In this paper, we study terahertz (THz) simultaneous wireless information and power transfer (SWIPT) systems. Since coherent information detection is challenging at THz frequencies and Schottky diodes may not be efficient for THz energy harvesting (EH), we propose a novel THz SWIPT system design that employs unipolar amplitude shift keying (ASK) modulation at the transmitter (TX) and a resonant-tunnelling diode (RTD)-based EH circuit at the receiver (RX) to extract both information and power from the received signal. Furthermore, we propose a novel model for the dependence of the instantaneous output power of the RTD-based RX on the instantaneous received power, which is based on a non-linear and non-monotonic piecewise function, whose parameters are adjusted to fit circuit simulation results. To determine the information rate-harvested power tradeoff of the considered THz SWIPT system, we derive the distribution of the transmit signal that maximizes the mutual information between the transmit and received signals subject to constraints on the required average harvested power at the RX and the peak signal amplitude at the TX. Since the computational complexity needed for maximization of the mutual information may be infeasible for real-time THz SWIPT systems, we derive low-complexity suboptimal input signal distributions that maximize an achievable information rate numerically and in closed form for high and low required average harvested powers, respectively. Furthermore, based on the obtained results, we propose a suboptimal closed-form distribution of the transmit signal which can also guarantee a desired harvested power at the RX. Our simulation results show that while the proposed EH model can capture the non-monotonicity of RTD-based EH circuits in the THz band, baseline linear and non-linear EH models, developed for Schottky-diode-based EH circuits, cannot. Furthermore, we demonstrate that a lower reverse current flow and a higher breakdown voltage of the employed RTD are preferable when the input signal power at the RX is low and high, respectively. We also show that all proposed input distributions yield practically identical SWIPT system performance. Moreover, we reveal that the information rate-harvested power tradeoff of THz SWIPT systems is determined by the peak amplitude of the TX signal and the maximum instantaneous harvested power for low and high received signal powers, respectively. Finally, we compare the proposed THz SWIPT system with two baseline schemes and confirm that the RX circuit parameters, mathematical EH models, and optimal transmit signal distributions have to be carefully designed to achieve high performance in THz SWIPT systems.

Integrating different functionalities, conventionally implemented as dedicated systems, into a single platform allows utilising the available resources more efficiently. We consider an integrated sensing and power transfer (ISAPT) system and propose the joint optimisation of the rectangular pulse-shaped transmit signal and the beamforming vector to combine sensing and wireless power transfer (WPT) functionalities efficiently. In contrast to prior works, we adopt an accurate non-linear circuit-based energy harvesting (EH) model. We formulate and solve a non-convex optimisation problem for a general number of EH receivers to maximise a weighted sum of the average harvested powers at the EH receivers while ensuring the received echo signal reflected by a sensing target (ST) has sufficient power for estimating the range to the ST with a prescribed accuracy within the considered coverage region. The average harvested power is shown to monotonically increase with the pulse duration when the average transmit power budget is sufficiently large. We discuss the trade-off between sensing performance and power transfer for the considered ISAPT system. The proposed approach significantly outperforms a heuristic baseline scheme based on a linear EH model, which linearly combines energy beamforming with the beamsteering vector in the direction to the ST as its transmit strategy.

Wireless information and energy transfer (WIET) represents an emerging paradigm that employs controllable transmission of radio frequency signals for the dual purpose of data communication and wireless charging. As such, WIET is widely regarded as an enabler of envisioned sixth-generation (6G) use cases that rely on energy-sustainable Internet-of-Things (IoT) networks, such as smart cities and smart grids. Meeting the quality-of-service demands of WIET, in terms of both data transfer and power delivery, requires effective codesign of the information and energy signals. In this article, we present the main principles and design aspects of WIET, focusing on its integration in 6G networks. First, we discuss how conventional communication notions, such as resource allocation and waveform design, need to be revisited in the context of WIET. Next, we consider various candidate 6G technologies that can boost WIET efficiency, namely, holographic multiple-input multiple-output, near-field beamforming, terahertz communication, intelligent reflecting surfaces (IRSs), and reconfigurable (fluid) antenna arrays. We introduce respective WIET design methods, analyze the promising performance gains of these WIET systems, and discuss challenges, open issues, and future research directions. Finally, a near-field energy beamforming scheme and a power-based IRS beamforming algorithm are experimentally validated using a wireless energy transfer testbed. The vision of WIET in communication systems has been gaining momentum in recent years, with constant progress with respect to theoretical and also practical aspects. The comprehensive overview of the state of the art of WIET presented in this article highlights the potential of WIET systems and their overall benefits in 6G networks.

