This paper addresses the challenge of efficient and accurate channel parameter estimation for Orthogonal Time Frequency Space (OTFS) systems in doubly selective channels. The existing approaches perform well when the channel delay and Doppler shifts are integer multiples of the delay and Doppler resolution but suffer performance degradation and require high computational complexity when dealing with a practical system with non-integer delay and Doppler shifts. We develop a novel two-stage method called Multi-scale Adaptive Channel Estimation (MACE) to overcome these limitations. Using a coarse grid search, MACE estimates the integer components of channel delay and Doppler parameters. Subsequently, a parameter reassignment rule is applied to estimate the fractional part of the delay and Doppler, enhancing accuracy. Simulation results demonstrate that MACE significantly improves the channel estimation accuracy and BER at a much lower computational complexity than existing methods.

Sweep spread carrier (S2C) acoustic communication uses wideband chirp waveforms as they are well suited for communicating in an undersea multipath environment. While the gradient heterodyne receivers in the S2C systems can handle the multipath arrivals, we show that they are extremely sensitive to a time scaling of the communication waveform induced by the Doppler effect or carrier frequency offset. The time-scaling can be estimated, and compensated for by re-scaling the received waveform. However, estimation errors leave out a residual timescale even in the re-scaled waveform. We show that even a timescale estimation error of the order of ±10–5can lead to deleterious variations in the symbol phases within a received S2C frame. We also show the existence of multiple, frequency-dependent, approximately affine symbol phase migration trajectories in a received frame in the presence of small residual time-scales and timing errors. Motivated by this observation, we propose a parallel bank of low complexity decision-aided progressive symbol phase (DAPSP) equalizers to effectively suppress the symbol phase migration in the S2C receiver.

This work focuses on compressing vast amounts of time series data from IoT sensors while achieving low reconstruction error, at a low compression ratio (ratio of output data size to input data size), for efficient storage and transmission. We investigate two lossy compression techniques: Adaptive Piecewise Linear Approximation (APLA) and Sparse Encoding (SE). APLA reduces data volume by leveraging the temporal correlation in time series data, while SE captures small, complex variations that APLA might miss. We identify a signal characteristic parameter, denoted by σd, which is used to derive error bounds for SE using σd, the dictionary used, and the input compression parameters. We also introduce APLASE, a novel compression technique that combines the two techniques by applying SE to the reconstruction error generated by APLA. We evaluate APLA, SE and APLASE using data from the NASA flight data recorder database consisting of recordings from 116 sensors. Our results show that APLASE consistently outperforms both APLA and SE individually, as well as the recent QoZ algorithm, particularly at lower compression ratios.

In wideband systems operating at mmWave frequencies, intelligent reflecting surfaces (IRSs) equipped with many passive elements can compensate for channel propagation losses. Then, a phenomenon known as the beam-split (B-SP) occurs in which the phase shifters at the IRS elements fail to beamform at a desired user equipment (UE) over the total allotted bandwidth (BW). Although B-SP is usually seen as an impairment, in this paper, we take an optimistic view and exploit the B-SP effect to enhance the system performance via an orthogonal frequency division multiple access (OFDMA). Due to the B-SP, we argue that when an IRS is tuned to beamform at a particular angle on one frequency, it also forms beams in different directions on other frequencies. Then, by opportunistically scheduling different UEs on different subcarriers (SCs), we show that, almost surely, the optimal array gain that scales quadratically in the number of IRS elements can be achieved on all SCs in the system. We derive the achievable throughput of the proposed scheme and deduce that the system also enjoys additional multi-user diversity benefits on top of the optimal beamforming gain over the full BW. Finally, we verify our findings via numerical simulations.

We address the problem of the beam split (B-SP) caused by the spatial wideband (SW) effect in intelligent reflecting surface (IRS) aided wideband systems. The SW effect arises when the signal delay across the IRS aperture is comparable to the system sampling time. This leads to a B-SP, wherein the IRS phase shifters fail to form a beam at the desired user equipment (UE) over the complete bandwidth (BW) allotted to that UE, in turn reducing the system throughput. This paper proposes a distributed IRS design that aids in naturally combating the SW/BSP effects by parallelizing the spatial delays across the multiple IRSs. Specifically, we propose to split a single large IRS into multiple smaller IRSs and distribute them over the geographical area. Then, by specifying the maximum permissible number of elements at each IRS, we ensure that the B-SP remains within a specified tolerance level. Finally, we derive the sum-rate and show that the proposed solution yields a nonzero rate over the whole BW of operation and circumvents the SW/B-SP effects. We verify our findings numerically and illustrate the low complexity of the proposed method compared to state-of-the-art techniques.

Intelligent reflecting surfaces (IRSs) are envisioned to enhance the performance of mmWave wireless systems. In practice, multiple mobile operators (MO) coexist in an area and provide simultaneous and independent services to user-equipments (UEs) on different frequency bands. Then, if each MO deploys an IRS to enhance its performance, the IRSs also alter the channels of UEs of other MOs. In this context, this paper addresses the following questions: can an MO still continue to control its IRS independently of other MOs and IRSs? Is joint optimization of IRSs deployed by different MOs and inter-MO cooperation needed? To that end, by considering the mmWave bands, we first derive the ergodic sum spectral efficiency (SE) in a 2-MO system for the following schemes: 1) joint optimization of an overall phase angle of the IRSs with MO cooperation, 2) MO cooperation via time-sharing, and 3) no cooperation between the MOs. We find that even with no cooperation between the MOs, the performance of a given MO is not degraded by the presence of an out-of-band (OOB) MO deploying and independently controlling its own IRS. On the other hand, the SE gain obtained at a given MO using joint optimization and cooperation over the no-cooperation scheme decreases inversely with the number of elements in the IRS deployed by the other MO. We generalize our results to a multiple MO setup and show that the gain in the sum-SE over the no-cooperation case increases at least linearly with the number of OOB MOs. Finally, we numerically verify our findings and conclude that every MO can independently operate and tune its IRS; cooperation via optimizing an overall phase only brings marginal benefits in practice.

We consider the problem of identifying the defectives from a population of items via anon-adaptive group testingframework with a random pooling-matrix design. We analyze the sufficient number of tests needed forapproximate set identification, i.e., for identifyingalmostall the defective and non-defective items with highconfidence. To this end, we view the group testing problem as a function learning problem in the probably approximately correct (PAC) framework. We derive sufficiency bounds on the number of tests for popular binary group testing recovery algorithms, namely, combinatorial orthogonal matching pursuit under Bernoulli and near-constant row-weight test designs, and definite defectives under a Bernoulli test design. We compare the derived bounds with the existing ones in the literature for exact recovery both theoretically and using simulations. Finally, we contrast the three cases under consideration in terms of the sufficient testing ratesurfaceand the sufficient number of testscontoursacross the range of the approximation and confidence levels.

We consider the problem of jointly estimating the states and sparse inputs of a linear dynamical system using noisy low-dimensional observations. We exploit the underlying sparsity in the inputs using fictitious sparsity-promoting Gaussian priors with unknown variances (as hyperparameters). We develop two Bayesian learning-based techniques to estimate states and inputs: sparse Bayesian learning and variational Bayesian inference. Through numerical simulations, we illustrate that our algorithms outperform the conventional Kalman filtering based algorithm and other state-of-the-art sparsity-driven algorithms, especially in the low-dimensional measurement regime.

The benefits of cell-free multiple-input multiple-output (CF MIMO) systems over traditional cellular systems, viz., a dramatic improvement of spectral efficiency (SE) and uniform quality of service, crucially depend on the quality of the estimated channels at the access points (APs). However, in a CF MIMO system, where a large number of user-equipments (UEs) are served by distributed APs, ensuring pilot contamination-free channel estimates across all the APs requires inordinately high pilot length, which substantially reduces the time available for data transmission. This paper proposes a novel pilot design and allocation algorithm that ensures no pilot contamination among any pair of UEs that are proximal to a common AP, and this is guaranteed at all APs. Further, our algorithm procures the pilot allocation with a minimum number of orthogonal pilots being reused across the UEs. Specifically, we recast the problem as a graph-vertex coloring problem and solve it via a low-complexity algorithm known to be optimal for all bipartite graphs. Unlike existing solutions, our algorithm does not require additional signaling overhead, e.g., signal-to-interference plus noise ratio exchanges, for pilot assignment. Numerical results illustrate the superiority of the proposed technique over existing methods from the literature.

The time varying nature of the wireless propagation channel causes a mismatch between the true channel at the time of data transmission and its available estimate based on previously received pilot symbols, and is known to impair the performance of the massive multiple input multiple output (MIMO) systems. In this paper, we develop and evaluate adaptive data aided channel tracking and data detection algorithms to counter the effects of channel aging for uplink and downlink massive MIMO systems. We first present a recursive least squares (RLS) algorithm for tracking the matrix uplink channel at the base station (BS), and derive bounds on its MSE performance. We also derive a linear complexity stochastic gradient descent (SGD) algorithm for tracking the uplink channel, along with its performance bounds. Following this, we develop RLS and SGD based algorithms for tracking the scalar effective downlink channel at each UE, and derive their performance guarantees. Finally, via Monte Carlo simulations, we validate the efficacy of the algorithms in terms of their mean squared error performance, and demonstrate the gains achievable by channel tracking in the form of the improvement in the symbol error rates.

In this paper, we present a comparative study of half-duplex (HD) access points (APs) with dynamic time-division duplex (DTDD) and full-duplex (FD) APs in cell-free (CF) systems. Although both DTDD and FD CF systems support concurrent downlink (DL) transmission and uplink (UL) reception capability, the sum spectral efficiency (SE) is limited by various cross-link interferences. We first present a novel pilot allocation scheme that minimizes the pilot length required to ensure no pilot contamination among the user equipments (UEs) served by at least one common AP. Then, we derive the sum SE in closed form, considering zero-forcing combining and precoding along with the signal-to-interference plus noise ratio optimal weighting at the central processing unit. We also present a provably convergent algorithm for joint UL-DL power allocation and UL/DL mode scheduling of the APs (for DTDD) to maximize the sum SE. Further, the proposed algorithms are precoder and combiner agnostic and come with closed-form update equations for the UL and DL power control coefficients. Our numerical results illustrate the superiority of the proposed pilot allocation and power control algorithms over several benchmark schemes and show that the sum SE with DTDD can outperform an FD CF system with similar antenna density. Thus, DTDD combined with CF is a promising alternative to FD that attains the same performance using HD APs, while obviating the burden of intra-AP interference cancellation.

Intelligent reflecting surfaces (IRSs) were introduced to enhance the performance of wireless communication systems. However, from a service provider’s viewpoint, a concern with the use of an IRS is its effect on out-of-band (OOB) quality of service. Specifically, if two operators, say X and Y, provide services in a given geographical area using non-overlapping frequency bands, and if operator X uses an IRS to enhance the spectral efficiency (SE) of its users (UEs), does it degrade the performance of UEs served by operator Y? We answer this by analyzing the average and instantaneous performances of the OOB operator considering both sub-6 GHz and mmWave bands. Specifically, we derive the ergodic sum-SE achieved by the operators under round-robin scheduling. We also derive the outage probability and analyze the change in the SNR caused by the IRS at an OOB UE, using stochastic dominance theory. Surprisingly, even though the IRS is randomly configured from operator Y’s point of view, the OOB operator still benefits from the presence of the IRS, witnessing a performance enhancement for free in both sub-6 GHz and mmWave bands. This is because the IRS introduces additional paths between the transmitter and receiver, increasing the overall signal power arriving at the UE and providing diversity benefits. Finally, we show that the use of opportunistic scheduling schemes can further enhance the benefit of the uncontrolled IRS at OOB UEs. We numerically illustrate our findings and conclude that an IRS is always beneficial to every operator, even when the IRS is deployed & controlled by only one operator.

