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This paper studies the coordinated beamforming design problem for the multiple-input single-output (MISO) interference channel, assuming only channel distribution information (CDI) known to the transmitters. Under a given requirement on the rate outage probability for receivers, we aim to maximize the system utility (e.g., the weighted sum rate, weighted geometric mean rate, and the weighed harmonic mean rate) subject to the rate outage constraints and individual power constraints. The outage constraints, however, lead to a complicated, nonconvex structure for the considered beamforming design problem and render the optimization problem difficult to handle. Although this nonconvex optimization problem can be solved in an exhaustive search manner, this brute-force approach is only feasible when the number of transmitter-receiver pairs is small. For a system with a large number of transmitter-receiver pairs, computationally efficient alternatives are necessary. Hence, the focus of this paper is the design of such efficient approximation methods. In particular, by employing semidefinite relaxation (SDR) and first-order approximation techniques, we propose an efficient successive convex approximation (SCA) algorithm that provides high-quality approximate beamforming solutions via solving a sequence of convex approximation problems. The solution thus obtained is further shown to be a stationary point for the SDR of the original outage constrained beamforming design problem. Furthermore, we propose a distributed SCA algorithm where each transmitter optimizes its own beamformer using local CDI and information obtained from limited message exchange with the other transmitters. Our simulation results demonstrate that the proposed SCA algorithm and its distributed counterpart indeed converge, and promising performance can be achieved for all the considered system utilities. [ABSTRACT FROM PUBLISHER]

This paper proposes for single carrier signaling a simple structure that combines turbo equalization and decoding of correlated sources in multipath-rich multiuser Rayleigh fading multiple access channels (MAC), where the correlation between the sources is exploited by vertical iterations (VI) between the decoders. The bit-flipping model with a flipping probability pe is used to express the correlation ρ between the sources as ρ = 1-2pe. The proposed simple structure, spatially concatenated codes (SpCC), can achieve turbo-like performance over MAC channels suffering from severe inter-symbol interference (ISI), even though it uses short memory convolutional codes. First of all, to achieve turbo-like performance we introduce VIs to exploit the knowledge about the source correlation by exchanging extrinsic log-likelihood ratio (LLR) between the two decoders. We then add a rate-1 doped accumulator (D-ACC) to flexibly adapt for the variation of correlations between the sources. The results of computer simulations and extrinsic information transfer (EXIT) analysis confirm that in multipath-rich environments the proposed structure can achieve excellent performances, 1.02-1.28 dB away from the Slepian-Wolf-Shannon limit, at 1% outage probability for 0 <; ρ ≤ 1. [ABSTRACT FROM PUBLISHER]

Systems of bionanomachines may benefit future applications which require interaction with biological systems at the nano- to microscale. Molecular communication is a suitable communication mechanism for autonomous bionanomachines which are limited in size and capability and for interfacing with biological systems. In molecular communication, a bionanomachine transmits information to a receiver bionanomachine by modulating the concentration of molecules in the environment. One promising direction for molecular communication is for a bionanomachine to measure the distance to another bionanomachine in order to perform location-based functionality or to adapt communications using the measured distance. In this paper, a bionanomachine measures the distance to another bionanomachine by requesting the other bionanomachine to transmit a feedback signal of many molecules transmitted over a short time interval (i.e., a single spike of molecules). Upon receiving the feedback signal, the bionanomachine which requested the feedback signal then estimates distance by measuring the Round Trip Time (RTT) or Signal Attenuation (SA) of the received feedback signal. The propagation of molecules and the receiving of molecules are modeled to investigate how distance impacts measured RTT and SA. Simulations are performed to measure the accuracy of the distance measurement, the time required to measure distance, and how the number of molecules transmitted affects accuracy. [ABSTRACT FROM PUBLISHER]

In this paper, we propose low precision BER-optimal analog-to-digital converters (ADC) where quantization levels and thresholds are set nonuniformly to minimize the bit-error rate (BER) in a high data rate communication link. This is in contrast to how ADCs are used today, in which they act as transparent waveform preservers. Simulations for various communication channels show that for 2-PAM modulation, the BER-optimal ADC achieves shaping gains that range from 2.5 dB for channels with low intersymbol interference (ISI) to more than 30 dB for channels with high levels of ISI. Moreover, a 3-bit BER-optimal ADC achieves the same or even lower BER than a 4-bit uniform ADC. With a DFE and a high-ISI channel, a 3-bit BER-optimal ADC achieves lower BER than a 5-bit uniform ADC. [ABSTRACT FROM PUBLISHER]

This paper discusses the joint design of receive filters and transmit signals for active sensing applications such as radar and active sonar. The goal is to minimize the mean-square error (MSE) of target's scattering coefficient estimate in the presence of clutter and interference, which is equivalent to maximizing the signal-to-clutter-plus-interference ratio. A discrete-time signal model is assumed and practical constant-modulus or low peak-to-average-power ratio (PAR) constraints are imposed on the transmit signal. Several optimization methods are proposed for this joint design. Furthermore, the MSE criterion is expressed in the frequency domain and a corresponding MSE lower bound is derived. Numerical examples for different types of interferences are included to demonstrate the effectiveness of the proposed designs. [ABSTRACT FROM PUBLISHER]

This paper presents new linear time-invariant (LTI) block-based transceivers which employ a reduced amount of redundancy to eliminate the interblock interference. The proposals encompass both multicarrier and single-carrier systems with either zero-forcing or minimum mean-square error (MSE) equalizers. The amount of redundancy ranges from the minimum, \lceilL\over 2\rceil, to the most commonly used value, L, assuming a channel-impulse response of order L. The resulting transceivers allow for superfast equalization of the received data blocks, since they only use fast Fourier transforms and single-tap equalizers in their structures. The paper also includes an MSE analysis of the proposed transceivers with respect to the amount of redundancy. Indeed, we demonstrate that larger amounts of transmitted redundant elements lead to lower MSE of symbols at the receiver end. Computer simulations indicate that, by choosing an appropriate amount of redundancy, our proposals can achieve higher throughputs than the standard superfast multicarrier and single-carrier systems, while keeping the same asymptotic computational complexity for the equalization process. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a Partial Interference Alignment and Interference Detection (PIAID) design for K-user quasi-static MIMO interference channels with discrete constellation inputs. Each transmitter has M antennas and transmits L independent data streams to the desired receiver with N receive antennas. We focus on the case where not all K-1 interfering transmitters can be aligned at every receiver. As a result, there will be residual interference at each receiver that cannot be aligned. Each receiver detects and cancels the residual interference based on the constellation map. However, there is a window of unfavorable interference profile at the receiver for Interference Detection (ID). In this paper, we propose a low complexity Partial Interference Alignment scheme in which we dynamically select the user set for IA so as to create a favorable interference profile for ID at each receiver. We first derive the average symbol error rate (SER) by taking into account of the non-Guassian residual interference due to discrete constellation. Using graph theory, we then devise a low complexity user set selection algorithm for the PIAID scheme, which minimizes the asymptotically tight bound for the average end-to-end SER performance. Moreover, we substantially simplify interference detection at the receiver using Semi-Definite Relaxation (SDR) techniques. It is shown that the SER performance of the proposed PIAID scheme has significant gain compared with various conventional baseline solutions. [ABSTRACT FROM PUBLISHER]

