transmission at the helper and random vector quantization of the channels, an analytic expression for the achievable ergodic secrecy rate due to the resulting quantization errors is derived. While direct optimization of the secrecy rate is difficult, an approximate upper bound for the mean loss in secrecy rate is derived and a feedback bit allocation method that minimizes the average upper bound on the secrecy rate loss is studied. A closed-form solution is shown to be possible if the integer constraint on the bit allocation is relaxed. Numerical simulations indicate the significant advantage that can be achieved by adaptively allocating the available feedback bits. [ABSTRACT FROM PUBLISHER]

In this paper we present a model for the hidden Markovian bandit problem with linear rewards. As opposed to current work on Markovian bandits, we do not assume that the state is known to the decision maker before making the decision. Furthermore, we assume structural side information where the decision maker knows in advance that there are two types of hidden states; one is common to all arms and evolves according to a Markovian distribution, and the other is unique to each arm and is distributed according to an i.i.d. process that is unique to each arm. We present an algorithm and regret analysis to this problem. Surprisingly, we can recover the hidden states and maintain logarithmic regret in the case of a convex polytope action set. Furthermore, we show that the structural side information leads to expected regret that does not depend on the number of extreme points in the action space. Therefore, we obtain practical solutions even in high dimensional problems. [ABSTRACT FROM AUTHOR]

Existing Orthogonal Frequency-Division Multiplexing (OFDM) variants based on cyclic prefix (CP) allow for efficient time synchronization, but suffer from lower power efficiency compared to zero-padded (ZP)-OFDM. Because of its power efficiency, ZP-OFDM is considered as an appealing solution for the emerging low-power wireless systems. However, in the absence of CP, time synchronization in ZP-OFDM is a challenging task. In this paper, the non-data-aided (NDA) maximum-likelihood (ML) time synchronization for ZP-OFDM is analytically derived. We show that the optimal NDA-ML synchronization algorithm offers a high lock-in probability and can be efficiently implemented using Monte Carlo sampling (MCS) technique in combination with golden-section search method in one dimension. To obtain the optimal NDA-ML time synchronization algorithm, we first derive a closed-form expression for the joint probability density function (PDF) of the received ZP-OFDM samples in frequency-selective fading channels. The derived expression is valid for doubly-selective fading channels with mobile users as well. The performance of the proposed synchronization algorithm is evaluated under various practical settings through simulation experiments. It is shown that the proposed optimal NDA-ML synchronization algorithm and its MCS implementation substantially outperforms existing NDA time synchronization algorithms in terms of lock-in probability and mean-squared error (MSE). [ABSTRACT FROM AUTHOR]

This paper introduces novel transmit precoding approaches for the cognitive radio (CR) Z-channel. The proposed transmission schemes exploit noncausal information about the interference at the secondary base station to redesign the CR precoding optimization problem. This is done with the objective to improve the quality of service (QoS) of secondary users by taking advantage of constructive interference in the secondary link. The precoders are designed to minimize the worst secondary user's symbol error probability (SEP) under constraints on the instantaneous total transmit power, and the power of the instantaneous interference in the primary link. The problem is formulated as a bivariate probabilistic constrained programming (BPCP) problem. We show that the BPCP problem can be transformed for practical SEPs into a convex optimization problem that can be solved, for example, by the barrier method. A computationally efficient tight approximate approach is also developed to compute the near-optimal solutions. Simulation results and analysis show that the average computational complexity per downlink frame of the proposed approximate problem is comparable to that of the conventional CR downlink beamforming problem. In addition, both the proposed methods offer significant performance improvements as compared to the conventional CR downlink beamforming, while guaranteeing the QoS of primary users on an instantaneous basis, in contrast to the average QoS guarantees of conventional beamformers. [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]

In this paper, we consider an OFDM radio link corrupted by oscillator phase noise in the receiver, namely the problem of estimating and compensating for the impairment. To lessen the computational burden and delay incurred onto the receiver, we estimate phase noise using only scattered pilot subcarriers, i.e., no tentative symbol decisions are used in obtaining and improving the phase noise estimate. In particular, the phase noise estimation problem is posed as an unconstrained optimization problem whose minimizer suffers from the so-called amplitude and phase estimation error. These errors arise due to receiver noise, estimation from limited scattered pilot subcarriers and estimation using a dimensionality reduction model. It is empirically shown that, at high signal-to-noise-ratios, the phase estimation error is small. To reduce the amplitude estimation error, we restrict the minimizer to be drawn from the so-called phase noise geometry set when minimizing the cost function. The resulting optimization problem is a nonconvex program. However, using the S-procedure for quadratic equalities, we show that the optimal solution can be obtained by solving the convex dual problem. We also consider a less complex heuristic scheme that achieves the same objective of restricting the minimizer to the phase noise geometry set. Through simulations, we demonstrate improved coded bit-error-rate and phase noise estimation error performance when enforcing the phase noise geometry. For example, at high signal-to-noise-ratios, the probability density function of the phase noise estimation error exhibits thinner tails which results in lower bit-error-rate. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an algorithm to design interference alignment (IA) precoding and decoding matrices for arbitrary MIMO X networks. The proposed algorithm is rooted in the homotopy continuation techniques commonly used to solve systems of nonlinear equations. Homotopy methods find the solution of a target system by smoothly deforming the solution of a start system which can be trivially solved. Unlike previously proposed IA algorithms, the homotopy continuation technique allows us to solve the IA problem for both unstructured (i.e., generic) and structured channels such as those that arise when time or frequency symbol extensions are jointly employed with the spatial dimension. To this end, we consider an extended system of bilinear equations that include the standard alignment equations to cancel the interference, and a new set of bilinear equations that preserve the desired dimensionality of the signal spaces at the intended receivers. We propose a simple method to obtain the start system by randomly choosing a set of precoders and decoders, and then finding a set of channels satisfying the system equations, which is a linear problem. Once the start system is available, standard prediction and correction techniques are applied to track the solution all the way to the target system. We analyze the convergence of the proposed algorithm and prove that, for many feasible systems and a sufficiently small continuation parameter, the algorithm converges with probability one to a perfect IA solution. The simulation results show that the proposed algorithm is able to consistently find solutions achieving the maximum number of degrees of freedom in a variety of MIMO X networks with or without symbol extensions. Further, the algorithm provides insights into the feasibility of IA in MIMO X networks for which theoretical results are scarce. [ABSTRACT FROM PUBLISHER]

