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This paper considers the detection of a distributed target in a partial observation scenario. A distributed targetmodel is usually adopted when the target size is larger than the range bin, for example, in some high-resolution radars. Based on the distributed target model, joint processing of several consecutive range bins can be adopted to achieve performance improvement with respect to processing just one range bin. In applications in complex electromagnetic environments, the radars may miss some of their observations, a phenomenon that usually caused by interference, spectrum sharing, and so on. This partial observation problem leads to degradation of the estimation accuracy for the disturbance (clutter plus noise) covariance matrix and target amplitude vector, which results in decline of the target detection performance. In this paper, a scheme is proposed by using the low-rank priori knowledge of clutter covariance matrix to estimate the detector's unknown parameters. Specifically, the target amplitude vector is obtained by maximizing the likelihood function, and the disturbance covariance matrix is reconstructed by solving an optimization that considers both the likelihood maximization and low-rank property of the clutter covariance matrix. The simulation results indicate that this algorithm improves the estimation accuracy, and achieves a better detection performance in the partial observation scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a new framework for the design of transmit and receive beamformers for interference alignment (IA) without symbol extensions in multi-antenna cellular networks. We consider IA in a G cell network with K users/cell, N antennas at each base station (BS) and M antennas at each user. The proposed framework is developed by recasting the conditions for IA as two sets of rank constraints, one on the rank of interference matrices, and the other on the transmit beamformers in the uplink. The interference matrix consists of all the interfering vectors received at a BS from the out-of-cell users in the uplink. Using these conditions and the crucial observation that the rank of interference matrices under alignment can be determined beforehand, this paper develops two sets of algorithms for IA. The first part of this paper develops rank minimization algorithms for IA by iteratively minimizing a weighted matrix norm of the interference matrix. Different choices of matrix norms lead to reweighted nuclear norm minimization (RNNM) or reweighted Frobenius norm minimization (RFNM) algorithms with significantly different per-iteration complexities. Alternately, the second part of this paper devises an alternating minimization (AM) algorithm where the rank-deficient interference matrices are expressed as a product of two lower-dimensional matrices that are then alternately optimized. Simulation results indicate that RNNM, which has a per-iteration complexity of a semidefinite program, is effective in designing aligned beamformers for proper-feasible systems with or without redundant antennas, while RFNM and AM, which have a per-iteration complexity of a quadratic program, are better suited for systems with redundant antennas. [ABSTRACT FROM PUBLISHER]

Widely linear (WL) receivers have the capability to perform single antenna interference cancellation (SAIC) of one rectilinear (R) or quasi-rectilinear (QR) co-channel interference (CCI), a function which is operational in global system for mobile communications (GSM) handsets in particular. Moreover, SAIC technology for QR signals is still required for voice services over adaptive multiuser channels on one slot (VAMOS) standard, a recent evolution of both GSM and enhanced data rates for GSM evolution (EDGE) standards, to mitigate legacy GSM CCI in particular. It is also required for filter bank multicarrier offset quadrature amplitude modulation (FBMC-OQAM) networks, which are candidate for 5G mobile networks, to mitigate intercarrier interference at reception for frequency selective propagation channels in particular. In this context, the purpose of this paper is twofold. The first one is to get more insights into the existing SAIC technology, and its extension to multiple antenna called multiple antenna interference cancellation (MAIC), by showing analytically that, contrary to what is accepted as true in the literature, SAIC/MAIC implemented from standard WL filtering may be less efficient for QR signals than for R ones. From this result, the second purpose of the paper is to propose and to analyze, forQRsignals and frequency selective fading channels, an SAIC/MAIC enhancement based on a three-input WL frequency shift receiver, making QR signals always almost equivalent to R ones for WL filtering in the presence of CCI. The results of the paper, completely new, may contribute to develop elsewhere new powerful WL receivers for QR signals and for both VAMOS and FBMC-OQAM networks in particular. [ABSTRACT FROM AUTHOR]

In this paper, we study the achievable degrees of freedom (DoFs) for $K$ -user multiple-input single-output broadcast channel (BC) in the absence of channel state information at the transmitter under the finite channel coherent time. For the considered $K$ -user BC, a transmitter is equipped with multiple conventional antennas, and receivers are equipped with a reconfigurable antenna that is capable of dynamically modifying the receiving beam radiation pattern. In this system, we propose a blind interference alignment scheme that is able to allocate an asymmetric number of information symbols to each user, which is essentially required to improve DoFs under limited symbol extension. A generalized systematic construction method of transmit beamforming vectors and receiving mode selection patterns to deal with such asymmetric allocation is established. As a consequence, for a broad class of network configurations and symbol extension constraints, the proposed scheme attains an improved sum DoF compared to the previous works, allowing only for the same number of information symbols for all users. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of the interference among multiple simultaneous transmissions in the downlink channel of a multiantenna wireless system. A symbol-level precoding scheme is considered, in order to exploit the multiuser interference and transform it into useful power at the receiver side, through a joint utilization of the data information and the channel state information. In this context, this paper presents novel strategies that exploit the potential of symbol-level precoding to control the per-antenna instantaneous transmit power. In particular, the power peaks among the transmitting antennas and the instantaneous power imbalances across the different transmitted streams are minimized. These objectives are particularly relevant with respect to the nonlinear amplitude and phase distortions induced by the per-antenna amplifiers, which are important sources of performance degradation in practical systems. More specifically, this paper proposes two different symbol-level precoding approaches. The first approach performs a weighted per-antenna power minimization, under quality-of-service constraints and under a lower bound constraint on the per-antenna transmit power. The second strategy performs a minimization of the spatial peak-to-average power ratio, evaluated among the transmitting antennas. Numerical results are presented in a comparative fashion to show the effectiveness of the proposed techniques, which outperform the state-of-the-art symbol-level precoding schemes in terms of spatial peak-to-average power ratio, spatial dynamic range, and symbol error rate over nonlinear channels. [ABSTRACT FROM AUTHOR]