Molecular communication (MC) will enable the exchange of information among nanoscale devices. In this novel bio-inspired communication paradigm, molecules are employed to encode, transmit and receive information. In the most general case, these molecules are propagated in the medium by means of free diffusion. An information theoretical analysis of diffusion-based MC is required to better understand the potential of this novel communication mechanism. The study and the modeling of the noise sources is of utmost importance for this analysis. The objective of this paper is to provide a mathematical study of the noise at the reception of the molecular information in a diffusion-based MC system when the ligand-binding reception is employed. The reference diffusion-based MC system for this analysis is the physical end-to-end model introduced in a previous work by the same authors, where the reception process is realized through ligand-binding chemical receptors. The reception noise is modeled in this paper by following two different approaches, namely, through the ligand-receptor kinetics and through the stochastic chemical kinetics. The ligand-receptor kinetics allows to simulate the random perturbations in the chemical processes of the reception, while the stochastic chemical kinetics provides the tools to derive a closed-form solution to the modeling of the reception noise. The ligand-receptor kinetics model is expressed through a block scheme, while the stochastic chemical kinetics results in the characterization of the reception noise using stochastic differential equations. Numerical results are provided to demonstrate that the analytical formulation of the reception noise in terms of stochastic chemical kinetics is compliant with the reception noise behavior resulting from the ligand-receptor kinetics simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a constellation reshaping method for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. The key idea of the proposed method is to generate a reshaped quadrature amplitude modulation (R-QAM) constellation, in which the minimum distance of the R-QAM is the same as that of the traditional QAM constellation. When the novel R-QAM constellation is employed with the traditional partial transmit sequence (PTS) and selected mapping (SLM) methods in OFDM systems, called as the R-PTS and R-SLM methods in this paper, respectively, the mean square errors between the constellations of the received data and each R-QAM rotated by different phase rotation factors are calculated at the receiver, and the phase rotation factor with the minimum mean square error is selected to recover the original data without side information. Therefore, the flexibility in phase rotation choice and the ability to avoid data rate loss make the R-PTS and R-SLM methods more suitable for good bit-error-rate performance in OFDM systems. Theoretical analysis and simulation results show that the R-PTS and R-SLM methods could provide the same PAPR reduction and bit-error-rate as those of the conventional PTS and SLM methods with perfect side information, respectively. [ABSTRACT FROM AUTHOR]

In the class of systems with linear precoder and decision feedback equalizers (DFE) for zero-padded (ZP) multiple-input multiple-output (MIMO) frequency selective channels, existing optimal transceiver designs present two drawbacks. First, the optimal systems require a large number of feedback bits from the receiver to encode the full precoding matrix. Second, the full precoding matrix leads to complex computations. These disadvantages become more severe as the bandwidth (BW) efficiency increases. In this paper, we propose using block diagonal geometric mean decomposition (BD-GMD) to design the transceiver. Two new BD-GMD transceivers are proposed: the ZF-BD-GMD system, where the receiver is a zero-forcing DFE (ZF-DFE), and the MMSE-BD-GMD system, where the receiver is a minimum- mean-square-error DFE (MMSE-DFE). The BD-GMD systems introduced here have the following four properties: a) They use the block diagonal unitary precoding technique to reduce the required number of encoding bits and simplify the computation. b) For any block size, the BD-GMD systems are optimal within the family of systems using block diagonal unitary precoders and DFEs. As block size gets larger, the BD-GMD systems produce uncoded bit error rate (BER) performance similar to the optimal systems using unitary precoders and DFEs. c) For the two ZF transceivers (ZF-Optimal and ZF-BD-GMD) and the two MMSE transceivers (MMSE-Optimal and MMSE-BD-GMD), the average BER degrades as the BW efficiency increases. d) In the case of single-input single-output (SISO) channels, the BD-GMD systems have the same performance as those of the lazy precoder transceivers. These properties make the proposed BD-GMD systems more favorable designs in practical implementation than the optimal systems. [ABSTRACT FROM AUTHOR]

In this paper, we design robust precoders, under the minimum mean square error (MMSE) criterion, for different types of channel state information (CSI) in multiple-input multiple-output (MIMO) channels. We consider low-complexity pre-fixed receivers that may adapt to the channel but are oblivious to the existence of a precoder at the transmitter. In particular, three types of CSI are taken into account: i) perfect CSI, ii) statistical CSI in the form of mean feedback, and iii) deterministic imperfect CSI assuming that the actual channel is within the neighborhood of a nominal channel, which leads to the worst-case robust design that is the focus of this paper. Interestingly, it is found that, under some mild conditions, the optimal transmit directions, i.e., the left singular vectors of the precoder, are equal to the right singular vectors of the channel, the channel mean, and the nominal channel for perfect CSI, statistical CSI, and the worst-case design, respectively. Consequently, the matrix-valued problems can be simplified to scalar power allocation problems that either admit closed-form solutions or can be efficiently solved by the proposed algorithm. [ABSTRACT FROM PUBLISHER]

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]