Automotive radars at the Terahertz (THz) frequency band have the potential to be compact and lightweight while providing high (nearly-optical) angular resolution. In this paper, we propose a bistatic THz automotive radar that employs the recently proposed orthogonal chirp division multiplexing (OCDM) multi-carrier waveform. As a standalone communications waveform, OCDM has been investigated for robustness against interference in time-frequency selective channels. The THz-band path loss, and, hence, radar signal bandwidth, are range-dependent. We address this unique feature through a multicarrier wideband OCDM sensing transceiver that exploits the coherence bandwidth of the THz channel. We develop an optimal scheme to combine the returns at different ranges/bandwidths by assigning weights based on the Cramér-Rao lower bound on the range and velocity estimates. Numerical experiments demonstrate improved target estimates that can be obtained using our proposed combined estimation from measurements with varied attenuation at THz bands.

A new variable bandwidth multicarrier (VBMC) waveform was presented in [1] for communicating over wideband rapidly time-varying multi-scale multi-lag (MSML) channels. Perfect channel state information was assumed to be available at the receiver in [1]. In this work, we address the problem of channel estimation for VBMC based communications over wideband MSML channels. Using the Variational Bayesian (VB) inference framework, we estimate the channel from short preamble and postamble waveforms that are primarily used for timing and carrier frequency synchronization, and then decode the data symbols in VBMC communications. We also derive the Bayesian Cramér-Rao bound (BCRB) of the channel estimate as a benchmark for assessing the normalized mean squared error (NMSE) performance of the estimators. We numerically illustrate the efficacy of our approach in the context of underwater acoustic channel estimation.

In this paper, we consider intelligent reflecting surface (IRS) assisted opportunistic communications (OC), and present a comparative analysis of the system throughput over independent and identically distributed (i.i.d.) and line-of-sight (LoS) channels. In the system we consider, the phase configuration of an N-element IRS is set randomly and independently over time. Then, with high probability, at least one of the K users in the system will see an effective channel with IRS that is close to the beamforming configuration. The BS opportunistically schedules this user for data transmission, thereby avoiding deep fade events. We analyze the convergence rate of the opportunistic throughput to the beamforming throughput as a function of N and K, and derive the rate scaling laws in both i.i.d. and LoS channels. We show that i.i.d. channels require a much larger K compared to LoS channels in order to achieve a performance comparable to the beamforming configuration. Further, the average SNR of the scheduled user under i.i.d. channels scales as $\mathcal{O}\left( N \right)$, while that under LoS channels scales as $\mathcal{O}\left( {{N^2}} \right)$. We corroborate our analysis, and provide interesting insights, via numerical simulations.

Irregular repetition slotted aloha (IRSA) is a massive random access protocol which can be used to serve a large number of users while achieving a packet loss rate (PLR) close to zero. However, if the number of users is too high, then the system is interference limited and the PLR is close to one. In this paper, we propose a variant of IRSA in the interference limited regime, namely Censored-IRSA (C-IRSA), wherein users with poor channel states censor themselves from transmitting their packets. We theoretically analyze the throughput performance of C-IRSA via density evolution. Using this, we derive closed-form expressions for the optimal choice of the censor threshold which maximizes the throughput while achieving zero PLR among uncensored users. Through extensive numerical simulations, we show that C-IRSA can achieve a 4 × improvement in the peak throughput compared to conventional IRSA.

Intelligent reflecting surfaces (IRSs) are envisioned as a key enabler for next generation wireless communications due to their capability to boost system performance without requiring additional bandwidth or transmit power. However, estimating the cascaded channels between the user equipment (UE) through the IRS to the base station (BS) is a major bottleneck in IRS-assisted systems. In this work, we present a novel method to estimate all the cascaded IRS channels in a multiple-IRS (called as distributed IRS in this paper) aided mmWave massive MIMO system. We exploit the inherent structure in mmWave channels to reformulate the channel estimation problem as one of direction of arrival (DoA) and departure (DoD) estimation in the cascaded channel. In turn, this allows us to use subspace based methods from array processing to develop a joint ESPRIT-MUSIC algorithm for estimating the DoA at the BS and the DoD from the UE. An attractive feature of the scheme is its low pilot overhead requirement: unlike existing methods, the number of pilot symbols does not scale with number of IRS elements or the number of antennas at the BS; it only depends on the number of IRSs deployed and the number of antennas at the UE. We compare our method against state-of-the-art methods, and numerically illustrate its superior performance and robustness to the number of IRSs in the system.

We consider a bistatic vehicular integrated sensing and communications (ISAC) system that employs the recently proposed orthogonal chirp division multiplexing (OCDM) multicarrier waveform. As a stand-alone communications waveform, OCDM has been shown to be robust against the interference in time-frequency selective channels. In a bistatic ISAC, we exploit this property to develop efficient receive processing algorithms that achieve high target resolution as well as high communications rate. We derive statistical bounds for our proposed Sequential symbol decoding and radar parameter estimation (SUNDAE) algorithm and compare its competitive performance with other multicarrier waveforms through numerical experiments.

In this paper, we derive sufficiency results on the number of group tests required, in a non-adaptive random pooling matrix setting, to find almost all the defective and non-defective items with high confidence, via two popular algorithms in the group testing literature, namely CoMa and DD. To this end, we propose viewing the group testing problem as an online function learning problem and develop our analysis using the probably approximately correct (PAC) framework. We compare the derived bounds with existing bounds literature for exact recovery both theoretically and using simulations. We also illustrate the savings in the number of tests required for approximate defective set recovery compared to exact recovery.

This paper considers the uplink performance of a multi-user massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system with mobile users. Mobility brings two major problems to a MIMO-OFDM system: inter carrier interference (ICI) and channel aging. In practice, it is common to allot multiple contiguous subcarriers to a user as well as schedule multiple users on each subcarrier. Motivated by this, we consider a general subcarrier allocation scheme and derive expressions for the ICI power, uplink signal to interference plus noise ratio and the achievable uplink sum-rate, taking into account the ICI and the multi-user interference due to channel aging. We show that the system incurs a near-constant ICI power that depends linearly on the ratio of the number of users per subcarrier to the number of subcarriers per user, nearly independently of how the UEs distribute their power across the subcarriers. Further, we exploit the coherence bandwidth of the channel to reduce the length of the pilot sequences required for uplink channel estimation. We consider both zero-forcing and maximal-ratio combining at the receiver and compare the respective sum-rate performances. In either case, the proposed subcarrier allocation scheme leads to significantly higher sum-rates compared to previous work, owing to the near-constant ICI property as well as the reduced pilot overhead.

We consider a dynamic time division duplex (DTDD) enabled cell-free massive multiple-input multiple-output (CF-mMIMO) system, where each half-duplex (HD) access point (AP) is scheduled to operate in the uplink (UL) or downlink (DL) mode based on the data demands of the user equipments (UEs), with the goal of maximizing the sum UL-DL spectral efficiency (SE). We develop a new, low complexity, greedy algorithm for the combinatorial AP scheduling problem, with an optimality guarantee theoretically established via showing that a lower bound of the sum UL-DL SE is sub-modular. We also consider pilot sequence reuse among the UEs to limit the channel estimation overhead. In CF systems, all the APs estimate the channel from every UE, making pilot allocation problem different from the cellular case. We develop a novel algorithm that iteratively minimizes the maximum pilot contamination across the UEs. We compare the performance of our solutions, both theoretically and via simulations, against a full duplex (FD) multi-cell mMIMO system. Our results show that, due to the joint processing of the signals at the central processing unit, CF-mMIMO with dynamic HD AP-scheduling significantly outperforms cellular FD-mMIMO in terms of the sum SE and 90% likely SE. Thus, DTDD enabled HD CF-mMIMO is a promising alternative to cellular FD-mMIMO, without the cost of hardware for self-interference suppression.

In this work, we provide non-asymptotic, probabilistic guarantees for successful recovery of the common nonzero support of jointly sparse Gaussian sources in the multiple measurement vector (MMV) problem. The support recovery problem is formulated as the marginalized maximum likelihood (or type-II ML) estimation of the variance hyperparameters of a joint sparsity inducing Gaussian prior on the source signals. We derive conditions under which the resulting nonconvex constrained optimization perfectly recovers the nonzero support of a joint-sparse Gaussian source ensemble with arbitrarily high probability. The support error probability decays exponentially with the number of MMVs at a rate that depends on the smallest restricted singular value and the nonnegative null space property of the self Khatri-Rao product of the sensing matrix. Our analysis confirms that nonzero supports of size as high as$O(m^{2})$are recoverable from$m$measurements per sparse vector. Our derived sufficient conditions for support consistency of the proposed constrained type-II ML solution also guarantee the support consistency of any global solution of the multiple sparse Bayesian learning (M-SBL) optimization whose nonzero coefficients lie inside a bounded interval. For the case of noiseless measurements, we further show that a single MMV is sufficient for perfect recovery of the$k$-sparse support by M-SBL, provided all subsets of$k + 1$columns of the sensing matrix are linearly independent.

We study the problem of recovering the common k-sized support of a set of$n$samples of dimension$d$, using$m$noisy linear measurements per sample. Most prior work has focused on the case when$m$exceeds$k$, in which case$n$of the order$(k/m)\log(d/k)$is both necessary and sufficient. Thus, in this regime, only the total number of measurements across the samples matter, and there is not much benefit in getting more than$k$measurements per sample. In the measurement-constrained regime where we have access to fewer than$k$measurements per sample, we show an upper bound of$O((k^{2}/m^{2})\log d)$on the sample complexity for successful support recovery when$m\geq 2\log d$. Along with the lower bound from our previous work, this shows a phase transition for the sample complexity of this problem around$k/m=1$. In fact, our proposed algorithm is sample-optimal in both the regimes. It follows that, in the$m\ll k$regime, multiple measurements from the same sample are more valuable than measurements from different samples.

In the problem of multiple support recovery, we are given access to linear measurements of multiple sparse samples in$\mathbb{R}^{d}$. These samples can be partitioned into$\ell$groups, with samples having the same support belonging to the same group. For a given budget of$m$measurements per sample, the goal is to recover the$\ell$underlying supports, in the absence of the knowledge of group labels. We study this problem with a focus on the measurement-constrained regime where$m$is smaller than the support size$k$of each sample. We design a two-step procedure that estimates the union of the underlying supports first, and then uses a spectral algorithm to estimate the individual supports. Our proposed estimator can recover the supports with$m < k$measurements per sample, from$\tilde{O}(k^{4}\ell^{4}/m^{4})$samples. Our guarantees hold for a general, generative model assumption on the samples and measurement matrices.

We consider the optimal design of the frame-based irregular repetition slotted ALOHA (IRSA) protocol for minimizing the average age of information (AAoI) in grant-free massive machine-type communications (mMTC). To this end, we first characterize the AAoI as a function of the number of user elements (UEs), the frame duration, and the repetition distribution of IRSA. We present this characterization for IRSA schemes with packet recovery at the end of frame and packet generation either at the beginning of the frame or just in time before first transmission in a frame. We also propose and characterize the AAoI of a novel early packet recovery method which further reduces the average age of information. In all cases, the analysis reveals that, as a function of normalized channel traffic (defined as the ratio of number of UEs to frame duration), the AAoI first decreases linearly due to more frequent updates received from the UEs, and increases sharply beyond a critical point due to packet recovery failures caused by collisions. We then consider the problem of minimizing AAoI by optimizing over the normalized channel traffic and repetition distribution for all the proposed sampling and recovery schemes. The optimization problem is challenging since the objective function is semi-analytical and can only be completely characterized using simulations. In an asymptotic regime where the number of UEs as well as the frame size is large, we characterize the AAoI using upper and lower bounds. We also obtain a locally optimal normalized channel traffic and repetition distribution using differential evolution. Based on the insights obtained from the asymptotic analysis, we also propose a pragmatic approach to obtain a normalized channel traffic and repetition distribution for AAoI reduction in the non-asymptotic case. Finally, we empirically show that our AAoI minimizing schemes outperform conventional throughput optimal schemes.

Compressed sensing deals with recovery of sparse signals from low dimensional projections, but under the assumption that the measurement setup has infinite dynamic range. In this letter, we consider a system with finite dynamic range, and to counter the clipping effect, the measurements crossing the range are folded back into the dynamic range of the system through modulo arithmetic. For this setup, we derive theoretical results on the minimum number of measurements required for unique recovery of sparse vectors. We also show that recovery using the minimum number of measurements is achievable by using a measurement matrix whose entries are independently drawn from a continuous distribution. Finally, we present an algorithm based on convex relaxation and develop a mixed integer linear program (MILP) for recovering sparse signals from the modulo measurements. Our empirical results demonstrate that the minimum number of measurements required for recovery using the MILP algorithm is close to the theoretical result for signals with low variance.

