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This study is motivated by the fact that localization in millimeter wave(mmWave) Vehicle-to-Vehicle communication becomes a more critical problem because both the terminals of the communication link are in motion. The positional awareness merely based on GPS or local sensors has an error margin of around 10 meters, which can worsen in uncertain real-time conditions such as road topology and highway traffic. The paper analyses the relation between vehicle dynamics, beamforming, and beam alignment for highly directive antennas. When the subsystem models presented in this paper are taken into consideration, the joint vehicle dynamics-beamforming approach will optimize the link connectivity and SNR. The vehicle dynamics model is designed to be more realistic considering the non-linear acceleration based on the throttle-brake jerks due to induced noises. The Unscented Kalman Filter based prediction subsystem predicts the alignment values even during non-linear acceleration and jerks. The feasibility of antenna design for the beamforming strategies is supported by the requirements of phase shifters and the number of elements. [ABSTRACT FROM AUTHOR]

A zero forcing (ZF) assisted single layer beamforming is developed for spatial modulation (SM) and space shift keying (SSK) modulation multiple input multiple output (MIMO) systems, termed as SZF-SM/SZF-SSK, to achieve both transmit beamforming and receive diversity gain in this paper. Specifically, the proposed SZF-SM/SZF-SSK maps part of the bits to spatial domain, simultaneously forming a ZF precoding assisted single layer data both in multiple input single output (MISO) and MIMO systems, that differs greatly from conventional ZF beamforming assisted MIMO systems. With MISO configuration, the ZF precoder of SZF-SM/SZF-SSK converges to the maximum ratio transmission (MRT), achieving only the transmit beamforming gain. With MIMO configuration, SZF-SM/SZF-SSK tranceiver could be further modified along with the MIMO channel to achieve single layer transmission and joint detection, attaining both the transmit beamforming and receive diversity gain in SM-MIMO system. The theoretical analysis and the simulation results of the proposed SZF-SM/SZF-SSK with MISO and MIMO configurations are presented in this paper respectively, which show an attractive performance gain over the conventional SM/SSK. Compared with the conventional ZF beamforming assisted MIMO systems and some other CSI assisted SM-MIMO schemes, the proposed scheme exhibits better performance, especially under highly correlated MIMO channels, rendering a new and practical way to achieve low rank MIMO transmission with good performance and high data rate. [ABSTRACT FROM AUTHOR]

The performance of the recently proposed orientational beamforming (OBF) system for ultra-wideband (UWB) signals degrades seriously in multiuser situation. Although various techniques have been proposed in the literature to suppress multiuser interference in the time-hopping UWB system, there has been no research on interference suppression in the OBF UWB system. In this paper, we propose a nonlinear OBF system via a modified radial basis function (RBF) neural network, named the RBF-OBF system, to reject orientational UWB interferences caused by multiuser transmission in the OBF system. The RBF neural network is selected mainly because of its simple structure, convenient training process, and fast convergence speed. However, the conventional Euclidean-distance-based RBF neural network is not suitable for the training process used in this work, as the training samples are noiseless orientational steering vectors in frequency domain. Therefore, a modified RBF neural network is proposed in this paper, which evaluates the similarity between an input vector and the center vector of a hidden layer neuron by their signed complex correlation coefficient. Numerous simulations demonstrate that compared to the conventional OBF system, the proposed RBF-OBF system can significantly reduce the bit error rates (BERs) under different noise and interference situations. The BER reduction rate of the proposed RBF-OBF system is higher than 92% in additive white Gaussian noise channel and higher than 89% in line-of-sight multipath channel. [ABSTRACT FROM AUTHOR]

This paper considers the downlink of an ultra-reliable low-latency communication (URLLC) system in which a base station (BS) serves multiple single-antenna users in the short (finite) blocklength (FBL) regime with the assistance of a reconfigurable intelligent surface (RIS). In the FBL regime, the users' achievable rates are complex functions of the beamforming vectors and of the RIS's programmable reflecting elements (PREs). We propose the joint design of the transmit beamformers and PREs to maximize the geometric mean (GM) of these rates (GM-rate) and show that this approach provides fair rate distribution and thus reliable links to all users. A novel computational algorithm is developed, which is based on closed forms to generate improved feasible points. Simulations show the merit of our solution. [ABSTRACT FROM AUTHOR]

This paper proposes a Hierarchical Rate Splitting (HRS)-based and Macro Base Station (MBS)-assisted cooperative content delivery scheme for Cache-enabled Cloud Radio Access Networks (C $^{2}$ -RANs). In this scheme, a user is served by a cluster of Small Base Stations (SBSs) that cache contents. If the user-requested content is missing in its cache, the SBS retrieves it from the central processor (backhaul link) and the MBS (wireless broadcast). We minimize the total delay of this system. We thus optimize user grouping, decoding order, RS variables, BS clustering, and beamforming subject to the transmit power and backhaul capacity constraints. This process formulates a non-convex problem, which we decouple into three subproblems. First, we must pair users to reduce intra-group interference. We thus develop a Low-Complexity User Pairing (LCUP) algorithm. Second, we optimize the decoding order to minimize the broadcast link delay. We, therefore, propose a Dynamic Delay-based Decoding Order Update (D $^{3}$ OU) algorithm. Third, we jointly design the RS variables, beamforming, and BS clustering. We use surrogate optimization via the Quadratic Transform (QT), Woodbury matrix identity, and Taylor expansion. We then recast this problem to a Two-Tier Alternating (TTA) problem, which we solve with closed-form (outer tier) and convex (inner tier) formulations. We integrate these algorithms to propose an overall algorithm. It achieves significant performance gains and a 3-fold reduction in time-complexity over the standard Semi-Definite Relaxation (SDR)-based algorithm and several benchmark schemes. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the high-dimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks. [ABSTRACT FROM AUTHOR]

