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Localization of wireless transmitters is traditionally done using Radio Frequency (RF) sensors that measure the propagation delays between the transmitter and a set of anchor receivers. One of the major challenges of wireless localization systems is the need for anchor nodes to be time-synchronized to achieve accurate localization of a target node. Using a reference transmitter is an efficient way to synchronize the anchor nodes Over-The-Air (OTA), but such algorithms require multiple periodic messages to achieve tight synchronization. In this paper, we propose a new synchronization method that only requires a single message from a reference transmitter. The main idea is to use the Carrier Frequency Offset (CFO) from the reference node, alongside the Time of Arrival (ToA) of the reference node messages, to achieve tight synchronization. The ToA allows the anchor nodes to compensate for their absolute time offset, and the CFO allows the anchor nodes to compensate for their local oscillator drift. Additionally, using the CFO of the messages sent by the reference nodes and the target nodes also allow us to estimate the speed of the targets. The error of the proposed algorithm is derived analytically and is validated through controlled laboratory experiments. Finally, the algorithm is validated by realistic outdoor vehicular measurements with a software-defined radio testbed. [ABSTRACT FROM AUTHOR]

Summary: Non‐orthogonal multiple access (NOMA) is a great contender for future cellular modulation due to its desirable properties like massive connectivity, high data rate transmission, and high spectral efficiency. However, its peak‐to‐average power ratio (PAPR) is significant, which becomes a significant disadvantage for the efficient operability of the NOMA waveform compared to current techniques. Several PAPR reduction algorithms like selective mapping (SLM), partial transmission sequence (PTS), and companding techniques have been proposed to lower the PAPR of multicarrier waveforms (MCWs). PTS reduces the PAPR but has high complexity. On the other hand, SLM has a less complex framework, but its PAPR performance is not as efficient as PTS. Companding methods reduce the PAPR by compressing the signals at the transmitter, which unfortunately reduces the dynamic range of the signal. In this work, we propose a hybrid algorithm (SLM + PTS) with a companding method for the first time for the NOMA waveform, which efficiently reduces the PAPR with low computational complexity. Furthermore, we compare the performances of a host of candidate algorithms like SLM, PTS, hybrid (SLM + PTS), hybrid + A law (SLM–PTS–A law), and hybrid + Mu law (SLM–PTS–Mu law). The results of the experiments show that the hybrid + Mu law did a better job than the existing PAPR reduction algorithms. [ABSTRACT FROM AUTHOR]

Since there is a frequency offset between each adjacent antenna of FDA radar, there exists angle-range two-dimensional dependence in the transmitter. For bistatic FDA-multiple input multiple output (MIMO) radar, range-direction of departure (DOD)-direction of arrival (DOA) information is coupled in transmitting the steering vector. How to decouple the three information has become the focus of research. Aiming at the issue of target parameter estimation of bistatic FDA-MIMO radar, a real-valued parameter estimation algorithm based on high-order-singular value decomposition (HOSVD) is developed. Firstly, for decoupling DOD and range in transmitter, it is necessary to divide the transmitter into subarrays. Then, the forward–backward averaging and unitary transformation techniques are utilized to convert complex-valued data into real-valued data. The signal subspace is obtained by HOSVD, and the two-dimensional spatial spectral function is constructed. Secondly, the dimension of spatial spectrum is reduced by the Lagrange algorithm, so that it is only related to DOA, and the DOA estimation is obtained. Then the frequency increment between subarrays is used to decouple the DOD and range information, and eliminate the phase ambiguity at the same time. Finally, the DOD and range estimation automatically matched with DOA estimation are obtained. The proposed algorithm uses the multidimensional structure of high-dimensional data to promote performance. Meanwhile, the proposed real-valued tensor-based method can effectively cut down the computing time. Simulation results verify the high efficiency of the developed method. [ABSTRACT FROM AUTHOR]

SWIPT can effectively deal with the energy limitation problem of communication terminals, and IRS can assist in enhancing the efficiency of SWIPT. Therefore, to overcome the shortcoming of limited coverage of deploying a single IRS and further improve the time and spectrum resource utilization efficiency of SWIPT, this paper studied a double-IRS-assisted NOMA SWIPT system, where the beamforming vector of the transmitter, the phase shifts of each IRS, and the power splitting coefficients of the receivers were jointly optimized to maximize the minimum user rate. To solve the above non-convex optimization problem with highly coupled variables, this paper proposed an AO algorithm based on SDR and SCA techniques to solve it efficiently. The simulation results show that the double-IRS-assisted system can achieve a higher minimum rate than that of its single-IRS, which reveals the superiority of deploying double IRS, the effectiveness of the proposed algorithm, and the importance of jointly optimizing IRS phase shifts and power splitting coefficients in improving the user rate performance. [ABSTRACT FROM AUTHOR]