We consider the problem of joint channel estimation and data decoding in uplink massive multiple input multiple output systems with low resolution analog-to-digital converters (ADCs) at the base station. The nonlinearities introduced by the ADCs make the problem challenging: in particular, the existing linear detectors perform poorly. Also, the channel coding used in commercial wireless systems necessitates soft symbol detection to obtain satisfactory performance. In this paper, we present a low-complexity variational Bayesian (VB) inference procedure to jointly solve the (possibly correlated) channel estimation and soft symbol decoding problem. We present the approach in progressively more complex scenarios, including the case where even the channel statistics are not available at the receiver. Finally, we combine our proposed VB procedure with a belief propagation (BP) based channel decoder, which further enhances the performance without any additional complexity. We numerically evaluate the bit error rate (BER) and the normalized mean squared error (NMSE) in the channel estimates obtained by our algorithm as a function of various system parameters, and benchmark the performance against genie-aided and state-of-the-art receivers. The results show that VB procedure is a promising technique for the design of low-complexity advanced receivers in low resolution ADC based systems.

Data samples from${\mathbb{R}}^ {d}$with a common support of size$k$are accessed through$m$random linear projections (measurements) per sample. It is well-known that roughly$k$measurements from a single sample are sufficient to recover the support. In the multiple sample setting, do$k$overallmeasurements still suffice when only$m$measurementsper sampleare allowed, with$m < k$? We answer this question in the negative by considering a generative model setting with independent samples drawn from a subgaussian prior. We show that$n=\Theta ((k^{2}/ m^{2})\cdot \log k(d- k))$samples are necessary and sufficient to recover the support exactly. In turn, this shows thatwhen$m < k$,$k$overall measurements are insufficient for support recovery; instead we need about$m$measurements each from$k^{2}/ m^{2}$samples, and therefore$k^{2}/ m$overall measurements are necessary.

In this work, we consider control of linear dynamical systems using sparse inputs. We provide an algorithm for determining a sequence of sparse inputs that will take the system from any given initial state to a desired final state, and stay in that state thereafter. We formulate this as a sparse vector recovery problem and obtain conditions on the sparse recovery algorithm that will enable the state of the system to be within an ε-ball of the desired state. Further, we also give a computationally efficient test that checks whether it is possible to remain in a given desired state using only sparse inputs.

This paper presents a new tomography-based anomaly mapping algorithm for composite structures. The system consists of an array of piezoelectric transducers which sequentially excites the structure and collects the resulting waveform at the remaining transducers. Anomaly indices computed from the sensor waveforms are fed as input to the mapping algorithm. The output of the algorithm is a color map indicating the outline of damage on the structure when present. Unlike prior work on this topic, the algorithm of this paper explicitly accounts for both sparsity and cluster pattern structures that are typical of structural anomalies. Hence, the algorithm of this paper provides excellent reconstruction accuracy by incorporating the available prior information on the anomaly map. Experimental results on a unidirectional composite plate confirms that the algorithm of this paper outperforms two competing methods in terms of reconstruction accuracy.

Disjunct matrices play a central role in non-adaptive group testing, as they provide necessary and sufficient conditions for identifying defective items from a large population using a small number of tests. In this paper, we show that binary disjunct matrices can also be very useful for recovering sparse signals from underdetermined linear measurements. They admit non-iterative, ultra-low complexity recovery of sparse signals. Binary measurement matrices have the added benefit of being friendly for hardware implementation. Further, we generalize the notion of disjunctness to matrices with arbitrary (non-binary) entries and show that such matrices also admit similar fast sparse vector recovery algorithms. We empirically demonstrate that disjunct matrices can recover denser signals than recent non-iterative sparse recovery algorithms.

In energy harvesting (EH) communications, it is customary to use a battery to temporarily store harvested energy prior to using it for communication. In practice, these batteries suffer from degradation in the usable capacity when they are repeatedly charged after being partially discharged and vice versa. The capacity can be recovered by imposing the full-cycle constraint, which says that a battery must be charged only after it is fully discharged and vice versa. Further, practical batteries cannot be charged and discharged simultaneously. With the above constraints, we consider and compare EH communication systems under two cases: (a) the single-battery case and (b) the dual-battery case, in which the transmitters are equipped with a single battery of capacity 2B joules and two batteries, each having capacity of B joules, respectively. Under (a) and (b), our goal is to obtain the long-term average throughputs and throughput regions in a point-to-point (P2P) channel and a multiple access channel (MAC), respectively. For the P2P channel, we derive the optimal solution in the single-battery case, and propose optimal and suboptimal power allocation policies for the dual-battery case, assuming Bernoulli energy arrivals. Based on these policies, we obtain long-term average achievable throughput regions in MACs by jointly allocating rates and powers. From numerical simulations, we find that the optimal throughput in the dual-battery case is significantly higher than that in the single-battery case, although the total storage capacity in both cases is 2B joules.

Data samples from ℝdwith common support of size k are accessed through m linear projections per sample. In the measurement-starved regime of m2/m2) log(k(d - k))) samples are necessary and sufficient to exactly recover the support. Our proposed sample-optimal estimator has a closed-form expression and has computational complexity of O(dnm).

In this paper, a method is proposed to overcome the consensus error in an average consensus problem, under a distributed setting and with finite-bit communications between the network agents. Previous works have illustrated that the average consensus problem can be solved under a distributed setting, using the Alternating Direction Method of Multipliers (ADMM) method, in which consensus can be attained by using locally available information along with information from neighboring nodes. This holds true even under finite-bit exchanges between neighbouring nodes, but suffers from consensus errors and cyclic states due to the introduced quantization schemes. This work deals with achieving perfect consensus with finite-bit communications between neighboring nodes. We propose an algorithm which leads to perfect consensus in an asymptotic sense, without the need to increase the per-exchange communication rate of the network.

Machine learning and data driven approaches have recently received much attention as a key enabler for future 5G and beyond wireless networks. Yet, the evolution towards learning-based data driven networks is still in its infancy, and much of the realization of the promised benefits requires thorough research and development. Fundamental questions remain as to where and how ML can really complement the well-established, well-tested communication systems designed over the last four decades. Moreover, adaptation of machine learning methods is likely needed to realize their full potential in the wireless context. This is particularly challenging for the lower layers of the protocol stack, where the constraints, problem formulation, and even the objectives may fundamentally differ from the typical scenarios to which machine learning has been successfully applied in recent years. In addition, a thorough understanding of the fundamental performance limits is also essential in order to establish quality-of-service guarantees that are common in communication system design. Such challenges, which lie at the core of the special issue, can be categorized into a number of research topics ranging from the optization of neural networks architectures that are suited to wireless communication links (inclusing autoencoders, generative adversarial networks, reinforcement based networks etc) to performance analysis, to the acceleration of data-driven training, and possibly in distributed settings. The application domains within the wireless realm are also quite diverse in nature with promising preliminary results in the area of physical layer design and resource allocation as well as for network service orchestrations. Testbeds and experimental evaluations are also begining to be reported.

Machine learning and data driven approaches have recently received much attention as a key enabler for future 5G and beyond wireless networks. Yet, the evolution towards learning-based data driven networks is still in its infancy, and much of the realization of the promised benefits requires thorough research and development. Fundamental questions remain as to where and how ML can really complement the well-established, well-tested communication systems designed over the last four decades. Moreover, adaptation of machine learning methods is likely needed to realize their full potential in the wireless context. This is particularly challenging for the lower layers of the protocol stack, where the constraints, problem formulation, and even the objectives may fundamentally differ from the typical scenarios to which machine learning has been successfully applied in recent years. In addition, a thorough understanding of the fundamental performance limits is also essential in order to establish quality-of-service guarantees that are common in communication system design. Such challenges, which lie at the core of the special issue, can be categorized into a number of research topics ranging from the optization of neural networks architectures that are suited to wireless communication links (inclusing autoencoders, generative adversarial networks, reinforcement based networks etc) to performance analysis, to the acceleration of data-driven training, and possibly in distributed settings. The application domains within the wireless realm are also quite diverse in nature with promising preliminary results in the area of physical layer design and resource allocation as well as for network service orchestrations. Testbeds and experimental evaluations are also begining to be reported.

Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story - ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.

We analyze the effect of channel aging on the achievable rate of time division duplexed massive multiple input multiple output systems serving a number of users under aging channels, using nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA). Using the recently proposed shared uplink pilot based channel estimation for NOMA, we derive bounds on the channel estimation error variance for the two schemes. We then derive the achievable spectral efficiencies of the two schemes. Using numerical results, we show that, in slowly varying channels, using NOMA with shared pilots is preferable over OMA, while the reverse is true under fast varying channels.

The objective of this paper is to design a power control policy that maximizes the long-term time-averaged sum throughput of a Gaussian multiple access channel (MAC), where the transmitters as well as the access point (AP) are energy harvesting nodes (EHNs). In addition, the policy is required to facilitate uncoordinated operation of the network. That is, in each slot, the transmitting nodes and the AP need to independently take their actions, e.g., the amount of energy to be used for transmission or whether to turn on and receive the data. First, in order to benchmark the performance of any policy, we derive an upper bound on the throughput achievable, by analyzing a centralized genie-aided system where the nodes have infinite capacity batteries and can freely share the available energy among themselves. In addition, the genie-aided system has non-causal knowledge of the energy arrivals at all the nodes. Next, we show that, surprisingly, a simple time sharing based online policy which requires no coordination among the transmitters and uses time-dilation at the receiver achieves the upper bound asymptotically in the battery size. We also present a policy that requires an occasional one-bit feedback from the AP about its battery state, and show that it requires a smaller sized battery at the receiver compared to a policy which operates without any feedback from the AP, to achieve the same performance. We use Monte Carlo simulations to validate our theoretical results and illustrate the performance of the proposed policies.

In this paper, we study the problem of downlink (DL) sum rate maximization in codebook based multiuser (MU) multiple input multiple output (MIMO) systems. The user equipments (UEs) estimate the DL channels using pilot symbols sent by the access point (AP) and feedback the estimates to the AP over a control channel. We present a closed form expression for the achievable sum rate of the MU-MIMO broadcast system with codebook constrained precoding based on the estimated channels, where multiple data streams are simultaneously transmitted to all users. Next, we present novel, computationally efficient, minorization-maximization (MM) based algorithms to determine the selection of beamforming vectors and power allocation to each beam that maximizes the achievable sum rate. Our solution involves multiple uses of MM in a nested fashion. Based on this approach, we propose and contrast two algorithms, which we call the square-root-MM (SMM) and inverse-MM (IMM) algorithms. The algorithms are iterative and converge to a locally optimal beamforming vector selection and power allocation solution from any initialization. We evaluate the performance and complexity of the algorithms for various values of the system parameters, compare them with existing solutions, and provide further insights into how they can be used in system design.

In this paper, we consider physical layer security in wireless sensor networks (WSNs) using distributed co-phasing (DCP)-based transmissions. For this protocol, we first analyze the achievable ergodic secrecy rate of a single stream DCP system in the presence of one or more eavesdroppers. We show that the coherent combining gain offered by DCP leads to the signal-to-interference-plus-noise-ratio (SINR) over the main channel increasing as the square of the number of SNs N and that over the eavesdropper channel increasing linearly with N. This results in a strictly positive ergodic secrecy rate that increases as log N. We then analyze the performance of multi-stream DCP and show that using K data streams in DCP leads to a K -fold increase in the achievable secrecy rate at high SNRs. We also discuss an alternative power allocation scheme for multi-stream DCP, such as distributed maximal ratio transmission with a per-user power constraint and show that this improves the achievable secrecy rates as compared to standard multi-stream DCP. Finally, we analyze the role of artificial noise in improving the achievable secrecy rates. We validate the accuracy of these derived results and illustrate the efficacy of DCP in ensuring secure data fusion in WSNs using Monte Carlo simulations.