In this paper, we study simultaneous lightwave information and power transfer (SLIPT) systems employing photovoltaic optical receivers (RXs). We consider the case, where the optical RX is illuminated by ambient light and an intensity-modulated information-carrying free space optical (FSO) signal. To overcome the possible absence of ambient light, e.g., indoors or at night, we additionally assume that the optical RX receives a dedicated energy-bearing broadband optical signal. Additionally, to efficiently harvest energy from broadband light, we propose a novel optical RX based on multi-junction photovoltaic cells. Exploiting the analysis of the equivalent two-diode electrical circuit for the multi-junction photovoltaic RX, we carefully model the current flow through the photovoltaic cell and derive an accurate energy harvesting (EH) model. Furthermore, we also derive novel approximate EH models for the two cases, where the optical RX is equipped with a single and multiple p-n junctions, respectively. Next, we derive the distribution of the transmit information signal that maximizes the achievable information rate and, for a practical pulse amplitude modulated information signal, we determine the symbol error rate at the RX. We validate the proposed EH models by circuit simulations and show that the photovoltaic RXs saturate for high received signal powers. For single-junction RXs, we compare the proposed EH model with two well-known baseline EH models, which are based on maximum point tracking and a single-diode electrical circuit, respectively. We demonstrate that, in contrast to the proposed EH model, both baseline EH models are not able to fully capture the non-linear behavior of photovoltaic RXs. Moreover, our results reveal that, since multi-junction RXs allow a more efficient allocation of the optical power, they are more robust against saturation, and thus, are able to harvest significantly more power and achieve higher data rates than RXs employing a single p-n junction. Finally, we highlight a tradeoff between the information rate and harvested power in SLIPT systems and demonstrate that the proposed transmit signal distribution yields significantly higher achievable information rates compared to uniformly distributed transmit signals, which are optimal for linear optical information RXs.

In this paper, we study optical simultaneous wireless information and power transfer (SWIPT) systems, where a photo-voltaic optical receiver (RX) is illuminated by ambient light and an intensity-modulated free space optical (FSO) signal. To facilitate simultaneous information reception and energy harvesting (EH) at the RX, the received optical signal is first converted to an electrical signal, and then, its alternating current (AC) and direct current (DC) components are separated and utilized for information decoding and EH, respectively. By accurately analysing the equivalent electrical circuit of the photovoltaic RX, we model the current flow through the photovoltaic p-n junction in both the low and high input power regimes using a two-diode model of the p-n junction and we derive a closed-form non-linear EH model that characterizes the harvested power at the RX. Furthermore, taking into account the non-linear behaviour of the photovoltaic RX on information reception, we derive the optimal distribution of the transmit information signal that maximizes the achievable information rate. The proposed EH model is validated by circuit simulation results. Furthermore, we compare with two baseline models based on maximum power point (MPP) tracking at the RX and a single-diode p-n junction model, respectively, and demonstrate that in contrast to the proposed EH model, they are not able to fully capture the non-linearity of photovoltaic optical RXs. Finally, our numerical results highlight that the proposed optimal distribution of the transmit signal yields significantly higher achievable information rates compared to uniformly distributed transmit signals, which are optimal for linear optical information RXs.

Current trends in communication system design precipitate a change in the operating regime from the traditional far-field to the radiating near-field (Fresnel) region. We investigate the optimal transmit antenna placement for a multiple-input single-output (MISO) wireless power transfer (WPT) system designed for a three-dimensional cuboid room under line-of-sight (LoS) conditions in the Fresnel region. We formulate an optimisation problem for maximising the received power at the worst possible receiver location by considering the spherical nature of the electromagnetic (EM) wavefronts in the Fresnel region while assuming perfect knowledge of the channel at the transmitter. For the case of two transmit antennas, we derive a closed-form expression for the optimal positioning of the antennas which is purely determined by the geometry of the environment. If the room contains locations where the far-field approximation holds, the proposed positioning is shown to reduce to the far-field solution. The analytical solution is validated through simulation. Furthermore, the maximum received power at the locations yielding the worst performance is quantified and the power gain over the optimal far-field solution is presented. For the considered cuboid environment, we show that a distributed antenna system is optimal in the Fresnel region, whereas a co-located antenna architecture is ideal for the far-field.

The optimal energy signal design for wireless powered communication networks (WPCNs) enabling energy-sustainable communication for a large number of low-power devices is still an open problem in practical systems. In this work, we study a multi-user WPCN, where a multi-antenna base station (BS) sends an energy signal to multiple single-antenna users, which, in turn, harvest energy from the received signal and utilize it for information transmission in the uplink. In contrast to the existing works on multiple-input single-output (MISO) WPCN design, in this paper, we jointly optimize the energy signal waveform and downlink beamforming at the BS for energy harvesting (EH) devices described by non-linear circuit-based models. To this end, we assume that the BS broadcasts a pulse-modulated signal employing multiple energy signal vectors and we formulate an optimization problem for the joint design of the downlink transmit energy signal vectors, their number, the durations of the transmit pulses, and the time allocation policy for minimization of the average transmit power at the BS. We show that for single-user WPCNs, a single energy signal vector, which is collinear with the maximum ratio transmission (MRT) vector and drives the EH circuit at the user device into saturation, is optimal. Next, for the general multi-user case, we show that the optimal signal design requires a maximum number of energy signal vectors that exceeds the number of users by one and propose an algorithm to obtain the optimal energy signal vectors. Since the complexity of the optimal design is high, we also propose two suboptimal schemes for WPCN design. First, for asymptotic massive WPCNs, where the ratio of the number of users to the number of BS antennas, i.e., the system load, tends to zero, we show that the optimal downlink transmit signal can be obtained in closed-form and comprises a sequence of weighted sums of MRT vectors. Next, based on this result, for general WPCNs with finite system loads, we propose a suboptimal closed-form MRT-based design and a suboptimal semidefinite relaxation (SDR)-based scheme. Our simulation results reveal that the proposed optimal scheme and suboptimal SDR-based design achieve nearly identical performance and outperform two baseline schemes, which are based on linear and sigmoidal EH models. Furthermore, we show that, if the system load of the WPCN is low, the performance gap between the proposed suboptimal solutions is small and becomes negligible as the number of BS antennas tends to infinity.

