Fifth-generation (5G) wireless networks harness the extensive spectrum available in the millimeter-wave (mmWave) frequency bands which set them apart from current wireless systems in terms of directivity, propagation loss, and susceptibility to blockages. Sub-6 GHz systems can attain omni-directional coverage, displaying limited sensitivity to physical obstacles. Still, they are incapable of achieving the same level of service quality as systems outfitted with electronically steerable directional antennas offering reduced propagation loss and higher gains due to the beam directionality. In our framework, we investigate the utilization of directional, steerable mmWave antennas as integral components. The nodes communicate by manipulating the orientation of their antennas, i.e., steering their beams. To minimize dependence on a base station, the nodes are categorized into primary antennas (PAs) and secondary antennas (SAs), and they communicate in three phases: Uplink (SA to PA), Downlink (PA to SA), and PA-PA. We delve into the impact of optimal steering of the main lobe beams transmitted by these antennas as well as optimizing the time sharing among three phases. Within each phase, we assume that the nodes follow a random transmission scheduling scheme and derive the achievable rates accordingly. Through meticulous design of polynomial-time heuristics, we maximize the overall network capacity.

We consider a multi-cell massive multiple-input multiple-output (MIMO) system operating under spatially correlated Rayleigh fading channels, where pilot reuse is permitted both within and across cells, and each base station (BS) employs multi-cell minimum mean square error (M-MMSE) processing. We derive a novel deterministic approximation of the uplink signal-to-interference-and-noise ratio (SINR), asymptotically tight in the large-system limit, even under pilot reuse and spatial correlation, addressing a key gap in the existing literature. Building on this result, we propose a multi-cell pilot assignment (PA) scheme that fully eliminates pilot contamination by exploiting the spatial correlation matrices of all users. To ensure scalability in large networks, we further introduce a scalable PA scheme with partial M-MMSE (P-MMSE) processing, which reduces inter-BS information exchange while maintaining high spectral efficiency (SE). Additionally, we design pilot and data power allocation strategies for both weighted sum SE and max-min SE objectives. A detailed complexity analysis confirms the practicality of the proposed algorithms. Simulation results demonstrate the robustness and superiority of our PA schemes across various network conditions, showing substantial SE gains and good user fairness with significantly lower pilot overhead compared to existing approaches, offering valuable insights for the design of future massive MIMO systems.

We address distributed detection problem in a wireless sensor network, where each sensor harvests and stores randomly arriving energy units in a finite-size battery. Sensors transmit their symbols simultaneously to a fusion center (FC) with M >1 antennas, over temporally correlated fading channels. To characterize the channel time variation we adopt a Markovian model and assume that the channel time-correlation is defined by Jakes-Clark’s correlation function. We consider limited feedback of channel gain, defined as the Frobenius norm of MIMO channel matrix, at a fixed feedback frequency (e.g., every T time slots) from the FC to sensors. Modeling the randomly arriving energy units as a Poisson process and the quantized channel gain and the battery dynamics as homogeneous finite-state Markov chains, we propose an adaptive transmit power control strategy such that the J-divergence based detection metric is maximized at the FC, subject to an average transmit power per-sensor constraint.

In this paper, we consider a device-to-device (D2D) millimeter Wave (mmWave) network that allocates a spectrum band with bandwidth BcHz exclusively to support communication of N cooperative D2D pairs over Rayleigh fading channels. The available bandwidth is divided into Ncnon-overlapping sub-bands. Each node is equipped with a directional antenna that is capable of steering its beam within its field of view. Also, each transmitter can adjust its transmit power. Aiming at maximizing the network throughput, the cooperative D2D pairs form Ncdisjoint coalitions, where the D2D pairs in a particular coalition share the same sub-band for communication and hence cause co-channel interference. We address this question: What is the best coalition among the D2D pairs, the optimal beams steering angles of directional antennas of the D2D pairs within each coalition, and the optimal transmit powers such that the network throughput is maximized? We formulate the network throughput maximization problem, subject to certain constraints, and we propose an iterative method, based on the block coordinate descent (BCD) algorithm, to solve the constrained optimization problem. Specially, we propose a coalitional game approach for coalition formation among the D2D pairs. We numerically investigate the effects of different system parameters (e.g., N, Nc, the antenna gain, the maximum allowed total transmit power), as well as the impact of optimizing coalition formation only, and optimizing transmit power only, on the network throughput maximization.