The goal in this paper is to design an optimal scheduling and power control policy that maximizes the long-term time-averaged sum throughput of a Gaussian multiple access channel (MAC) with energy harvesting (EH) nodes, and \emph{facilitates uncoordinated operation} of the nodes. In order to benchmark the performance of any policy, we derive an upper bound on the sum throughput by considering a genie-aided system where the nodes have infinite capacity batteries and can freely share the available energy between them. Next, we design a time-sharing based power control policy for the EH MAC, which operates in an uncoordinated fashion. We show that, surprisingly, the sum throughput obtained by the proposed policy achieves the genie-aided upper bound asymptotically in the battery size at each node. Simulation results validate the theoretical findings and illustrate the relative impact of various system parameters (e.g., the battery size required to achieve the upper bound) on the number of nodes and the variation in the harvesting rates across the nodes.

We consider the problem of recovering the common support of a set of k-sparse signals {xi}Li=1from noisy linear underdetermined measurements of the form {Φxi+ wi}Li=1where Φ ϵ Rm×N(m <; N) is the sensing matrix and wi is the additive noise. We employ a Bayesian setup where we impose a Gaussian prior with zero mean and a common diagonal covariance matrix Γ across all xi, and formulate the support recovery problem as one of covariance estimation. We develop an algorithm to find the approximate maximum-likelihood estimate of Γ using a modified reweighted minimization procedure. Empirically, we find that the proposed algorithm succeeds in exactly recovering the common support with high probability in the k <; m regime with L of the order of m and in the k ≥ m regime with larger L. The key advantage of the proposed algorithm is that its complexity is independent of L, unlike existing sparse support recovery algorithms.

In this letter, we address the problem of observability of a linear dynamical system from compressive measurements and the knowledge of its external inputs. Observability of a high dimensional system state may require a large number of measurements in general, but we show that if the initial state vector admits a sparse representation, the number of measurements can be significantly reduced by using random projections for obtaining the measurements. We derive guarantees for the observability of the system using tools from probability theory and compressed sensing. Our analysis uses properties of the transfer matrix and random measurement matrices to derive concentration of measure bounds, which lead to sufficient conditions for the restricted isometry property of the observability matrix to hold. Hence, under the derived conditions, the initial state can be recovered by solving a computationally tractable convex optimization problem.

In this letter, we address the problem of reconstructing the common nonzero support of multiple joint sparse vectors from their noisy and underdetermined linear measurements. The support recovery problem is formulated as the selection of nonnegative hyperparameters of a correlation-aware, joint sparsity inducing Gaussian prior. The hyperparameters are recovered as a nonnegative sparse solution of covariance-matching constraints formulated in the observation space by solving a sequence of proximal regularized convex optimization problems. For proximal regularization based on Von Neumann Bregman matrix divergence, an exponentiated gradient (EG) update is proposed, which when applied iteratively, converges to hyperparameters with the correct sparse support. Compared to existing multiple measurement vector support recovery algorithms, the proposed multiplicative EG update has a significantly lower computational and storage complexity and takes fewer iterations to converge. We empirically demonstrate that the support-recovery algorithm based on the proposed EG update can solve million variable support recovery problems in tens of seconds. Additionally, by leveraging its correlation-awareness property, the proposed algorithm can recover supports of size as high as O(m2) from only m linear measurements per joint sparse vector.

In this paper, we study the effect of channel aging on the uplink and downlink performance of an FDD massive MIMO system, as the system dimension increases. Since the training duration scales linearly with the number of transmit dimensions, channel estimates become increasingly outdated in the communication phase, leading to performance degradation. To quantify this degradation, we first derive bounds on the mean squared channel estimation error. We use the bounds to derive deterministic equivalents of the receive SINRs, which yields a lower bound on the achievable uplink and downlink spectral efficiencies. For the uplink, we consider maximal ratio combining and MMSE detectors, while for the downlink, we consider matched filter and regularized zero forcing precoders. We show that the effect of channel aging can be mitigated by optimally choosing the frame duration. It is found that using all the base station antennas can lead to negligibly small achievable rates in high user mobility scenarios. Finally, numerical results are presented to validate the accuracy of our expressions and illustrate the dependence of the performance on the system dimension and channel aging parameters.

In this paper, we consider an energy harvesting (EH) node that periodically takes a measurement and conveys it to a destination over multiple EH relays operating in the decode-and-forward fashion, using the automatic repeat request protocol. Packets that are not delivered to the destination before the next measurement is taken are dropped. We seek to design an online retransmission-index based power control policy (RIP) for each node which minimizes the packet drop probability (PDP). To this end, we first derive an expression for the PDP in terms of the RIPs at the nodes. Next, when the energy cost for decoding a packet is negligible, we obtain closed form expressions for the optimal RIPs. We also extend the results to the case where the peak transmit power is constrained. When the energy cost of decoding is non-negligible, we present a geometric programming based iterative algorithm to obtain near-optimal RIPs. In both the scenarios, in order to obtain insight into the impact of channel coherence time, we design the RIPs for both slow and fast fading channels. Through Monte Carlo simulations, we show that the proposed policies significantly outperform state-of-the-art solutions.

The problem of topology change detection in wireless sensor networks (WSNs) is addressed. The network is modeled as a directed graph comprising sensor nodes and edges over which the sensors communicate with each other. The problem, formulated using a linear model of noisy measurements, is to distinguish between hypotheses 0 (null) and 1 (alternative) corresponding to “no-change” and “change”, respectively, in the topology of the network over a period of time. For a fixed probability of false alarm, computable expressions for the test threshold and the probability of detection are derived by efficiently approximating the distribution functions of the test statistic under 0 and 1. The performance of the test is comparable to that of the likelihood ratio test, which requires a priori knowledge of the time of change and the topology of the network after the change. Simulations results are presented to verify the theoretical findings of the paper. The test presented here is especially suited for large-scale WSNs, and is an important component in topology control which has received significant attention in the design of WSNs.

In this work, we consider the problem of data decoding in media-based modulation systems. The underlying problem is sparse because only a subset of the available transmit antennas is activated in each symbol; additionally, only one of the different mirror patterns is activated depending on the unknown data bits. Thus, the data recovery problem involves recovery of a block-sparse vector, with the additional structure that only one entry is active within each block. We term this structure as inclusion-exclusion sparsity, as the inclusion of an index in the active set precludes several other indices from being active. Devising efficient algorithms for recovering such sparse signals from noisy underdetermined linear measurements is an open problem. To this end, we propose a general, non-convex cost function that, when optimized, yields a sparse vector with additional structure, including, but not limited to, the inclusion-exclusion sparsity. Further, we propose a convex concave procedure (CCP) based algorithm for optimizing the cost function. The algorithm has low computational complexity and is globally convergent to a local optimum. Finally, we demonstrate the efficacy of our algorithm and its superior performance over existing data recovery schemes via Monte Carlo simulations.

In this paper, we consider automatic repeat request based multihop communication between a source and a destination. We present the design of near-optimal power control policies which minimize the packet drop probability (PDP), when the energy cost to receive and decode a packet is non-negligible. The design problem is a nonconvex mixed integer nonlinear program, which, in general, is NP-hard to solve. We transform the problem into a complementary geometric program (CGP), and solve the CGP using a series of GP approximations. Simulations show that for slow (fast) fading channels, the obtained policy offers approximately one (two) orders of magnitude better performance compared to a conventional equal power policy. In addition, the results quantify the relative impact of the available power at the nodes and the effect of decoding cost on the PDP.

In this paper, we consider a point-to-point link between an energy harvesting transmitter and receiver, where neither node has the information about the battery state or energy availability at the other node. We consider a model where data is successfully delivered only in slots where both nodes are active. Energy loss occurs whenever one node turns on while the other node is in sleep mode. In each slot, based on their own energy availability, the transmitter and receiver need to independently decide whether or not to turn on, with the aim of maximizing the long-term time-average throughput. We present an upper bound on the throughput achievable by analyzing a genie-aided system that has noncausal knowledge of the energy arrivals at both the nodes. Next, we propose an online policy requiring an occasional one-bit feedback whose throughput is within one bit of the upper bound, asymptotically in the battery size. In order to further reduce the feedback required, we propose a time-dilated version of the online policy. As the time dilation gets large, this policy does not require any feedback and achieves the upper bound asymptotically in the battery size. Inspired by this, we also propose a near-optimal fully uncoordinated policy. We use Monte Carlo simulations to validate our theoretical results and illustrate the performance of the proposed policies.

The construction of binary sensing matrices is one of the active directions in the emerging field of compressed sensing (CS). Due to their sparse structure and competitive performance, they provide multiplier-less and faster dimensionality reduction in applications such as data compression. This letter attempts to relate the notion of extremal set theory to the construction of good CS matrices. In particular, we show that extremal set theory is useful for constructing binary sensing matrices and bounding their maximum column size (i.e., number of columns). We also prove the existence of binary sensing matrices whose column size meets the upper bound. Simulation results show that the constructed matrices outperform Gaussian and Bernoulli random matrices, as well as other deterministic binary and bipolar constructions from the literature.

In this paper, the role of unidirectional limited rate transmitter cooperation is studied for the two-user symmetric Z interference channel (Z-IC) with secrecy constraints at the receivers, in achieving two conflicting goals simultaneously: mitigating interference and ensuring secrecy. First, the problem is studied under the linear deterministic model. A novel scheme for partitioning the encoded messages and outputs based on the relative strengths of the signal and interference is proposed. The partitioning reveals the side information that needs to be provided to the receiver and facilitates the development of tight outer bounds on the secrecy capacity region. The achievable schemes for the deterministic model use a fusion of cooperative precoding and transmission of a jamming signal. The optimality of the proposed scheme is established for the deterministic model for all possible parameter settings. The insights obtained from the deterministic model are used to derive inner and outer bounds on the secrecy capacity region of the two-user Gaussian symmetric Z-IC. The achievable scheme for the Gaussian model uses stochastic encoding in addition to cooperative precoding and transmission of a jamming signal. For the Gaussian case, the secure sum generalized degrees of freedom (GDOF) is characterized and shown to be optimal for the weak/moderate interference regime. It is also shown that the secure sum capacity lies within 2 bits/s/Hz of the outer bound for the weak/moderate interference regime for all values of the capacity of the cooperative link. Interestingly, in the deterministic model, it is found that there is no penalty on the capacity region of the Z-IC due to the secrecy constraints at the receivers in the weak/moderate interference regimes. Similarly, it is found that there is no loss in the secure sum GDOF for the Gaussian case due to the secrecy constraint at the receiver, in the weak/moderate interference regimes. The results highlight the importance of cooperation in facilitating secure communication over the Z-IC.

In the classical non-adaptive group testing setup, pools of items are tested together, and the main goal of a recovery algorithm is to identify the complete defective set given the outcomes of different group tests. In contrast, the main goal of a non-defective subset recovery algorithm is to identify a subset of non-defective items given the test outcomes. In this paper, we present a suite of computationally efficient and analytically tractable non-defective subset recovery algorithms. By analyzing the probability of error of the algorithms, we obtain bounds on the number of tests required for non-defective subset recovery with arbitrarily small probability of error. Our analysis accounts for the impact of both the additive noise (false positives) and dilution noise (false negatives). By comparing with information theoretic lower bounds, we show that the upper bounds on the number of tests are orderwise tight up to a log2K factor, where K is the number of defective items. We also provide simulation results that compare the relative performance of the different algorithms and reveal insights into their practical utility. The proposed algorithms significantly outperform the straightforward approaches of testing items one-by-one, and of first identifying the defective set and then choosing the non-defective items from the complement set, in terms of the number of measurements required to ensure a given success rate.