This paper deals with the imaging problem for sliding spotlight (SS) and terrain observation by progressive scan (TOPS) modes in bistatic configuration of the geostationary (GEO) transmitter with a low earth orbit satellite (LEO) receiver, named GTLR-BiSAR system. A unified imaging algorithm is proposed to process the GTLR-BiSAR data acquired in SS or TOPS modes. Our main contributions include four aspects. Firstly, the imaging geometry of this novel configuration is described in detail. Furthermore, the GTLR-BiSAR signal expressions were deduced in both time and frequency domains. These signal expressions provide great support for the design of processing the algorithm theoretically. Secondly, we present a unified deramping-based technique according to the special geometry of GTLR-BiSAR to overcome the azimuth spectrum aliasing phenomenon, which typically affects SS and TOPS data. Thirdly, the spatial variance of GTLR-BiSAR data were thoroughly analyzed based on the range-Doppler (RD) geolocation functions. On the basis of a former analysis, we put forward the azimuth variance correction strategy and modified the conventional chirp scaling function to solve the range variance problem. Finally, we completed the derivation of the two-dimensional spectrum after the range chirp scaling. On the basis of spectrum expressions, we compensated for the quadratic and residue phase, and the azimuth compression was completed by SPECAN operation. In addition, we provide a flow diagram to visually exhibit the processing procedures. At the end of this paper, the simulation and real data experiment results are presented to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Geo-localization services are an important functionality in cellular networks. Besides, the use of Ultra Dense Networks and small cells, in current and future cellular networks, greatly increases the complexity of centralized localization approaches. Consequently, we propose a Self-Synchronization Positioning Estimation (SSPE) algorithm that estimates the transmitter position in a distributed fashion. The proposed SSPE algorithm reaches consensus for the posterior distribution of the transmitter position rather than on the final estimates. Such consensus ensures that the proposed SSPE algorithm converges to the centralized Direct Positioning Estimation (DPE) approach, which has the best performance of all localization approaches. We show that the proposed algorithm is related to the Iterative Positioning Estimation (IPE) algorithm, since both exploit the self-synchronization mechanism. As a result, the improvements and extensions for IPE, previously studied in other works, can be directly applied to the proposed SSPE algorithm. In addition, the proposed algorithm is able to localize the transmitter even when it is not time synchronized with the network as it is usually the case. The performance of the algorithms is numerically assessed through Monte-Carlo simulations by the mean distance error and mean range offset error. Finally, we not only show that our approach gets close to the DPE performance after a few iterations, but also that it converges for different logical network configurations. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor signal detection effect caused by many interference factors in large-scale MIMO technology scene, this paper proposes a 5G massive MIMO signal detection algorithm based on deep learning. Firstly, the MIMO system model based on neural network is constructed, and Deep Neural Network (DNN) detection is introduced into the receiver of the traditional MIMO system to obtain the information bits or codewords and channel state information transmitted by transmitters. Then, the end-to-end training method is adopted to make neural network learn the mapping relationship of information bits or codewords transmitted by system transceivers. Furthermore, DNN detector is improved based on Simplified Message Passing Detection (sMPD) algorithm, and the correction factor is updated continuously to optimize network parameters to realize the accurate detection and decoding of the MIMO system. Finally, the proposed algorithm is experimentally analyzed based on the TensorFlow deep learning framework. Experimental results show that when signal-to-noise ratio is 10 dB, the bit error rate and mean square error are lower than 0.005 and 0.1, respectively. [ABSTRACT FROM AUTHOR]

We propose new transmission schemes and receiver algorithms for unsourced multiple access (UMA) in MIMO and massive MIMO channels. Each active transmitter’s information bits are first channel encoded. The coded bits are divided into sub-blocks and each sub-block is modulated and transmitted. For both MIMO and massive MIMO channels, the conventional nonlinear modulation can be employed where each sub-block of coded bits is mapped to a transmitted signal vector. For the massive MIMO channel, we propose a new hybrid modulation scheme to reduce the receiver complexity, where the first sub-block is nonlinearly modulated, and the subsequent sub-blocks are linearly modulated and spread by the first sub-block signal. We also propose sparsity-exploiting blind receiver algorithms. Specifically, for the MIMO case, we exploit the codeword sparsity inherent in the UMA system, and a channel clustering technique, to estimate the channel and the transmitted signal of each transmitter. For the massive MIMO, in addition to the codeword sparsity, we further exploit the channel sparsity and user sparsity in estimating the channel and transmitted signal of each transmitter. The proposed receiver algorithms for both MIMO and massive MIMO channels output either hard or soft estimates of the coded bits, and therefore single-user channel decoding of the information bits can be performed for each transmitter. Extensive simulation results are provided to demonstrate the performances of the proposed algorithms. [ABSTRACT FROM AUTHOR]

For the multiple-input multiple-output (MIMO) downlink employing high-order quadrature amplitude modulation signaling and with nonlinear high power amplifiers (HPAs) at base station transmitter, the existing precoding designs relying on the linear MIMO channel can no longer work. We propose an efficient and accurate predistorter design to enable transmit precoding for nonlinear MIMO downlink. Specifically, we obtain the closed-form least squares estimates of the nonlinear HPA's amplitude and phase response using two B-spline neural networks during training. The estimated HPA's phase response automatically yields the estimate of the predistorter's phase response. Based on the B-spline neural network estimate of the HPA's amplitude response, we construct a B-spline neural network model for the predistorter amplitude response, and we adopt a particle swarm optimization (PSO) algorithm to solve this highly nonlinear optimization problem. Using our accurate predistorter estimate to pre-compensate for the nonlinear distortions of the transmit HPAs, a standard full-digital transmit precoding design can readily be adopted to combat the MIMO channel interference. A simulation study is conducted to demonstrate the effectiveness of our proposed PSO assisted predistorter design. [ABSTRACT FROM AUTHOR]

Although there are many articulations of SWIPT architecture implementations, the hardware impairment aspect involved in the SWIPT architecture system is not given much attention. This paper evaluates the performance of SWIPT PS Reciever architecture in the presence of IQ imbalance hardware impairment with 16-QAM transmitter and AWGN channel. The parameters SNR, BER is evaluated in the presence of amplitude, phase imbalance, and PS factor at the SWIPT receiver side. Further, the IQ imbalance is estimated and compensated using a blind compensation algorithm. The system achieved a maximum BER of 10-7 in the presence of amplitude and phase imbalance of 0.2 and 1.6 respectively. [ABSTRACT FROM AUTHOR]

A neural network (NN)‐assisted probabilistic shaping (PS) distribution matcher is proposed, in which the model is simplified by a structured optimization method. The NN algorithm can encode the information sequence, making the signal obey the Gaussian distribution, and can directly restore the received signal. In addition, the algorithm uses the novel training method at both ends of the transmitter and receiver so that the system performance is significantly improved. PS system verification experiments have been carried out under 16QAM‐DMT modulation format. Under the hard decision forward error correction (FEC) threshold of 3.8*10−3 BER, the proposed system achieves 1.1 dB improvement compared to the traditional 16QAM‐DMT system. [ABSTRACT FROM AUTHOR]

Quantum computing is a field of study aimed at developing technologies based on the principles of quantum theory. In this research area, enormous theoretical progress has been made, but at the current state, few quantum algorithms have been developed. It is very hard to efficiently construct new ones. The complexity is due to the non-intuitive nature of quantum operations. To overcome this obstacle, we opt for the integration of an automatic design approach. In this paper, we propose an adapted version of BackTracking algorithm allowing complete but optimal exploration of the quantum circuits space. For a given problem, the test of possible quantum gate sequences is optimized by using a constraint making it possible to ignore all unnecessary calculation in an efficient manner. In addition, solutions are generated automatically without resorting to mathematical formulas. Our approach allowed us to generate two new versions of controlled quantum teleportation. In the first version, the controller can switch the teleported Qubit between two possible distant receivers, while in the second version, this controller can choose between two different transmitters. [ABSTRACT FROM AUTHOR]