Interference alignment (IA) is a promising technique to efficiently mitigate interference and to enhance the capacity of a wireless communication network. This paper proposes a grouping-based interference alignment (GIA) with optimized IA-Cell assignment for the multiple cells interfering multiple-input multiple-output (MIMO) multiple access channel (MAC) network under limited feedback. This work consists of three main parts: 1) an improved version (including some new improvements) of the GIA with respect to the degrees of freedom (DoF) and optimal linear transceiver design is provided, which allows for low-complexity and distributed implementation; 2) based on the GIA, the concept of IA-Cell assignment is introduced. Three IA-Cell assignment algorithms are proposed with different backhaul overhead and their DoF and rate performance is investigated; 3)the performance of the proposed GIA algorithms is studied under limited feedback of IA precoders. To enable efficient feedback, a dynamic feedback bit allocation (DBA) problem is formulated and solved in closed-form. The practical implementation, the backhaul overhead requirements, and the complexity of the proposed algorithms are analyzed. Numerical results show that our proposed algorithms greatly outperform the traditional GIA under both unlimited and limited feedback. [ABSTRACT FROM PUBLISHER]

Terahertz band communication is envisioned as a key technology to satisfy the increasing demand for ultra-high-speed wireless links. In this paper, a multi-wideband waveform design for the THz band is proposed, by exploiting the channel peculiarities including the distance-varying spectral windows, the delay spread and the temporal broadening effects. This scheme allows the dynamical variation of the rate and the transmit power on each sub-window and improves the distance. Moreover, the closed-form expressions of the signal-to-interference-plus-the-noise and bit-error-rate for the multi-wideband waveform are derived, by considering the inter-symbol and inter-band interferences. Then, an optimization framework is formulated to solve for the multi-wideband waveform design parameters of the transmit power and the number of frames, with the aim to maximize the communication distance while satisfying the rate and the transmit power constraints. Four sub-optimal solutions are proposed and compared. The results show that the SINR increases with the transmit power and the number of frames, at the cost of the power consumption and the rate decrease. With the transmit power of 10 dBm, the largest distance to support 10 Gbps for the multi-path propagation is 4 m, which is realized via the power allocation scheme to minimize the power/bit on each sub-window and is 10% improvement over the fixed scheme. However, for the directional transmission, this scheme under-exploits the transmit power severely. Instead, the allocation scheme that minimizes the number of frames outperforms the other three schemes. In terms of the maximum distance that achieves 30 Gbps, this scheme reaches 22.5 m. [ABSTRACT FROM AUTHOR]

This paper studies the performance of hierarchical modulation-based multimedia transmission in cognitive radio (CR) systems with imperfect channel sensing results under constraints on both transmit and interference power levels. Unequal error protection (UEP) of data transmission using hierarchical quadrature amplitude modulation (HQAM) is considered in which high priority (HP) data is protected more than low priority (LP) data. In this setting, closed-form bit error rate (BER) expressions for HP data and LP data are derived in Nakagami-m fading channels in the presence of sensing errors. Subsequently, the optimal power control that minimizes weighted sum of average BERs of HP bits and LP bits or its upper bound subject to peak/average transmit power and average interference power constraints is derived and a low-complexity power control algorithm is proposed. Power levels are determined in three different scenarios, depending on the availability of perfect channel side information (CSI) of the transmission and interference links, statistical CSI of both links, or perfect CSI of the transmission link and imperfect CSI of the interference link. The impact of imperfect channel sensing decisions on the error rate performance of cognitive transmissions is also evaluated. In addition, tradeoffs between the number of retransmissions, the severity of fading, and peak signal-to-noise ratio (PSNR) quality are analyzed numerically. Moreover, performance comparisons of multimedia transmission with conventional quadrature amplitude modulation (QAM) and HQAM, and the proposed power control strategies are carried out in terms of the received data quality and number of retransmissions. [ABSTRACT FROM PUBLISHER]

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

This paper investigates the application of blind and semi-blind interference alignment methods in clustered small cell networks. First, we examine two simple straightforward extensions of blind interference alignment and observe that neither of them is uniformly superior under all user distribution. Then, we exploit the location information of the users and base stations (BSs) in the cluster to define the semi-blind interference alignment problem, whose objective function is to minimize the supersymbol length by grouping the users that can be served in the same time slot. We show that this problem is NP-hard and propose two heuristic centralized algorithms that can provide effective solutions. The first scheme, top-level semi-blind supersymbol design, employs a grouping indicator matrix to determine the supersymbol while the second scheme, ordered semi-blind supersymbol design, constructs the supersymbol in an ordered and greedy fashion. Finally, we propose a more decentralized scheme which does not have the location information of all users and BSs in the cluster, but the BS utilizes the long-term SINR values, obtained via feedback, of each user it serves. By numerical simulations, we show that the proposed semi-blind algorithms perform uniformly better than pure blind interference alignment schemes for any possible user distribution scenario. [ABSTRACT FROM AUTHOR]