We study two-user multiple-input single-output (MISO) wireless powered communication networks (WPCNs), where the user devices are equipped with non-linear energy harvesting (EH) circuits. We consider time-division duplex (TDD) transmission, where the users harvest power from the signal received in the downlink phase, and then, utilize this harvested power for information transmission in the uplink phase. In contrast to existing works, we adopt a non-linear model of the harvested power based on a precise analysis of the employed EH circuit. We jointly optimize the beamforming vectors in the downlink and the time allocated for downlink and uplink transmission to minimize the average transmit power in the downlink under per-user data rate constraints in the uplink. We provide conditions for the feasibility of the resource allocation problem and the existence of a trivial solution, respectively. For the case where the resource allocation has a non-trivial solution, we show that it is optimal to employ no more than three beamforming vectors for power transfer in the downlink. To determine these beamforming vectors, we develop an iterative algorithm based on semi-definite relaxation (SDR) and successive convex approximation (SCA). Our simulation results reveal that the proposed resource allocation scheme outperforms two baseline schemes based on linear and sigmoidal EH models, respectively.

In this paper, we study multi-user multi-antenna wireless power transfer (WPT) systems, where each antenna at the energy harvesting (EH) nodes is connected to a dedicated non-linear rectifier. We propose optimal transmit strategies which maximize a weighted sum of the average harvested powers at the EH nodes subject to a constraint on the power budget of the transmitter. First, for multiple-input single-output (MISO) WPT systems, we prove that the optimal strategy employs maximum ratio transmission (MRT) beamforming and scalar symbols with arbitrary phases and discrete amplitudes following a probability density function (pdf) with at most two mass points. Then, we prove that for single-input multiple-output (SIMO) WPT systems, the optimal transmit symbol amplitudes are discrete random variables, whose pdf also has no more than two mass points. For general multi-user MIMO WPT, we show that the optimal transmit strategy employs scalar unit-norm symbols with arbitrary phases and at most two beamforming vectors. To determine these vectors, we formulate a non-convex optimization problem and obtain an optimal solution based on monotonic optimization. Since the computational complexity of the optimal solution is high, we propose a low-complexity iterative algorithm to obtain a suboptimal solution, which achieves near-optimal performance. Our simulation results reveal that the proposed transmit strategy for multi-user MIMO WPT systems outperforms baseline schemes based on a linear EH model and a single beamforming vector. For a given transmit power budget, we show that the harvested power saturates when increasing the number of transmit antennas. Finally, we observe that the harvested power region spanned by multiple EH nodes is convex and the power harvested at one EH node can be traded for a higher harvested power at the other nodes.

This paper tackles the problem of downlink data transmission in massive multiple-input multiple-output (MIMO) systems where user equipments (UEs) exhibit high spatial correlation and channel estimation is limited by strong pilot contamination. Signal subspace separation among UEs is, in fact, rarely realized in practice and is generally beyond the control of the network designer (as it is dictated by the physical scattering environment). In this context, we propose a novel statistical beamforming technique, referred to asMIMO covariance shaping, that exploits multiple antennas at the UEs and leverages the realistic non-Kronecker structure of massive MIMO channels to target a suitable shaping of the channel statistics performed at the UE-side. To optimize the covariance shaping strategies, we propose a low-complexity block coordinate descent algorithm that is proved to converge to a limit point of the original nonconvex problem. For the two-UE case, this is shown to converge to a stationary point of the original problem. Numerical results illustrate the sum-rate performance gains of the proposed method with respect to spatial multiplexing in scenarios where the spatial selectivity of the base station is not sufficient to separate closely spaced UEs.

In this paper, we study multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems, where the energy harvesting (EH) node is equipped with multiple nonlinear rectennas. We characterize the optimal transmit strategy by the optimal distribution of the transmit symbol vector that maximizes the average harvested power at the EH node subject to a constraint on the power budget of the transmitter. We show that the optimal transmit strategy employs scalar unit-norm input symbols with arbitrary phase and two beamforming vectors, which are determined as solutions of a non-convex optimization problem. To solve this problem, we propose an iterative algorithm based on a two-dimensional grid search, semi-definite relaxation, and successive convex approximation. Our simulation results reveal that the proposed MIMO WPT design significantly outperforms two baseline schemes based on a linear EH model and a single beamforming vector, respectively. Finally, we show that the average harvested power grows linearly with the number of rectennas at the EH node and saturates for a large number of TX antennas.

In this paper we consider cell-free (CF) massive MIMO (MaMIMO) systems, which comprise a very large number of geographically distributed access points (APs) serving a much smaller number of users. We exploit channel sparsity to tackle pilot contamination, which originates from the reuse of pilot sequences. Specifically, we consider semi-blind methods for joint channel estimation and data detection. Under the challenging assumption of deterministic parameters, we determine sufficient conditions and necessary conditions for semi-blind identifiability, which guarantee the non-singularity of the Fisher Information Matrix (FIM) and the existence of the Cramer-Rao bound (CRB). We propose a message passing (MP) algorithm which determines the exact channel coefficients in the case of semiblind identifiability. We show that the system is identifiable if the Karp-Sipser algorithm yields an empty core. Additionally, we propose a Bayesian semi-blind approach which results in an effective algorithm for joint channel estimation and multi-user detection. This algorithm alternates between channel estimation and linear multi-user detection. Numerical simulations verify the analytical derivations.