Massive MIMO is a promising air interface technology for 5G wireless communications. Such antenna systems offer capabilities to utilize channel correlations to create beams suitable for user specific transmissions. Most of the prior research is devoted to user-specific transmission in such systems, where a narrow beam is formed towards a user to improve its reception. Meanwhile, public-channel transmission to multiple users at once by means of broad beams has been understudied. In this paper, we introduce the concept of array-size invariant (ASI) beamforming, enabling generation of broad beams from very large antenna arrays where all elements are transmitting at maximum power. The ASI technique offers the possibility to achieve a perfectly flat array factor by exploiting the additional degree of freedom coming from the dual-polarization beamforming. The proposed technique is applicable to both uniform linear and rectangular arrays. The benefits of the method are shown by means of several numerical examples. In particular, the practical usefulness of the ASI beamforming, in terms of coverage and energy efficiency, is illustrated on an example of designing a cell-specific broad beam for public-channel transmission in a realistic network deployment. [ABSTRACT FROM AUTHOR]

In this paper, we study a novel reconfigurable intelligent surface (RIS) enabled symbiotic radio system, where a RIS is used to enhance the communication between the primary transmitter (PTx) and the primary receiver (PRx), and concurrently transmit its information (e.g., environmental monitoring information) to the PRx by varying the phase shifts. The objective is to cooperatively optimize the active transmit beamforming at the PTx and passive reflecting beamforming at the RIS to minimize the PTx's transmit power, subject to the signal-to-noise ratio constraints of primary and RIS transmissions. A new optimization problem is formulated where the RIS phase shifts are not only related to the channel state information (CSI), but also related to its message. First, we consider the perfect CSI setup to draw useful insights into the cooperative beamforming design between the PTx and RIS. Then, the worst-case robust beamforming design is carried out under the imperfect CSI setup. In particular, we take into account the imperfect successive interference cancellation at the PRx. Finally, simulation results show the effectiveness of the RIS information transfer and the integration of RIS into a symbiotic radio system can significantly improve the performance. [ABSTRACT FROM AUTHOR]

This article studies the transmit beamforming security in the dual-functional radar-communication (DFRC) system, where the radar beam also serves as the artificial jamming to suppress potential eavesdropper channels. Unlike previous researches, which assumed that the lone target might be a potential eavesdropper on the adversary side, this paper analyses a more generic scenario in which the eavesdroppers’ channel state information (CSI) is unknown. The eavesdropping rate should be limited to guarantee transmission confidentiality, which is intractable owing to the unknown CSI. To address this issue, we present the long-term eavesdropping signal-to-interference-plus-noise ratio (SINR) as a more tractable security metric. Based on the long-term eavesdropping SINR, the sum rate maximization (SRM) and jamming power maximization (JPM) transmission scheme are investigated. For the SRM scheme, we propose a zero-forcing (ZF) suboptimal algorithm to solve the non-convex fractional programming (FP) problem, and for the JPM scheme, the resultant problem is solved via the semidefinite relaxation (SDR) technique. Finally, numerical results validate the effectiveness of the proposed beamforming approaches. [ABSTRACT FROM AUTHOR]

In this paper, a robust uplink transmission scheme combining beamforming (BF) with rate splitting multiple access is proposed to improve the spectrum efficiency of a multibeam satellite system. By assuming that only imperfect channel state information is available, we first formulate an optimization problem to minimize the total transmit power under the constraints of individual transmit power budget and users’ rate requirements. Then, based on the successive convex approximation and difference-of-convex, we present an approach to convert the nonconvex constraints into convex ones, and obtain the BF vectors and power control parameters through iterative procedure. Finally, numerical results confirm the validity and superiority of our proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the anti-jamming problem of a directional modulation (DM) system with the aid of intelligent reflecting surface (IRS). As an efficient tool to combat malicious jamming, receive beamforming (RBF) is usually designed by using the statistical properties of the jamming received by Bob. Thus, it is very necessary to estimate the receive jamming covariance matrix (JCM) at Bob. To achieve a precise JCM estimation, three JCM estimation methods, including eigenvalue decomposition (EVD), parametric estimation method by gradient descend (PEM-GD) and parametric estimation method by alternating optimization (PEM-AO), are proposed. Here, the proposed EVD is derived according to the rank-2 constraint of JCM. The PEM-GD method fully explores the structure features of JCM and the PEM-AO is proposed to decrease the computational complexity of the former via dimensionality reduction. The simulation results show that the proposed three methods perform better than directly using sample covariance matrix. Additionally, the proposed PEM-GD and PEM-AO outperform EVD method and the clutter and disturbance covariance estimator RCML. [ABSTRACT FROM AUTHOR]

In future wireless communication systems, intelligent reflecting surface (IRS) plays an important role since it is capable of dynamically reconfiguring the channel conditions. In this paper, we apply IRS to facilitate the communication of an uplink multiple-input multiple-output non-orthogonal multiple access system. The objective is to maximize the system energy-efficiency (EE), requiring a joint optimization of the active beamforming at the base station, passive beamforming at the IRS, and power allocation. An iterative method is proposed to solve the formulated multi-variable non-convex optimization problem. Moreover, a low-complexity user ordering method is provided. Numerical results verify the rapid convergence of the iterative optimization scheme and the effectiveness of the user ordering. In addition, a substantial gain can be obtained by employing the proposed scheme over the benchmark schemes without IRS and with orthogonal multiple access. [ABSTRACT FROM AUTHOR]

This paper focuses on securing confidential communication in multiple intelligent reflecting surfaces (IRS) assisted terahertz (THz) systems, where a potential eavesdropper can intercept either the base station (BS)-IRS link or the IRS-user link. Notably, the secure transmission may be intercepted and blocked by the eavesdropper due to the blockage-prone nature in THz bands. To characterize the blocking effects of the eavesdropper, the blocking-based path loss is first investigated. With the imperfect eavesdropper channel state information (ECSI), the worst-case secrecy rate (WCSR) is derived, and a joint optimization problem of hybrid beamforming at the BS and reflecting beamforming at the IRS is formulated. For the BS-IRS link eavesdropping, the zero-forcing (ZF) principle-based hybrid beamforming and the closed-form phase shifts of multiple IRSs are respectively proposed. For the IRS-user link eavesdropping, an iterative algorithm is proposed to tackle the non-convex optimization problem with a given information leakage threshold. Finally, a robust secure transmission strategy for multi-eavesdropper systems is further investigated. Simulation results demonstrate that compared with blockage-unaware scenarios, our proposed scheme can resist the adverse effects of the blockage-prone nature of THz waves on information security, and significantly boost secrecy performance. [ABSTRACT FROM AUTHOR]