This paper investigates the security problem of a multi-eavesdrop multiple-input-single-output (MISO) wiretap channel, where an N-antenna transmitter communicates with a single-antenna legitimate user in the presence of multiple single-antenna smart eavesdroppers. To overcome the security risk of the traditional secure constructive interference-based (CI-based) scheme when facing the smart eavesdroppers, we propose a novel deception scheme (DS) via a random transmission strategy, where the eavesdroppers are expected to decode the deception symbols correctly but unable to distinguish the authenticity of the decoded symbol. Then, an efficient algorithm is proposed for the deception signal-to interference-plus-noise (SINR)-balancing problem when perfect channel state information (CSI) is assumed. Furthermore, we consider a practical scenario where only imperfect CSI is available, and explore two different methods for the deception optimization problem, i.e., convexification relaxation approach (CRA) and Lagrangian relaxation approach (LRA), respectively. For both CSI cases, a closed-form solution to the considered CI-based deception scheme is obtained. Simulation results validate the superiority of the proposed approach over traditional secure precoding schemes, and also demonstrate the significant computation efficiency improvements for the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this article, we consider the problem of optimally selecting a subset of transmitters from a transmitter set available to a multiple-input and multiple-output radar network. The aim is to minimize the location estimation error of underlying targets under a power constraint. We formulate it as a minimum-variance estimation problem and show that the underlying variance reduction function is submodular. From the properties of submodularity, we present a balanced selection policy which minimizes the worst-case error value using a minimax strategy. A greedy algorithm with guaranteed performance with respect to optimal solutions is given to efficiently implement the scheduling policy. The effectiveness and the efficiency of the proposed algorithm are demonstrated in simulated examples. [ABSTRACT FROM AUTHOR]

Massive multiple-input-multiple-output (M-MIMO) features a capability for spatial multiplexing of large number of users. This number becomes even more extreme in extra-large (XL-MIMO), a variant of M-MIMO where the antenna array is of very large size. Yet, the problem of signal processing complexity in M-MIMO is further exacerbated by the XL size of the array. The basic processing problem boils down to a sparse system of linear equations that can be addressed by the randomized Kaczmarz (RK) algorithm. This algorithm has recently been applied to devise low-complexity M-MIMO receivers; however, it is limited by the fact that certain configurations of the linear equations may significantly deteriorate the performance of the RK algorithm. In this article, we embrace the interest in accelerated RK algorithms and introduce three new RK-based low-complexity receiver designs. In our experiments, our methods are not only able to overcome the previous scheme, but they are more robust against inter-user interference (IUI) and sparse channel matrices arising in the XL-MIMO regime. In addition, we show that the RK-based schemes use a mechanism similar to that used by successive interference cancellation (SIC) receivers to approximate the regularized zero-forcing (RZF) scheme. [ABSTRACT FROM AUTHOR]

In this paper, a novel reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) symbiotic radio (SR) system is proposed, in which an RIS, operating as a secondary transmitter (STx), sends messages to a multi-antenna secondary receiver (SRx) by using cognitive backscattering communication, and simultaneously, it enhances the primary transmission from a multi-antenna primary transmitter (PTx) to a multi-antenna primary receiver (PRx) by intelligently reconfiguring the wireless environment. We are interested in the joint design of active transmit beamformer at the PTx and passive reflecting beamformer at the STx to minimize the total transmit power at the PTx, subject to the signal-to-noise-ratio (SNR) constraint for the secondary transmission and the rate constraint for the primary transmission. Due to the non-convexity of the formulated problem, we decouple the original problem into a series of subproblems using the alternating optimization method and then iteratively solve them. The convergence performance and computational complexity of the proposed algorithm are analyzed. Furthermore, we develop a low-complexity algorithm to design the reflecting beamformer by solving a backscatter link enhancement problem through the semi-definite relaxation (SDR) technique. Then, theoretical analysis is performed to reveal the insights of the proposed system. Finally, simulation results are presented to validate the effectiveness of the proposed algorithms and the superiority of the proposed system. [ABSTRACT FROM AUTHOR]

In this paper, we consider a general K-user Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). We assume that the channel state is deterministic and known to all the nodes. While the private-message capacity region is well known to be achievable with dirty paper coding (DPC), we are interested in the simpler linearly precoded transmission schemes. In particular, we focus on linear precoding schemes combined with rate-splitting (RS). First, we derive an achievable rate region with minimum mean square error (MMSE) precoding at the transmitter and joint decoding of the sub-messages at the receivers. Then, we study the achievable sum rate of this scheme and obtain two findings: 1) an analytically tractable upper bound on the sum rate that is shown numerically to be a close approximation, and 2) how to reduce the number of active streams – crucial to the overall complexity – while preserving the sum rate to within a constant loss. The latter results in two practical algorithms: a stream elimination algorithm and a stream ordering algorithm. Finally, we investigate the constant-gap optimality of linearly precoded RS with respect to the capacity. Our result reveals that, while the achievable rate of linear precoding alone can be arbitrarily far from the capacity, the introduction of RS can help achieve the capacity region to within a constant gap in the two-user case. Nevertheless, we prove that the RS scheme’s constant-gap optimality does not extend to the three-user case. Specifically, we show, through a pathological example, that the gap between the sum rate and the sum capacity can be unbounded. [ABSTRACT FROM AUTHOR]