Massive access for Internet-of-Things (IoT) in beyond 5G networks represents a daunting challenge for conventional bandwidth-limited technologies. Millimeter-wave technologies (mmWave)-which provide large chunks of bandwidth at the cost of more complex wireless processors in harsher radio environments-is a promising alternative to accommodate massive IoT but its cost and power requirements are an obstacle for wide adoption in practice. In this context, meta-materials arise as a key innovation enabler to address this challenge by Re-configurable Intelligent Surfaces (RISs). In this article we take on the challenge and study a beyond 5G scenario consisting of a multi-antenna base station (BS) serving a large set of single-antenna user equipments (UEs) with the aid of RISs to cope with non-line-of-sight paths. Specifically, we build a mathematical framework to jointly optimize the precoding strategy of the BS and the RIS parameters in order to minimize the system sum mean squared error (SMSE). This novel approach reveals convenient properties used to design two algorithms, RISMA and Lo- RISMA, which are able to either find simple and efficient solutions to our problem (the former) or accommodate practical constraints with low-resolution RISs (the latter). Numerical results show that our algorithms outperform conventional benchmarks that do not employ RIS (even with low-resolution meta-surfaces) with gains that span from 20% to 120% in sum rate performance.

In this paper, we study simultaneous wireless information and power transfer (SWIPT) systems employing practical non-linear energy harvester (EH) circuits. Since the voltage across the reactive elements of realistic EH circuits cannot drop or rise instantaneously, EHs have memory which we model with a Markov decision process (MDP). Moreover, since an analytical model that accurately models all non-linear effects and the unavoidable impedance mismatch of EHs is not tractable, we propose a learning based model for the EH circuit. We optimize the input signal distribution for maximization of the harvested power under a constraint on the minimum mutual information between transmitter (TX) and information receiver (IR). We distinguish the cases where the MDP state is known and not known at TX and IR. When the MDP state is known, the formulated optimization problem for the harvested power is convex. In contrast, if TX and IR do not know the MDP state, the resulting optimization problem is non-convex and solved via alternating optimization, which is shown to yield a limit point of the problem. Our simulation results reveal that the rate-power region of the considered SWIPT system depends on the symbol duration, the EH input power level, the EH impedance mismatch, and the type of EH circuit. In particular, a shorter symbol duration enables higher bit rates at the expense of a significant decrease in the average harvested power. Furthermore, whereas half-wave rectifiers outperform full-wave rectifiers in the low and medium input power regimes, full-wave rectifiers are preferable if the input power at the EH is high.

Cell-free MIMO, employing distributed antenna systems (DAS), is a promising approach to deal with the capacity crunch of next generation wireless communications. In this paper, we consider a wireless network with transmit and receive antennas distributed according to homogeneous point processes. The received signals are jointly processed at a central processing unit. We study if the favorable propagation properties, which enable almost optimal low complexity detection via matched filtering in massive MIMO systems, hold for DAS with line of sight (LoS) channels and general attenuation exponent. Making use of Euclidean random matrices (ERM) and their moments, we show that the analytical conditions for favorable propagation are not satisfied. Hence, we propose multistage detectors, of which the matched filter represents the initial stage. We show that polynomial expansion detectors and multistage Wiener filters coincide in DAS and substantially outperform matched filtering. Simulation results are presented which validate the analytical results.

In this paper, we study the rate-power region of a simultaneous wireless information and power transfer (SWIPT) system where a transmitter (TX) broadcasts a common signal to an information receiver (IR) and an energy harvester (EH). Since practical EH circuits include a reactive element as part of their signal rectifier and the voltage on this element cannot drop or rise instantaneously, the EH circuit has memory. We model the memory effect of the EH by a Markov reward chain. Furthermore, since an analytical model that includes all nonlinear and memory effects of the EH circuit is not available, we employ a deep neural network (DNN) to model the Markov chain. We formulate an optimization problem to determine the rate-power region of the considered SWIPT system and propose an iterative algorithm based on sequential quadratic programming (SQP) to solve it. Our numerical results show that the optimal input distribution and the rate-power region depend on both the input power level at the EH and the symbol duration.

We consider a cell-free MIMO system in uplink, comprising a massive number of distributed transmit and receive antennas. In our distributed antenna system (DAS), transmit and receive antennas are distributed according to homogeneous point processes (PP) and the received signals are processed jointly at a central processing unit (CPU). In centralized massive MIMO systems, the phenomenon of favorable propagation has been observed: when the number of receive antennas tends to infinity while the number of transmit antennas remains finite, the users' channels become almost orthogonal and low complexity detection via matched filtering is almost optimal. We analyze the properties of DASs in asymptotic conditions when the network dimensions go to infinity with given intensities of the transmit and receive antenna PPs. We study the analytical conditions of favorable propagation in DASs with two kinds of channels, namely, channels with path loss and transmit and receive antennas in line of sight (LoS) or in multipath Rayleigh fading. We show that the analytical conditions of favorable propagation are satisfied for channels impaired by path loss and Rayleigh fading while they do not hold in the case of LoS channels, motivating the use and analysis of multi-stage receivers. Simulation results of the favorable propagation conditions and the performance of multi-stage detectors for finite systems validate the asymptotic analytical results.