To extend the transmission range governed by limited transmit power, collaborative beamforming (CB) can be employed with a virtual antenna array (VAA) constructed by an unmanned aerial vehicle (UAV) swarm. CB techniques combined with physical layer security (PLS) algorithms are known to better secure transmissions of sensitive information in the presence of eavesdropping attacks, compared to co-located antenna-based schemes. In particular, analog collaborative beamforming (ACB) with random VAA topologies, which is suitable for UAV networks due to its lower signaling/channel estimation overhead compared to the digital CB, can provide a significantly higher secrecy rate by creating noise-like signals at eavesdroppers. However, the increase in energy consumption to improve secrecy rate had been ignored in the existing ACB-based PLS schemes, which is of paramount importance considering the limited onboard energy of UAVs. Thus, motivated by such limitation, in this paper, we examine the security energy efficiency of the ACB-based PLS technique, where a group of UAVs (or VAA elements) randomly change their locations in a distributed manner to randomize the array factor of the VAA, i.e., a stochastic VAA. The proposed stochastic VAA outperforms the existing static VAA in terms of secrecy energy efficiency as verified by analytic and numerical results. Further, the impact of phase errors of the proposed scheme is also presented, which indicates the significance of the accurate phase estimation. [ABSTRACT FROM AUTHOR]

The success of fog radio access networks (F-RANs) is critically dependent on the potential quality of service (QoS) that they can offer to users in the face of capacity-constrained fronthaul links and limited caches at their remote radio heads (RRHs). In this context, the collaborative beamforming design is very challenging, since it constitutes a large-dimensional nonlinearly constrained optimization problem. The paper develops a new technique for tackling these critical challenges in fog computing. We show that all the associated constraints can be efficiently dealt with maximizing the geometric mean (GM) of the user throughputs (GM-throughput) subject to the affordable total transmit power constraints. To elaborate, the GM-throughput maximization judiciously exploits the fronthaul links and the RRHs’ caches by relying on our novel algorithm, which evaluates low-complexity closed-form expressions in each of its iterations. The problem of F-RAN energy-efficiency is also addressed while maintaining the target throughput. Numerical examples are provided for quantifying the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

From adapting to the propagation environment to reconstructing the propagation environment, reconfigurable intelligent surface (RIS) changes the design paradigm of wireless communications. To reconstruct the propagation environment, joint beamforming of RIS and multi-input multi-output (MIMO) is crucial. Unfortunately, due to the coupling effect of the active beamforming of MIMO and passive beamforming of RIS, it is difficult to find the optimal solution to the joint beamforming problem, so a serious performance loss will be caused. In this paper, inspired by the end-to-end (E2E) learning of communication system, we propose the E2E learning based RIS-aided communication system to mitigate the performance loss via deep learning techniques. The key idea is to simultaneously optimize the signal processing functions at base station (BS), RIS, and user, including active beamforming for BS and user, passive beamforming for RIS. This way is able to avoid the performance loss caused by alternately optimizing each function of the RIS-aided system. Specifically, we firstly utilize a deep neural network (DNN) to realize the modulation and beamforming for BS and utilize another DNN to realize the demodulation and combining for user. Then, the RIS passive beamforming is also represented by trainable parameters, which could be simultaneously optimized with the DNNs at BS and user. Simulation results show that the proposed E2E learning based RIS-aided communication system could achieve the better bit error rate (BER) performance than traditional RIS-aided communication systems. [ABSTRACT FROM AUTHOR]

As the number of antennas increases, the massive multiple-input multiple-output (MIMO) system can precisely point to user equipments (UEs) with narrow beams. Accurate and timely channel state information (CSI) feedback is crucial to keep UEs in service. Mobile UEs, however, may suffer from the narrow beam nature of the massive MIMO system since UEs can move out of the beam coverage. When intelligent reflecting surface (IRS) is applied to the massive MIMO system, the adjustment of the IRS cannot be frequent as the IRS is controlled remotely by the base station (BS). Limiting the number of CSI feedback and the number of both BS and IRS adjustments significantly reduces the overhead of transmission and computation to the system. In this paper, we consider the UEs’ mobility adaptation problem in an IRS assisted multiuser massive MIMO downlink system with infrequent CSI feedback. We propose a beam control method that adapts to UEs’ mobility. The problem is constructed as a sum rate problem where both the BS and IRS are taken into account to jointly optimize the beamforming matrices. Simulation results show that our proposed algorithm can converge quickly and provide satisfactory performance for mobile UEs. At the same time, our proposed algorithm reduces the frequency of updating the beamforming matrices effectively both at the BS and at the IRS. [ABSTRACT FROM AUTHOR]

Most of beamforming methods rely on the proper estimation of the steering vectors of the incoming signals in order to calculate appropriate array feeding weights. However, this estimation is often performed on a theoretical basis, since most methods assume that the array elements radiate like isotropic sources and consider only the phase differences between them due to their different spatial positions. This is a rough estimation as it ignores both the non-isotropic radiation pattern of the array elements and the mutual coupling between them. To overcome these limitations, we use a different approach for the construction of steering vectors based on the embedded patterns of the array elements, extracted through full-wave analysis. This approach is used in this paper to modify two well-known beamforming methods, namely the null steering beamforming and the minimum variance distortionless response method. Both modified methods are applied to a realistic model of a microstrip planar antenna array, and thus, both the azimuth and polar angles of the main lobe and nulls directions are controlled (beamforming in 3D space). Extensive statistical analyses are performed by implementing several scenarios of escalating difficulty, in order to examine the performance and temporal response of the modified beamforming methods in comparison to the respective conventional methods and also in comparison to beamforming methods based on evolutionary techniques. [ABSTRACT FROM AUTHOR]