We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting randomly arrived energy and storing it in a finite capacity battery. Sensors are informed of their fading channel states, via a bandwidth- limited feedback channel from the FC. Each sensor has the knowledge of its current battery state and its channel state (quantized channel gain). Our goal is to study how sensors should choose their transmit powers such that J-divergence of the received signal densities under two hypotheses at the FC is maximized, subject to certain (battery and power) constraints. We derive the optimal power map, which depends on the energy arrival rate, the battery capacity, and the battery states probabilities at the steady state. Using the optimal power map, each sensor optimally adapts its transmit power, based on its battery state and its channel state. Our simulation results demonstrate the performance of our proposed power adaptation scheme for different system parameters.

Utilization of directional antennas is a promising solution for efficient spectrum sensing and accurate source localization and tracking. Spectrum sensors equipped with directional antennas should constantly scan the space in order to track emitting sources and discover new activities in the area of interest. In this paper, we propose a new formulation that unifies received-signal-strength (RSS) and direction of arrival (DoA) in a compressive sensing (CS) framework. The underlying CS measurement matrix is a function of beamforming vectors of sensors and is referred to as the propagation matrix. Comparing to the omni-directional antenna case, our employed propagation matrix provides more incoherent projections, an essential factor in the compressive sensing theory. Based on the new formulation, we optimize the antenna beams, enhance spectrum sensing efficiency, track active primary users accurately and monitor spectrum activities in an area of interest. In many practical scenarios there is no fusion center to integrate received data from spectrum sensors. We propose the distributed version of our algorithm for such cases. Experimental results show a significant improvement in source localization accuracy, compared with the scenario when sensors are equipped with omni-directional antennas. Applicability of the proposed framework for dynamic radio cartography is shown. Moreover, comparing the estimated dynamic RF map over time with the ground truth demonstrates the effectiveness of our proposed method for accurate signal estimation and recovery.

Repetitive visual stimuli induce periodic Visual Evoked Potentials (VEPs) in the brain that can be potentially identified in an EEG trace. The ability to distinguish frequencies and patterns due to different stimuli is the basis for brain computer interfaces (BCIs) used for communication and control of neurologically disabled patients. Since such responses are recorded in presence of high levels of noise from background brain processes, the detection task is rather challenging. In this work, we propose a detection approach for VEPs based on Ramanujan Periodicity Transform matrices (RPT), which have shown promise in detecting periodicities in data. Our results show that the RPT-based approach can outperform conventional spectral techniques and the state-of-the-art correlation analysis, and is more compatible with real-time BCIs which have to work with short duration EEG epochs. The proposed approach is fairly robust to unknown natural latencies in brain response.

In this paper we consider distributed estimation of an unknown Gaussian random variable with known mean and variance, where each sensor observation is affected by both multiplicative and additive Gaussian observation noises. We derive the corresponding Cramer Rao Lower Bound (CRLB) for both quantized and full precision observations. In sequel we provide some closed-form approximations for both CRLB expressions which provide us with better understanding of behavior of CRLBs. Afterwards through analytic and simulation results we report some scenarios that multiplicative observation noise can play an enhancive role in terms of estimation accuracy. We call this phenomena enhancement mode of multiplicative noise.