In this paper, we propose a novel joint caching and massive multiple-input multiple-output (MIMO) transmission scheme, referred to as cache-aided massive MIMO, for advanced downlink cellular communications. In addition to reaping the conventional advantages of caching and massive MIMO, the proposed scheme also exploits the side information provided by cached files for interference cancellation at the receivers. This interference cancellation increases the degrees of freedom available for precoding design. In addition, the power freed by the cache-enabled offloading can benefit the transmissions to the users requesting non-cached files. The resulting performance gains are not possible if caching and massive MIMO are designed separately. We analyze the performance of cache-aided massive MIMO for cache-dependent maximum-ratio transmission (MRT), zero-forcing (ZF) precoding, and regularized zero-forcing (RZF) precoding. Lower bounds on the ergodic achievable rates are derived in closed form for MRT and ZF precoding. The ergodic achievable rate of RZF precoding is obtained for the case when the numbers of transmit antennas and users are large but their ratio is fixed. Compared to conventional massive MIMO, the proposed cache-aided massive MIMO scheme achieves a significantly higher ergodic rate especially when the number of users approaches the number of transmit antennas.

The low-rank behavior of massive multiple-input multiple-output (MIMO) channel covariance matrices and its exploitation for pilot decontamination and statistical beamforming are well documented. Existing algorithms, however, rely on signal subspace separation among user equipments (UEs) and, as such, they tend to fail when the distance between UEs becomes small. This paper proposes a solution to this problem via covariance shaping at the UE-side in the case where the UEs are equipped with (a small number of) multiple antennas. The key resides in: i) exploiting general non-Kronecker MIMO channel structures that allow the transmitter to suitably alter the channel statistics perceived by the base station, and ii) sacrificing some spatial degrees of freedom at each UE so as to improve the statistical orthogonality between closely spaced UEs. Numerical results illustrate the sum-rate performance gains of the proposed covariance shaping method with respect to existing ones.

The problem of joint Angle-of-Arrival (AoA) and calibration parameters in a wideband scenario is addressed. The system consists of multiple sources transmitting from different directions and in certain subcarriers. Sources in the same beam are regarded as transmitted from the same AoA and their signals are allocated to different subcarriers while users in different beams may transmit on the same subcarrier, i.e. the signals are multiplexed in space and frequency. Then, signals from a given beam not necessarily occupy the full bandwidth but only specific subcarriers. In addition, due to the wideband of the signal, each RF chain introduces frequency dependent gain and phase shift that need to be calibrated to perform a subsequent demultiplexing in a digital beamforming matrix. We propose a novel blind algorithm that (i) estimates the AoAs of all present sources in the bandwidth of interest and (ii) estimates the different gains/phases at each antenna per frequency up to an unknown impairment at a reference antenna. We provide identifiability conditions that ensure a successful parameter estimation. Finally, the potential of the proposed algorithm compared to the case of known calibration parameters is assessed by simulations.

We propose a local message passing algorithm based on Belief Propagation (BP) to detect a small hidden Erdos-Rényi (ER) subgraph embedded in a larger sparse ER random graph in the presence of side-information. We consider side-information in the form of revealed subgraph nodes called cues, some of which may be erroneous. Namely, the revealed nodes may not all belong to the subgraph, and it is not known to the algorithm a priori which cues are correct and which are incorrect. We show that asymptotically as the graph size tends to infinity, the expected fraction of misclassified nodes approaches zero for any positive value of a parameter λ, which represents the effective Signal-to-Noise Ratio of the detection problem. Previous works on subgraph detection using BP without side-information showed that BP fails to recover the subgraph when λ <; 1/e. Our results thus demonstrate the substantial gains in having even a small amount of side-information.

Massive multiple-input multiple-output (massive MIMO) and small cell densification are complementary key 5G enablers. Given a fixed number of the entire basestation antennas per unit area, this paper fairly compares (i) to deploy few base stations (BSs) and concentrate many antennas on each of them, i.e. massive MIMO, and (ii) to deploy more BSs equipped with few antennas, i.e. small cell densification. We observe that small cell densification always outperforms for both signal-to-interference ratio (SIR) coverage and energy efficiency (EE), when each BS serves multiple users via L number of sub-bands (multicarrier transmission). Moreover, we also observe that larger L increases SIR coverage while decreasing EE, thus urging the necessity of optimal 5G network design. These two observations are based on our novel closed-form SIR coverage probability derivation using stochastic geometry, also validated via numerical simulations.

Channel state information (CSI) feedback is a challenging issue in frequency division duplexing (FDD) massive MIMO systems. This paper studies a cooperative feedback scheme, where the users first exchange their CSI with each other through device-to-device (D2D) communications, then compute the precoder by themselves, and feedback the precoder to the base station (BS). Analytical results are derived to show that the cooperative precoder feedback is more efficient than the CSI feedback in terms of interference mitigation. To reduce the delays for CSI exchange, we develop an adaptive CSI exchange strategy based on signal subspace projection and optimal bit partition. Numerical results demonstrate that the proposed cooperative precoder feedback scheme with adaptive CSI exchange significantly outperforms the CSI feedback scheme, even under moderate delays for CSI exchange via D2D.