Multistatic time-of-arrival (TOA) localization has recently attracted great attention due to performance advantages offered by multistatic radar. In target localization, the relative sensor-target geometry is an important factor that can significantly affect the localization performance. In this paper, we analyze the optimal geometries for a two-dimensional TOA localization configuration commonly presented in the literature with a single transmitter and multiple receivers. Our analysis is based on minimizing the area of estimation confidence region, which is equivalent to maximizing the determinant of the Fisher information matrix. If the coordinate system is rotated such that the bearing angle of the transmitter with respect to the target is zero, an optimal geometry is obtained when a subset of the receivers are collinear with the target at a bearing angle of $\pi /3$ rad and the remaining receivers at a bearing angle of $-\pi /3$ rad. The arrangement of the receivers on either branch of the bearing angles, i.e., $\pi /3$ rad or $-\pi /3$ rad, is decided based on the measurement noise variances at the receivers. We conclude the study with numerical solutions generated by the genetic algorithm and simulation examples for UAV path optimization to verify the accuracy of the analytical findings. [ABSTRACT FROM PUBLISHER]

This paper deals with blind equalization based on the constant norm algorithm (CNA) in orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM). The CNA is designed to fit the complex square constellations with high order. As a consequence, the received signal is reshaped in order to change the real OQAM symbols into complex ones. Although it increases the intrinsic interferences of the OFDM/OQAM signal, it is proved by analysis and through simulations that the CNA with proposed reshaped signal achieves better mean square error (MSE) performance than CMA applied to the received OFDM/OQAM signal. Furthermore, a suboptimal initialization strategy is proposed, which ensures a high convergence speed. The behavior of the proposed blind equalization method is analyzed under synchronization mismatch as well as in time-varying propagation environment. [ABSTRACT FROM PUBLISHER]

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a recently developed transmission technique that extends the principle of spatial modulation to OFDM subcarriers. In this paper, the performance of OFDM-IM is studied in terms of the achievable rate assuming an M-ary constellation and that channel state information is available at the receiver. A closed-form lower bound is derived, based on which an interleaved grouping method is proposed for the use of subcarriers. In comparison with the existing grouping method, the proposed one can better benefit from the diversity effects over frequency-selective fading channels, especially when the spacing of any two subcarriers within a subcarrier group is larger than the coherence bandwidth. Through numerical results, it is revealed that OFDM-IM with interleaved grouping outperforms classical OFDM for small M and certain ranges of signal-to-noise ratio. Finally, the effects of modulation types on the performance of OFDM-IM are studied. It is found that the superiority of OFDM-IM over classical OFDM is greater for phase-shift keying than for quadrature amplitude modulation. [ABSTRACT FROM PUBLISHER]

This paper investigates the outage behavior and power allocation problem in a two-user network-coded cooperative cognitive radio network over Rayleigh fading channels. First, we derive an exact expression for the outage probability of the secondary transmissions, and then we propose an approximate expression whose validity limits are later evaluated. To improve the power efficiency of the secondary network, two power allocation schemes are proposed. In the first scheme, the transmit power is adjusted as a function of the average channel gains and the power control is formulated as a convex optimization problem where the objective is to minimize the total transmit power subject to outage probability constraints. In the second scheme, the transmit power is adjusted as a function of the instantaneous channel gains to guarantee a target signal-to-interference-plus-noise ratio at the receiver. We evaluate the performance of both schemes under cooperative and non-cooperative scenarios considering different network geometries. Our results indicate that the benefits of network coding cooperation are substantial for both power allocation schemes. [ABSTRACT FROM AUTHOR]

A two-way active parallel channel refers to a communication link between two transceivers, where the subchannel gains between the two transceivers can be adjusted such that a given performance criterion is optimized. A two-way active channel can have reciprocal subchannels, meaning that the subchannel gains in both communication directions are identical. Otherwise, if the gains of each subchannel in the two communication directions are different, the active channel is referred to as non-reciprocal. In this paper, we consider the problem of sum-rate maximization for reciprocal and non-reciprocal two-way active channels under two constraints on the transceivers’ transmit powers and a third constraint on the channel power (i.e., the sum of squared of the subchannel gains). We prove rigorously that for such active channels, in order to maximize the sum-rate, only a subset of the subchannels will have to be active. We also provide the optimal values of the subchannel gains and the optimal values of the transceivers’ transmit powers over different subchannels in closed forms. Simulation results show that parallel active channels can outperform their passive counterparts, where the subchannel gains are fixed, and thus, they cannot be adjusted. [ABSTRACT FROM PUBLISHER]

In this paper, adaptive training beam sequence design for efficient channel estimation in large millimeter-wave (mmWave) multiple-input multiple-output (MIMO) channels is considered. By exploiting the sparsity in large mmWave MIMO channels and imposing a Markovian random walk assumption on the movement of the receiver and reflection clusters, the adaptive training beam sequence design and channel estimation problem is formulated as a partially observable Markov decision process (POMDP) problem that finds non-zero bins in a two-dimensional grid. Under the proposed POMDP framework, optimal and suboptimal adaptive training beam sequence design policies are derived. Furthermore, a very fast suboptimal greedy algorithm is developed based on a newly proposed reduced sufficient statistic to make the computational complexity of the proposed algorithm low to a level for practical implementation. Numerical results are provided to evaluate the performance of the proposed training beam design method. Numerical results show that the proposed training beam sequence design algorithms yield good performance. [ABSTRACT FROM PUBLISHER]