This article investigates the intelligent reflecting surface (IRS)-aided multi-antenna covert communications. In particular, with the help of an IRS, a favorable communication environment can be established via controllable intelligent signal reflection, which facilitates the covert communication between a multi-antenna transmitter (Alice) and a legitimate full-duplex receiver (Bob) in the existence of a watchful warden (Willie). In order to shelter the desired communication, Bob generates jamming signals with a varying power to confuse Willie. The beamforming vector employed by Alice and the passive phase shifts of the IRS are optimized jointly to maximize the covert rate under the constraints of the successful detection probability at Willie and the communication outage experienced by Bob. We focus on the worst case by characterizing the minimum successful detection probability at Willie. The formulated problem is non-convex, due to the coupling between the beamforming vector of Alice and the phase shifts of the IRS, and the unit modulus constraint on the phase shifts of the IRS. To tackle the above issues, we first employ the penalty dual decomposition (PDD) method to handle the coupling effect. After that, we apply the successive convex approximation (SCA) method to develop an iterative algorithm for locating a Karush-Kuhn-Tucker (KKT) solution of the joint design problem. Moreover, we show that our proposed iterative algorithm can be adapted to handle the multi-antenna Willie case. Simulation results validate the effectiveness of the proposed iterative algorithm and show the great potential brought by the IRS for covert communications. [ABSTRACT FROM AUTHOR]

In this paper, the main research object is evolution algorithm applied for an ultra-wideband three-dimensional (3D) circular antenna array and the significance of elevation angles and their influence on the system performance are highlighted. The 3D channel is calculated by means of 3D shooting and bouncing ray/image techniques which specify multipath elevation angles and azimuth for horizontal plane angles. The capacities of the two-dimensional (2D) and three-dimensional transmitting antenna arrays have been presented. Beam-synthesizing techniques are applied at the transmitter to focus the transmitter energy for the sake of mitigating the multi-path effect and improving channel capacity. A combination of asynchronous particle swarm optimization (APSO) and dynamic differential evolution (DDE) methods are introduced to make adjustment to the length of the feed line on each array element for the maximum capacity. In comparison with 2D azimuth-only antenna arrays, a higher directive gain can be achieved from the 3D antenna arrays results in high channel capacity. Besides, it is discovered that the capacity for the 3D array improves by as much as over 10% when compared to that for the 2D array. As revealed by numerical results, APSO is capable of obtaining better beam-forming pattern to improve the channel capacity than DDE by about 9%. Not only does our research provide qualitative outcomes, it also presents quantitative results. [ABSTRACT FROM AUTHOR]

The huge ubiquitous deployment of wireless technologies and the pervasive availability of smart devices, indoor localization has become one of the most interesting topics in numerous location-based services. Where several methods have been adopted to estimate and localize the mobile and user location. The earliest and most interesting method was based on received signal strength (RSS) measurement. However, such method has showed weakness in handling the accurate estimation results. Recently, researchers start gather several different methods to achieve the advantages of each method and achieve higher accurate estimation. Hence, in this paper a hybrid localization algorithm has been designed and proposed based on the use of both RSS and angle-of-arrival (AoA). The algorithm has been tested in a three-dimensional indoor building; we are taking the effects of different building materials. The results were obtained depending on the measurement of RSS and AoA gathered from each received point and by using two AP devices for more accuracy in positioning coordination, the type of the transmitters is directional. [ABSTRACT FROM AUTHOR]

This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramér–Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms. [ABSTRACT FROM AUTHOR]

Bistatic forward-looking spotlight synthetic aperture radar with stationary transmitter and maneuvering receiver (STMR-BFSSAR) is a promising sensor for various applications, such as the automatic navigation and landing of maneuvering vehicles. Because of the bistatic forward-looking configuration and the receiver’s maneuvers, conventional image formation algorithms suffer from high computational complexity or small size of a well-focused scene if applied to STMR-BFSSAR. In this article, we propose a wavenumber-domain algorithm for STMR-BFSSAR image formation, which is termed the bistatic-range-Doppler-aperture wavenumber algorithm (BDWA). First, a novel range model in bistatic-range and Doppler-aperture coordinate space instead of conventional Cartesian coordinate space is established by employing the elliptic polar coordinate system and the method of series reversion. The novel range model not only makes the echo’s samples to be regular along the direction of the bistatic-range wavenumber axis but also constructs a curved wavefront close to the true wavefront. Second, an operation termed wavenumber-domain gridding is conceived to regularize the echo’s samples along the Doppler-aperture wavenumber axis, which can be implemented by 1-D interpolation. The proposed algorithm significantly outperforms the conventional algorithms in terms of computational complexity and scene size limits. Both point and distributed targets are simulated for two STMR-BFSSAR systems with different parameters. The simulation results verify the validity and superiority of the proposed BDWA. [ABSTRACT FROM AUTHOR]

Methods based on received signal strength measurements (RSS measurements) are used to determine the unmanned aerial vehicle (UAV) location using a wireless sensor network. The UAV transmitter power is usually unknown. In real conditions, it often becomes necessary to consider existence of anomalous measurement results. The reasons for the violation of the measurement process can be: the influence of interference, errors in the identification of signals during primary processing, failures of the equipment and similar. The optimum and quasi-optimal adaptive algorithms of UAV movement parameters filtration based on the RSS-measurement sensor networks in the presence of anomalous measurements at the unknown power of the transmitter are developed. These algorithms are obtained using Bayes’ theorems and the Markov property of a mixed process, including a vector of target movement parameters and a discrete component characterizing the type of measurement. Analysis of developed algorithm performance was carried out by Monte Carlo method on 2D plane. The quasi-optimal adaptive filtering algorithm detects the appearance of anomalous measurements with probabilities close to unity and allows one to eliminate their influence on the accuracy of UAV movement parameters estimation and also to estimate the UAV unknown transmitter power. [ABSTRACT FROM AUTHOR]

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems. [ABSTRACT FROM AUTHOR]

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems. [ABSTRACT FROM AUTHOR]