This paper investigates a heterogeneous cellular network (HCN) topology, where multiple sub-6 GHz bands are available for access and device-to-device (D2D) links with the full-duplex (FD) mode, and an integrated millimeter-wave (mm-wave) band is added to the D2D links’ sub-channel resource options. D2D links share spectrum resources of cellular users or dedicated mm-wave sub-channel. Densely deployed small cells scenario provides an ideal platform to implement mm-wave, FD and D2D communications. With large bandwidths available, mm-wave has the potential to meet the expected extreme data rate demands. Leveraging highly directional beamforming, severe transmission loss and the blockage phenomenon of mm-wave can be alleviated. Theoretically, FD communications can double the spectral efficiency due to bi-directional communications with the same spectral and temporal resources. However, such benefit comes at the cost of residual self-interference (RSI). Multi-user interference (MUI) and RSI among network uplink, downlink and D2D links are the main challenges. In the paper, we formulate an optimization problem for FD access and D2D links sub-channel allocation underlaying HCNs combining sub-6 GHz and mm-wave bands to make the system transmission rate maximized. Then, a scheme based on the coalition formation game is proposed to deal with this challenging NP-complete optimization. Finally, performance evaluation investigates the suitable conditions for FD and HD operations. Under different network parameter settings, we quantify associated performance of proposed scheme. [ABSTRACT FROM AUTHOR]

As more and more people choose high-speed rail (HSR) as a means of transportation for short trips, there is ever growing demand of high quality of multimedia services. With its rich spectrum resources, millimeter wave (mm-wave) communications can satisfy the high network capacity requirements for HSR. Also, it is possible for receivers (RXs) to be equipped with antenna arrays in mm-wave communication systems due to its short wavelength. However, as HSRs run with high speed, the received signal power (RSP) varies rapidly over a cell and it is the lowest at the edge of the cell compared to other locations. Consequently, it is necessary to conduct research on RX beamforming for HSR in mm-wave band to improve the quality of the received signal. In this paper, we focus on RX beamforming for a mm-wave train-ground communication system. To improve the RSP, we propose an effective RX beamforming scheme based on deep reinforcement learning (DRL), and develop a deep Q-network (DQN) algorithm to train and determine the optimal RX beam direction with the purpose of maximizing average RSP. Through extensive simulations, we demonstrate that the proposed scheme has better performance than the four baseline schemes in terms of average RSP at most positions on the railway. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an intelligent reflecting surface (IRS) aided millimeter wave (mmWave) communication system using hybrid precoding of subarray-connected structure at the base station. To maximize the received signal power of the user, we jointly optimize the active beamforming at the BS and passive beamforming at the IRS, respectively. Exploiting the rank-one property of mmWave channels, we transform the original problem into an approximate optimization problem and give the closed-form solution of the approximated problem. Simulation results show that our proposed scheme can effectively improve the received signal quality of the user with a high energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cost-efficient beamforming optimization algorithm for multi-user wireless communication systems associated with the intelligent reflecting surface (IRS). From the baseline successive refinement algorithm, which gives a sub-optimal solution for the power minimization problem under the signal-to-interference-plus-noise-ratio (SINR) constraint at each user, several optimization techniques are proposed to reduce the computation complexity while maintaining the algorithm-level performance. To reduce the number of required multiply-accumulate (MAC) operations, we first simplify the complicated matrix inversion by utilizing the channel hardening effect. We also present the two-phase refinement process for the group-level optimization of phase-shift elements, further relaxing the computation complexity as well as the processing latency. Applying the proposed optimization techniques, as a result, numerical results show that the fully-optimized algorithm can reduce the computational costs by up to 89.4% while showing less than 1 dB power loss, leading to the practical solution for the next-generation IRS-aided communication. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the secure vehicle communications in cybertwin-enabled integrated satellite-terrestrial networks, where the digital twins (DTs) in the cybertwin space reflects the physical entities (i.e., satellite, terrestrial base station (BS), and vehicles). Particularly, considering the channel similarity between different satellite links versus the randomness difference in terrestrial links, it is challenging to reach the secure transmission in satellite and terrestrial links independently with limited resources. Considering the information exchange in the cybertwin space can support an information sharing between such physical entities, the secure transmission design by using the heterogeneous satellite-terrestrial resources can be conducted from a global perspective. With the channel feedback information of vehicles gathered at the cybertwin, the co-channel interference caused by the spectrum sharing is leveraged to assist the implementation of secure transmissions in the integrated satellite-terrestrial vehicle network. Specifically, the problems of maximizing the secrecy rate of satellite-to-vehicle link and the terrestrial BS-to-vehicle link are formulated, respectively. To solve such two problems, we propose two corresponding beamforming optimization approaches, where semi-definite relaxation (SDR) and semi-definite programming (SDP) are adopted due to the non-convexity. In addition, the tightness of SDR is proved and the complexity of proposed approaches is also analyzed. Finally, extensive numerical simulations are carried out and results show the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, an unmanned aerial vehicle (UAV)-enabledspace-air-ground integrated relay system, in which UAV is equipped with phased-array antennas to receive satellite’s signal while utilizing non-orthogonal multiple access (NOMA) schemes to transmit data to land-based users, is proposed. For UAV signal collection, a detection-vector optimization problem can be addressed by introducing a serial of transformations into a second-order cone programming problem. Furthermore, the UAV-ground NOMA communication is summarized into a max-min problem in terms of UAV’s energy efficiency (EE), which is a fractional mixed-integer non-convex problem and has been widely recognized as hard work. Thus, a two-step solution is proposed. Firstly, we transfer the max-min fractional problem into a subtractive one. After that, the transformed problem is further divided into three sub-problems, which are used for jointly performing UAV trajectory optimization, NOMA scheduling optimization, and NOMA power allocation. In addition, the non-convexity of the above-mentioned problem is well solved by using the proposed iterative algorithm, achieving a rapid convergence. Numerical results illustrate the superiority of the proposed EE-NOMA scheme, which achieves three times higher energy-efficient than the conventional spectrum efficiency scheme, and NOMA is shown to globally outperform the orthogonal multiple access in UAV EE circumstance. [ABSTRACT FROM AUTHOR]