The energy spectral efficiency maximization (ESEM) problem of a multi-user, multi-relay, multi-cell system is considered, where all the network nodes are equipped with multi-antenna transceivers. To deal with the potentially excessive interference originating from a plethora of geographically distributed transmission sources, a pair of transmission protocols based on interference alignment (IA) are conceived. The first, termed the full-IA, avoids all intra-cell interference (ICI) and other-cell interference by finding the perfect interference-nulling receive beamforming matrices (RxBFMs). The second protocol, termed partial-IA, only attempts to null the ICI. Employing the RxBFMs computed by either of these protocols mathematically decomposes the channel into a multiplicity of non-interfering multiple-input–single-output channels, which we term as spatial multiplexing components (SMCs). The problem of finding the optimal SMCs as well as their power control variables for the ESEM problem considered is formally defined and converted into a convex optimization form with carefully selected variable relaxations and transformations. Thus, the optimal SMCs and power control variables can be distributively computed using both the classic dual decomposition and subgradient methods. Our results indicate that indeed, the ESEM algorithm performs better than the baseline equal power allocation algorithm in terms of its ESE. Furthermore, surprisingly the partial-IA outperforms the full-IA in all cases considered, which is because the partial-IA is less restrictive in terms of the number of available transmit dimensions at the transmitters. Given the typical cell sizes considered in this paper, the path-loss sufficiently attenuates the majority of the interference, and thus the full-IA over-compensates, when trying to avoid all possible sources of interference. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for the artificial noise assisted secure transmission in multiple-input, multiple-output, multiple antenna eavesdropper (MIMOME) wiretap channels in frequency-division duplexed (FDD) systems. We focus on a practical scenario that only the eavesdroppers’ channel distribution information (CDI) is available and the imperfect channel state information (CSI) of the legitimate receiver is acquired through training and analog feedback. By taking explicitly into account the signaling overhead and training power overhead incurred by channel estimation and feedback, we define the achievable effective ergodic secrecy rate (ESR), and investigate a joint power allocation and training overhead optimization problem for the maximization of effective ESR. We first derive a deterministic approximation for the achievable effective ESR which facilitates the joint optimization. Then, efficient iterative algorithms are proposed to solve the considered nonconvex optimization problem. In particular, in the high-SNR regime, a block coordinate descent method (BCDM) is proposed to handle the joint optimization. In the low-SNR regime, we transform the problem into a sequence of geometric programmings (GPs) and locate its Karush–Kuhn–Tucker (KKT) solution using the successive convex approximation (SCA) method. For the general case of SNR, we maximize the lower bound of the achievable effective ESR. Simulation results corroborate the theoretical analysis and illustrate the secrecy performance of the proposed secure transmission scheme. [ABSTRACT FROM AUTHOR]

A multistatic system uses a transmitter to illuminate the object of interest and collects the reflected signal by a number of receivers to determine its location. In some scenarios such as passive coherent localization or for gaining flexibility, the position of the transmitter is not known. This paper investigates the use of the indirect path measurements reflected off the object alone, or together with the direct path measurements from the transmitter to receivers for locating the object in the absence of the transmitter position. We show that joint estimation of the object and transmitter positions from both the indirect and direct measurements can yield a better object location estimate than using the indirect measurements only by eliminating the dependency of the transmitter position. An algebraic closed-form solution is developed for the nonlinear problem of joint estimation and is shown analytically to achieve the Cramér–Rao lower bound performance under Gaussian noise over the small error region. To complete the study and gain insight, the optimal receiver placement in the absence of transmitter position is derived by minimizing the estimation confidence region or the mean-square estimation error for the object location. The performance lost due to unknown transmitter position under the optimal geometries is quantified. Simulations confirm well with the theoretical developments. [ABSTRACT FROM AUTHOR]

In this paper, we construct two tests for the weights of the global minimum variance portfolio (GMVP) in a high-dimensional setting, namely, when the number of assets $p$ depends on the sample size $n$ such that $\frac{p}{n}\rightarrow c \in (0,1)$ as $n$ tends to infinity. In the case of a singular covariance matrix with rank equal to $q$ we assume that $q/n\rightarrow \tilde{c}\in (0, 1)$ as $n\rightarrow \infty$. The considered tests are based on the sample estimator and on the shrinkage estimator of the GMVP weights. We derive the asymptotic distributions of the test statistics under the null and alternative hypotheses. Moreover, we provide a simulation study where the power functions and the receiver operating characteristic curves of the proposed tests are compared with other existing approaches. We observe that the test based on the shrinkage estimator performs well even for values of $c$ close to one. [ABSTRACT FROM AUTHOR]

In this paper, optimal deterministic encoding of a vector parameter is investigated in the presence of an eavesdropper. The objective is to minimize the expectation of the conditional Cramér–Rao bound at the intended receiver, while satisfying an individual secrecy constraint on the mean-squared error of estimating each parameter at the eavesdropper. The eavesdropper is modeled to employ the linear minimum mean-squared error estimator based on the noisy observation of the encoded parameter without being aware of encoding. First, the problem is formulated as a constrained optimization problem in the space of vector-valued functions. Then, two practical solution strategies are developed based on nonlinear individual encoding and affine joint encoding of parameters. Theoretical results on the solutions of the proposed strategies are provided for various scenarios on channel conditions and parameter distributions. Finally, numerical examples are presented to illustrate the performance of the proposed solution approaches. [ABSTRACT FROM AUTHOR]

In this paper, we develop efficient methods for devising lower complexity receivers that can achieve performance close to the full complexity receivers for passive/active multiple-input multiple-output (MIMO) radar. The method employed eliminates some parts of the test statistic to lower either hardware or software complexity. For the case of spatially uncorrelated reflection coefficients and spatially white clutter-plus-noise, the test statistic requires the computation of a set of matched filters, each matched to a signal from a different transmitter. In this case, our method is equivalent to selecting a specific set of transmitters to provide optimum performance. In the more general case of correlated clutter-plus-noise and reflection coefficients, then the test statistic requires the computation of a larger set of matched filters. These matched filters correlate the clutter-plus-noise free signal received at one receive antenna due to the signal transmitted from some transmit antenna and the signal received at another receive antenna. In the more general case, our algorithm picks the best of these matched filters to implement when the total number of these matched filters one can implement is limited. [ABSTRACT FROM AUTHOR]

Media-based modulation (MBM), which utilizes radiation patterns of a reconfigurable antenna to convey information, appears as a promising index modulation (IM) scheme for beyond 5G networking. In this paper, we present a general framework for MBM from the perspective of space-time coding, and introduce a novel space-time coded IM concept, which is called space-time media-based modulation (ST-MBM). The proposed scheme is based on one of the prominent IM solutions, space shift keying, along with Hurwitz–Radon family of matrices in order to achieve transmit diversity gain with a single radio frequency (RF) chain by utilizing the unique RF mirror activation principle of MBM. We derive the theoretical pairwise error probability of the ST-MBM scheme for correlated and uncorrelated channel states and obtain the average bit error probability. Additionally, a lower bound is derived for the mutual information of the ST-MBM scheme to gain insights into the information theoretical bounds of the proposed scheme. Furthermore, extensive computer simulations are provided to show the superior error performance of the ST-MBM scheme over the state-of-the-art multiple-input multiple-output-based transmission systems. [ABSTRACT FROM AUTHOR]