In a cellular network (CN), cellular users (CUs) located nearby machine type communications (MTC) devices (MTC-Ds) may act as uplink gateways to relay data to the base station (BS). We consider non-orthogonal multiple access (NOMA) and successive interference cancellation (SIC) at the receiver to increase the number of connected devices and the spectrum efficiency. Both underlay and overlay spectrum access modes (SAMs) are considered. We introduce a dynamic user grouping (UG) concept as a practical constraint for SIC, in order to decrease the receiver complexity. Moreover, SIC constraints are introduced based on a minimum signal-to-noise-plus-interference ratio (SINR) at the receiver. We formulate a joint dynamic UG, power allocation, and resource block (RB) assignment problem, aiming at maximizing the total sum-rate of both CUs and MTC-Ds. We transform the obtained non-convex mixed-integer programming problem into a convex problem by using a quadratic fractional programming (FP). A heuristic method is also proposed to reduce complexity. Simulation results demonstrate that the proposed solution outperforms the conventional CN (C-CN) method and the results of the proposed optimization methods are close to optimal solution obtained by the exhaustive search (ES) algorithm in terms of total average sum-rate and network connectivity, while requiring less transmit power. The heuristic method decrease the computational complexity of the FP method at the expense of a small reduction in spectrum efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we consider an energy harvesting device-to-device (D2D) communication system, where D2D transmitter can use mode A to directly communicate with D2D receiver or use mode B as a relay to assist cellular communication while communicating with D2D receiver by adopting non-orthogonal multiple access technology. Firstly, the outage probability expression in two modes is derived, and the communication mode is determined according to outage performance. Then, assuming that the full system information is available, the channel allocation and relay selection are completed by Kuhn–Munkres algorithm, and the offline power allocation of D2D users is realized by reinforcement learning. Next, the offline optimization results are taken as the training data set to train the neural network, and the optimal model of the transmission power is obtained. Considering the transmission power constraint, the online power allocation optimization algorithm is further proposed. Numerical results demonstrate the accuracy of derived outage probability, and the proposed resource allocation algorithm can improve the performance of hybrid system. [ABSTRACT FROM AUTHOR]

In this work, we investigate the problem of secure communications in a downlink large-scale multi-antenna assisted simultaneous wireless information and power transfer (SWIPT) system, where a base station (BS) transmits signals to serve a number of information decoding (ID) and energy harvesting (EH) users. Considering that the EH users can potentially eavesdrop the ID users’ confidential information, we study the robust joint design of the hybrid analog-digital (A/D) beamforming (BF) matrices and of the artificial redundant signal (ARS) covariance matrix at the BS, where the aim is to maximize the worst-case sum secrecy rate for the ID users under a transmit power constraint, a nonlinear EH constraint and a unit-modulus constraint on the entries of the analog BF matrix. The corresponding optimization problem is very challenging due to the nonlinear and nonconvex objective function and constraints. Using innovative optimization techniques, we first transform the original problem into an equivalent but more tractable form, and then develop a novel joint iterative algorithm based on the penalty-concave-convex procedure (CCCP) for solving the resultant problem. We show that the proposed penalty-CCCP based algorithm for ARS-aided robust joint hybrid BF design converges to a Karush-Kuhn-Tucker solution of the original problem, and also analyze its computational complexity. Our simulation results verify that the resultant robust joint hybrid BF design algorithm relying on ARS significantly outperforms the conventional hybrid BF benchmark algorithms and efficiently achieves the performance of the fully-digital BF with reduced number of radio frequency chains and energy consumption. [ABSTRACT FROM AUTHOR]

We consider a homogeneous cellular network where a multi-antenna base station (BS) in each cell transmits messages to its intended user over a common frequency band. To improve the system capacity of this multi-cell multi-input single-output (MISO) interference channel, one of the state-of-the-art algorithms, namely, downlink-beamforming coordination, allows all BSs to cooperate with one another to mitigate the effect of inter-cell interference. However, most existing algorithms are suboptimal and impractical in a dynamic wireless environment, due to the high computational complexity and the overhead involved in collecting global channel state information (CSI). In this study, we exploit deep reinforcement learning (DRL) and propose a distributed dynamic downlink-beamforming coordination (DDBC) method with partial observability of the CSI. Each BS is able to train its own deep Q-network and employs appropriate beamformer depending on its environment, which is observed through a designed limited-information exchange protocol. The simulation results show that the proposed DRL-based DDBC method, with a considerably lower system overhead, achieves a system capacity that is very close to that of the fractional programming algorithm with global and instantaneous CSI measurements. In addition, this work demonstrates the potential of utilizing DRL to solve DDBC problems in a more practical manner. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with the proportional fairness (PF) of the spectral efficiency (SE) maximization of uplinks in a cell‐free (CF) massive multiple‐input multiple‐output (MIMO) system in which a large number of single‐antenna access points (APs) connected to a central processing unit (CPU) serve many single‐antenna users. To detect the user signals, the APs use matched filters based on the local channel state information while the CPU deploys receiver filters based on knowledge of channel statistics. We devise the maximization problem of the SE PF, which maximizes the sum of the logarithm of the achievable user rates, as a jointly nonconvex optimization problem of receiver filter coefficients and user power allocation subject to user power constraints. To handle the challenges associated with the nonconvexity of the formulated design problem, we develop an iterative algorithm by alternatively finding optimal filter coefficients at the CPU and transmit powers at the users. While the filter coefficient design is formulated as a generalized eigenvalue problem, the power allocation problem is addressed by a gradient projection (GP) approach. Simulation results show that the SE PF maximization not only offers approximately the achievable sum rates as compared to the sum‐rate maximization but also provides an improved trade‐off between the user rate fairness and the achievable sum rate. [ABSTRACT FROM AUTHOR]

Extracting the unknown frequency of a radar pulse has been one of the most challenging aspects of instantaneous frequency measurement (IFM) receivers. The conventional IFM receivers are limited in accurate frequency extraction by their design. In this article, a digital processing unit is added to the conventional IFM to extract the frequency with higher accuracy. A 2–4 GHz IFM has been designed combining the compact conventional RF section with the new digital processing unit using a novel efficient frequency extraction algorithm. The algorithm of minimum mean square error (MMSE) estimator is explored for use within the IFM, and it is proven that it can achieve much higher frequency accuracy. The novelty of this article includes both investigation and development of the algorithm to be a good fit for the frequency extraction in IFM receivers and also a digital implementation of the algorithm which meets the minimum requirements of an industrial IFM. The design has been implemented using microstrip filters on Rogers substrate and a Spartan-3E field-programmable gate array (FPGA) processor. The measurement results show promising achievements and verify the principle idea of digital processing for accurate frequency extraction in IFM receivers. The designed and fabricated receiver reaches 1.7 MHz rms frequency error in the frequency band of 2–4 GHz, which is at least $10\times $ improvement with respect to the state-of-the-art works. [ABSTRACT FROM AUTHOR]