In this paper, we consider a cognitive radio system, consisting of a primary user (PU), a secondary user (SU) transmitter, and a SU receiver. The SUs are equipped with directional antennas. The SU transmitter first performs spectrum sensing (with errors) and then transmits data. We assume the SU and PU can coexist and the SU transmits at two power levels, according to the result of spectrum sensing (i.e., whether the spectrum is sensed idle or busy). We establish a lower bound on the ergodic capacity of the channel between SU transmitter and receiver, and study how spectrum sensing errors affect the bound. Furthermore, we explore the optimal SU transmit power levels and the optimal directions of SU transmit and receive antennas, such that the lower bound is maximized, subject to average transmit power and average interference power constraints. Through numerical simulations, we show that (compared with the case when the SUs use omni-directional antennas) directional antennas can significantly improve the lower bound in the presence of spectrum sensing errors, subject to the constraints.

We consider the problem of distributed estimation of an unknown zero-mean Gaussian random variable in a bandwidth constrained network, when only partial information of observation model is available. Sensors employ uniform quantizers with variable rates and transmit their quantized observations to a fusion center (FC). Assuming the FC employs the linear minimum mean-square error (LMMSE) estimator, we provide a closed-form expression for the corresponding MSE, and propose a rate allocation scheme that minimizes the MSE, subject to a total bandwidth constraint (measured in quantization bits). Simulation results show the superiority of the proposed scheme over uniform bit allocation scheme.

We consider a wireless sensor network (WSN), consisting of several sensors and a fusion center (FC), which is tasked with solving an M-ary hypothesis testing problem. Sensors make M-ary decisions and transmit their digitally modulated decisions over orthogonal channels, which are subject to Rayleigh fading and noise, to the FC. Adopting Bayesian optimality criterion, we consider training and non-training based distributed detection systems and investigate the effect of imperfect channel state information (CSI) on the optimal maximum a posteriori probability (MAP) fusion rules and detection performance, when the sum of training and data symbol transmit powers is fixed. Our results show that, when sensors employ M-FSK modulation, the error probability is minimized when training symbol transmit power is zero (regardless of the reception mode at the FC). However, for coherent reception and M-PSK modulation the error probability is minimized when half of transmit power is allocated for training symbol.

In this paper we study the problem of distributed estimation of a random vector in wireless sensor networks (WSN) with linear observation model. Each sensor makes a noisy observation, quantizes its observation, maps it to a digitally modulated symbol, and transmits the symbol over erroneous wireless channels (subject to fading and noise) to a fusion center (FC), which is tasked with fusing the received signals and estimating the unknown vector. We derive the Bayesian Cramer-Rao Bound (CRB) matrix and study the behavior of its trace (through analysis and simulations), with respect to the system parameters, including observation and communication channel signal-to-noise ratios (SNRs). The derived CRB serves as a benchmark for performance comparison of different Bayesian estimators, including linear MMSE estimator.

We derive the capacity region of two classes of the discrete memoryless state-dependent cognitive interference channels (SD-CICs) with noncausal channel state information known to only the cognitive transmitter: semideterministic SD-CIC and deterministic SD-CIC. We also provide new inner and outer bounds on the capacity region of the general SD-CIC. We prove that the new outer bound is the capacity region of the SD-CIC in the better cognitive decoding regime when both the cognitive transmitter and its corresponding receiver are aware of the channel state information in a noncausal manner.

We derive a new upper bound on the capacity region of the discrete memoryless partially cooperative relay cognitive interference channel (PC-RCIC). We show that our new upper bound is the capacity region of the semideterministic discrete memoryless PC-RCIC, where the channel output observed by the relay is a deterministic function of the channel inputs.

We consider a binary distributed detection problem in a wireless sensor network with inhomogeneous sensors, in which sensors send their binary phase shift keying (BPSK) modulated decisions to the fusion center (FC) over orthogonal channels that are subject to pathloss, Rayleigh fading, and Gaussian noise. Assuming training based channel estimation, we consider a linear fusion rule which employs imperfect channel state information (CSI) to form the global decision at the FC. Under the constraint that the total transmit power of training and decision symbols at each sensor is fixed, we analytically derive the optimal power allocation between training and data at each sensor such that the deflection coefficient at the FC is maximized. Our analysis shows that the proposed optimal power allocation scheme is a function of signal-to-noise (SNR) and local detection indices, and at high SNR regime, the proposed scheme outperforms the uniform power allocation.