Nonlinear transceiver based on Tomlinson–Harashima (TH) precoding outperforms the linear minimum mean-square-error (MSE) architecture in terms of minimum achievable MSE. In this paper, we investigate nonlinear TH precoding design problem for simultaneous wireless information and power transfer in multiple-input-multiple-output broadcast channels, where there are one information-decoding (ID) receiver and multiple energy harvesting (EH) receivers. We consider the scenario that the transmitter knows imperfect channel state information of all links and the channel uncertainties are modeled by Gaussian random variables. Our objective is to jointly optimize TH precoding matrices and linear equalizer to minimize average MSE subject to transmit power constraint and EH constraints on average harvested power. We transform the robust transceiver design problem into a difference of convex programming and propose a constrained concave convex procedure (CCCP) based locally optimal solution. To reduce computational complexity, we propose a noniterative upper bound based suboptimal solution that minimizes the upper bound on average MSE. We also extend the CCCP-based locally optimal solution to the single group multicast scenario where there are multiple ID receivers. Simulation results demonstrate that our proposed TH precoding transceiver design achieves lower average MSE than linear precoding transceiver design. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the channel estimation problem in millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we first propose a fast channel estimation (FCE) algorithm based on a novel overlapped beam pattern design, which can increase the amount of information carried by each channel measurement compared to the existing nonoverlapped designs and thus reduce the required channel estimation time. We develop a maximum likelihood estimator to optimally extract the path information from the channel measurements. Then, we propose a novel rate-adaptive channel estimation (RACE) algorithm, which can dynamically adjust the number of channel measurements based on the expected probability of estimation error (PEE). The performance of both proposed algorithms is analyzed. For the FCE algorithm, an approximate closed-form expression for the PEE is derived. For the RACE algorithm, a lower bound for the minimum signal energy-to-noise ratio required for a given number of channel measurements is developed based on the Shannon–Hartley theorem. Simulation results show that the FCE algorithm significantly reduces the number of channel estimation measurements compared to the existing algorithms using nonoverlapped beam patterns. By adopting the RACE algorithm, we can achieve up to a 6 dB gain in signal energy-to-noise ratio for the same PEE compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

Spectrum sensing is a key technology enabling the cognitive radio system. In this paper, the problem of how to quickly and accurately find an unoccupied channel from a large amount of potential channels is considered. The cognitive radio system under consideration is equipped with a narrow band sensor, hence it can only sense those potential channels in a sequential manner. In this scenario, we propose a novel two-stage mixed-observation sensing strategy. In the first stage, which is named as scanning stage, the sensor observes a linear combination of the signals from a pair of channels. The purpose of the scanning stage is to quickly identify a pair of channels such that at least one of them is highly likely to be unoccupied. In the second stage, which is called refinement stage, the sensor only observers the signal from one of those two channels identified from the first stage, and selects one of them as the unoccupied channel. The problem under this setup is an ordered two concatenated Markov stopping time problem. The optimal solution is solved using the tools from the multiple stopping time theory. It turns out that the optimal solution has a rather complex structure, hence a low complexity algorithm is proposed to facilitate the implementation. In the proposed low complexity algorithm, the cumulative sum test is adopted in the scanning stage and the sequential probability ratio test is adopted in the refinement stage. The performance of this low complexity algorithm is analyzed when the presence of unoccupied channels is rare. Numerical simulation results show that the proposed sensing strategy can significantly reduce the sensing time when the majority of potential channels are occupied. [ABSTRACT FROM PUBLISHER]

Recently, studies on suboptimal precoding techniques for multiple-input multiple-output broadcast channels (MIMO-BC), which achieve performance near to that of the dirty paper coding (DPC), have drawn attention to vector perturbation (VP) precoding. In practice, each antenna or more generally each antenna group has its own limit on the transmitted power, which makes per-antenna-group power constraints more meaningful than the sum power constraint. In this paper, we introduce an optimization technique for VP precoding employing the minimum mean-square error (MMSE) criterion with per-antenna-group power constraints. This technique is inspired by the p-sphere encoding in a sense that it involves finding the node with the lowest mean-square error (MSE) over a lattice. We demonstrate that the MSE metric, as well as the p-norm one, can be enclosed in a proper Frobenius-norm ball. This Frobenius-norm ball shrinks until it captures the perturbing vector minimizing the MSE. Numerical results show that the proposed algorithm outperforms conventional VP precoding and the p-sphere encoding, but at higher complexity. Consequently, we investigate a couple of simplified techniques employing the MMSE criterion, which perform almost as well as the proposed precoding technique, but are less complex. [ABSTRACT FROM PUBLISHER]