We propose a fast and near-optimal approach to joint channel-estimation, equalization, and decoding of coded single-carrier (SC) transmissions over frequency-selective channels with few-bit analog-to-digital converters (ADCs). Our approach leverages parametric bilinear generalized approximate message passing to reduce the implementation complexity of joint channel estimation and (soft) symbol decoding to that of a few fast Fourier transforms. Furthermore, it learns and exploits sparsity in the channel impulse response. This paper is motivated by millimeter-wave systems with bandwidths on the order of Gsamples/sec, where few-bit ADCs, SC transmissions, and fast processing all lead to significant reductions in power consumption and implementation cost. We numerically demonstrate our approach using signals and channels generated according to the IEEE 802.11ad wireless local area network standard, in the case that the receiver uses analog beamforming and a single ADC. [ABSTRACT FROM AUTHOR]

In this paper, the optimal stochastic design of multiple parameters is investigated for an array of fixed estimators both in the absence and presence of an average power constraint. Two different performance criteria are considered: the total Bayes risk criterion and the maximum Bayes risk criterion. It is obtained that in the presence of K parameters and the average power constraint, the optimal stochastic parameter design results in randomization (time sharing) among at most two and (K+1) different signals for the total Bayes risk and the maximum Bayes risk criteria, respectively. The average transmitted signal powers corresponding to the optimal parameter design approaches are specified, and the characterization of the optimal approaches is provided in various scenarios. In addition, sufficient conditions are derived to specify when the stochastic parameter design or the deterministic parameter design is optimal. Finally, numerical examples are presented to investigate the theoretical results, and to illustrate performance improvements achieved via the proposed approaches. [ABSTRACT FROM PUBLISHER]

This paper proposes a sequence detector designed to retrieve data bits from compressively sampled frequency-hopping spread spectrum (FHSS) Gaussian frequency-shift keying (GFSK) signals. The received signal waveform is not reconstructed from the compressive measurements, nor are received bits detected on a symbol-by-symbol basis. Rather, the entire sequence of transmitted symbols is detected from the entire sequence of compressive measurements. Another novel aspect of the work is that a non-cooperative scenario is assumed. Specifically, the receiver is assumed to have no prior knowledge of the specific spread spectrum hopping sequence used by the transmitter. The most significant contribution of the work is the design of adaptive sampling kernels that exploit the structure of an FHSS-GFSK signal to obtain significant performance improvements over random-kernel sampling. The resulting system can automatically choose an appropriate compression ratio as a function of the signal-to-noise ratio (SNR) without explicit knowledge of the SNR. Additionally, the noise folding problem present in random-kernel sampling is greatly alleviated by use of the adaptive sampling kernels. Compared with Nyquist sampling, adaptive compressive sampling offers compression ratios ranging from 20 to 32, depending on the SNR while suffering less than 1 dB loss in the resulting bit error rate. [ABSTRACT FROM PUBLISHER]

In this paper, we develop linear precoding schemes for MIMO downlink networks with quasi-static channels that are scalable, in the sense that they can be implemented with moderate complexity in networks with increasing numbers of cells and users. The principle that underlies the proposed precoding scheme is to exploit the decomposable structure of the equivalent channel matrix by designing the precoders at the base stations to be decomposable as well. Thus, the channel matrices and the designed precoders can be expressed as the Kronecker product of constituent matrices. For networks with a finite number of cells, the proposed structured precoding schemes enable inter-cell interference cancellation without the need for inter-cell feedback and provide more degrees of freedom than conventional interference avoidance schemes, while incurring a latency that grows only linearly in the number of cells. The proposed structured precoding schemes also enable a scaling approach for unbounded networks that we have called “spatial reuse precoding” (SRP). SRP is based on the observation that at each receiver, the signals from interfering sources that employ the same precoder arrive in the same subspace, regardless of the particular channel matrices between the interfering sources and the receiver. In some typical cellular architectures we show how an SRP scheme based on the proposed structured precoders can be designed to eliminate the dominant sources of interference without requiring cooperation between cells. In addition, we show that SRP can provide substantial performance gains in certain heterogeneous networks. [ABSTRACT FROM PUBLISHER]

This paper deals with the power allocation problem in a multipoint-to-multipoint network, which is heterogenous in the sense that each transmit and receiver pair can arbitrarily choose whether to selfishly maximize its own rate or energy efficiency. This is achieved by modeling the transmit and receiver pairs as rational players that engage in a noncooperative game in which the utility function changes according to each player’s nature. The underlying game is reformulated as a quasi variational inequality (QVI) problem using convex fractional program theory. The equivalence between the QVI and the noncooperative game provides us with all the mathematical tools to study the uniqueness of its Nash equilibrium points and to derive novel algorithms that allow the network to converge to these points in an iterative manner, both with and without the need for a centralized processing. Numerical results are used to validate the proposed solutions in different operating conditions. [ABSTRACT FROM PUBLISHER]

In this paper, we consider variable-rate transmission over a slowly varying multiple-input multiple-output (MIMO) channel with a decision feedback receiver. The transmission rate is adapted to the channel by dynamically assigning bits to the subchannels of the MIMO system. Predictive quantization is used for the feedback of bit loading to take advantage of the time correlation inherited from the temporally correlated channel. Due to the use of decision feedback at the receiver, the bit loading is related to the Cholesky decomposition of the channel Gram matrix. Assuming the channel is modeled by a slowly varying Gauss–Markov process, we show that the nested submatrices generated during the process of Cholesky decomposition can be updated as time evolves. Based on the update, we derive the optimal predictor of the next bit loading for predictive quantization. Furthermore, we derive the statistics of the prediction error, which are then exploited to design the quantizer to achieve a smaller quantization error. Simulations are given to demonstrate that the proposed predictive quantization gives a good approximation of the desired transmission rate with a low feedback rate. [ABSTRACT FROM PUBLISHER]