This paper considers the problem of efficient feedback design for massive multiple-input multiple-output (MIMO) downlink transmissions in frequency division duplexing (FDD) bands, where some partial channel state information (CSI) can be directly exchanged between users via device-to-device (D2D) communications. Drawing inspiration from classical point-to-point MIMO, where efficient mechanisms are obtained by feeding back directly the precoder, this paper proposes a new approach to bridge the channel feedback and the precoder feedback by the joint design of the feedback and precoding strategy following a team decision framework. Specifically, the users and the base station (BS) minimize a common mean squared error (MSE) metric based on their individual observations on the imperfect global CSI. The solutions are found to take similar forms as the regularized zero-forcing (RZF) precoder, with additional regularizations that capture any level of uncertainty in the exchanged CSI, in case the D2D links are absent or unreliable. Numerical results demonstrate superior performance of the proposed scheme for an arbitrary D2D link quality setup.

In this paper we propose a novel robust channel estimation algorithm exploiting path diversity in both angle and power domains, relying on a suitable combination of the spatial filtering and amplitude based projection. The proposed approach is able to cope with a wide range of system and topology scenarios, including those where interference channel may overlap with desired channels in terms of multipath angles of arrival (AoA) or exceed them in terms of received power. We establish the analytical conditions under which the proposed channel estimator is fully decontaminated.

We propose power allocation among femto-base stations (femto-BSs) in a heterogeneous network (HetNet) based on non cooperative games. A minimum level of quality of service has to be guaranteed at macro-user terminals (macro-UTs). Femto-BSs are unaware of the exact values of the channel parameters between them and macro-UTs because of the lack of cooperation and fading. First, we consider the design criterion where the outage probability has to be below a certain threshold at macro-UTs. The equilibrium concept is based on the Normalized Nash Equilibrium (NNE) since it caters to the distributed setting. NNE is unique only for a few strictly concave utility functions in this case. We introduce the concept of Weakly Normalized Nash Equilibrium (WNNE) which keeps the most of the appealing features of NNE but can be extended to a wide class of utility functions and can be incorporated with low complexity. Finally, we consider the design criterion where the expected SINR at a macro-UT has to be greater than a threshold. In this case, the NNE is always unique for any strictly concave utility functions.

We consider an extension of Erdös-Rényi graph known in literature as Stochastic Block Model (SBM). We analyze the limiting empirical distribution of the eigenvalues of the adjacency matrix of SBM. We derive a fixed point equation for the Stieltjes transform of the limiting eigenvalue empirical distribution function (e.d.f.), concentration results on both the support of the limiting e.s.f. and the extremal eigenvalues outside the support of the limiting e.d.f. Additionally, we derive analogous results for the normalized Laplacian matrix and discuss potential applications of the general results in epidemics and random walks.

We consider heterogeneous networks with multiple femtocells and macrocells. Femto-base stations (femto-BS) are constrained to allocate transmitting powers such that the total interference at each macro-user terminal (macro-UT) is below a given threshold. We formulate a power allocation problem as a concave game with femto-BSs as players and multiple macro-UTs enforcing coupled constraints. Equilibrium selection is based on the concept of normalized Nash equilibrium (NNE). When the interference at a femto-user terminal (femto-UT) from adjacent femto-BSs is negligible, for any strictly concave nondecreasing utility the NNE is unique and the NNE is the solution of a concave potential game. We also propose a distributed algorithm which converges to the unique NNE. When the interference is not negligible, an NNE may not be unique and the computation of NNE has exponential complexity. We introduce the concept of weakly normalized Nash equilibrium (WNNE) which keeps the most of NNEs' interesting properties but, in contrast to the latter, the WNNE can be determined with low complexity. We show the usefulness of the WNNE concept for the relevant case of Shannon capacity as femto-BS's utility.

We consider an extended wireless network with transmit and receive nodes distributed according to Bernoulli lattice processes in ID and 2D spaces. The received signals are jointly processed at a central unit. The channel is characterized by pathloss attenuation depending on distances between transmit and receive antennas. We introduce a new class of Euclidean random matrix (ERM) to characterize the distributed antenna system (DAS). By leveraging on a suitable decomposition of these ERMs, we propose an approximated analysis of their spectra and use it to provide an analytical approximation of the capacity per unit area of the DAS.

This paper considers the problem of interference control in networks where base stations signals are coherently combined (aka network MIMO). Building on an analogy with so-called massive MIMO, we show how second-order statistical properties of channels can be exploited when the massive MIMO array corresponds in fact to many antennas randomly spread over a two-dimensional network. Based on the classical one-ring model, we characterize the low-rankness of channel covariance matrices and show the rank is related to the scattering radius. The application of the low-rankness property to channel estimation's denoising and low complexity interference filtering is highlighted.

A subspace method for channel estimation is proposed for asymmetric antenna array systems. The so-called pilot contamination problem reported in [1] is found to be due to the linearity of channel estimation in [2]. We show that it does not occur in cellular systems with power control and power-controlled handoff when the nonlinear channel estimation method proposed in this paper is used. Power-control hand-off is needed to guarantee separability between signal and interference subspaces. We derive the transmission conditions for subspace separability based on free probability and perturbation theory.

Body area networks (BANs), referring to embedded wireless systems in, on, and around bodies, are expected to take an important role for health, leisure, sports, and all the facets of our daily life. In many cases, several BANs coexist in a small area, resulting in very strong inter-BAN interference, which seriously disturbs intra-BAN communications. The goal of this paper is to decrease inter-BAN interference by cooperative scheduling, hence increasing packet reception rate (PRR) of intra-BAN communications. Cooperative scheduling here is divided into two sub-problems: single-BAN scheduling as an assignment problem and multi-BAN concurrent scheduling as a game. For the first sub-problem, a low complexity algorithm, horse racing scheduling, is proposed, which achieves near-optimal PRR for the BAN performing scheduling. For the second sub-problem, we prove the existence of a set of mixed strategy Nash equilibria (MSNE). Then, we propose a distributed cooperative scheduling scheme, which efficiently achieves higher PRR than the MSNE without degrading fairness.