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

This paper investigates the problem of the spectrally compatible waveform design for multiple-input multiple-output (MIMO) radar in the presence of multiple targets and signal-dependent interference. Unlike the existing approaches to designing the waveform for a single target via output signal-to-interference-plus-noise ratio (SINR) maximization, a new method is proposed to deal with a more general problem, i.e., designing a spectrally compatible waveform for multiple targets, by minimizing the waveform energy of the overlayed space-frequency bands under constraints of waveform similarity and individual SINR requirements. Due to the existence of signal dependence, the formulated optimization problem is NP-hard. Toward this end, an iterative algorithm is developed. Concretely, at each iteration, the receive filter is first obtained with the aid of the minimum variance distortionless response method, then the transmit waveform is optimized by employing the dual ascent method. Both the convergence and asymptotic optimality of this algorithm are discussed. Numerical simulations are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we consider the potential of data transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material. We refer to this as a large intelligent surface (LIS), which is a newly proposed concept and conceptually goes beyond contemporary massive MIMO technology. First, we consider capacities of single-antenna autonomous terminals communicating to the LIS where the entire surface is used as a receiving antenna array in a perfect line-of-sight propagation environment. Under the condition that the surface area is sufficiently large, the received signal after a matched-filtering operation can be closely approximated by a sinc-function-like intersymbol interference channel. Second, we analyze a normalized capacity measured per unit surface, for a fixed transmit power per volume unit with different terminal deployments. As terminal density increases, the limit of the normalized capacity [nats/s/Hz/volume-unit] achieved when wavelength $\lambda$ approaches zero is equal to half of the transmit power per volume unit divided by the noise spatial power spectral density. Third, we show that the number of independent signal dimensions that can be harvested per meter deployed surface is $2/\lambda$ for one-dimensional terminal deployment, and $\pi /\lambda ^2$ per square meter for two- and three-dimensional terminal deployments. Finally, we consider implementations of the LIS in the form of a grid of conventional antenna elements, and show that the sampling lattice that minimizes the surface area and simultaneously obtains one independent signal dimension for every spent antenna is the hexagonal lattice. [ABSTRACT FROM AUTHOR]

Time-division duplex (TDD)-based massive MIMO systems rely on the reciprocity of the wireless propagation channels when calculating the downlink precoders based on uplink pilots. However, the effective uplink and downlink channels incorporating the analog radio front-ends of the base station (BS) and user equipments (UEs) exhibit non-reciprocity due to non-identical behavior of the individual transmit and receive chains. When the downlink precoder is not aware of such channel non-reciprocity (NRC), system performance can be significantly degraded due to the NRC-induced interference terms. In this paper, we consider a general TDD-based massive MIMO system where frequency-response mismatches at both the BS and UEs, as well as the mutual coupling mismatches at the BS large-antenna system all coexist and induce channel NRC. Based on the NRC-impaired signal models, we first propose a novel iterative estimation method for acquiring both the BS and UE side NRC matrices and then also propose an efficient NRC-aware downlink precoder design which utilizes the obtained estimates. Furthermore, an efficient pilot signaling scheme between the BS and UEs is introduced in order to facilitate executing the proposed estimation method and the NRC-aware precoding technique in practical systems. Comprehensive numerical results indicate substantially improved spectral efficiency performance when the proposed NRC estimation and NRC-aware precoding methods are adopted, compared to the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Self-interference is the main obstacle to overcome in order to enable a wireless device to simultaneously transmit and receive in overlapped frequency bands. There is much interest to suppress this interference employing compact and efficient cancelers, which operate in the digital domain, extending this way the benefits of in-band full-duplex to innumerable applications. However, this is not an easy task since the desired signal results hidden in quantization noise during analog-to-digital conversion because of the tremendous power asymmetry between the selfinterference and desired signal. This paper proposes an alternative receiver structure in which the received signal is sampled at the zero-amplitude instants of the interference. Our analysis demonstrates that the resulting sampling process preserves the information conveyed in the desired signal and that demodulation is possible. The performance of this novel full-duplex receiver is evaluated using simulations, proving that this sampling strategy enables all digital self-interference cancelation. [ABSTRACT FROM AUTHOR]

In this paper, we consider the joint design of both transmit waveforms and receive filters for a colocated multipleinput- multiple-output (MIMO) radar with the existence of signaldependent interference and white noise. The design problem is formulated into a maximization of the signal-to-interference-plusnoise ratio (SINR), including various constraints on the transmit waveforms. Comparedwith the traditional alternating semidefinite relaxation approach, a general and flexible algorithm is proposed based on the majorization-minimization method with guaranteed monotonicity, lower computational complexity per iteration and/or convergence to a B-stationary point. Many waveform constraints can be flexibly incorporated into the algorithm with only a few modifications. Furthermore, the connection between the proposed algorithm and the alternating optimization approach is revealed. Finally, the proposed algorithm is evaluated via numerical experiments in terms of SINR performance, ambiguity function, computational time, and properties of the designed waveforms. The experiment results show that the proposed algorithms are faster in terms of running time and meanwhile achieve as good SINR performance as the the existing methods. [ABSTRACT FROM AUTHOR]

The paper investigates the joint design of transmit waveform and receive filter for multiple-input multiple-output radar in the presence of signal-dependent interference, subject to a peak-to-average-power ratio constraint as well as a waveform similarity constraint. Owing to this kind of signal dependence and constraints, the formulated optimization problem of the output signal-to-interference-plus-noise ratio (SINR) maximization is NPhard. To this end, an auxiliary variable is first introduced to modify the original problem, and then a primal-dual algorithm based on the block successive upper-bound minimization method of multipliers (BSUM-M) is developed to deal with the resulting problem. Moreover, an active set method is exploited to solve the quadratic programming problem involved in each update procedure of the proposed BSUM-M algorithm. Finally, numerical simulations are performed to demonstrate the superiority of the proposed algorithm over state-of-the-art methods in terms of the output SINR, beampattern, computational complexity, pulse compression, and ambiguity properties. [ABSTRACT FROM AUTHOR]