We consider a distributed detection system, in which sensors send their decisions over orthogonal noisy channels to a fusion center (FC). We study how the optimal integrated fusion rule and its low signal-to-noise ratio (SNR) approximation, as well as the suboptimal non-integrated fusion rule, are related to the physical layer specifications (reception, modulation, channel model, and availability of channel state information (CSI) at FC). In particular, we consider training and non-training based systems and investigate the effect of imperfect CSI on the fusion rules, detection performance and error exponent, assuming that the sum of training and data symbol transmit powers is fixed. Our results show that the detection performance of the system with noncoherent reception is maximized when training symbol transmit power is zero. This performance is attainable with the statistics-based likelihood ratio test (LRT) rule for random channel model and the generalized LRT rule for deterministic channel model. For a system with BPSK modulation and coherent reception, subject to Rayleigh fading, the detection probability and error exponent are maximized when half of transmit power is allocated for training symbol. For Rician fading, however, optimal power allocation between training and decision symbols depends on the SNR and Rice factor. Comparing the integrated and non-integrated fusion rules, we show that the former always outperforms the latter.

We study the problem of joint optimization of Slepian-Wolf (SW) source coding and transmission rates over a Gaussian multiple access channel with the considerations of circuit power consumption and average transmit power constraint. The goal is to maximize the sample rate at the source nodes. We first derive a criterion to determine the optimality of different multiple access schemes such that the highest sample rate can be achieved at the source nodes when SW coding is used. Based on the derived optimality criterion, we propose a rate allocation procedure to determine the jointly optimal SW coding and transmission rates corresponding to orthogonal code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA) and superposition coding with successive interference cancellation (SCSIC) schemes. Several demonstrative numerical examples are provided to show the performance gain of the proposed joint rate allocation scheme.

We consider a two-way amplify-and-forward (AF) relaying system consisting of two source nodes and a half-duplex relay. We assume that the source nodes are equipped with the linear minimum mean squared error (LMMSE) channel estimators. We investigate the effect of uncertainty due to channel estimation error on the system outage probability, considering the imposed bandwidth and energy costs of channel estimation. For a fixed transmission block length and a total transmit power constraint, we provide a compact-form expression for the system outage probability upper bound and we explore the optimal number of training symbols, the optimal power allocation between data and training, and the optimal power allotment between the two users and the relay, such that this bound is minimized. Our numerical results show that channel estimation error does not limit the performance in high signal-to-noise ratio (SNR). Also, the optimal power allocation between data and training and between the users and the relay provides a significant SNR improvement, compared with the suboptimal schemes, including fixed power allocation. The optimization gain increases as the relay moves away from the middle point.

This paper studies the impact of channel estimation error on the performance of a two-way amplify-and-forward (AF) relay network and investigates the optimal transmit resource allocation that minimizes the impact. In particular, we consider a three node network, consisting of two user terminals \mathbb{T}_A and \mathbb{T}_B and a half duplex relay node \mathbb{R}, where only \mathbb{T}_A and \mathbb{T}_B are equipped with channel estimators. Assuming block flat fading channel model, we adopt two estimation theoretic performance metrics, namely the Bayesian Cramer-Rao lower bound (CRLB) and the mean-squared error (MSE) of the linear minimum mean square error (LMMSE) channel estimate, and an information theoretic performance metric, namely the average sum-rate lower bound, as our optimality criteria. For a fixed transmission block length and under the total transmit power constraint, we investigate the optimal training vector design, the optimal number of training symbols in the training vector, the optimal power allocation between training and data in a transmission block, and the optimal power allotment between three nodes, such that these performance metrics are optimized, via utilizing bi-objective optimization methods. Our simulation results demonstrate that the optimal solutions corresponding to each performance metric vary, as the relay location and the system signal-to-noise ratio (SNR) change. They also reveal interesting symmetry relationship between these optimal solutions and the relay location.