In this paper, we present unified performance analysis framework of distributed transmit beamforming (DTB) with residual phase errors which arise from various sources such as imperfect synchronization and channel feedback quantization. More specifically, we derive statistics of the receive signal-to-noise ratio of DTB in Rayleigh and Rician fading channels in the presence of residual phase errors. In the case of residual phase errors caused by channel feedback quantization, we show that the distribution of residual phase errors can be modeled by uniform distribution in Rayleigh fading channels and Gaussian distribution in Rician fading channels. Also, our results reveal that a small number of bits are sufficient for quantized feedback to achieve comparable performance to DTB with perfect channel feedback. Finally, capitalizing on the derived statistics, we present closed-form expressions for the outage probability and the ergodic capacity of DTB with quantized channel feedback. [ABSTRACT FROM AUTHOR]

This paper studies joint cache placement and cooperative multicast beamforming to provide content-centric data services for mobile users in the integrated satellite-terrestrial network (ISTN). Specifically, in the ISTN, users requesting the same content are arranged into a multicast group and served by the cache-enabled base stations (BSs) and low earth orbit (LEO) satellite via cooperative beamforming. To maximize the network utility that takes network throughput and backhaul traffic into consideration, the cache placement, LEO satellite and BS clustering, and multicast beamforming are jointly designed and formulated as a two-timescale optimization problem. However, the original problem is anti-causal since the cache placement strategy and content delivery policy are coupled in different timescales. By utilizing historical information, we propose a two-step scheme to decompose the problem into a short-term content delivery subproblem and a long-term cache placement subproblem. As the former subproblem is nonconvex with mixed-integer variables and coupling constraints, we transform it into an equivalent problem and propose a penalty concave-convex procedure based algorithm to solve it. To address the latter subproblem, a centralized iterative algorithm and a distributed alternating algorithm with low complexity are developed, respectively. Simulation results validate that the proposed schemes can effectively enhance the network throughput and reduce the backhaul traffic compared with benchmark scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider secure communications in a multi-cell downlink multi-antenna system, where each multi-antenna base station (BS) sends confidential messages to one intended legitimate user (LU) with a passive multi-antenna eavesdropper trying to intercept the messages in this cell. To enhance the secure communications of the cell-edge users, the multiple BSs employ coordinated beamforming with the aid of artificial-noise (AN) beamforming. We conduct a mathematically rigorous secrecy performance analysis of the considered system and obtain new accurate closed-form expressions of a lower bound on the ergodic rate of each LU and an upper bound of the ergodic rate of each eavesdropper without assuming asymptotes for any system parameter (except for the “worst-case” scenario with zero noise power). Then, a lower bound on the ergodic secrecy rate (ESR) of each LU follows. Based on the derived analytical results, we propose a low-complexity algorithm to optimize the channel sate information (CSI) feedback bits allocation for the channels of target signal link and inter-cell interference links. Furthermore, we develop explicit sufficient conditions on the set of system parameters under which there are at least two BSs to coordinated secure transmission. Through both theoretical analysis and numerical results, we can verify that the obtained analytical lower bound on ESR of each LU as a function of the set of power allocation coefficients is generally neither convex nor concave. Thus, we propose to employ some numerical method to find a sub-optimal power allocation solution. Finally, numerical results are also provided to validate our theoretical results and the proposed feedback bits and power allocation methods. [ABSTRACT FROM AUTHOR]

Distributed phased Multiple-Input Multiple-Output (phased-MIMO) radar system consisting of separated subarrays possesses a high angle resolution but with sharing high sidelobes. This paper therefore focuses on the design of a virtual low-sidelobe beampattern in distributed phased-MIMO radar. An iterative framework with respect to subarray layout and receive weighting coefficients is proposed to minimize beampattern Peak Sidelobe Level (PSL) accounting for practical position constraints as well as the mainlobe level restriction. In each iteration, an efficient cyclic optimization algorithm based on the Coordinate Descent (CD) framework is developed to seek for the subarray layout through splitting high dimensional layout optimization problem into multiple one-dimensional optimization problems, and the SemiDefinite Relaxation (SDR) technique and rank one approximation are explored to design weighting coefficients. Numerical simulations are provided to assess the performance of the proposed algorithms in term of the achieved beampattern under different constraints and parameter settings. [ABSTRACT FROM AUTHOR]

This paper proposes a downlink transmission scheme exploiting robust beamforming in conjunction with non-orthogonal multiple access for multibeam satellite systems to enhance the spectral efficiency and user fairness. Specifically, by employing the imperfect channel state information and considering the fairness among multiple satellite terminals (STs), we first formulate an optimization problem to maximize the sum $\alpha$ -fair utility, while guaranteeing the transmit power budget and quality-of-service requirement of each ST. Since the original problem is nonconvex, we then adopt a discretization method to convert the channel uncertainty into deterministic forms, and propose an iterative penalty function algorithm combined with sequential convex approximation to obtain the optimal solution. Finally, simulation results are given to confirm the effectiveness and superiority of the proposed scheme over some existing works. It is also shown that our scheme can achieve a good balance between the system performance and user fairness. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep neural network (DNN) aided codebook based beamforming scheme for frequency-division-duplex (FDD) millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) systems under multipath. Different from the time-division-duplex (TDD) systems, for FDD systems the reciprocity between the down-link (DL) and up-link (UL) channel state information (CSI) does not hold in general, requiring an extra CSI feedback stage. Based on the previous theoretical analysis and measurements, however, partial reciprocities, including the spatial directional angles and number of propagation paths, do exist for FDD mmWave systems. Given such partial reciprocities, we propose a beamforming scheme based on a predefined codebook. Furthermore, we consider the channels with multipath where multiple codewords can be selected for some mobile stations (MSs) to utilize more than one propagation paths, which improves the link reliability. We then propose the corresponding beamforming strategy that not only identifies the number of paths, but also reconstructs the individual single-path channels which are easier to deal with at the beamforming stage. To select the optimal codewords for these single-path channels with lower complexity, we then present a DNN-aided approach that predicts the optimal codewords. The integration of DNN resolves not only the issue of power leakage, but also angular ambiguity in the codebook. Simulation results demonstrate that the performance of the proposed DNN-aided approach is very close to that of an exhaustive search. [ABSTRACT FROM AUTHOR]