In this paper, optimal power allocation strategies are investigated for jammer nodes in a wireless localization system. Building upon the concept of the restricted Bayesian approach, a generalized optimization strategy, called the restricted scheme, is proposed for power allocation of jammer nodes, and its theoretical properties are characterized. In the restricted scheme, the aim is to maximize the average Cramer-Rao lower bound (CRLB) of target nodes while keeping their minimum CRLB above a predefined level in the presence of average (total) and peak power constraints. It is proved that the average CRLB achieved by the restricted scheme is a strictly decreasing and concave function of the constraint on the minimum CRLB level. A closed-form solution is obtained for the restricted scheme when the tradeoff parameter and the total power limit are below certain thresholds. In addition, it is shown that the optimal solution of the restricted scheme corresponds to the use of at most NT jammer nodes, where NT is the number of target nodes, and that the optimal solution of the minimum CRLB maximization scheme is determined by at most NJ target nodes, where NJ is the number of jammer nodes. Extensions of the restricted scheme and an alternative scheme that aims to maximize the number of disabled target nodes (whose CRLBs are above a preset level) are considered, and the corresponding optimal strategies for jammer power allocation are identified. Numerical examples are provided to verify the theoretical derivations for various network configurations. [ABSTRACT FROM AUTHOR]

It is known that if the objective of a wireless sensor network is not to reconstruct individual sensor readings at a fusion center but rather to compute a linear function of them, then the interference property of the wireless channel can be beneficially harnessed by letting nodes transmit simultaneously. Recently, an analog computation scheme was proposed to show that it is possible to take the advantage of the interference property even if nonlinear functions are to be computed. The scheme involves some pre-processing on the sensor readings and post-processing on the superimposed signals observed by the fusion center. Correspondingly, this paper provides a thorough base for a theory of analog-computing functions over wireless channels by specifying what is the maximum achievable. This means it is determined for networks of arbitrary topology which functions are generally analog-computable over the channel and how many wireless resources are needed. It turns out that the considerations are closely related to the famous 13th Hilbert problem and that analog-computations can be universally performed in the sense that the pre-processing at sensor nodes is independent of the function to be computed. Universality reduces the complexity of transmitters and the signaling overhead, and it is shown that this property is preserved if nodes leave or join the network. Analog-computability is therefore of high practical relevance as it allows for an efficient computation of functions in sensor networks. [ABSTRACT FROM PUBLISHER]

Localization via received signal strength (RSS) is often employed in cases where the received signal is fairly weak, either due to distance or due to deliberate covert operation or interference avoidance. However, most research on source localization via RSS implicitly assumes that the background noise is negligible, and that parameters of the transmitter and environment are known. Many commercial chipsets provide per-frame RSS measurements obtained when demodulating the signal, which do not include background noise; however, noise can still cause signal outages. In law enforcement, surveillance, and emergency situations, RSS may be obtained more crudely, such as by energy detection, in which case the RSS will include contributions from the background noise as well. This paper proposes new probabilistic RSS models that account for background noise in both types of RSS measurements. We also derive and evaluate maximum likelihood estimators (MLEs) for these new models, as well as for differential RSS, which has hitherto not been rigorously analyzed in the literature. Several of these MLEs are extended to estimate the transmit power and/or path loss if they are unknown. The new models are justified by extensive measured data. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel low-complexity variable forgetting factor (VFF) mechanism for blind adaptive constrained constant modulus (CCM) recursive least squares (RLS) algorithms applied to linear interference suppression in direct-sequence code-division multiple access (DS-CDMA) systems. The proposed variable forgetting factor mechanism employs an updated component related to the time average of the constant modulus (CM) cost function to automatically adjust the forgetting factor in order to ensure good tracking of the interference and the channel. Convergence and tracking analyses are carried out. Analytical expressions for predicting the mean-squared error of the proposed adaptation technique are obtained. Simulation results are presented for nonstationary environments and show that the proposed variable forgetting factor mechanism achieves superior performance to previously reported methods at a reduced complexity. [ABSTRACT FROM PUBLISHER]

In this paper, the linear Gaussian relay problem is considered. Under the linear time-invariant (LTI) model the rate maximization problem in the linear Gaussian relay channel is formulated in the frequency domain based on the Toeplitz distribution theorem. Under the further assumption of realizable input spectra, the rate maximization problem is converted to the problem of joint source and relay filter design with two power constraints, one at the source and the other at the relay, and a practical solution to this problem is proposed based on the (adaptive) projected (sub)gradient method. Numerical results show that the proposed method yields a considerable gain over the instantaneous amplify-and-forward (AF) scheme in inter-symbol interference (ISI) channels. Also, the optimality of the AF scheme within the class of one-tap relay filters is established in flat-fading channels. [ABSTRACT FROM PUBLISHER]

Linear matrix precoding, also known as vectoring, is a well-known technique to mitigate multiuser interference in the downlink digital subscriber line (DSL) transmission. While effective in canceling interference, vectoring does incur major communication overhead and computational overhead in terms of the transmission of idle symbols and precoder-data multiplications at each data frame, resulting in significant energy consumption when the number of lines is large. To facilitate energy efficient transmission, it has been recently proposed (in the G.fast standard) that each data frame is divided into a normal operating interval (NOI) and a discontinuous operating interval (DOI). In the NOI, all lines (or users) are involved in a common vectoring group, which requires a large matrix precoder, whereas in the DOI, the lines are subdivided into multiple small nonoverlapping vectoring subgroups and are transmitted in a time division multiple access manner, requiring small matrix multiplications and, thus, improving the energy efficiency. In this paper, we consider the key dynamic resource allocation problems in downlink DSL: given the real-time demands, determine the optimal transmission scheme: The optimal NOI and DOI size in each data frame as well as the optimal grouping strategy in the DOI, and optimally adjust the transmission scheme. We formulate these optimal dynamic resource allocation problems and propose efficient real-time algorithms to solve them to global optimality. Simulation results are shown to demonstrate the efficiency and the effectiveness of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we consider compressive interference mitigation and data recovery in cloud radio access networks (C-RANs). Specifically, we exploit the inherent sparsity to reduce the fronthaul capacity requirement for joint narrowband interference (NBI) mitigation and data recovery in the uplink of a C-RAN. As the data sent by uplink users in C-RANs and the NBI from other systems are sparse in some application scenarios, a joint NBI mitigation and data recovery problem can be naturally formulated as a compressive sensing problem. To further exploit the individual sparse structure of the information and interference, we formulate a sparse maximum likelihood estimation (MLE) problem which maximizes the associated likelihood function under individual sparsity levels (ISLs) constraints. We further adopt an alternating optimization (AO) algorithm to solve this Sparse MLE problem. Besides this, we introduce a new kind of restricted isometry property (RIP) called the ISLs-RIP and show that the normalized C-RAN measurement matrix satisfies the ISLs-RIP. Based on the ISLs-RIP, we establish the convergence conditions for the AO algorithm. Finally, we analyze the MSE satisfaction probability for the C-RAN and further obtain some design insights for C-RANs by studying the impact of some key parameters on C-RAN performance. [ABSTRACT FROM PUBLISHER]