We consider a multiuser MIMO Mobile Satellite System (MSS) and model its channel as a cascade of a slow varying component, directivity vector, and a fast fading component, propagation component. We study the estimation of the slow varying part of the satellite channel at the gateway. Since the channel model is nonlinear, we propose a nonlinear parametric least squares approach. This optimization problem is shown to be equivalent to an eigenvalue complementary problem. The equivalent problem does not require an intermediate estimation of the nuisance (fast fading component) with relevant benefits in terms of computational complexity. The performance of the proposed algorithm is assessed by simulations based on realistic satellite channels.

We deal with interference channels with (i) delayed channel state information at the transmitter (CSIT), and (ii) delayed output feedback at the receiver and no CSIT. We extend the algorithm by Maleki et al. [8] for M = 3 users to any M ≥ 3 under the constraint that all the transmitter/receiver pairs are active simultaneously. We propose a retrospective interference alignment algorithm achieving M2/(M2-1) degrees of freedom (DoF) in the case (i) and an algorithm attaining [M/2]M/([M/2](M-1) + 1) DoF in the case (ii). However, larger DoF - 9/8 in (i) and 6/5 in (ii) - are easily achievable, for any M, when orthogonal channels (e.g. in time, frequency, etc.) are shared by triplets of transmitter/receiver pairs, and within each subchannel the algorithm by Maleki et al. [8] is applied. Hence, our work suggests that, for all M and both in the case (i) and in the case (ii), the strategy maximizing the DoF entails that three transmitter/receiver pairs are simultaneously active on the same channel.

We consider a block fading interference channels with partial channel state information and we address the issue of joint power and rate allocation in a game theoretic framework. The system is intrinsically affected by outage events. Resource allocation algorithms based on Bayesian games are proposed. The existence, uniqueness, and some stability properties of Nash equilibriums (NE) are analyzed. For some asymptotic setting, closed form expressions of NEs are also provided.

Spectral efficiency for asynchronous code division multiple access (CDMA) with random spreading is calculated in the large system limit allowing for arbitrary chip waveforms and frequency-flat fading. Signal-to-interference and noise ratios (SINRs) for suboptimal receivers, such as the linear minimum mean square error (MMSE) detectors, are derived. The approach is general and optionally allows even for statistics obtained by undersampling the received signal. All performance measures are given as a function of the chip waveform and the delay distribution of the users in the large system limit. It turns out that synchronizing users on a chip level impairs performance for all chip waveforms with bandwidth greater than the Nyquist bandwidth, e.g., positive roll-off factors. For example, with the pulse shaping demanded in the UMTS standard, user synchronization reduces spectral efficiency up to 12% at 10 dB normalized signal-to-noise ratio. The benefits of asynchronism stem from the finding that the excess bandwidth of chip waveforms actually spans additional dimensions in signal space, if and only if the users are desynchronized at chip-level. The analysis of linear MMSE detectors shows that the limiting interference effects can be decoupled both in the user domain and in the frequency domain such that the concept of effective interference spectral density arises. This generalizes and refines Tse and Hanly's concept of effective interference. In Part II, the analysis is extended to any linear detector that admits a representation as multistage detector and guidelines for the design of low complexity multistage detectors with universal weights are provided.

Totally asynchronous code-division multiple-access (CDMA) systems are addressed. In Part I, the fundamental limits of asynchronous CDMA systems are analyzed in terms of spectral efficiency and SINR at the output of the optimum linear detector. The focus of Part II is the design of low-complexity implementations of linear multiuser detectors in systems with many users that admit a multistage representation, e.g., reduced rank multistage Wiener filters, polynomial expansion detectors, weighted linear parallel interference cancellers. The effects of excess bandwidth, chip-pulse shaping, and time delay distribution on CDMA with suboptimum linear receiver structures are investigated. Recursive expressions for universal weight design are given. The performance in terms of SINR is derived in the large-system limit and the performance improvement over synchronous systems is quantified. The considerations distinguish between two ways of forming discrete-time statistics: chip-matched filtering and oversampling.

We considered a single hop ad-hoc network consisting of N source-destination pairs. Each transmitter is endowed with a finite buffer and accepts packets from a Poisson distributed arrival process. The channel is described by a Markov chain. We investigate distributed algorithms for joint admission control, rate and power allocation aiming at maximizing the individual or the global throughput defined as the average information rate successfully received. The decisions are based on the statistical knowledge of the channel and buffer states of the other communication pairs and on the exact knowledge of their own channel and buffer states. The problems are formulated as a cooperative and noncooperative games and reduced to the mathematical framework of the variational inequalities problems. The proposed algorithms provide sizable improvements with respect to straightforward extension of decentralized algorithms for multiple access channels to ad hoc networks.