Pilot spoofing attack brings new challenges to physical layer secure transmission. However, this method will not work without any knowledge about the pilot sequence and active eavesdropping can be detected by constructing random pilot sequence. Intelligent reflecting surface (IRS), with the real-time programmable features for wireless channels, provides new possibilities for effective pilot spoofing. In this paper, the IRS is deployed near the legitimate users and the legitimate signal can always be passively reflected. Then the control strategy is embedded into the communication process under time-division duplex mode to assist eavesdroppers to conduct pilot spoofing. By setting different phase shifts at the IRS during the uplink phase and downlink phase, the channel reciprocity disappears, and thus secure beamforming vector is biased towards the eavesdropper. Furthermore, in order to overhear more information, the average secrecy rate minimization problem based on statistical channel state information is established by carefully designing the phase shifts, which is non-trivial to solve. With alternating optimization and Charnes-Cooper transformation technique, the original problem is transformed into convex form and a near optimal solution is achieved. Finally, simulation results show that our proposed scheme can pose serious secure threat without any energy footprint. What's more, if the IRS is not utilized by the internal users properly, it will bring more threat. [ABSTRACT FROM AUTHOR]

This paper investigates rate splitting multiple access (RSMA) for multigroup multicast beamforming in a cache-enabled cloud radio access network (C $^2$ -RAN). To maximize the minimum weighted rate among all users and ensure the base station (BS) transmit power and backhaul capacity constraints, we jointly optimize the dynamic BS clustering, message splitting, and beamforming vectors. More specifically, users requesting the same content are collected to a multicast group and served by a cluster of BSs. Each BS has limited storage space and connects to the central processor (CP) via a backhaul link. However, the resulting optimization problem is discrete and nonconvex and has a non-smooth objective function. To alleviate these problems, we construct two surrogates to approximate the objective function locally. These surrogates are strictly lower bounds and concave. Also, we build an upper-bound convex surrogate function to handle the backhaul cost constraint and derive a minorization-maximization (MM)-based iterative algorithm. Besides, we adopt a quadratic transform method to recast the achievable rate and binary BS selection problems to a more tractable form to reduce time cost. We transform the resulting problem into a two-tier alternating optimization problem; the outer-tier problem is handled with closed-form expressions while the inner-tier one is solved by a convex framework. We thus develop a quadratic transform-based alternating (QTA) algorithm. Extensive simulation results confirm the superiority of our proposed transmit strategy and algorithms. [ABSTRACT FROM AUTHOR]

Thanks to the short wavelength at high frequency (HF), it is promising to combine massive multiple-input and multiple-output (MIMO) with HF communications. In particular, HF massive MIMO differs from the conventional MIMO in array structure and radio propagation. In this paper, we investigate the HF massive MIMO system with a compact planar array (CPA) deployed at the base station (BS) in the presence of electromagnetic mutual coupling (MC) for the asymptotic case under unlimited antenna elements assumption. Specifically, the HF sparse channel is analyzed asymptotically to show that the randomness characteristic of channel decreases in the asymptotic regime, which is conducive to the design of communication systems. Moreover, on the basis of the asymptotic analysis results, the optimal beamforming to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) is derived by applying random matrix theory. Numerical results verify the accuracy of the asymptotic analysis, indicate the performance superiority of the proposed scheme to the traditional minimum mean squared error (MMSE) and maximum ratio transmission (MRT) schemes, and reveal the robustness of the proposal to electromagnetic MC. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) band, i.e., 30 to 300 GHz, is characterized by short range transmissions and vulnerability to path blockage necessitating the use of relaying. Probing more relays finds out the best relay having the highest spectral efficiency but at the expense of increasing the probing overhead due to excessive beamforming training (BT) causing a decrease in the overall throughput. In this paper, mmWave two-hop relaying will be formulated as a single player multi-armed bandit (MAB) problem enabling one relay probing while maximizing the achievable spectral efficiency. Moreover, the relays could not establish the mmWave link due to blockage for instance will be identified as sleeping relays and eliminated from the rest of the MAB game. Thus, sleeping non contextual MAB (S-MAB) algorithm, namely sleeping Thompson sampling (S-TS) will be proposed to handle the problem. Furthermore, by utilizing the multiband capability of standardized WiGig devices containing both 2.4/5 GHz WiFi and 60 GHz mmWave bands, WiFi information will be used as contexts of the MAB game. Therefore, sleeping contextual MAB (S-CMAB) algorithm, namely S-CTS, will be proposed as well. Numerical and regret analysis ensure the superior performance of the S-CMAB algorithm over the S-MAB counterpart and the existing mmWave relay probing solutions accompanied with high convergence rates. [ABSTRACT FROM AUTHOR]

In this paper, we consider the secure transmission problem in an unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) assisted mmWave networks in the presence of an eavesdropper. The UAV base station (UAV-BS) and IRS are deployed to overcome the blockages. Artificial noise (AN) is exploited against the eavesdropper. Aiming to maximize the secrecy rate, we jointly design the positions and beamforming of UAV-BS and IRS, where the positions here represent the deployed position of the UAV-BS and the activation position of the IRS. Meanwhile, the maximum transmits power, minimum height constraint for UAV-BS, and the legitimate receiver minimum rate constraint are required. To obtain the optimal UAV-BS position, the elevation angle-dependent probabilistic LoS air-to-ground channel is exploited in this network. To tackle the formulated non-convex problem, we divide it into two subproblems: (1) design UAV-BS and IRS positions; (2) design UAV-BS and IRS beamforming. Moreover, to reduce the complexity, we propose an ideal beamforming model to find the near-optimal positions of UAV-BS and IRS. Then, semidefinite relaxation (SDR) is utilized to cope with the highly coupled and high-dimensional variable vectors. Finally, we obtain a sub-optimal solution by an efficient and low-complexity alternating optimization algorithm. Numerical results demonstrate the improvement of secrecy rate by jointly designing the positions as well as the significant performance gains achieved by our proposed schemes over benchmark schemes. [ABSTRACT FROM AUTHOR]