In this paper, we consider retransmission-based point-to-point dual energy harvesting (EH) links, where both the transmitter and receiver are EH nodes (EHNs). The transmitter needs to periodically send a packet to the receiver and the packet is dropped if it is not delivered within a given number of slots. The goal is to find a retransmission index-based power management policy (RIP), which minimizes the packet drop probability (PDP). To this end, first, we establish the near-optimality of policies that operate in the energy unconstrained regime (EUR), i.e., the regime where the average rate of energy use at each EHN is less than the average harvesting rate. Specifically, we analytically show that for such policies, the gap between the PDP of the dual EH systems with finite and infinite capacity batteries decreases exponentially with the size of the battery at the transmitter and receiver. Next, we show that, in the EUR, the non-convex problem of designing optimal RIPs can be reformulated as a geometric program, which leads to a provably convergent and computationally efficient solution. We design the RIPs for both slow and fast fading channels, and with two different retransmission protocols, namely, the automatic repeat request (ARQ) and hybrid ARQ with chase combining. Numerical results obtained through Monte Carlo simulations show that the proposed RIPs outperform the state-of-the-art policies.

We consider the problem of estimating the intensity map of a spatially random phenomenon over a geographical area observed by a sensor network. The spatial phenomenon of interest is modeled using a Gaussian random field specified by its nonlinear mean and covariance functions. Our proposed algorithm includes two stages: a novel greedy sparse recovery algorithm to estimate the parameters of the mean function, and a spatial interpolation stage using an algorithm called simple kriging. Further, we study the application of the proposed algorithm to radio spectrum cartography, and show that it offers a significant advantage in terms of accuracy of map reconstruction compared to existing methods.

In this paper, we consider point-to-point dual energy harvesting (EH) links, where both transmitter and receiver are EH nodes (EHNs), with a retransmission protocol: either an automatic repeat request (ARQ) or hybrid ARQ with chase combining. We develop a framework to analyze the impact of various physical layer parameters, e.g., the energy harvesting profiles, size of energy buffer, and power management policies, at both the transmitter and receiver, and the channel statistics and coherence time, on the packet drop probability (PDP) of dual EH links over block fading channels. We derive the closed-form expressions for the PDP of a retransmission index-based power management policy. The presented framework naturally extends to obtain the PDP of mono EH links, i.e., links where only one of the two nodes harvests energy. To obtain further insights, we analyze the PDP of links with zero and infinite energy buffer size, and characterize the energy unconstrained regime, where the PDP is governed only by the average harvesting rate. Through extensive Monte Carlo simulations, we demonstrate the accuracy of the theoretical expressions, compare performance against existing power management schemes, and illustrate the performance tradeoffs involved.

We study the problem of throughput optimization in large multiple-input multiple-output (MIMO) beamforming systems with channel aging, as the number of transmit and receive antennas is increased. We quantify the effective channel coherence time in terms of the intertraining interval, which is the time after which the channel state information needs to be reestimated to avoid loss in performance due to outdated channel estimates. Conventional wisdom suggests that the coherence time of large MIMO systems should decrease with increasing number of antennas, because the system has a large number of independently varying channel coefficients. On the contrary, we show that the effective channel coherence time increases with the number of antennas. We optimize the throughput with respect to the symbol and pilot energies, the number of antennas, frame duration, and target SNR. This analysis brings out the important fact that the effective channel coherence time critically depends on the underlying signaling scheme, and not only on the temporal correlation coefficient of the wireless channel.

This paper studies the value of limited rate cooperation between the transmitters for managing interference and simultaneously ensuring secrecy, in the two-user Gaussian symmetric interference channel (IC). First, the problem is studied in the symmetric linear deterministic IC (SLDIC) setting, and achievable schemes are proposed, based on interference cancelation, relaying of the other user's data bits, and transmission of random bits. In the proposed achievable scheme, the limited rate cooperative link is used to share a combination of data bits and random bits depending on the model parameters. Outer bounds on the secrecy rate are also derived, using a novel partitioning of the encoded messages and outputs depending on the relative strength of the signal and the interference. The partitioning helps to bound certain negative entropy terms, leading to a tractable outer bound. The inner and outer bounds are derived under all possible parameter settings. It is found that, for some parameter settings, the inner and outer bounds match, yielding the capacity of the SLDIC under transmitter cooperation and secrecy constraints. In some other scenarios, the achievable rate matches with the capacity region of the two-user SLDIC without secrecy constraints derived by Wang and Tse; thus, the proposed scheme offers secrecy for free, in these cases. Inspired by the achievable schemes and outer bounds in the deterministic case, achievable schemes and outer bounds are derived in the Gaussian case. The proposed achievable scheme for the Gaussian case is based on Marton's coding scheme and stochastic encoding along with dummy message transmission. One of the key techniques used in the achievable scheme for both the models is interference cancelation, which simultaneously offers two seemingly conflicting benefits: it cancels interference and ensures secrecy. Many of the results derived in this paper extend to the asymmetric case also. The results show that limited transmitter cooperation can greatly facilitate secure communications over two-user ICs.

This work addresses the problem of sequential recovery of temporally correlated sparse vectors with common support from noisy under-determined linear measurements. The Kalman sparse Bayesian learning (SBL) algorithm is an efficient tool for solving the problem when the temporal correlation is modeled using a first order autoregressive model. However, this method processes the input data in a batch mode, which results in high latency. We propose two online SBL algorithms which operate on the observations in a serial fashion. They are sequential expectation maximization (EM) schemes, implemented using fixed lag smoothing and sawtooth lag smoothing. The online algorithms require significantly lower computational and memory resources compared to their offline counterparts. Also, estimates of the sparse vectors become available after a fixed delay from the time observations arrive. Using Monte Carlo simulations, we illustrate that the mean square error and support recovery performance of the proposed algorithms is very close to the offline Kalman SBL algorithm.

In this paper, we propose algorithms for finding the optimum multi-hop routes and corresponding transmit powers that maximize the throughput between a pair of device-to-device (D2D) nodes, under a constraint on the maximum interference caused to the cellular network. Our solution involves two steps. In the first step, we determine the set of feasible D2D links, based on the interference constraint. In the second step, we use the celebrated Dijkstra's algorithm to find throughput-optimal routes between a given pair of D2D nodes under two scenarios: a) The Fixed Rate Scheme and b) The Fixed Power Scheme. The dependency of the net D2D throughput on the system parameters such as target SINR is analyzed for both the schemes, and a procedure to find the optimum parameter setting is proposed. The performance of the algorithms is illustrated using computer simulations. The results show that, depending on the network topology, a significantly higher throughput can be achieved by using multi-hop paths compared to using single-hop, direct D2D communication.

We present computationally efficient and analytically tractable algorithms for identifying a given number of “non-defective” items from a large population containing a small number of “defective” items under a noisy Non-adaptive Group Testing (NGT) framework. In contrast to the classical NGT, where the main goal is to identify the complete set of defective items, the main goal of a non-defective subset recovery algorithm is to identify a subset of non-defective items given the test outcomes. In this paper, we present three algorithms and corresponding bounds on the number of tests required for successful non-defective subset recovery. We consider a random, non-adaptive pooling strategy with noisy test outcomes, where we account for the impact of both additive noise (false positives) and dilution noise (false negatives). We provide simulation results to highlight the relative performance of the algorithms, and to demonstrate the significant improvement they offer over existing approaches, in terms of the number of tests required for a given success rate.

In this work, we address the recovery of sparse and compressible vectors in the presence of colored noise possibly with a rank-deficient noise covariance matrix, from overcomplete noisy linear measurements. We exploit the structure of the noise covariance matrix in a Bayesian framework. In particular, we propose the CoNo-SBL algorithm based on the popular and efficient Sparse Bayesian Learning (SBL) technique. We also derive Bayesian and Marginalized Cramér Rao lower Bounds (CRB) for the problem of estimating compressible vectors. We consider an unknown compressible vector drawn from a Student-t prior distribution, and derive CRBs that encompass the random nature of the unknown compressible vector and the parameters of the prior distribution, in the presence of colored noise and rank-deficient noise covariance matrix. Using Monte Carlo simulations, we demonstrate the efficacy of the proposed CoNo-SBL algorithm as compared to compressed sensing and greedy techniques. Further, we demonstrate the mean squared error performance of the proposed estimator compared to the CRBs, for different ranks of the noise covariance matrix.

In this work, an algorithm is proposed for self-localization of a target node using power measurements from beacon nodes transmitting from known locations. The geographical area is overlaid with a virtual grid, and the problem is treated as one of testing overlapping subsets of grid cells for the presence of the target node. The proposed algorithm is validated both by Monte Carlo simulations as well as using experimental data collected from commercially-off-the-shelf bluetooth low energy (BLE) beacon nodes.

This paper derives the capacity region of the 2- user symmetric linear deterministic Z-interference channel (Z-IC) with limited-rate unidirectional cooperation between the transmitters and secrecy constraints at the receivers. The proposed achievable scheme uses a combination of transmission of random bits and interference cancelation. A novel outer bound is derived, based on carefully selecting the side information, and partitioning the encoded message/output depending on the relative strength of the signal and the interference. The derived bounds match, thereby characterizing the secrecy capacity region of the symmetric Z-IC with unidirectional transmitter cooperation. Interestingly, it is found that, in the weak/moderate interference regime, the capacity region does not decrease due to the secrecy constraint at the receiver. Hence, the proposed scheme achieves the capacity of the Z-IC for the weak/moderate interference regime with and without secrecy constraints at the receivers. The results highlight the role of the unidirectional transmitter cooperation in facilitating secure communication over a 2-user ZIC in the high interference regime.

It is known that the capacity of the discrete-time memoryless noncoherent Rayleigh fading channels (DTM-NRFC) is achieved by a discrete constellation with finite number of mass points and when one of the mass points is located at the origin [1]. In this paper, we present the maximum likelihood detection (MLD) error performance on DTM-NRFC for a discrete constellation with coding and spatial diversity. In the absence of outer coding, the error probability with MLD is derived in a surprisingly simple closed-form. On the other hand, with coding and diversity, our error probability expressions can be evaluated via saddle-point approximation techniques.

This paper considers the problem of receive antenna selection (AS) in a multiple-antenna communication system having a single radio-frequency (RF) chain. The AS decisions are based on noisy channel estimates obtained using known pilot symbols embedded in the data packets. The goal here is to minimize the average packet error rate (PER) by exploiting the known temporal correlation of the channel. As the underlying channels are only partially observed using the pilot symbols, the problem of AS for PER minimization is cast into a partially observable Markov decision process (POMDP) framework. Under mild assumptions, the optimality of a myopic policy is established for the two-state channel case. Moreover, two heuristic AS schemes are proposed based on a weighted combination of the estimated channel states on the different antennas. These schemes utilize the continuous-valued received pilot symbols to make the AS decisions, and are shown to offer performance comparable to the POMDP approach, which requires one to quantize the channel and observations to a finite set of states. The performance improvement offered by the POMDP solution and the proposed heuristic solutions relative to existing AS training-based approaches is illustrated using Monte Carlo simulations.

In this paper, we analyze the effect of channel estimation errors on the performance of an uplink cellular network. We use a stochastic geometric approach, where the Mobile Users (MUs) and the Base Stations (BSs) are modeled as being randomly located on the 2-dimensional plane according to independent Poisson point processes. Each MU makes use of fractional distance-dependent power control, while transmitting both the training signal as well as the data signal in the uplink direction. We derive an analytical expression for the uplink coverage probability for a typical BS-MU pair, accounting for the effects of channel estimation errors and fractional power control. We numerically obtain the fractional power control that maximizes the coverage probability, and show that the optimal power control factor does not depend on the training duration. Further, we numerically compute the optimal training duration that maximizes the area spectral efficiency. The results provide critical insights into the design and optimization of uplink cellular networks in the presence of pilot contamination due to practical channel estimation.

In this work, we consider the estimation of multiple jointly sparse vectors (or signals) from noisy, undetermined, linear measurements acquired by multiple nodes connected in a network. We propose a decentralized Bayesian algorithm, which is able to exploit the joint sparsity structure across the nodes. In the proposed algorithm, each node seeks the maximum a posterior probability (MAP) estimate of a local sparse signal vector by learning the parameters of a sparsity inducing signal prior, which is assumed to be common to the nodes, in a distributed fashion. Through simulations, we show that our algorithm significantly outperforms DCS-SOMP, an existing algorithm, in terms of number of measurements required per node for exact recovery of the common support. We also propose a tuning procedure to accelerate the convergence of our algorithm.