In this paper, we derive the maximum a posteriori (MAP) symbol detector for a multiple-input multiple-output system in the presence of Wiener phase noise due to noisy local oscillators, and quasi-static fading channels. As in single-antenna systems, the computation of the optimum receiver is an analytically intractable problem and is unimplementable in practice. In this purview, we propose three suboptimal, low-complexity algorithms for approximately implementing the MAP symbol detector, which involve joint phase noise estimation and data detection. Our first algorithm is obtained by means of the sum-product algorithm, where we use the multivariate Tikhonov canonical distribution approach. In our next algorithm, we derive an approximate MAP symbol detector based on the smoother-detector framework, wherein the detector is properly designed by incorporating the phase noise statistics from the smoother. The third algorithm is derived based on the variational Bayesian framework. By simulations, we evaluate the performance of the proposed algorithms for both uncoded and coded data transmissions and observe that the proposed techniques significantly outperform the other important algorithms from prior works, which are considered herein. [ABSTRACT FROM PUBLISHER]

The application of nonlinear transformations to a cyclostationary signal for the purpose of revealing hidden periodicities has proven to be useful for applications requiring signal selectivity and noise tolerance. The fact that the hidden periodicities, referred to as cyclic moments, are often compressible in the Fourier domain motivates the use of compressive sensing (CS) as an efficient acquisition protocol for capturing such signals. In this paper, we consider the class of Temporal Higher Order Cyclostationary Statistics (THOCS) estimators when CS is used to acquire the cyclostationary signal assuming compressible cyclic moments in the Fourier domain. We develop a theoretical framework for estimating THOCS using the low-rate nonuniform sampling protocol from CS and illustrate the performance of this framework using simulated data. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of three-dimensional target motion analysis using a combination of bearing, elevation, and time difference of arrival (TDOA) measurements. We propose a hybrid closed-form solution based on the weighted instrumental variables for this highly nonlinear problem. The proposed solution avoids the high computational complexity and convergence problems associated with iterative estimators. Furthermore, the simulation results suggest that the proposed estimator is nearly efficient, since its performance is very close to the best achievable performance as predicted by the Cramer-Rao lower bound. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a method to localize a wireless and stationary blind node based on received signal strength measurements; the positioning scheme relies on the statistical path loss model. The operating environment is either non-homogeneous, i.e., the attenuation factors of the various links are different, or homogeneous, i.e., all links share one and the same attenuation factor. We introduce a cognitive procedure to circumvent the a priori uncertainty on the actual scenario being in force: the first stage is a properly designed hypothesis test, fed by measurements between anchors, moving along known trajectories, to decide whether or not the environment is homogeneous. Based upon the output of the test, a model-dependent maximum-likelihood localization algorithm is adopted using the measurements made by the blind node and the parameters estimated by the anchors at previous stage. The performance assessment shows that the proposed approach could be a viable means to handle localization in uncertain scenarios. [ABSTRACT FROM PUBLISHER]

For many wireless sensor networks, high accuracy and low complexity localization techniques are crucial to their successful deployment. The radio interferometric positioning system (RIPS) has been introduced for accurate localization with low complexity. In this paper, a dual-tone radio interferometric positioning system (DRIPS) is proposed to localize multiple targets simultaneously. In DRIPS, multiple asynchronous targets emit dual-tone signals, and synchronous anchor receivers (nodes with known positions) extract phase information of low-frequency differential tones created by squaring the received dual-tone signals. Multiple time-of-arrivals (TOAs) coupled with unknown offsets due to the asynchronous targets are estimated based on the phase information. Accordingly, a localization algorithm taking the integer ambiguity issue into account is developed. Moreover, considering the case without accurate knowledge of the frequencies of the differential tones, an ESPRIT-type algorithm is proposed to estimate the frequencies. The proposed DRIPS is robust to flat-fading channels, immune to uncertainties of local oscillators, and able to distinguish different targets. In order to show more insights of the performance limit of the DRIPS, Cramér-Rao bounds (CRBs) are derived. Simulation results illustrate the merits of the proposed DRIPS. [ABSTRACT FROM PUBLISHER]

This paper considers the transmitter design for wireless information and energy transfer (WIET) in a multiple-input single-output (MISO) interference channel (IFC). The design problem is to maximize the system throughput subject to individual energy harvesting constraints and power constraints. It is observed that the ideal scheme, where the receivers simultaneously perform information detection (ID) and energy harvesting (EH) from the received signal, may not always achieve the best tradeoff between information transfer and energy harvesting, but simple practical schemes based on time splitting may perform better. We therefore propose two practical time splitting schemes, namely the time-division mode switching (TDMS) and time-division multiple access (TDMA), in addition to the existing power splitting (PS) scheme. In the two-user scenario, we show that beamforming is optimal to all the schemes. Moreover, the design problems associated with the TDMS and TDMA schemes admit semi-analytical solutions. In the general K-user scenario, a successive convex approximation method is proposed to handle the WIET problems associated with the ideal scheme, the PS scheme and the TDMA scheme, which are known NP-hard in general. Simulation results show that none of the schemes under consideration can always dominate another in terms of the sum rate performance. Specifically, it is observed that stronger cross-link channel power improves the achievable sum rate of time splitting schemes but degrades the sum rate performance of the ideal scheme and PS scheme. As a result, time splitting schemes can outperform the ideal scheme and the PS scheme in interference dominated scenarios. [ABSTRACT FROM AUTHOR]

Vector orthogonal frequency division multiplexing (V-OFDM) for single transmit antenna systems is a generalization of OFDM where single-carrier frequency domain equalization (SC-FDE) and OFDM are just two special cases. It has been shown to be able to collect multipath diversity and thus generally outperforms OFDM. The performance of OFDM under phase noise has been extensively studied in the literature. These effects can be summarized as introducing a common phase error (CPE) and inter carrier interference (ICI) resulting in poor performance. The performance of V-OFDM under phase noise still remains uninvestigated and in this paper it is shown that phase noise in V-OFDM systems leads to a common vector block phase error (CVBPE) as well as an inter vector block carrier interference (IVBCI) effect. An expression for the signal plus interference noise ratio (SINR) for a V-OFDM system is derived and found to agree closely with simulation results. A pilot vector CVBPE based phase noise estimation technique is derived, and then combined with the maximum likelihood (ML) and the linear minimum mean square error (LMMSE) estimators to estimate the phase noise. It turns out that compared with OFDM, V-OFDM systems can perform phase noise estimation with lower complexity. The decorrelation and cancellation techniques are then used to compensate for the phase noise impairment. Numerical results for both coded and uncoded systems with perfect and practical channel estimation are provided to validate the analysis and effectiveness of the proposed phase noise estimation and compensation methods. [ABSTRACT FROM AUTHOR]