When the wireless communication system evolves to massive multiple-input multiple-output (MIMO) system, the system becomes more and more complex. Hybrid beamforming architecture provides excellent trade-off between the system performance and the complexity for the massive MIMO system. Hybrid beamforming architecture can be classified as fully-connected (FC) and sub-connected (SC) architecture. Existing studies on the system performance comparison of the two architectures are limited. In this paper, we investigate the performance of the FC and SC architecture in the multi-user multiple-input single-output (MU-MISO) system. We derive the closed-form sum rate of two architectures under the same total transmission power constraint. From the results, we conclude that the system performance of FC architecture is always better than that of SC architecture. When the number of users is small or the signal noise ratio (SNR) is low, the performance difference between the FC and SC architecture is small. Our analysis can serve as a guideline of choosing hybrid beamforming architectures. [ABSTRACT FROM AUTHOR]

Phase-only beamforming (POB) plays an important role in modern radar and communication systems. The NP-hard nature of POB makes it difficult to be solved for large-scale antenna arrays. This paper studies two fast and simple GP algorithms for the problem of POB. First we give a new interpretation of the GP method under the framework of sequential quadratic programming (SQP). Then we prove that the GP operation gives a strict global minimizer of the subproblem of SQP. Moreover, the larger the step size in GP operation, the smaller the minimum value of the subproblem. Based on this observation we design two variable step size GP (VSGP) algorithms that improve the convergence speed of the GP method significantly. Furthermore, the VSGP algorithms have a simple structure, making them easy to be implemented in chips. Finally, we derive a necessary and sufficient condition for the Karush-Kuhn-Tucker (KKT) points of the POB problem. We also prove that, for any initialization, all limit points of the iterates generated by the VSGP algorithms converge to the KKT points. Simulation results show that the proposed algorithms outperform the state-of-the-art algorithms in terms of convergence speed and computational cost. [ABSTRACT FROM AUTHOR]

Both the jammer and the eavesdropper pose severe threat to wireless communications due to the broadcast nature of wireless channels. In this paper, an intelligent reflecting surface (IRS) assisted secure communication system is considered, where a base station (BS) wishes to reliably convey information to a user, in the presence of both a jammer and an eavesdropper whose transmit informations are not completely known. Specifically, with the imperfect third-party node's channel state information (CSI) and no knowledge of the jammer's transmit beamforming, we aim to maximize the system achievable rate by jointly designing the BS's transmit beamforming and the IRS's reflect beamforming, while limiting the information leakage to the potential eavesdropper. Due to the non-convexity and intractability of the original problem induced by the incompleted information, we utilize the auxiliary variables, Cauchy-Schwarz inequality, and General Sign-Definiteness transformation to convert the original optimization problem into a tractable convex optimization problem, and then obtain the high-quality optimal solution by using the successive convex approximation and penalty convex concave procedure. Numerical simulations demonstrate the superiority of our proposed optimization algorithm compared with existing approaches, and also reveal the impact of key parameters on the achievable system performance. [ABSTRACT FROM AUTHOR]

This paper investigates the application of nonorthogonal multiple access (NOMA) in millimeter-Wave (mmWave) communications to address resource allocation issues for multiuser downlink transmission. Inspired by the K-means clustering algorithm, a NOMA user grouping policy is first employed in accordance with the channel correlation between users. The first-stage decoding order for each NOMA group is then determined by merely utilizing the users’ channel gain to formulate a joint power allocation and hybrid beamforming optimization problem. Specifically, we use signal-to-leakage-plus-noise ratio (SLNR) as the performance index of the optimization problem to reduce the computational complexity, because it can decouple the design of the beamforming and power allocation issues so they can be executed iteratively. Under given beamforming matrix, the power allocation issue is expressed as a quadratic programming (QP) problem, and then transformed into a convex problem by introducing an auxiliary-positive real variable to solve through the Lagrange multiplier method. On the contrary, the beamforming optimization problem is non-convex and very difficult to solve due to the constant modulus constraints in the hybrid architecture. However, the optimum solution of the ideal full-digital beamforming can be acquired primarily by generalized eigenvalue decomposition (GED). In this condition, two hybrid beamforming algorithms are proposed, in which normalized phase matching and equal gain transmission are performed respectively in the analog domain. On this basis, a second-stage decoding order can be employed according to the effective channel gain of users to perform successive interference cancellation (SIC) successfully. Numerical results show that the proposed joint power allocation and hybrid beamforming algorithms are superior to the classical mmWave-NOMA and orthogonal multiple access (OMA) schemes not only in lower implementation complexity, but also in better spectrum efficiency and energy efficiency performance. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has recently emerged as a promising technology for 6 G wireless systems, due to its capability to reconfigure the wireless propagation environment. In this paper, we investigate a Rate-Splitting Multiple Access (RSMA) for IRS-assisted downlink system, where the base station (BS) communicates with single-antenna users with the help of an IRS. RSMA relies on rate-splitting (RS) at the BS and successive interference cancellation (SIC) at the users and provides a generalized multiple access framework. We derive a new architecture called IRS-RS that leverages the interplay between RS and IRS. For performance analysis, we utilize an on-off control technique to control the passive beamforming vector of the IRS-RS and derive the closed-form expressions for outage probability of cell-edge users and near users. Moreover, we also analyze the outage behavior of cell-edge users for a sufficiently large number of reflecting elements. Additionally, we also analyze the outage performance of cooperative RS based decode-and-forward (DF)-assisted framework called DF-RS. Through simulation results, it is shown that the proposed framework outperforms the corresponding DF-RS, RS without IRS and IRS-assisted conventional non-orthogonal multiple access (NOMA) schemes. Furthermore, the impact of various system's parameters such as the number of IRS reflecting elements and the number of users on the system performance is revealed. [ABSTRACT FROM AUTHOR]