This paper develops power control algorithms for energy efficiency (EE) maximization (measured in bit/Joule) in wireless networks. Unlike previous related works, minimum-rate constraints are imposed and the signal-to-interference-plus-noise ratio takes a more general expression, which allows one to encompass some of the most promising 5G candidate technologies. Both network-centric and user-centric EE maximizations are considered. In the network-centric scenario, the maximization of the global EE and the minimum EE of the network is performed. Unlike previous contributions, we develop centralized algorithms that are guaranteed to converge, with affordable computational complexity, to a Karush–Kuhn–Tucker point of the considered non-convex optimization problems. Moreover, closed-form feasibility conditions are derived. In the user-centric scenario, game theory is used to study the equilibria of the network and to derive convergent power control algorithms, which can be implemented in a fully decentralized fashion. Both scenarios above are studied under the assumption that single or multiple resource blocks are employed for data transmission. Numerical results assess the performance of the proposed solutions, analyzing the impact of minimum-rate constraints, and comparing the network-centric and user-centric approaches. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating a signal which has been corrupted with structured noise. When the signal of interest accepts a sparse representation, only a small number of measurements are required to retain all the information. The measurements are mapped to a lower dimensional space through a projection matrix. We propose a method to optimize the design of this matrix where the objective is not only to reduce the amount of data to be processed but also to reject the undesired signal components. As a result, we reduce the computation time and the error on the estimation of the unknown parameters of the sparse model, with respect to the uncompressed data. The proposed method has tunable parameters that can affect its performance. Optimal tuning would require a comprehensive study of parameter variations and options. To avoid this learning burden, we also introduce a variant of the algorithm that is free from tuning, without significant loss of performance. Using synthetic data, we analyze the performance of the proposed algorithms and their robustness against errors in the model parameters. Additionally, we illustrate the performance of the method through a radar application using real clutter data with a still target and with a synthetic moving target. [ABSTRACT FROM PUBLISHER]

Consider a MIMO multi-cellular network (also known as an interfering broadcast channel) where each base station transmits signals to the users in its own cell. The basic problem is to design linear transmit/receive beamformers and schedule users across a fixed set of time slots so as to maximize the system throughput in the presence of both inter and intra cell interference. In this paper, we propose a joint linear transceiver design and user grouping scheme for sum utility maximization that is based on iterative minimization of weighted mean squared error (MSE). The proposed algorithm only needs local channel knowledge and its convergence to a stationary point is guaranteed for some well-known utility functions, while ensuring user fairness. The simulation results show that the proposed formulation/algorithm can offer significantly higher system throughput than the standard multi-user MIMO techniques such as the SVD-MMSE strategy, while maintaining user fairness. Furthermore, the proposed algorithm exhibits fast convergence and is amenable to distributed implementation with limited information exchange. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the robust beamforming schemes for a multi-user multiple-input-single-output (MU-MISO) system with per-antenna power constraints and quantized channel direction information (CDI) feedback. Our design objective is to maximize the expectation of the weighted sum-rate performance by means of controlling the interference leakage and properly allocating the power among user equipments (UEs). First, we prove the optimality of the non-robust zero-forcing (ZF) beamforming scheme in the sense of generating the minimum amount of average inter-UE interference under quantized CDI. Then we derive closed-form expressions of the cumulative density function (CDF) of the interference leakage power for the non-robust ZF beamforming scheme, based on which we adjust the leakage thresholds and propose two robust beamforming schemes under per-antenna power constraints with an iterative process to update the per-UE power allocations using the geometric programming (GP). Simulation results show the superiority of the proposed robust beamforming schemes compared with the existing schemes in terms of the average weighted sum-rate performance. [ABSTRACT FROM AUTHOR]

In this paper we deal with the problem of detecting an extended target embedded in homogeneous Gaussian interference with unknown but structured covariance matrix. We model the possible target echo, from each range bin under test, as a deterministic signal with an unknown scaling factor accounting for the target response. At the design stage, we exploit some a-priori knowledge about the operating environment enforcing the inverse interference plus noise covariance matrix to belong to a set described via unitary invariant continuous functions. Hence, we derive the constrained Maximum Likelihood (ML) estimates of the unknown parameters, under both the H0 and H1 hypotheses, and design the Generalized Likelihood Ratio Test (GLRT) for the considered decision problem. At the analysis stage, we assess the performance of the devised GLRT for some covariance matrix uncertainty sets of practical relevance both for spatial and Doppler processing. The results highlight that correct use of the a-priori knowledge can lead to a detection performance quite close to the optimum receiver which supposes the perfect knowledge of the interference plus noise covariance matrix. [ABSTRACT FROM PUBLISHER]