This paper considers the problem of passive detection with a multistatic radar system involving a noncooperative illuminator of opportunity (IO) and multiple receive platforms. An unknown source signal is transmitted by the IO, which illuminates a target of interest. These receive platforms are geographically dispersed, and collect independent target echoes due to the illumination by the same IO. We consider a generalized canonical correlation (GCC) detector for passive detection which requires the knowledge of the noise power. We derive closed-form expressions for the probabilities of false alarm and detection of this detector. For the case where the noise power is unknown, we propose a generalized likelihood ratio test (GLRT) detector to deal with the passive detection problem. Moreover, a closed-form expression for the probability of false alarm of this GLRT detector is given, which shows that the proposed GLRT detector exhibits a constant false alarm rate property with respect to the noise power. Numerical simulations demonstrate that the proposed GLRT detector generally outperforms the generalized coherence detector, a previous popular passive detector that neither requires the knowledge of the noise power. In addition, the GLRT also outperforms the GCC detector when the latter has an uncertainty in its knowledge of the noise power. [ABSTRACT FROM PUBLISHER]

Due to the insertion of cyclic prefix (CP), orthogonal-frequency-division multiplexing (OFDM) systems with offset quadrature amplitude modulation (OQAM) (OQAM-OFDM) and CP, denoted as CP-OQAM-OFDM, systems have more robustness to combat a multi-path fading channel and simpler and better receivers than the conventional OQAM-OFDM systems. In this paper, two channel estimators, i.e., weighted least square (WLS) and pairs of pilots (POP) are presented in CP-OQAM-OFDM systems. To evaluate the proposed WLS and POP estimators, the corresponding Cramér–Rao bounds are given for comparison. In addition, the computational complexities of the proposed estimators are analyzed as well. Simulation results demonstrate that both of the proposed channel estimators can achieve good bit error rate (BER) performance. [ABSTRACT FROM PUBLISHER]

Multi-antenna or multiple-input multiple-output (MIMO) techniques are appealing to enhance the transmission efficiency and range for radio frequency (RF) signal enabled wireless energy transfer (WET). In order to reap the energy beamforming gain in MIMO WET, acquiring the channel state information (CSI) at the energy transmitter (ET) is an essential task. This task is particularly challenging, since existing channel training and feedback methods used for communication receivers may not be implementable at the energy receiver (ER) due to its hardware limitation. To tackle this problem, we consider in this paper a multiuser MIMO WET system, and propose a new channel learning method that requires only one feedback bit from each ER to the ET per feedback interval. Specifically, each feedback bit indicates the increase or decrease of the harvested energy by each ER in the present as compared to the previous intervals, which can be measured without changing the existing structure of the ER. Based on such feedback information, the ET adjusts transmit beamforming in subsequent training intervals and at the same time obtains improved estimates of the MIMO channels to different ERs by applying an optimization technique called analytic center cutting plane method (ACCPM). For the proposed ACCPM based channel learning algorithm, we analyze its worst-case convergence, from which it is revealed that the algorithm is able to estimate multiuser MIMO channels simultaneously without reducing the analytic convergence speed. Also, we provide extensive simulations to show its performances in terms of both convergence speed and energy transfer efficiency. [ABSTRACT FROM PUBLISHER]

In this paper we consider variable-rate transmission for time-correlated MIMO (multi-input multi-output) channels with limited feedback. The number of bits loaded on each subchannel of the MIMO system is dynamically assigned according to the current channel condition and fed back to the transmitter. As the channel is time-correlated, bit loading is a vector signal that is also time-correlated. We propose to feedback bit loading using predictive coding, which is known to be a powerful quantization technique when the underlying signal is correlated in time. Assuming the channel is a first-order Gauss-Markov random process, we derive the predictor for the bit loading to be coded and analyze the corresponding prediction error variance when the channel is varying slowly. By exploiting the prediction error variance, we adapt the quantizer of the prediction error to have a smaller quantization error. Furthermore we show that the prediction error variance is proportional to a term that depends only on the time-correlation coefficient. This leads to the conclusion that, a codebook that is designed for a particular time correlation coefficient can be easily modified to a codebook for a different correlation coefficient without redesign. Simulations are presented to demonstrate that the proposed predictive coding can achieve a very good approximation of the desired transmission rate with a very low feedback rate. [ABSTRACT FROM PUBLISHER]

We consider a point-to-point multiple-input-single-output (MISO) system where a receiver harvests energy from a transmitter. To achieve high-efficiency wireless power transfer (WPT), the transmitter performs energy beamforming by using an instantaneous channel state information (CSI). The CSI is estimated at the receiver by training via a preamble and fed back to the transmitter. In this paper, we address the key challenge of balancing the time resource used for channel estimation and WPT to maximize the harvested energy and also investigate the allocation of energy resource used for WPT. First, we consider the general scenario where the preamble length is allowed to vary dynamically depending on channel conditions. The optimal preamble length is obtained online by solving a dynamic programming (DP) problem. The DP problem is proved to reduce to an optimal stopping problem. The optimal policy is then shown to depend only on the channel estimate power. Next, we consider the scenario in which the preamble length is fixed by an offline optimization. Furthermore, we derive the optimal power allocation schemes. For the dynamic-length-preamble scenario, the power is allocated according to both the optimal preamble length and the channel estimate power, while for the fixed-length-preamble scenario, the power is allocated according to only the channel estimate power. By numerical simulations, our results show that with optimal power allocation, the energy harvested by using the optimized fixed-length preamble is close to that harvested by using a dynamic-length preamble, hence allowing a low-complexity yet close-to-optimal WPT system to be implemented in practice. [ABSTRACT FROM AUTHOR]

A considerable volume of research into adaptive schemes for transmit beamforming in distributed networks has emerged in recent years. A noise-free received signal strength (RSS) measurement and a static environment were often considered. However, in practical scenarios, system uncertainties often arise, which may lead to the aforementioned idealistic assumptions failing. In this paper, we focus on the robust design of distributed beamforming and proposed a systematic analytical framework for the convergence of a general set of adaptive schemes under the condition that the RSS at the receiver is corrupted by noise. Furthermore, we presented theoretical analysis using stochastic stability to demonstrate the tracking capability of the general adaptive schemes when channels are subject to fast time variations. We defined a set of robustness criteria that can be used as comparison metrics for existing adaptive schemes, under time-varying channels and time-varying network topologies. Through the utilization of the proposed analytical frameworks and metrics, we developed a bio-inspired scheme, BioRARSA2, which possess significantly superior robustness with respect to environmental variations and system uncertainties. The improved robustness of the proposed algorithm was validated further through extensive numerical simulations. [ABSTRACT FROM PUBLISHER]