We consider an energy constrained wireless sensor network, with arbitrary number of nodes, where source nodes utilize Slepian-Wolf (SW) coding before transmission to a joint decoder. We investigate optimal and near-optimal SW coding rates, transmit powers, and transmit durations that maximize the number of collected samples during the network lifetime, subject to channel capacity, SW rate region, and residual energy constraints. We find optimal (near-optimal) closed-form solutions in the absence (presence) of an energy constraint at the joint decoder. We take into account the energy consumption of SW encoding and decoding and communication circuitry. Numerical results demonstrate the effectiveness of the proposed optimization, especially when the joint decoder is not energy constrained.

Considering a two-way amplify-and-forward (AF) relay network and aiming to simultaneously maximize the two users' mutual information lower bounds in the presence of channel estimation errors, we study the Pareto-front of users' mutual information lower bounds. Based on the Pareto-front we investigate the optimal power allocation among the two users and the relay, as well as the optimal power allotment between training and data symbols that maximize the average sum-rate lower bound, and explore the variations of these optimal power allocation as the relay position changes. We also show that the mean square error (MSE) of channel estimation is minimized when training vectors transmitted by the two users are orthogonal.

We investigate the problem of maximizing the sample rate at the transmitters in a wireless network using distributed source coding (DSC) and lossless transmission. With the consideration of circuit power consumption, average power constraints and DSC rate constraints, we study different orthogonal multiple access channels (MACs) and prove that the optimal orthogonal MAC is either TDMA or CDMA, depending on the joint entropy of the correlated sources, average power constraints, circuit power consumption, noise power and path loss.

For a three node amplify-and-forward (AF) relay system in which only destination D is equipped with a channel estimator, we derive lower bound on training-based mutual information per transmission block. For a given transmission block length and a fixed total transmit power constraint between source S and relay R, we investigate jointly optimal number of training symbols, optimal power allocation between training and data, and optimal power allotment between S and R, such that the mutual information lower bound is maximized. For the AF relay system without direct link we provide analytical solutions to the joint optimization problem and relate the solutions to the relay location. For the AF relay system with direct link we resort to numerical evaluations to find the optimal solutions.

We consider an energy constrained two-way wireless relay system, where a bidirectional connection between two terminals TAand TBis established via one decode-and-forward (DF) relay R. We study the optimal power allocation that minimizes the total transmit power consumption, under given requirements on the end-to-end bit error rate (BER) at TAand TB. By approximating the non-convex BER constraints, we formulate a convex optimization problem and provide closed form solutions for the optimal power assignment problem. Comparing the resulting overall transmit power consumption with that achieved through the individual link requirement strategy (where power is assigned under a per-link BER constraint), we show that the proposed power allocation scheme can achieve a maximum of 50% power reduction.

In this paper, we propose an iteration-free algorithm to find the optimal configuration, including transmit power and source coding rates, to maximize the lifetime of a cluster utilizing Slepian-Wolf source coding of data sent to a fusion center. Exact closed form solutions are derived when the fusion center is not energy constrained. When the fusion center is energy constrained, a near optimum solution is provided. Numerical results demonstrating the performance of the proposed algorithms are also provided.

Recently, a new set of dyadic wavelet frames based on oversampled filter banks is introduced that provides a higher sampling in both time and frequency, compared to the usual dyadic wavelets. This transform (called HDDWT) is not shift-invariant; a feature which is desirable particularly for signal denoising. In this paper we propose a new transform, referred to as nonsubsampled HDDWT (NS-HDDWT), which is the shift-invariant version of HDDWT. The NS-HDDWT filter bank is built upon iterated nonsubsampled filter banks which are derived from the HDDWT filter bank in a way that is similar to the a trous algorithm. We employ HDDWT and NS-HDDWT for decomposition of images by performing the separable filtering. The performance of both HDDWT and NS-HDDWT is assessed in image denoising. Experimental results show that the performance of NS-HDDWT is superior to that of HDDWT, and in some cases NS-HDDWT outperforms powerful wavelet-based image denoising methods.