In this work, the problem of receive antenna selection (AS) is considered, in a multiple antenna communication system having a single radio frequency (RF) chain at the receiver. The AS is performed on a per-packet basis, and AS decisions are based on noisy estimates of the channel gains obtained using pilot symbols embedded in the data packet for coherent demodulation, along with the receiver's knowledge of the time correlation of the channel. The problem is posed as a partially observable Markov decision process (POMDP) with the goal of minimizing the average packet error rate (PER). The performance of a myopic policy is compared with that of the POMDP solution, and it is shown that the former is optimal under certain conditions. As the POMDP approach requires the channel gains to be quantized to a finite set of states, we also propose two heuristic AS schemes that use the continuous-valued received pilot symbols to make AS decisions, and thereby offer comparable or better performance than the POMDP approach. Unlike previous work, the schemes proposed here for AS do not require a lengthy AS training phase to precede each data packet. The performance improvement offered by the POMDP solution and the proposed heuristic solutions relative to existing AS training-based approaches is illustrated using Monte Carlo simulations.

This paper considers the problem of achieving binary consensus among a set of nodes using physical layer communication over noisy wireless links. Starting with initial binary values, the nodes exchange messages over i.i.d. fading channels, detect the majority bit across the sensors, and update their majority bit estimates, over multiple cycles. The bits are updated using either an LMMSE-based scheme or a co-phased combining scheme. The channel state information (CSI) available at the nodes are imperfect due to practical estimation errors. The evolution of network consensus is modeled as a Markov chain, and the average transition probability matrix (TPM) is analytically derived for the co-phased combining scheme. The two schemes are compared in terms of the probability of accurate consensus and the second largest eigen value of the TPM. It is found that the co-phased combining scheme is better at low to intermediate pilot SNRs, in addition to having a lower computational complexity and better analytical tractability, compared to the LMMSE-based scheme.

In this work, we address the recovery of block sparse vectors with intra-block correlation, i.e., the recovery of vectors in which the correlated nonzero entries are constrained to lie in a few clusters, from noisy underdetermined linear measurements. Among Bayesian sparse recovery techniques, the cluster Sparse Bayesian Learning (SBL) is an efficient tool for block-sparse vector recovery, with intrablock correlation. However, this technique uses a heuristic method to estimate the intra-block correlation. In this paper, we propose the Nested SBL (NSBL) algorithm, which we derive using a novel Bayesian formulation that facilitates the use of the monotonically convergent nested Expectation Maximization (EM) and a Kalman filtering based learning framework. Unlike the cluster-SBL algorithm, this formulation leads to closed-form EM updates for estimating the correlation coefficient. We demonstrate the efficacy of the proposed NSBL algorithm using Monte Carlo simulations.

In this work, spectrum sensing for cognitive radios is considered in the presence of Primary Users (PU) using frequency-hopping communication over multiple frequency bands. The detection performance of the Fast Fourier Transform (FFT) Average Ratio (FAR) algorithm is obtained in closed-form, for a given FFT size and number of PUs. The effective throughput of the Secondary Users (SU) is formulated as an optimization problem with a constraint on the maximum allowable interference on the primary network. Given the hopping period of the PUs, the sensing duration that maximizes the SU throughput is derived. The results are validated using Monte Carlo simulations. Further, an implementation of the FAR algorithm on the Lyrtech (now, Nutaq) small form factor software defined radio development platform is presented, and the performance recorded through the hardware is observed to corroborate well with that obtained through simulations, allowing for implementation losses.

In this paper, we consider the diversity order achievable in a Rayleigh fading Multi-User (MU)-MIMO system when CSI is available at the transmitter (CSIT), but not at the receiver (CSIR). Such a scenario is relevant, for example, in Time Division Duplex (TDD) communications, where, due to channel reciprocity, CSIT can be acquired directly by sending a known training sequence from the receiver to the transmitter. We propose a novel, simple-to-implement transmit precoding scheme that converts fading MU-MIMO channels (the multiple access channel, broadcast channel and interference channel) into fixed-gain parallel Gaussian channels, while satisfying an average power constraint. Hence, the proposed precoding scheme achieves an infinite diversity order, which is in contrast with schemes based on perfect CSIR, which at best achieve a finite diversity order. Monte Carlo simulations illustrate the improvement in the BER performance obtainable from the proposed precoding schemes compared to existing diversity schemes.

In this paper, we design and analyze pilot-assisted Distributed Co-Phasing (DCP) schemes for information fusion in a wireless sensor network. First, using a cutoff rate analysis, we show that higher order constellations significantly improve the throughput performance of DCP in comparison with the binary constellation considered in past work. However, using a higher order constellation in the DCP setting requires estimation of the composite channel from the sensors at the Fusion Center (FC), which is not available in current DCP schemes. We propose two blind algorithms for channel estimation, namely, a power method and a modified K-means algorithm. In particular, the latter is computationally efficient and converges significantly faster and more accurately than the conventional K-means algorithm. We derive closed-form expressions for the probability of symbol error and study the performance of DCP both analytically and through simulations. Our simulation results show that even at moderate to low SNRs, the modified K-means algorithm achieves a probability of error comparable to that achievable with a perfect channel estimate at the FC. The proposed DCP and blind channel estimation schemes are thus a promising technique for energy-efficient data fusion in wireless sensor networks.

This paper considers antenna selection (AS) for packet reception at a receiver equipped with multiple antenna elements but only a single radio frequency chain. The receiver makes its AS decisions based on noisy channel estimates obtained from the training symbols (pilots). The time-correlation of the wireless channel and the results of the link-layer error checks upon data packet reception provide additional information that can be exploited for AS. This information can also be used to optimally distribute pilots among the antenna elements, so that packet loss due to selection errors is minimized. The task of the receiver, then, is to sequentially select (a) the pilot symbol allocation for channel estimation on each of the receive antennas and (b) the antenna to be used for data packet reception. The goal is to maximize the expected throughput, based on the history of allocation and selection decisions, and the corresponding noisy channel estimates and error check observations. This joint problem of pilot allocation and AS is solved as a partially observed Markov decision problem (POMDP) and the solutions yield the optimal policies that maximize the long-term expected throughput. The performance of the POMDP solution is compared with several other schemes for a 2-state Markov channel model, and it is illustrated that it outperforms the others.

This study considers linear filtering methods for minimising the end‐to‐end average distortion of a fixed‐rate source quantisation system. For the source encoder, both scalar and vector quantisation are considered. The codebook index output by the encoder is sent over a noisy discrete memoryless channel whose statistics could be unknown at the transmitter. At the receiver, the code vector corresponding to the received index is passed through a linear receive filter, whose output is an estimate of the source instantiation. Under this setup, an approximate expression for the average weighted mean‐square error (WMSE) between the source instantiation and the reconstructed vector at the receiver is derived using high‐resolution quantisation theory. Also, a closed‐form expression for the linear receive filter that minimises the approximate average WMSE is derived. The generality of framework developed is further demonstrated by theoretically analysing the performance of other adaptation techniques that can be employed when the channel statistics are available at the transmitter also, such as joint transmit–receive linear filtering and codebook scaling. Monte Carlo simulation results validate the theoretical expressions, and illustrate the improvement in the average distortion that can be obtained using linear filtering techniques.

This paper derives outer bounds on the sum rate of the K-user MIMO Gaussian interference channel (GIC). Three outer bounds are derived, under different assumptions of cooperation and providing side information to receivers. The novelty in the derivation lies in the careful selection of side information, which results in the cancellation of the negative differential entropy terms containing signal components, leading to a tractable outer bound. The overall outer bound is obtained by taking the minimum of the three outer bounds. The derived bounds are simplified for the MIMO Gaussian symmetric IC to obtain outer bounds on the generalized degrees of freedom (GDOF). The relative performance of the bounds yields insight into the performance limits of multiuser MIMO GICs and the relative merits of different schemes for interference management. These insights are confirmed by establishing the optimality of the bounds in specific cases using an inner bound on the GDOF derived by the authors in a previous work. It is also shown that many of the existing results on the GDOF of the GIC can be obtained as special cases of the bounds, e.g., by setting K=2 or the number of antennas at each user to 1.

The K-user multiple input multiple output (MIMO) Gaussian symmetric interference channel where each transmitter has M antennas and each receiver has N antennas is studied from a generalized degrees of freedom (GDOF) perspective. An inner bound on the GDOF is derived using a combination of techniques such as treating interference as noise, zero forcing (ZF) at the receivers, interference alignment (IA), and extending the Han-Kobayashi (HK) scheme to K users, as a function of the number of antennas and the log INR / log SNR level. Several interesting conclusions are drawn from the derived bounds. It is shown that when K >; N/M + 1, a combination of the HK and IA schemes performs the best among the schemes considered. When N/M <; K ≤ N/M + 1, the HK-scheme outperforms other schemes and is found to be GDOF optimal in many cases. In addition, when the SNR and INR are at the same level, ZF-receiving and the HK-scheme have the same GDOF performance.

In this paper, we analyze the coexistence of a primary and a secondary (cognitive) network when both networks use the IEEE 802.11 based distributed coordination function for medium access control. Specifically, we consider the problem of channel capture by a secondary network that uses spectrum sensing to determine the availability of the channel, and its impact on the primary throughput. We integrate the notion of transmission slots in Bianchi's Markov model with the physical time slots, to derive the transmission probability of the secondary network as a function of its scan duration. This is used to obtain analytical expressions for the throughput achievable by the primary and secondary networks. Our analysis considers both saturated and unsaturated networks. By performing a numerical search, the secondary network parameters are selected to maximize its throughput for a given level of protection of the primary network throughput. The theoretical expressions are validated using extensive simulations carried out in the Network Simulator 2. Our results provide critical insights into the performance and robustness of different schemes for medium access by the secondary network. In particular, we find that the channel captures by the secondary network does not significantly impact the primary throughput, and that simply increasing the secondary contention window size is only marginally inferior to silent-period based methods in terms of its throughput performance.

This paper considers antenna selection (AS) at a receiver equipped with multiple antenna elements but only a single radio frequency chain for packet reception. As information about the channel state is acquired using training symbols (pilots), the receiver makes its AS decisions based on noisy channel estimates. Additional information that can be exploited for AS includes the time-correlation of the wireless channel and the results of the link-layer error checks upon receiving the data packets. In this scenario, the task of the receiver is to sequentially select (a) the pilot symbol allocation, i.e., how to distribute the available pilot symbols among the antenna elements, for channel estimation on each of the receive antennas; and (b) the antenna to be used for data packet reception. The goal is to maximize the expected throughput, based on the past history of allocation and selection decisions, and the corresponding noisy channel estimates and error check results. Since the channel state is only partially observed through the noisy pilots and the error checks, the joint problem of pilot allocation and AS is modeled as a partially observed Markov decision process (POMDP). The solution to the POMDP yields the policy that maximizes the long-term expected throughput. Using the Finite State Markov Chain (FSMC) model for the wireless channel, the performance of the POMDP solution is compared with that of other existing schemes, and it is illustrated through numerical evaluation that the POMDP solution significantly outperforms them.

This paper addresses the problem of finding optimal power control policies for wireless energy harvesting sensor (EHS) nodes with automatic repeat request (ARQ)-based packet transmissions. The EHS harvests energy from the environment according to a Bernoulli process; and it is required to operate within the constraint of energy neutrality. The EHS obtains partial channel state information (CSI) at the transmitter through the link-layer ARQ protocol, via the ACK/NACK feedback messages, and uses it to adapt the transmission power for the packet (re)transmission attempts. The underlying wireless fading channel is modeled as a finite state Markov chain with known transition probabilities. Thus, the goal of the power management policy is to determine the best power setting for the current packet transmission attempt, so as to maximize a long-run expected reward such as the expected outage probability. The problem is addressed in a decision-theoretic framework by casting it as a partially observable Markov decision process (POMDP). Due to the large size of the state-space, the exact solution to the POMDP is computationally expensive. Hence, two popular approximate solutions are considered, which yield good power management policies for the transmission attempts. Monte Carlo simulation results illustrate the efficacy of the approach and show that the approximate solutions significantly outperform conventional approaches.