A CMOS ON–OFF-keying 3–10.6-GHz transmitter with low power consumption and low complexity used to Impulse Radio Ultra-Wide Band (IR-UWB) communication system is presented in this work. This architecture is designed and optimized via particle swarm optimization (PSO) algorithm. The IR-UWB transmitter is adapted to generate a high bandwidth frequency and it has a band switching capability. It consists of a switching inductance–capacitance voltage-controlled oscillator (LC_VCO), a pulse generator circuit, an injection-locked frequency divider (ILFD) circuit, a buffer and an antenna. The VCO is switched ON/OFF by the pulse signal produced by a generator circuit which is realized through synchronizing the received data by a clock signal. The used technique for transmitting a discontinuous signal is based on a complementary switch-mode ON–OFF LC_VCO, whose main advantage is to reduce power consumption. In this work, a best agreement between the results of the optimization technique and those of the simulation is obtained. The simulated results illustrate a signal of pulse width of 2.5 ns and a pulse repetition rate (PRR) of 200 MHz. The output spectra are centered at 4-GHz and 8-GHz frequencies with 1,332-MHz and 1,350-MHz bandwidths, respectively. The peak-to-peak amplitude of a UWB signal output is 154 mV. The IR-UWB transmitter power consumption is 11.4 mW which corresponds to the consumption energy of 28.5 pJ/pulse @ 200 MHz. The power spectral densities (PSDs) of the output signals of both circuits viz. ON–OFF LC_VCO and ILFD are less than − 4 1. 3 dBm/MHz, which agreed well with the Federal Communication Commission (FCC) regulation. The transmitter design is well implemented using a TSMC 0.18- μ m CMOS process technology in an Advanced Design System (ADS). [ABSTRACT FROM AUTHOR]

To balance the output stability and maximum efficiency of a system, an efficiency optimization method based on adaptive frequency control is proposed. In this paper, load transmission is carried out using a DC–DC converter at the receiving end. When the load changes, controlling the rectified output voltage is carried out to realize the maximum efficiency point tracking. Meanwhile, the duty cycle of the rear stage DC–DC converter is changed to keep the output voltage constant. The sampled rectified voltage is compared with the estimated value and the resulting error data are sent to the system transmitter through ASK modulation wireless communication. Then the transmitter demodulation communication information through PID algorithm control is used to achieve the frequency of the adaptive adjustment. The feasibility of the proposed method has been verified by the simulation and experimental results. When the output voltage is 5 V, the constant voltage accuracy is within ± 1%, the maximum efficiency is up to 79.2%, and the dynamic response time is only 160 ms. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks have attracted much attention in various applications such as natural disasters monitoring, defense, industries, etc. A new routing algorithm for underwater wireless sensor networks is developed and tested. The algorithm shows a better end-to-end delay yet less energy consumption. This was achieved by limiting the data transmission to a number of specific adjacent nodes to whom the transmitter is authorized to send the message. The algorithm performance was compared with other algorithms (depth based routing and cooperative depth based routing protocols) and the results show a better performance. [ABSTRACT FROM AUTHOR]

Massive multiuser multiple input multiple output (MU‐MIMO) system is aimed to improve throughput and spectral efficiency through a large number of antennas incorporated at the transmitter and/or receiver. However, the MU‐MIMO system usually suffers from interantenna interference (IAI) and multiuser interference (MUI). The IAI imposes due to closely spaced antennas at each user equipment (UE), and MUI is enforced when one user comes under the vicinity of another user in the same cellular network. Most of the previous literatures considered any one of these interferences. However, the present work proposes singular value decomposition (SVD) precoding‐assisted user‐level local likelihood ascent search (LLAS) algorithm to mitigate both IAI and MUI. In the uplink MU‐MIMO, the IAI is cancelled by SVD, and the residual MUI is mitigated by LLAS detection. The LLAS detection balances the trade‐off between the classical suboptimal likelihood ascent search (LAS) and optimal maximum likelihood (ML) detection techniques. The proposed LLAS performs local search among all 2MT‐dimensional neighborhood vectors at each UE, where MT represents number of transmitting antennas of each UE. Thus, its performance is near optimal, and its complexity is much lower than ML detector. [ABSTRACT FROM AUTHOR]

Covert communication is often limited in rate because it is difficult to hide the signal in the background noise. Recent work has shown that jamming can significantly improve the rate at which covert communication can be conducted, but this jamming signal has a devastating effect on the receiver. To tackle this issue, we equip the jammer with multiple antennas so that it can employ beamforming to place the intended receiver in the null space of the jamming. Moreover, in order to increase rate in this network, we equip the transmitter with an antenna array to be able to perform 3-dimensional (3D) beamforming toward its intended recipient. To evaluate this design, we formulate an optimization problem and present an iterative algorithm to solve it. Moreover, since Willie is passive, it is possible that Alice is not able to estimate the location of Willie perfectly. Hence, we investigate a new practical scenario in which the information about Willie's location is imperfect at Alice. Numerical results consider both the rate of covert communication with the proposed architecture and the gap between the result from our optimization and that obtained from exhaustive search. Numerical results highlight that the optimality gap is on average approximately 10%, and 3D beamforming compared to 2D beamforming increases covert rate by approximately 70%. [ABSTRACT FROM AUTHOR]

For the fast access with high reliability, a single grant allows a transmitter to transmit multiple redundant packets with new redundancy information in the time domain without waiting for feedback from a receiver. However, entire redundant packets shall be retransmitted in response to a negative acknowledgment of those redundant packets, which increases the service latency. To reduce a long HARQ RTT due to a number of redundant packets, we propose packet-by-packet parallel processing that achieves lower latency by alleviating the long HARQ RTT of repeated transmission. We also provide a model and analysis for repeated transmission functionality, in terms of the wireless transmission latency with packet-by-packet processing. Numerical analysis and simulation in various network environments show that the proposed algorithm achieves lower latency without trading reliability off. [ABSTRACT FROM AUTHOR]