In this paper we propose an M-ary biorthogonal pulse shape modulation (BPSM) scheme for an impulse radio ultra wideband (IR-UWB) system transmitting over lognormal fading channels. Our proposed modulation scheme enables different transmission rates for a fixed number of orthogonal pulses M/2, while the symbol period Tsis constant. In our scheme information bits are mapped into N pulses (N symbols), using M/2 orthogonal pulses and their negates. The N generated pulses are superimposed to form one pulse that will be transmitted over the channel. The transmission rate provided by this scheme is N(log2M/N)/Ts, which is significantly higher than (log2M/N)/Tsprovided by the conventional biorthogonal signaling. The RAKE receiver demodulates the N symbols. For the proposed modulation scheme we derive closed form expressions for symbol error probability, for both maximum ratio combining (MRC) and equal gain combining (EGC)-RAKE receivers, using Wilkinson's method. Furthermore, we numerically evaluate performances of the proposed transceiver, assuming perfect and imperfect channel estimation at the receiver, and compare them with our analytical expressions.

In a portable device, such as a digital camera, limitations on storage are an important consideration. In addition, due to constraints on the complexity of available hardware, image coding algorithms must be fairly simple in implementation. This work presents one such efficient method for coding multiple images of a scene, in a manner that complements a post-processing-based enhancement system. Super-resolution, image restoration and de-noising algorithms have demonstrated the ability to improve the quality of an image using multiple blurry, noisy copies of the same scene. This additional quality does not come without cost, however, since an image capture system must store each image. The proposed encoding scheme is derived from a general linear system model, and encodes multiple images of the same scene, with different amounts of blurring. It is also compared with a variety of methods based on current camera compression technology. For the tested images, this approach requires one-half the rate required by other methods at lower rates. In addition, for a small performance loss, it is essentially implementable without using any compression hardware

Considering two correlated vector sources x, y isin RN, we address the problem of lossy coding of x with uncoded side information y available at the decoder. The general non-linear mapping between y and x capturing their correlation can be approximated through a linear model y = Hx + n in which x is independent of x. Viewing this model as a virtual communication channel with input x and output y we utilize linear precoding and decoding technique to convert the original vector source coding problem into a set of manageable scalar source coding problems. The scalar source coding problems can be solved using the existing distributed source coding algorithms that are primarily designed for the simple correlation model y = x + n where x and y are scalar jointly Gaussian sources

We consider a block fading frequency selective multi-input multi-output (MIMO) channel in additive white Gaussian noise (AWGN). The channel input is a training vector superimposed on a linearly precoded vector of Gaussian symbols. To achieve a better performance over the conventional least-squares (LS), we utilize the linear mean square error (LMMSE) symbol estimate to improve the initial LS estimate and update the symbol estimation accordingly. We provide the guidelines to design training which minimizes the MSE of the initial LS estimate.

We consider a block fading frequency selective multi-input multi-output (MIMO) channel in additive white Gaussian noise (AWGN). The channel input is a training vector superimposed on a linearly precoded vector of Gaussian symbols. This form of precoding is referred to as affine precoding. We derive the Cramer-Rao bound (CRB) under two circumstances: the random parameter vector to be estimated contains (i) only fading channel coefficients, (ii) unknown data symbols as well as the channel coefficients. While case (i) corresponds to the decoding schemes in which the channel is estimated first and the channel measurement is utilized to recover the data symbols, case (ii) corresponds to methods in which channel and symbol estimation is performed jointly. The interesting outcome of our investigation is that minimizing trace of the channel CRB for cases (i) and (ii) under a total transmit power constraint leads to different affine precoder design guidelines.

Adopting affine precoding as the transmission strategy, we investigate the effect of channel estimation error on the mutual information between the input and the output of a frequency selective fading MIMO channel. For Bayesian receivers which perform joint channel and symbol estimation there is asymptotically no loss in information rate. In contrast, for the receivers which obtain the channel estimation and use it to estimate the symbols, the lower bound on information rate is maximized by enforcing a form of orthogonality between symbols and training