In this paper, we consider the problem of finding a spectrum hole of a specified bandwidth in a given wide band of interest. We propose a new, simple and easily implementable sub-Nyquist sampling scheme for signal acquisition and a spectrum hole search algorithm that exploits sparsity in the primary spectral occupancy in the frequency domain by testing a group of adjacent subbands in a single test. The sampling scheme deliberately introduces aliasing during signal acquisition, resulting in a signal that is the sum of signals from adjacent sub-bands. Energy-based hypothesis tests are used to provide an occupancy decision over the group of subbands, and this forms the basis of the proposed algorithm to find contiguous spectrum holes. We extend this framework to a multi-stage sensing algorithm that can be employed in a variety of spectrum sensing scenarios, including non-contiguous spectrum hole search. Further, we provide the analytical means to optimize the hypothesis tests with respect to the detection thresholds, number of samples and group size to minimize the detection delay under a given error rate constraint. Depending on the sparsity and SNR, the proposed algorithms can lead to significantly lower detection delays compared to a conventional bin-by-bin energy detection scheme; the latter is in fact a special case of the group test when the group size is set to 1. We validate our analytical results via Monte Carlo simulations.

This paper investigates the Diversity-Multiplexing gain Trade-off (DMT) of a training based reciprocal Single Input Multiple Output (SIMO) system, with (i) perfect Channel State Information (CSI) at the Receiver (CSIR) and noisy CSI at the Transmitter (CSIT), and (ii) noisy CSIR and noisy CSIT. In both the cases, the CSIT is acquired through Reverse Channel Training (RCT), i.e., by sending a training sequence from the receiver to the transmitter. A channel-dependent fixed-power training scheme is proposed for acquiring CSIT, along with a forward-link data transmit power control scheme. With perfect CSIR, the proposed scheme is shown to achieve a diversity order that is quadratically increasing with the number of receive antennas. This is in contrast with conventional orthogonal RCT schemes, where the diversity order is known to saturate as the number of receive antennas is increased, for a given channel coherence time. Moreover, the proposed scheme can achieve a larger DMT compared to the orthogonal training scheme. With noisy CSIR and noisy CSIT, a three-way training scheme is proposed and its DMT performance is analyzed. It is shown that nearly the same diversity order is achievable as in the perfect CSIR case. The time-overhead in the training schemes is explicitly accounted for in this work, and the results show that the proposed channel-dependent RCT and data power control schemes offer a significant improvement in terms of the DMT, compared to channel-agnostic orthogonal RCT schemes. The outage performance of the proposed scheme is illustrated through Monte Carlo simulations.

In this paper, we propose power management algorithms for maximizing the utility of energy harvesting sensors (EHS) that operate purely on the basis of energy harvested from the environment. In particular, we consider communication (i.e., transmission and reception) power management issues for EHS under an energy neutrality constraint. We also consider the fixed power loss effects of the circuitry, the battery inefficiency and its storage capacity, in the design of the algorithms. We propose a two-stage structure that exploits the inherent difference in the timescales at which the energy harvesting and channel fading processes evolve, without loss of optimality of the resulting solution. The outer stage schedules the power that can be used by an inner stage algorithm, so as to maximize the long term average utility and at the same time maintain energy neutrality. The inner stage optimizes the communication parameters to achieve maximum utility in the short-term, subject to the power constraint imposed by the outer stage. We optimize the algorithms for different transmission schemes such as the truncated channel inversion and retransmission strategies. The performance of the algorithms is illustrated via simulations using solar irradiance data, and for the case of Rayleigh fading channels. The results demonstrate the significant performance benefits that can be obtained using the proposed power management algorithms compared to the energy efficient (optimum when there is no storage) and the uniform power consumption (optimum when the battery has infinite capacity and is perfectly efficient) approaches.

In this paper, we determine packet scheduling policies for efficient power management in Energy Harvesting Sensors (EHS) which have to transmit packets of high and low priorities over a fading channel. We assume that incoming packets are stored in a buffer and the quality of service for a particular type of message is determined by the expected waiting time of packets of that type of message. The sensors are constrained to work with the energy that they garner from the environment. We derive transmit policies which minimize the sum of expected waiting times of the two types of messages, weighted by penalties. First, we show that for schemes with a constant rate of transmission, under a decoupling approximation, a form of truncated channel inversion is optimal. Using this result, we derive optimal solutions that minimize the weighted sum of the waiting times in the different queues.

This paper analyzes the error exponents in Bayesian decentralized spectrum sensing, i.e., the detection of occupancy of the primary spectrum by a cognitive radio, with probability of error as the performance metric. At the individual sensors, the error exponents of a Central Limit Theorem (CLT) based detection scheme are analyzed. At the fusion center, a K-out-of-N rule is employed to arrive at the overall decision. It is shown that, in the presence of fading, for a fixed number of sensors, the error exponents with respect to the number of observations at both the individual sensors as well as at the fusion center are zero. This motivates the development of the error exponent with a certain probability as a novel metric that can be used to compare different detection schemes in the presence of fading. The metric is useful, for example, in answering the question of whether to sense for a pilot tone in a narrow band (and suffer Rayleigh fading) or to sense the entire wide-band signal (and suffer log-normal shadowing), in terms of the error exponent performance. The error exponents with a certain probability at both the individual sensors and at the fusion center are derived, with both Rayleigh as well as log-normal shadow fading. Numerical results are used to illustrate and provide a visual feel for the theoretical expressions obtained.

This paper considers the problem of weak signal detection in the presence of navigation data bits for Global Navigation Satellite System (GNSS) receivers. Typically, a set of partial coherent integration outputs are non-coherently accumulated to combat the effects of model uncertainties such as the presence of navigation data-bits and/or frequency uncertainty, resulting in a sub-optimal test statistic. In this work, the test-statistic for weak signal detection is derived in the presence of navigation data-bits from the likelihood ratio. It is highlighted that averaging the likelihood ratio based test-statistic over the prior distributions of the unknown data bits and the carrier phase uncertainty leads to the conventional Post Detection Integration (PDI) technique for detection. To improve the performance in the presence of model uncertainties, a novel cyclostationarity based sub-optimal PDI technique is proposed. The test statistic is analytically characterized, and shown to be robust to the presence of navigation data-bits, frequency, phase and noise uncertainties. Monte Carlo simulation results illustrate the validity of the theoretical results and the superior performance offered by the proposed detector in the presence of model uncertainties.

This paper proposes an algorithm for joint data detection and tracking of the dominant singular mode of a time varying channel at the transmitter and receiver of a time division duplex multiple input multiple output beamforming system. The method proposed is a modified expectation maximization algorithm which utilizes an initial estimate to track the dominant modes of the channel at the transmitter and the receiver blindly; and simultaneously detects the un known data. Furthermore, the estimates are constrained to be within a confidence interval of the previous estimate in order to improve the tracking performance and mitigate the effect of error propagation. Monte-Carlo simulation results of the symbol error rate and the mean square inner product between the estimated and the true singular vector are plotted to show the performance benefits offered by the proposed method compared to existing techniques.

This paper considers the problem of identifying the footprints of communication of multiple transmitters in a given geographical area. To do this, a number of sensors are deployed at arbitrary but known locations in the area, and their individual decisions regarding the presence or absence of the transmitters' signal are combined at a fusion center to reconstruct the spatial spectral usage map. One straightforward scheme to construct this map is to query each of the sensors and cluster the sensors that detect the primary's signal. However, using the fact that a typical transmitter footprint map is a sparse image, two novel compressive sensing based schemes are proposed, which require significantly fewer number of transmissions compared to the querying scheme. A key feature of the proposed schemes is that the measurement matrix is constructed from a pseudo-random binary phase shift applied to the decision of each sensor prior to transmission. The measurement matrix is thus a binary ensemble which satisfies the restricted isometry property. The number of measurements needed for accurate footprint reconstruction is determined using compressive sampling theory. The three schemes are compared through simulations in terms of a performance measure that quantifies the accuracy of the reconstructed spatial spectral usage map. It is found that the proposed sparse reconstruction technique-based schemes significantly outperform the round-robin scheme.

This work derives inner and outer bounds on the generalized degrees of freedom (GDOF) of the K-user symmetric MIMO Gaussian interference channel. For the inner bound, an achievable GDOF is derived by employing a combination of treating interference as noise, zero-forcing at the receivers, interference alignment (IA), and extending the Han-Kobayashi (HK) scheme to K users, depending on the number of antennas and the INR/SNR level. An outer bound on the GDOF is derived, using a combination of the notion of cooperation and providing side information to the receivers. Several interesting conclusions are drawn from the bounds. For example, in terms of the achievable GDOF in the weak interference regime, when the number of transmit antennas (M) is equal to the number of receive antennas (N), treating interference as noise performs the same as the HK scheme and is GDOF optimal. For K >; N/M+1, a combination of the HK and IA schemes performs the best among the schemes considered. However, for N/M <; K ≤ N/M+1, the HK scheme is found to be GDOF optimal.

The well known noise rejection property of the cyclostationary spectrum makes it an ideal candidate for spectrum sensing in low SNR environments such as the IEEE 802.22 WRAN, which stipulates detection of primary signals at -20.8dB. In this paper, we propose two novel detector architectures that exploit cyclostationary properties: the Spectral Correlation Density (SCD), and the Magnitude Squared Coherence (MSC). Through extensive simulations, both on generated data and real world ATSC capture data, we show that our detector achieves an improvement of 2.5dB compared to existing proposals. Additionally, we compare our proposal against two popular choices for spectrum sensing in cognitive radio - the matched filter detection and energy detection, and show the superiority of cyclostationary spectrum sensing in such low SNR environments.

The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread. In this paper, a novel method of estimating such sparse multipath fading channels for OFDM systems is explored. In particular, Sparse Bayesian Learning (SBL) techniques are applied to jointly estimate the sparse channel and its second order statistics, and a new Bayesian Cramer-Rao bound is derived for the SBL algorithm. Further, in the context of OFDM channel estimation, an enhancement to the SBL algorithm is proposed, which uses an Expectation Maximization (EM) framework to jointly estimate the sparse channel, unknown data symbols and the second order statistics of the channel. The EM-SBL algorithm is able to recover the support as well as the channel taps more efficiently, and/or using fewer pilot symbols, than the SBL algorithm. To further improve the performance of the EM-SBL, a threshold-based pruning of the estimated second order statistics that are input to the algorithm is proposed, and its mean square error and symbol error rate performance is illustrated through Monte-Carlo simulations. Thus, the algorithms proposed in this paper are capable of obtaining efficient sparse channel estimates even in the presence of a small number of pilots.

This paper investigates the diversity-multiplexing gain tradeoff (DMT) of a time-division duplex (TDD) single-input multiple-output (SIMO) system with perfect channel state information (CSI) at the receiver (CSIR) and partial CSI at the transmitter (CSIT). The partial CSIT is acquired through a training sequence from the receiver to the transmitter. The training sequence is chosen in an intelligent manner based on the CSIR, to reduce the training length by a factor of r, the number of receive antennas. We show that, for the proposed training scheme and a given channel coherence time, the diversity order increases linearly with r for nonzero multiplexing gain. This is a significant improvement over conventional orthogonal training schemes.

This paper considers the problem of spectrum sensing in cognitive radio networks when the primary user is using Orthogonal Frequency Division Multiplexing (OFDM). For this we develop cooperative sequential detection algorithms that use the autocorrelation property of cyclic prefix (CP) used in OFDM systems. We study the effect of timing and frequency offset, IQ-imbalance and uncertainty in noise and transmit power. We also modify the detector to mitigate the effects of these impairments. The performance of the proposed algorithms is studied via simulations. We show that sequential detection can significantly improve the performance over a fixed sample size detector.