In this paper, we study K-user multiple-input multiple-output (MIMO) interference channels (IC) in the presence of antenna correlations. Although interference alignment (IA) achieves the maximum multiplexing gain, i.e., degrees of freedom (DOF), the actual performance is degraded due to ill-conditioned channels caused by the antenna correlations. To enhance the overall system performance, we thus consider a transmission mode selection scheme which adaptively determines the number of data streams at each transmitter-receiver pair in a distributed fashion using local channel state information (CSI). A filter update process is exploited in order to estimate the performance regarding each transmission mode successively by considering the actual channel conditions as well as signal-to-noise ratio (SNR). Simulation results show that our mode selection scheme enhances the sum rate performance compared to the conventional full data stream transmissions in spatially correlated MIMO IC. [ABSTRACT FROM PUBLISHER]

Second order cone (SOC) restrictions have been widely employed in the design of wireless communication systems to recast precoding optimizations in convex form. This paper highlights the flexibility of this framework to handle various transmit power and interference limitations also in the multistream multiple-input multiple-output broadcast channel with imperfect channel state information at the transmitter. In particular, we propose an iterative solution for mean square error (MSE) balancing with such constraints via the Lagrangian dual formulation. The MSE balancing criteria, contrary to signal-to-interference plus-noise ratio and rate criteria, exhibit an interesting property when considering different targets for the receivers. Users with low target values are strictly prioritized in comparison to users with large MSE targets that can even be switched off. Therefore, only a subset of the receivers is served at the solution with stringent limitations for the precoders. An example of such stringent limitations are small power constraints and probabilistic interference temperature constraints that set a measurable threshold on the generated interference in a cognitive radio system. We provide four different approaches to approximate these probabilistic constraints with deterministic SOC constraints, and compare their accuracy. [ABSTRACT FROM PUBLISHER]

The invariance principle is adopted to develop an exhaustive study for adaptive detection of range-spread targets in Gaussian noise sharing a block-diagonal covariance structure. For this problem, the usual generalized likelihood ratio principle is intractable. In this paper, we first determine the largest group of affine transformations that does not alter the decision problem. Then, a maximal invariant identified by this group is derived, which can characterize the totality of the invariant detectors and extends the existing results for the point-target case. A theoretical performance analysis of the maximal invariant is also given. Finally, we propose two classes of invariant detectors, which are distinguished by whether a constant false alarm rate (CFAR) is maintained. Numerical experiments are provided for a comparison of the proposed detectors, where a tradeoff between the CFARness and the improved performance has been observed and studied. [ABSTRACT FROM PUBLISHER]

Recent results have shown the benefits of widely linear precoding (WLP) in the MIMO interference channel (MIMO IC) assuming that all transmitters can follow the same strategy. Motivated by a transitional scenario where legacy linear transmitters coexist with widely linear ones, this work investigates the general K-user MIMO IC in a heterogeneous (linear and widely linear) transmitter deployment. In particular, we address the maximization of the weighted sum-rate (WSR) for (widely) linear transmit filters design through the use of the complex-valued formulation. Since the maximum WSR problem is non-convex, and thus difficult to be solved, we formulate an equivalent minimum weighted mean square error problem that allows deriving closed-form expressions for (widely) linear transceivers. Then an iterative procedure is proposed, which is proven to reach a stationary point of the maximum WSR problem. Simulations show that the proposed procedure allows increasing the sum-rate as compared to coordinated linear transceiver schemes. The gains are larger and significant in two different nonexclusive conditions: as the interference level increases or when the number of antennas is low. [ABSTRACT FROM PUBLISHER]

In future full-duplex communications, the cancellation of self-interference (SI) arising from hardware nonidealities will play an important role in the design of mobile-scale devices. To this end, we introduce an optimal digital SI cancellation solution for shared-antenna-based direct-conversion transceivers. To establish that the underlying widely linear signal model is not adequate for strong transmit signals, the impact of various circuit imperfections, including power amplifier distortion, frequency-dependent I/Q imbalance, quantization noise, and thermal noise, on the performance of the conventional augmented least mean square (LMS) based SI canceller, is analyzed. In order to achieve a sufficient signal-to-interference-plus-noise ratio when the nonlinear SI components are not negligible, we propose an augmented nonlinear LMS based SI canceller for a joint cancellation of both the linear and nonlinear SI components by virtue of a widely nonlinear model fit. A rigorous mean and mean square performance evaluation is conducted to justify the performance advantages of the proposed scheme over the conventional augmented LMS solution. Simulations on orthogonal frequency division multiplexing-based wireless local area network standard compliant waveforms support the analysis. [ABSTRACT FROM AUTHOR]

Estimation of both cascaded and residual self-interference (RSI) channels and a new training frame structure are considered for full-duplex (FD) amplify-and-forward (AF) one-way relay networks with rapidly time-varying individual channels. To estimate the RSI and the rapidly time-varying cascaded channels, we propose a new training frame structure in which orthogonal training blocks are sent by the source node and delivered to the destination over an FD-AF relay. Exploiting the orthogonality of the training blocks, we obtain two decoupled training signal models for the estimation of the RSI and the cascaded channels. We apply linear minimum mean square error (MMSE) based estimators to the cascaded channel as well as RSI channel. In order to investigate the mean square error (MSE) performance of the system, we also derive the Bayesian Cramer–Rao lower bound. As another performance benchmark, we also assess the symbol error rate (SER) performances corresponding to the estimated and the perfect channel state information available at the receiver side. Computer simulations exhibit the proposed training frame structure and the linear MMSE estimator MSE and SER performances are shown. [ABSTRACT FROM AUTHOR]