Rendezvous control of multiple robots without losing network connectivity has important implications in multi-robot system including formation control, coordinated task assignments, and cooperative robotic missions. This paper introduces a new coordinate-free, bearing-based algorithm to enable rendezvous of distributed mobile robots at any designated leader robot node using hierarchical tracking of wireless network topology. An assumption is made that the robot can only sense and communicate with their neighbors (i.e., local sensing). The proposed approach preserves connectivity during the rendezvous task, adapts to dynamic changes in the network topology (e.g., losing or re-gaining a communication link), and is tolerant of mobility faults in the robots. We theoretically analyze the proposed algorithm and experimentally demonstrate the approach through simulations and extensive field experiments. The results indicate that the method is effective in a variety of realistic scenarios in which the robots are distributed in a cluttered environment. [ABSTRACT FROM AUTHOR]

This paper studies the information-energy ($\mathrm{I}$ - $\mathrm{E}$) region for simultaneous wireless information and power transfer (SWIPT) networks in mobility scenarios, where a moving transmitter simultaneously transmits information and power to a fixed receiver, and the receiver adopts power splitting (PS) receiving architecture. In order to characterize the tradeoff between the received information and harvested energy, the $\mathrm{I}$ - $\mathrm{E}$ region is defined, and corresponding optimization problems are formulated to explore the system $\mathrm{I}$ - $\mathrm{E}$ regions by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver with three popular energy harvesting (EH) models, i.e., traditional linear EH model, the logistic nonlinear EH model and the piecewise nonlinear EH model. Particularly, to efficiently solve the nonconvex optimization problem with the logistic nonlinear EH model, a successive convex approximate (SCA)-based algorithm is proposed, which is able to characterize the lower bound of the $\mathrm{I}$ - $\mathrm{E}$ with low complexity. To solve the optimization problems with the linear and piecewise EH models, as they are convex, some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the $\mathrm{I}$ - $\mathrm{E}$ regions under the linear and piecewise EH models, that under the logistic nonlinear EH model is smaller due to the limitations of practical EH circuit features. Moreover, with a fixed moving speed, when the transmit power is relatively large, the logistic nonlinear EH model can be replaced with the piecewise one due to the relatively small gap between their achieved $\mathrm{I}$ - $\mathrm{E}$ regions. Additionally, for a fixed moving track length, the higher moving speed yields the smaller $\mathrm{I}$ - $\mathrm{E}$ region with all three EH models. [ABSTRACT FROM AUTHOR]

The coupling of space-time block codes (STBCs) with the spatial modulation (SM) scheme can introduce a spatial diversity to the SM systems. This paper presents antenna selection techniques to provide an additional coding gain with the space-time block coded spatial modulation (STBC-SM). Specifically, the selection of optimal transmit antenna elements at the transmitter to send a spatially modulated space-time block code is studied. This paper studies the effects of applying the capacity optimized antenna selection (COAS) algorithm with the STBC-SM. The COAS algorithm has a suboptimal performance with a trade-off between complexity and bit-error-rate (BER) enhancement. Simulation results for STBC-SM with a spectral efficiency of 3 bits/s/Hz have been introduced with and without antenna selection. [ABSTRACT FROM AUTHOR]

Conventional authentication mechanisms routinely used for validating communication devices are facing significant challenges. This is mainly due to their reliance on both ‘spoofable’ digital credentials and static binary characteristic, and inevitable misdetection in physical layer authentication using time-varying attributes, leading to the cascading risks of security and trust. To circumvent these impediments, we develop an adaptive trust management based soft authentication and progressive authorization scheme by intelligently exploiting the time-varying communication link-related attribute of the transmitter to improve wireless security. First of all, the trust relationship between the transmitter and receiver is established based on the evaluation of selected physical layer attribute for fast authentication and multiple-level authorization. Through the designed trust model, the transmitter is authorized by the specific level of services/resources corresponding to its trust level, so that soft security is achieved. To dynamically update the trust level of the transmitter, we propose an online conformal prediction-based adaptive trust adjustment algorithm relying on the real-time validation of its attribute estimates at the receiver, thus resulting in progressive authorization. The performance of our scheme is theoretically analyzed in terms of its individual risk and individual satisfaction. Our simulation results demonstrate that the proposed scheme significantly improves the security performance and robustness in time-varying environments, and performs better than the static binary authentication scheme and existing physical layer authentication benchmarker. [ABSTRACT FROM AUTHOR]

A frequency-domain system identification-based multiple-input multiple-output (FDSI-MIMO)-SAR algorithm using multiple phase center multiple azimuth beams is proposed to obtain high-resolution wide-swath (HRWS) imaging. Frequency-division multiple access is used in such a way that each transmitter emits a conventional linear frequency-modulated (LFM) waveform modulated by a different carrier frequency and the obtained range resolution corresponds to the total transmitted bandwidth. In this article, an MIMO-SAR problem is modeled, using the principle of displaced phase center, as multiple but separate multiple-input single-output (MISO) system identification problems. The channel impulse responses of the individual MISO problems are identified in the range dimension using an FDSI-based estimation algorithm in such a way that the estimated range profile is free of interrange cell interference. In addition, the proposed algorithm does not require separating the subband waveforms at the receiver as they are processed jointly without a need to add guard bands between the adjacent subbands, which would allow utilizing the available bandwidth to the maximum efficiency. The method of synthesizing a wide transmit antenna beam from a narrow antenna beam applied in single-input multiple-output (SIMO) SAR is extended to MIMO SAR to remove the sidelobes effects of the receive beams and hence leading to the desired signal-to-noise ratio. A pulse repetition frequency lower than the Doppler bandwidth is used to obtain wide swath without experiencing aliasing in the Doppler spectrum. Finally, both simulated and constructed raw data are used to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In a Non-Orthogonal Unicast and Multicast (NOUM) transmission system, a multicast stream intended to all the receivers is superimposed in the power domain on the unicast streams. One layer of Successive Interference Cancellation (SIC) is required at each receiver to remove the multicast stream before decoding its intended unicast stream. In this paper, we first show that a linearly-precoded 1-layer Rate-Splitting (RS) strategy at the transmitter can efficiently exploit this existing SIC receiver architecture. By splitting the unicast messages into common and private parts and encoding the common parts along with the multicast message into a super-common stream decoded by all users, the SIC is better reused for the dual purpose of separating the unicast and multicast streams as well as better managing the multi-user interference among the unicast streams. We further propose multi-layer transmission strategies based on the generalized RS and power-domain Non-Orthogonal Multiple Access (NOMA). Two different objectives are studied for the design of the precoders, namely, maximizing the Weighted Sum Rate (WSR) of the unicast messages and maximizing the system Energy Efficiency (EE), both subject to Quality of Service (QoS) rate requirements of all messages and a sum power constraint. A Weighted Minimum Mean Square Error (WMMSE)-based algorithm and a Successive Convex Approximation (SCA)-based algorithm are proposed to solve the WSR and EE problems, respectively. Numerical results show that the proposed RS-assisted NOUM transmission strategies are more spectrally and energy efficient than the conventional Multi-User Linear-Precoding (MU–LP), Orthogonal Multiple Access (OMA) and power-domain NOMA in a wide range of user deployments (with a diversity of channel directions, channel strengths and qualities of channel state information at the transmitter) and network loads (underloaded and overloaded regimes). It is superior for the downlink multi-antenna NOUM transmission. [ABSTRACT FROM AUTHOR]