In this paper, power management algorithms for energy harvesting sensors (EHS) that operate purely based on energy harvested from the environment are proposed. To maintain energy neutrality, EHS nodes schedule their utilization of the harvested power so as to save/draw energy into/from an inefficient battery during peak/low energy harvesting periods, respectively. Under this constraint, one of the key system design goals is to transmit as much data as possible given the energy harvesting profile. For implementational simplicity, it is assumed that the EHS transmits at a constant data rate with power control, when the channel is sufficiently good. By converting the data rate maximization problem into a convex optimization problem, the optimal load scheduling (power management) algorithm that maximizes the average data rate subject to energy neutrality is derived. Also, the energy storage requirements on the battery for implementing the proposed algorithm are calculated. Further, robust schemes that account for the insufficiency of battery storage capacity, or errors in the prediction of the harvested power are proposed. The superior performance of the proposed algorithms over conventional scheduling schemes are demonstrated through computations using numerical data from solar energy harvesting databases.

A wireless Energy Harvesting Sensor (EHS) needs to send data packets arriving in its queue over a fading channel at maximum possible throughput while ensuring acceptable packet delays. At the same time, it needs to ensure that energy neutrality is satisfied, i.e., the average energy drawn from a battery should equal the amount of energy deposited in it minus the energy lost due to the inefficiency of the battery. In this work, a framework is developed under which a system designer can optimize the performance of the EHS node using power control based on the current channel state information, when the EHS node employs a single modulation and coding scheme and the channel is Rayleigh fading. Optimal system parameters for throughput optimal, delay optimal and delay-constrained throughput optimal policies that ensure energy neutrality are derived. It is seen that a throughput optimal (maximal) policy is packet delay-unbounded and an average delay optimal (minimal) policy achieves negligibly small throughput. Finally, the influence of the harvested energy profile on the performance of the EHS is illustrated through the example of solar energy harvesting.

This paper proposes a new receiver optimized semihard-decision vector quantization (SHDVQ) for noisy channels, as a technique to alleviate the drastic increase in distortion incurred when the output of the classical source optimized vector quantizer (SOVQ) is sent over a noisy channel. The advantages of the proposed method are that it is computationally simple, requires minimal extra storage, and can be implemented solely at the receiver; thus allowing the encoder to be independent of channel conditions. Another advantage is that it can be used in conjunction with an index assignment to obtain the benefits of index assignment (IA). The decoder considers errors and erasures based on thresholding the log-likelihood ratio (LLR) of the bits comprising the transmitted index. Then, the proposed decoder computes the output as a linear combination of the codebook vectors based on the erasure bit locations and the IA. A novel performance analysis is presented, where the overall distortion is expressed as a convex combination of the distortion with an ideal IA and the distortion with random IA. The analysis is used to find the erasure threshold that minimizes the overall distortion. Finally, Monte-Carlo simulation results are presented to corroborate the derived theoretical expressions.

Energy harvesting sensors (EHS), which harvest energy from the environment in order to sense and then communicate their measurements over a wireless link, provide the tantalizing possibility of perpetual lifetime operation of a sensor network. The wireless communication link design problem needs to be revisited for these sensors as the energy harvested can be random and small and not available when required. In this paper, we develop a simple model that captures the interactions between important parameters that govern the communication link performance of a EHS node, and analyze its outage probability for both slow fading and fast fading wireless channels. Our analysis brings out the critical importance of the energy profile and the energy storage capability on the EHS link performance. Our results show that properly tuning the transmission parameters of the EHS node and having even a small amount of energy storage capability improves the EHS link performance considerably.

In this paper, we introduce the three-user cognitive radio channels with asymmetric transmitter cooperation, and derive achievable rate regions under several scenarios depending on the type of cooperation and decoding capability at the receivers. Two of the most natural cooperation mechanisms for the three-user channel are considered here: cumulative message sharing (CMS) and primary-only message sharing (PMS). In addition to the message sharing mechanism, the achievable rate region is critically dependent on the decoding capability at the receivers. Here, we consider two scenarios for the decoding capability, and derive an achievable rate region for each one of them by employing a combination of superposition and Gel'fand-Pinsker coding techniques. Finally, to provide a numerical example, we consider the Gaussian channel model to plot the rate regions. In terms of achievable rates, CMS turns out to be a better scheme than PMS. However, the practical aspects of implementing such message-sharing schemes remain to be investigated.

This paper investigates the problem of designing reverse channel training sequences for a TDD-MIMO spatial-multiplexing system. Assuming perfect channel state information at the receiver and spatial multiplexing at the transmitter with equal power allocation to the m dominant modes of the estimated channel, the pilot is designed to ensure an estimate of the channel which improves the forward link capacity. Using perturbation techniques, a lower bound on the forward link capacity is derived with respect to which the training sequence is optimized. Thus, the reverse channel training sequence makes use of the channel knowledge at the receiver. The performance of orthogonal training sequence with MMSE estimation at the transmitter and the proposed training sequence are compared. Simulation results show a significant improvement in performance.

In this paper, an achievable rate region for the three-user discrete memoryless interference channel with asymmetric transmitter cooperation is derived. The three-user channel facilitates different ways of message sharing between the transmitters. We introduce a manner of noncausal (genie aided) unidirectional message-sharing, which we term cumulative message sharing. We consider receivers with predetermined decoding capabilities, and define a cognitive interference channel. We then derive an achievable rate region for this channel by employing a coding scheme which is a combination of superposition and Gel'fand-Pinsker coding techniques.

This paper addresses the design and analysis of practical distributed beamforming techniques for the uplink communication over a wireless sensor network. Since the conventional frequency-division duplexing techniques require a large feedback overhead, we focus on a time-division duplexing approach, and exploit the channel reciprocity to reduce the channel feedback requirement. We consider periodic broadcast of known pilot symbols by the fusion center, and maximum likelihood estimation of the channel phase by the sensor nodes for the subsequent uplink co-phasing transmission. For simplicity, we study binary signaling over frequency-flat fading channels, and quantify the system performance such as the expected gains in the received signal-to-noise ratio (SNR) and the average probability of error at the fusion center, as a function of the number of sensor nodes and the pilot overhead. Our results show that a modest amount of accumulated pilot SNR is sufficient to realize a large fraction of the maximum possible beamforming gain.

In this paper, we present robust semi-blind (SB) algorithms for the estimation of beamforming vectors for multiple-input multiple-output wireless communication. The transmitted symbol block is assumed to comprise of a known sequence of training (pilot) symbols followed by information bearing blind (unknown) data symbols. Analytical expressions are derived for the robust SB estimators of the MIMO receive and transmit beamforming vectors. These robust SB estimators employ a preliminary estimate obtained from the pilot symbol sequence and leverage the second-order statistical information from the blind data symbols. We employ the theory of Lagrangian duality to derive the robust estimate of the receive beamforming vector by maximizing an inner product, while constraining the channel estimate to lie in a confidence sphere centered at the initial pilot estimate. Two different schemes are then proposed for computing the robust estimate of the MIMO transmit beamforming vector. Simulation results presented in the end illustrate the superior performance of the robust SB estimators.

This paper considers the design and analysis of a filter at the receiver of a source coding system to mitigate the excess Mean-Squared Error (MSE) distortion caused due to channel errors. It is assumed that the source encoder is channel-agnostic, i.e., that a vector quantization (VQ) based compression designed for a noiseless channel is employed. The index output by the source encoder is sent over a noisy memoryless discrete symmetric channel, and the possibly incorrect received index is decoded by the corresponding VQ decoder. The output of the VQ decoder is processed by a receive filter to obtain an estimate of the source instantiation. In the sequel, the optimum linear receive filter structure to minimize the overall MSE is derived, and shown to have a minimum-mean squared error receiver type structure. Further, expressions are derived for the resulting high-rate MSE performance. The performance is compared with the MSE obtained using conventional VQ as well as the channel optimized VQ. The accuracy of the expressions is demonstrated through Monte Carlo simulations.

This paper considers the problem of power management and throughput maximization for energy neutral operation when using energy harvesting sensors (EHS) to send data over wireless links. It is assumed that the EHS are designed to transmit data at a constant rate (using a fixed modulation and coding scheme) but are power-controlled. A framework under which the system designer can optimize the performance of EHS when the channel is Rayleigh fading is developed. For example, the highest average data rate that can be supported over a Rayleigh fading channel given the energy harvesting capability, the battery power storage efficiency and the maximum allowed transmit energy per slot is derived. Furthermore, the optimum transmission scheme that guarantees a particular data throughput is derived. The usefulness of the framework developed is illustrated through simulation results for specific examples.

This paper considers the high-rate performance of channel optimized source coding for noisy discrete symmetric channels with random index assignment. Specifically, with mean squared error (MSE) as the performance metric, an upper bound on the asymptotic (i.e., high-rate) distortion is derived by assuming a general structure on the codebook. This structure enables extension of the analysis of the channel optimized source quantizer to one with a singular point density: for channels with small errors, the point density that minimizes the upper bound is continuous, while as the error rate increases, the point density becomes singular. The extent of the singularity is also characterized. The accuracy of the expressions obtained are verified through Monte Carlo simulations.

In this paper, new results are derived for the high-rate performance of source coding for symmetric error channels (i.e., a channel where all index errors are equally likely) for a large class of distortion measures. Expressions are derived for the expected distortion including the effect of channel errors as the number of quantization levels N gets large. It is shown that the distortion can asymptotically be approximated as the sum of the source quantization distortion and the channel error induced distortion. In addition, the expressions obtained can be used to glean key insights on the relative amounts of source and channel coding necessary to attain a balanced system, i.e., one where neither the distortion caused by the source quantization nor the distortion caused by channel errors dominate the performance. Optimization of the codebook for minimizing the expected distortion is also considered, and theoretical expressions are derived for the optimal point density.

In this paper, the sensitivity of the high-rate performance of conventional source coding to symmetric channel errors (i.e., a channel where all index errors are equally likely) with arbitrary distortion measures is analyzed. It is shown that, in general, the overall distortion due to source quantization and channel errors cannot be expressed as the sum of the distortion due to the finite bit representation of the source and the distortion due to channel errors. An exception to this is when the distortion is measured as the mean-squared error. The binary symmetric channel with random index assignment is a special case of the analysis, and as the number of code-points gets large, the performance approaches a nonzero constant. Finally, the framework is applied to the wideband speech spectrum quantization problem, where it correctly predicts the channel error rate permissible for operation at a particular distortion level

In this paper, we consider multiple-input, single-output (MISO) systems with per-antenna constrained beamforming at the transmitter, also known as equal gain transmission (EGT). The i.i.d. Rayleigh flat fading channel is assumed known to the receiver, and is quantized and sent to the transmitter through through a noisy finite-rate feedback channel. We model the noisy feedback channel as a symmetric error channel (i.e.,a channel where all index errors are equally likely), and analyze the effect of feedback channel errors on the performance of quantized EGT systems. It is found that the asymptotic performance (as the number of quantized codepoints gets large) of EGT with quantized feedback via a noisy channel depends on the channel behavior as the number of quantized points increase. It turns out that the binary symmetric channel (BSC) with random index assignment is a special case of the analysis, and the asymptotic performance approaches that of random beamforming. The accuracy of the expressions obtained are further verified through Monte Carlo simulations.

In this work, we consider quantization of the beamforming vector for finite rate feedback-based communication in flat-fading multi input single output (MISO) systems with a per-antenna power constraint. Using the capacity loss with respect to perfect channel feedback as performance metric, we derive a new criterion, the mean-square weighted inner product (MSwIP) criterion, for designing the quantized beamforming vectors. We develop an iterative algorithm (based on the Lloyd algorithm) to generate the beamforming vector codebook that locally optimizes the design criterion. For the case of i.i.d. flat fading channels, we analyze the performance of the proposed algorithm in terms of capacity loss and the outage probability. We show that the capacity loss (in terms of bits) of a MISO system with quantized beamforming under a per-antenna power constraint is roughly half that obtained under a total power constraint. Simulation results verify the accuracy of the analytical expressions

We investigate semi-blind channel estimation for multiple input multiple output (MIMO) quasi-static flat fading channels when maximum ratio transmission (MRT) is employed. We propose a closed-form semi-blind solution (CFSB) for estimating the optimum transmit and receive beamforming vectors of the channel matrix. Employing matrix perturbation theory, we develop expressions for the mean squared error (MSE) in the beamforming vector and average received SNR of both the semi-blind and the conventional least squares estimation (CLSE) schemes. It is found that the proposed estimation technique outperforms CLSE for a wide range of training lengths and training SNRs.