We propose a cognitive blind interference alignment (BIA) scheme for macro-femto two-tier networks where the users are equipped with a single reconfigurable antenna. The proposed scheme fully cancels the intracell interference in both the upper and lower tiers. Moreover, it provides the optimal Degrees of Freedom (DoF) in the macro-tier while the lower tier subject to inter-tier interference achieves non-zero DoF. To do so, the proposed scheme relies on a topology management scheme where the users belong to the macro-tier if and only if they can optimally treat the interference caused by the transmission at the femto-tier as noise. The proposed scheme does not require any channel state information at the transmitter or data sharing between the macro- and femto-tiers. An outer bound of the DoF is derived for the considered macro-femto network. It is demonstrated that the proposed scheme can attain the outer bound subject to optimal sum DoF for the macro-tier. Furthermore, compared to previous approaches, cognitive BIA can achieve better sum rates for macro-femto networks for femtocells subject to different levels of inter-tier interference. [ABSTRACT FROM PUBLISHER]

In contrast to active radar systems, in which the transmitted waveforms are carefully selected and designed in relation to the radar operational modes and requirements, passive radars (PRs) exploiting noncooperative illuminators of opportunity need to cope with waveforms that are not tailored for the radar applications. In the latter case, it is likely that target echoes are masked by echoes from other strong targets in multitarget scenarios. This fact inspired us to model the multitarget detection problem in the PRs as an M-ary hypothesis testing problem. We then employ the generalized likelihood ratio (GLR) principle to derive the GLR-based detector. A parallel and recursive implementation of the detector is presented for computationally efficient implementation, in which the targets are detected sequentially and the previously detected targets are treated as interferences to be removed yielding the detection of the weakest ones. The false alarm and detection performance of the proposed sequential GLR-based detector are analytically studied using asymptotic distribution and also their accuracies are verified numerically. Simulation results show that there is a high agreement between asymptotic performance and the one obtained by simulation results. Extensive simulation results for both FM- and DVB-T-based PRs are presented to demonstrate the effectiveness of the proposed detection algorithm. Furthermore, it can be revealed from our simulation results that the proposed detection algorithm significantly outperforms the existing methods without adding significant complexity to them. [ABSTRACT FROM PUBLISHER]

In full-duplex (FD) radios, phase noise leads to random phase mismatch between the self-interference (SI) and the reconstructed cancellation signal, resulting in possible performance degradation during SI cancellation. To explicitly analyze its impacts on the digital SI cancellation, an orthogonal frequency division multiplexing (OFDM)-modulated FD radio is considered with phase noises at both the transmitter and receiver. The closed-form expressions for both the digital cancellation capability and its limit for the large interference-to-noise ratio (INR) case are derived in terms of the power of the common phase error, INR, desired signal-to-noise ratio (SNR), channel estimation error and transmission delay. Based on the obtained digital cancellation capability, the achievable rate region of a two-way FD OFDM system with phase noise is characterized. Then, with a limited SI cancellation capability, the maximum outer bound of the rate region is proved to exist for sufficiently large transmission power. Furthermore, a minimum transmission power is obtained to achieve $\beta$ -portion of the cancellation capability limit and to ensure that the outer bound of the rate region is close to its maximum. [ABSTRACT FROM PUBLISHER]

Future wireless networks are expected to be heterogeneous with dense deployment of pico (small cell) base stations (BSs) overlaid with traditional macro BSs. There are two performance bottlenecks in such heterogeneous networks (HetNet): the interference issue and cell-edge effect. We propose to combine cell-free BS-user association (BUA), power control, and dynamic user-side interference cancellation (IC) to mitigate these bottlenecks. By dynamically selecting the “best” serving BSs for each user, the cell-free BUA can exploit the multi-BS diversity gain to mitigate the interference and cell-edge effect. Furthermore, the user-side IC eliminates the strong cross-tier interference. We formulate the joint optimization of cell-free BUA, power allocation, and user-side IC as a weighted sum-rate (WSR) maximization problem, and propose a WMMSE alternating optimization algorithm to solve it. Specifically, a generalized WMMSE method is proposed to solve the power optimization subproblem with nondifferentiable WSR function. Furthermore, by exploiting the specific problem structure, low complexity search methods are designed to find the optimal solutions of the combinatorial cell-free BUA and user-side IC subproblems. The proposed algorithm is shown to converge to a stationary solution of the joint problem. Simulations verify the significant gain of the proposed solution over existing solutions. [ABSTRACT FROM AUTHOR]

We consider a downlink multi-cell multiple-input multiple-output (MIMO) interference broadcast channel (IBC) using orthogonal frequency division multiplexing (OFDM) with multiple users contending for space-frequency resources in a given scheduling instant. The problem is to design precoders efficiently to minimize the number of backlogged packets queuing in the coordinating base stations (BSs). Conventionally, the queue weighted sum rate maximization (Q-WSRM) formulation with the number of backlogged packets as the corresponding weights is used to design the precoders. In contrast, we propose joint space-frequency resource allocation (JSFRA) formulation, in which the precoders are designed jointly across the space-frequency resources for all users by minimizing the total number of backlogged packets in each transmission instant, thereby performing user scheduling implicitly. Since the problem is nonconvex, we use the combination of successive convex approximation (SCA) and alternating optimization (AO) to handle nonconvex constraints in the JSFRA formulation. In the first method, we approximate the signal-to-interference-plus-noise ratio (SINR) by convex relaxations, while in the second approach, the equivalence between the SINR and the mean squared error (MSE) is exploited. We then discuss the distributed approaches for the centralized algorithms using primal decomposition and alternating directions method of multipliers. Finally, we propose a more practical iterative precoder design by solving the Karush–Kuhn–Tucker expressions for the MSE reformulation that requires minimal information exchange for each update. Numerical results are used to compare the proposed algorithms to the existing solutions. [ABSTRACT FROM AUTHOR]