This paper proposes a zone-based approach referred to as zone-based GrEEn (ZBGrEEn) algorithm, for energy-efficient spectrum sharing (SS) networks. The SS network is considered to encompass device-to-device (D2D) transmissions between a single primary transmitter (PT) and multiple primary receivers (PRs), placed in distinctive zones around the PT. Power allocation is performed optimally so that the target rate of each application demanded by the PRs are adequately met. When the PT fails to meet the target rates over ${\rm{PT}} \to {\rm{PR}}$ D2D links, it transmits cooperatively through an appropriate secondary transmitter (ST) in the SS network, which acts as an amplify-and-forward (AF) relay. The ST's basis for selection is the ${\rm{PT}} \to {\rm{ST}}$ channel state and power level at the ST. Use of game theory for solving the energy efficiency (EE) maximization problem has also been quantified. ZBGrEEn introduces zone factor for determining the transmission time for each zone in the SS network. This time determines the energy consumption at the user terminals, thereby impacting its battery lifetime. The efficiency of the proposed scheme has been validated through simulation results, with the enhancement in battery lifetime of up to 46.28% achievable for the PT. Game theory is also observed to be rewarding in battery lifetime augmentation, especially for ultradense networks (UDNs). Performance of other user terminals in the network can be similarly evaluated. [ABSTRACT FROM AUTHOR]

Caching in wireless device-to-device (D2D) networks can bring contents closer to users and reduce backhaul traffic. However, the major obstacle to realize cache-enabled D2D communications is the selfish nature of users. In this paper, we propose an incentive mechanism for pricing the contributions of D2D transmitters in cache-enabled D2D-underlaid cellular networks modeled by stochastic geometry. A Stackelberg game is employed to resolve the conflict of interests between the operator and D2D transmitters. The main objective of this game is to reach the Stackelberg equilibrium, in which both the operator and D2D transmitters achieve the maximum profit by designing the optimal incentive price and the optimal caching strategy. We show the existence of the equilibrium and propose a low-complexity algorithm to obtain the near-optimal caching strategy and incentive price. Numerical results verify the existence of the SE and the advantages of the proposed scheme in terms of the utilities for the operator and D2D transmitters over existing caching schemes with fixed incentive. [ABSTRACT FROM AUTHOR]

We investigate power optimization for throughput maximization in massive multiple-input multiple-output (MIMO) downlink networks with hybrid energy harvesting transmitter. In particular, a finite-capacity energy harvester is employed at the base station in conjunction with the fixed power grid for system energy supply. With availability of the noncausal knowledge about the channel state information (CSI) and the energy harvesting process, the throughput maximization is first formulated as a typical convex optimization problem, subject to a set of battery storage and power constraints. Especifically, this optimization is also featured by taking into account the circuit energy consumption that can not be neglected in massive MIMO systems with a large quality of radio frequency chains. Mathematically, Lagrange dual decomposition technique can be used to derive the optimal power variables. However, due to its offline calculation characteristics, the proposed approach is hard to implement in practice and can only serve as a performance upper bound. To facilitate real world application, we then heuristically present an online scheme that merely utilizes the causal information of the CSI and the statistics of the energy harvesting process, without significantly degrading the performance. Finally, simulation results are shown to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we discuss four different optimization problems for distributed antenna systems (DAS) with and without D2D communication, respectively. The first and the third problems are maximizing spectral efficiency (SE) and energy efficiency (EE) of the DAS with D2D communication on the conditions of the minimum SE of DAS as well as D2D pair, the maximum transmit power of each remote access unit (RAU) and maximum transmit power of D2D transmitter. The second and the forth problems are maximizing SE of the DAS on the conditions of the minimum SE as well as the maximum transmit power of RAUs. We exploit the sub-gradient iteration method to obtain the optimal power allocation and summarize optimal power allocation algorithms for the first and second problems. We exploit fractional programming method to investigate the third and fourth problems and develop corresponding optimal power allocation algorithms. Simulation results demonstrate the effectiveness of the proposed power allocation algorithms and show the SE and EE of the DAS by using D2D communication are much better than DAS without D2D communication. [ABSTRACT FROM AUTHOR]

Periodic/aperiodic sequence with good correlation and stopband properties has received widespread attention and been widely applied in radar and communication systems. To meet the hardware requirement and maximize the transmitter efficiency, the unimodular or peak-to-average ratio (PAR) constraint is always required in sequence design. In this paper, we consider the problem of designing PAR-constrained periodic/aperiodic sequence with good properties. After establishing the corresponding criterions for both correlation and stopband properties, the unified PAR-constrained problem is formulated and then transformed into an unconstrained minimization problem via sequence synthesis. To solve the problem, an efficient gradient-based algorithm is proposed to minimize the objective function directly. As the main steps can be implemented by fast Fourier transform (FFT) operations and Hadamard product, the whole algorithm is computationally efficient. In addition, the proposed algorithm can be applied to design both the periodic and aperiodic sequences by choosing proper parameters. Numerical experiments show that the proposed algorithm has better performance than the state-of-the-art algorithms in terms of the sequence quality and the running time. [ABSTRACT FROM AUTHOR]