In this paper, we propose and optimize a low-complexity precoding scheme for multiple antenna cognitive radio networks. In a cognitive network, a secondary transmitter is allowed to access the spectrum of the primary network only if the interference to the primary network remains below the predefined power limit. The proposed scheme, termed MSLNR, is a combination of the optimal minimum-mean-square-error (MMSE) receiver and the signal-to-leakage-plus-noise-ratio (SLNR) transmitter, with additional scaling to comply with the cognitive interference constraint. We also present a robust design method for the case where the secondary transmitter has only partial channel state information (CSI). The MSLNR scheme requires low implementation complexity. The transmit precoder is evaluated while taking into account the optimal receiver weight, but without any iterations. Yet, simulation results demonstrate that the performance of the proposed MSLNR scheme is close to the performance of the best known solution. [ABSTRACT FROM PUBLISHER]

In molecular communication, small particles such as molecules are used to convey information. These particles are released by a transmitter into a fluidic environment, where they propagate freely (e.g. through diffusion) or through externals means (e.g. different types of active transport) until they arrive at the receiver. Although there are a number of different mathematical models for the diffusion-based molecular communication, active transport molecular communication (ATMC) lacks the necessary theoretical framework. Previous works had to rely almost entirely on full Monte Carlo simulations of these systems. However, full simulations can be time consuming because of the computational complexities involved. In this paper, a Markov channel model has been presented, which could be used to reduce the amount of simulations necessary for studying ATMC without sacrificing accuracy. Moreover, a mathematical formula for calculating the transition probabilities in the Markov chain model is derived to complete our analytical framework. Comparing our proposed models with full simulations, it is shown that these models can be used to calculate parameters such channel capacity accurately in a timely manner. [ABSTRACT FROM PUBLISHER]

The paper proposes a new approach of joint space decomposition-and-synthesis (JSDS) for deriving achievable degree-of-freedom (DoF) region by interference alignment without symbol extension for K-user MIMO interference channel. Solving the interference alignment problem is to find the precoders that align desired signal and interference with disjoint subspaces at receivers. A related property of these precoders is that the solution range for each one of them is a union of linear spaces, termed transmit spaces. Based on this property, the JSDS approach is developed to derive transmit spaces that fulfill alignment conditions. From this approach, achievable DoF regions for 2 and 3-user interference channels are obtained. The region for the 2-user channel is shown to coincide with the existing result of the DoF capacity of the channel. For the K-user (K > 3) case, inner as well as outer bounds are given. [ABSTRACT FROM PUBLISHER]

Recent breakthroughs in machine learning shift the paradigm of wireless communication towards intelligence radios. One of their core operations is automatic modulation recognition (AMR). Existing research focuses on coherent modulation schemes such as QAM and FSK. The AMR of (noncoherent) space–time modulation remains an uncharted area despite its deployment in modern multiple-input-multiple-output (MIMO) systems. The scheme using a so-called Grassmann constellation enables rate enhancement. In this paper, we propose an AMR approach for Grassmann constellation based on data clustering, which differs from traditional AMR based on classification using a modulation database. The approach allows algorithms for clustering on the Grassmann manifold (or the Grassmannian), such as Grassmann K-means and depth-first search, to be applied to AMR. We further develop an analytical framework for studying and designing these algorithms in the context of AMR. First, the expectation-maximization algorithm for Grassmann constellation detection is proved to be equivalent to clustering (K-means) on the Grassmannian for a high SNR. Thereby, a well-known machine-learning result that was originally established only for the Euclidean space is rediscovered for the Grassmannian. Next, we tackle the challenge on theoretical analysis of data clustering by introducing probabilistic metrics for measuring the inter-cluster separability and intra-cluster connectivity of received space–time symbols and deriving them using tools from differential geometry. The results provide useful insights into the effects of various parameters ranging from the signal-to-noise ratio to constellation size, facilitating algorithmic design. [ABSTRACT FROM AUTHOR]

In this paper, we design a receiver that iteratively passes soft information between the channel estimation and data decoding stages. The receiver incorporates sparsity-based parametric channel estimation. State-of-the-art sparsity-based iterative receivers simplify the channel estimation problem by restricting the multipath delays to a grid. Our receiver does not impose such a restriction. As a result, it does not suffer from the leakage effect, which destroys sparsity. Communication at near capacity rates in high SNR requires a large modulation order. Due to the close proximity of modulation symbols in such systems, the grid-based approximation is of insufficient accuracy. We show numerically that a state-of-the-art iterative receiver with grid-based sparse channel estimation exhibits a bit-error-rate floor in the high SNR regime. On the contrary, our receiver performs very close to the perfect channel state information bound for all SNR values. We also demonstrate both theoretically and numerically that parametric channel estimation works well in dense channels, i.e., when the number of multipath components is large and each individual component cannot be resolved. [ABSTRACT FROM AUTHOR]

In this paper, we consider the design of statistical multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) beamformers for millimeter wave (mmWave) channels. The transmitter designs the subcarrier beamformers based on the statistics of the channel, without instantaneous channel information. To overcome the radio frequency (RF) limitation in mmWave application, the subcarrier beamformers are implemented in a hybrid structure, which imposes constraints on the design of subcarrier beamformers. We analyze the unconstrained statistical subcarrier beamformers using spectral analysis of the subcarrier channels. The analysis shows that, for each subcarrier channel, the optimal statistical beamformer is approximately a linear combination of optimal statistical beamformers for some appropriately defined narrowband single-cluster subchannels. The result suggests a design of the subcarrier beamformers that can be readily implemented in a hybrid structure. Furthermore, a hybrid design for the receiver is proposed based on the concept of vector quantization. Simulations are given to show that the use of a hybrid beamforming structure incurs a minor degradation in transmission rate. With three RF chains, the performance is close to that of all digital statistical beamforming. [ABSTRACT FROM AUTHOR]