We investigate a blind interference alignment (BIA) scheme through staggered antenna switching over various interference networks (e.g., broadcast channel, interference channel, and cellular networks) with realistic channel assumptions. In existing BIA, the coherence time of channel is assumed to be long enough, but that may not always be true in realistic scenarios. Therefore, we propose a dynamic supersymbol design method which can construct a supersymbol with limited symbol extension that is determined by the coherence time of channel. It is demonstrated that the supersymbol block length can be reduced significantly by aligning interferences in a hierarchical manner, referred to as hierarchical BIA. The key idea of hierarchical BIA is to align interferences in groups and to use the same supersymbol structure between groups, producing aligned inter-group interferences without inner-group interference. Consequently, it is observed that with a given coherence time the proposed dynamic supersymbol design that exploits hierarchical BIA achieves higher degrees of freedom than the conventional method. [ABSTRACT FROM PUBLISHER]

We consider the problem of joint delay-Doppler estimation of a moving target in a passive radar that employs a non-cooperative illuminator of opportunity (IO) for target illumination, a reference channel (RC) steered to the IO to obtain a reference signal, and a surveillance channel (SC) for target monitoring. We consider a practically motivated scenario, where the RC receives a noise-contaminated copy of the IO signal and the SC observation is polluted by a direct-path interference that is usually neglected by prior studies. We develop a data model without discretizing the parameter space, which may lead to a straddle loss, by treating both the delay and Doppler as continuous parameters. We propose an expectation-maximization based estimator, as well as a modified cross-correlation (MCC) estimator that is a computationally simpler solution resulting from an approximation of the former. In addition, we derive the Cramér-Rao lower bound for the estimation problem. Simulation results are presented to illustrate the performance of the proposed estimators and the widely used CC estimator. [ABSTRACT FROM PUBLISHER]

We propose a blind interference alignment scheme for partially connected cellular networks. The scheme cancels both intracell and intercell interference by relying on receivers with one reconfigurable antenna and by allowing users at the cell edge to be served by all the base stations in their proximity. An outer bound for the degrees of freedom is derived for general partially connected networks with single-antenna receivers when knowledge of the channel state information at the transmitter is not available. It is demonstrated that for symmetric scenarios, this outer bound is achieved by the proposed scheme. On the other hand, for asymmetric scenarios, the achievable degrees of freedom are not always equal to the outer bound. However, the penalty is typically small, and the proposed scheme outperforms other blind interference alignment schemes. Moreover, significant reduction of the supersymbol length is achieved compared with a standard blind interference alignment strategy designed for fully connected networks. [ABSTRACT FROM PUBLISHER]

We consider the problem of waveform design for Multiple-Input Multiple-Output (MIMO) radar in the presence of signal-dependent interference embedded in white Gaussian disturbance. We present two sequential optimization procedures to maximize the Signal to Interference plus Noise Ratio (SINR), accounting for a constant modulus constraint as well as a similarity constraint involving a known radar waveform with some desired properties (e.g., in terms of pulse compression and ambiguity). The presented sequential optimization algorithms, based on a relaxation method, yield solutions with good accuracy. Their computational complexity is linear in the number of iterations and trials in the randomized procedure and polynomial in the receive filter length. Finally, we evaluate the proposed techniques, by considering their SINR performance, beam pattern as well as pulse compression property, via numerical simulations. [ABSTRACT FROM AUTHOR]

We propose a widely linear multistage Wiener filter (WL-MSWF) receiver to suppress inter-/intra-symbol interference, multiuser interference, and narrowband interference in a high data rate direct-sequence ultra wideband (DS-UWB) system. The proposed WL receiver fully exploits the second-order statistics of the received signal, yielding a smaller Minimum Mean Square Error (MMSE) than the linear receiver. The WL-MSWF receiver mainly consists of a low-rank transformation and an adaptive reduced-rank after. The rank-reduction is achieved via a transformation matrix. Based on the linear MSWF concept, two constructions of this rank-reduction matrix, namely total WL (TWL) and quasi WL (QWL), are proposed. We develop stochastic gradient (SG) and recursive least squares (RLS) adaptive versions of the proposed TWL/QWL-MSWF and theoretically analyze their convergence behavior. The comparison of the proposed TWL/QWL-MSWF and the existing algorithms is carried out in terms of the computational complexity and the resulting MMSE performance. Extensive simulation results show that the proposed TWL/QWL-MSWF schemes outperform the existing schemes in both convergence and steady-state performance under various conditions. [ABSTRACT FROM PUBLISHER]

We study the performance region of a general multicell downlink scenario with multiantenna transmitters, hardware impairments, and low-complexity receivers that treat interference as noise. The Pareto boundary of this region describes all efficient resource allocations, but is generally hard to compute. We propose a novel explicit characterization that gives Pareto optimal transmit strategies using a set of positive parameters-fewer than in prior work. We also propose an implicit characterization that requires even fewer parameters and guarantees to find the Pareto boundary for every choice of parameters, but at the expense of solving quasi-convex optimization problems. The merits of the two characterizations are illustrated for interference channels and ideal network multiple-input multiple-output (MIMO). [ABSTRACT FROM PUBLISHER]

We show that the maximization of the sum degrees-of-freedom for the static flat-fading multiple-input multiple-output (MIMO) interference channel (IC) is equivalent to a rank constrained rank minimization problem (RCRM), when the signal subspaces span all available dimensions. The rank minimization corresponds to maximizing interference alignment (IA) so that interference spans the lowest dimensional subspace possible. The rank constraints account for the useful signal subspaces spanning all available spatial dimensions. That way, we reformulate the IA requirements to requirements involving ranks. Then, we present a convex relaxation of the RCRM problem inspired by recent results in compressed sensing and low-rank matrix completion theory that rely on approximating rank with the nuclear norm. We show that the convex envelope of the sum of ranks of the interference matrices is the normalized sum of their corresponding nuclear norms and replace the rank constraints with asymptotically equivalent and tractable ones. We then tune our heuristic relaxation for the multicell interference channel. We experimentally show that in many cases the proposed algorithm attains perfect interference alignment and in some cases outperforms previous approaches for finding precoding and receive matrices for interference alignment. [ABSTRACT FROM PUBLISHER]

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