We considered a dense interference network with a large number (K → ∞) of transmitter-receiver pairs. Each transmitter is endowed with a finite buffer and accepts packets from an arrival process. Each transmitter-receiver link is a fading vector channel with N diversity paths whose statistics are described by a Markov chain. We investigate distributed algorithms for joint admission control, rate and power allocation aiming at maximizing the individual throughput defined as the average information rate successfully received. The decisions are based on the statistical knowledge of the channel and buffer states of the other communication pairs and on the exact knowledge of their own channel and buffer states. In the case of a finite number of communication pairs this problem is computationally extremely intensive with an exponential complexity in the number of users. Assuming that K,N → ∞ with constant ratio the algorithm complexity becomes substantially independent of the number of active communications and grows with the groups of users having distinct asymptotic channel statistics. The cross-layer design is investigated for different kind of decoders at the receiver. The benefits of a cross layer approach compared to a resource allocation ignoring the states of the queues are assessed. The performance loss due to the use of policies designed for asymptotic conditions and applied to networks with a finite number of active communications is studied.

In this contribution we present a decentralized power allocation algorithm for the uplink interleave division multiple access (IDMA) channel. Within the proposed optimal strategy for power allocation, each user aims at selfishly maximizing its own utility function. An iterative chip-by-chip (CBC) decoder at the receiver and a rational selfish behavior of all the users according to a classical game-theoretical framework are the underlying assumptions of this work. This approach leads to a channel inversion policy where the optimal power level is set locally at each terminal based on the knowledge of its own channel realization, the noise level at the receiver and the number of active users in the network.

The design and analysis of multistage detectors with universal weights for asynchronous CDMA systems is presented. The use of a front-end that enables joint detection and provides sufficient statistics is proposed. With such a front end the proposed multistage detector has the same complexity order per bit as the matched filter. The proposed approach can also take into account other suboptimum statistics and the non-ideality of the chip pulse waveforms. In such a way, the universal weights can be designed and the performance can be computed for very realistic scenarios.

This correspondence analyzes the behavior of code-division multiple-access (CDMA) systems with correlated spatial diversity. The users transmit to one or more antenna arrays. The centralized receiver employs a linear multiuser detector. We derive the performance of a large system with random spreading sequences and weak assumptions on the flat-fading channel gains—the fading may be correlated and contain line-of-sight components. We show that, as the number of users and the spreading factor grow large with fixed ratio, the performance of the system is fully characterized by a square matrix with size equal to the number of receiving antennas and multiuser efficiencies are not identical for all users. Our general result includes the analysis of CDMA systems with spatial diversity discussed by Hanly and Tse ('01) for independent channel gains in case of both micro-diversity and macro-diversity and provides a rigorous proof for the macro-diversity case missing in their work. We also show that to any scenario with correlated Rayleigh fading, there exists a macro-diversity scenario with independent Rayleigh fading which is characterized by the same signal-to-interference-and-noise ratio (SINR). Furthermore, sufficient conditions are given which force the multiuser efficiencies of all users to become identical also in case of statistically dependent channel gains.

The large system performance analysis of linear multiuser detectors (e.g. MMSE, MSWF, multistage detectors) for asynchronous CDMA systems is provided. While the performance of synchronous systems with square-root waveforms is independent of the chip bandwidth, the performance of asynchronous systems depends on the pulse shape and the bandwidth. It increases as the bandwidth increases beyond half on the chip rate and, in such a case, asynchronous systems outperform the synchronous ones.

We consider linear multistage detectors with universal (large system) weighting for synchronous code-division multiple access (CDMA) in multipath fading channels with many users. A convenient choice of the basis of the projection subspace allows a joint projection of all users. Taking advantage of this property, the complexity per bit of multistage detectors with universal weights scales linearly with the number of users on the uplink CDMA channel, while other known multistage detectors with universal weights and different bases of the projection subspace keep the same quadratic complexity order per bit as the linear minimum mean-square error (LMMSE) detector. We focus on the design of two kinds of detectors with linear complexity. The detector of Type I is obtained as an asymptotic approximation of the polynomial expansion detector proposed by Moshavi et al. The detector of Type II has the same performance as the multistage Wiener filter (MSWF) in large systems. Additionally, general performance expressions for large systems, applicable to any multistage detector with the same basis of the projection subspace (e.g., linear parallel interference canceling detectors), are derived. As a by-product, the performance analysis disproves the widespread belief that the MSWF and the polynomial expansion detector are equivalent. We show that, in general, the MSWF outperforms the latter one and they are equivalent only asymptotically in the case of equal received powers.

The asymptotic (in the number of antennas) theoretic mutual information of a MIMO system in the case of Ricean fading is provided. The results are especially useful for understanding the effect of line of sight components on the overall capacity performance.

The asymptotic performance of the linear MMSE detector for any finite observation window and any symbol impinging the observed signal is derived for asynchronous but chip synchronous CDMA systems with random spreading. Additionally, a multistage detector that does not suffer from windowing effects and performs as well as the correspondent detector in synchronous systems is proposed. In contrast to the synchronous case, considering a sufficient large delay, the proposed multistage detector can even outperform the full rank linear MMSE detector constrained to a finite fixed observation window.

In this work we provide equations to precisely calculate the asymptotic weighting of multistage detectors satisfying the individually and jointly LMMSE criteria in the projection subspace for scenarios with unequal powers. Additionally, a general expression of the SINR achievable at the filter output as system size grows large is derived. Such an equation can be applied to any multistage detector. We specialize this result to both the individually and jointly LMMSE multistage detector with asymptotic weighting. We show that the individually LMMSE detector outperforms the other detector in the case of unequal received powers while both the detectors are equivalent in the case of equal received powers.