This paper investigates distributed beamforming networks composed of multiple groups of transmitters where each group consists of two transmitters. The goal is to jointly optimize the phase components at the transmitters and the orientation angles of the groups that maximize the radiated energy over an angular sector containing an adversary while placing a null towards the client in order not to obfuscate the client. Since the optimization problem is non-convex, it is intractable to obtain the optimal solution in closed-form. To this end, we propose a non-iterative closed-form solution by adopting a group-wise nullforming approach where each group independently creates a null to the client. The effectiveness of the proposed scheme is validated through numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate hybrid beamforming, user scheduling, and resource allocation optimization based on spectrum coexisting forward transmission in integrated terrestrial-satellite network (ITSN) with the purpose of improving system sum rate and energy efficiency. Considering the limitation of on-board beamforming, a hybrid analog-digital beamforming scheme is designed under the scenario of millimeter wave (mmWave) coexisting in the ITSN framework. Besides, in order to further mitigate intra-beam and inter-beam interference, we propose an adaptive user scheduling scheme, which first determines the cluster center based on adaptive threshold, and then selects users with less channel correlation into a scheduling cluster. Moreover, we model system sum rate maximization problem that incorporates maximum power constrains and minimum data rate requirements. Combined with the aforementioned hybrid beamforming and user scheduling strategy, we formulate the sum rate maximizing problem to a pure power allocation issue. In view of the non-convexity and high complexity, we propose a feasible optimization method based on the minimum mean square error (MMSE) criterion and logarithmic linearization to optimize the power allocation for each user terminal (UT). Simulation results show that our proposed joint beamforming and resource allocation optimization scheduling scheme can achieve an attractive gain in system sum rate and energy efficiency compared with conservative beamforming and allocations. [ABSTRACT FROM AUTHOR]

Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes. [ABSTRACT FROM AUTHOR]

Hybrid antenna array consisting of analog subarrays provides a low-cost solution to achieving high beamforming gain in millimeter-wave/terahertz communication systems. For beam adjustment, beam scanning can be readily applied to find the line-of-sight (LoS) path. Conventionally, the beam scanning process is inefficient because the number of grids, together with the scanning period, has to be large to achieve high resolution. To improve the beam scanning efficiency, in this paper we propose an off-gird direction-of-arrival (DoA) estimation method utilizing unequal-sized subarrays. Specifically, we first provide a new solution to overcome the ambiguity problem, i.e., the hybrid antenna array may render the DoA estimate non-unique. That new solution is formed by designing the antenna numbers in the subarrays and can be easily combined with the beam scanning process. We then propose a three-stage beam scanning strategy. At the first stage, coarse scanning is applied to roughly steer the subarrays along the LoS path. At the second stage, a simple DoA estimator is employed to obtain the candidate DoAs. Finally, beam scanning is performed again on the candidate DoAs to obtain the final DoA estimate. Simulation results demonstrate that the proposed method can achieve higher DoA estimation accuracy in shorter scanning period than the conventional beam scanning method; and compared with existing methods developed for equal-sized subarrays, the proposed method is more robust against multi-path interference. [ABSTRACT FROM AUTHOR]

In-band full-duplex (IBFD) systems require promising resource allocation (RA) strategies to fully exploit the available time-frequency resources. Furthermore, the acquisition of channel state information and signal reception in IBFD systems are significantly impacted by insufficient self-interference cancellation (SIC), impeding the applications of IBFD in practical wireless systems. Multicarrier-division duplex (MDD), which benefits low-budget SI mitigation in digital domain and flexible subcarrier assignment, is expected to be a promising transitional technique from half-duplex (HD) to IBFD. To demonstrate the advantages of MDD over HD, this paper first compares the upper-bound performance of MDD and HD by applying unfair greedy RA. Then, considering the millimeter-wave (mmWave) with hybrid beamforming, we propose the RA optimization with the quality-of-service constraints on both downlink (DL) and uplink (UL) mobile-stations (MSs). To solve this non-convex RA problem, we divide it into a suboptimal subcarrier allocation problem solved by the proposed improved fair greedy (IFG) algorithm, and a convex power allocation problem. Furthermore, we design two general hybrid precoder based on matrix factorization and direct approach, and a combiner having high SIC capability. Our results show that the proposed RA algorithm can achieve the performance near the upper-bound achieved by the unfair greedy algorithm, while guaranteeing the proportional fairness among all DL/UL MSs. The performance of the two precoding schemes is depended on the number of radio frequency chains supported. Finally, the proposed SIC algorithm is able to provide sufficient SI mitigation, which can be implemented without impacting the RA operation. [ABSTRACT FROM AUTHOR]

Hybrid precoding is used in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems instead of full digital precoding to reduce the cost and power consumption. Conventional hybrid precoding however requires complex channel estimation before beamforming. In this paper, we propose a low complexity low-cost channel estimation-free deep direct beamforming method composed of two basic steps-beam impulse and beamforming for time division duplex (TDD). This method is built upon randomized singular value decomposition (RSVD) and can directly complete the process of beamforming. Furthermore, a realistic dataset of mmWave channel-DeepMIMO is adopted to evaluate and determine the practicability of this method. The simulation results demonstrate that the proposed deep direct beamforming achieves high performance while its complexity and power consumption are very low. Compared with other methods without perfect channel status information (CSI), the proposed method costs less and requires lower computational resources while achieves higher spectral efficiency. Furthermore, the RSVD can be easily combined with conventional beamforming methods hence the proposed method is easily extendable to other applications. [ABSTRACT FROM AUTHOR]

The tradeoff between energy efficiency (EE) and spectral efficiency (SE) provides a new perspective for the transmit design in satellite communications (SATCOM), i.e., one can jointly optimize the system EE and SE of the SATCOM system. In addition, jointly considering that it is challenging to obtain accurate channel state information at the transmitter, we study the tradeoff between EE and SE for the robust beamforming in multi-beam SATCOM in this paper. Specifically, with the quality of service and total power constraints taken into account, we study the robust beamforming aimed at maximizing the resource efficiency, which is defined as the weighted sum of the system EE and SE, to achieve the tradeoff between EE and SE with more flexibility. For this problem, we first approximate the ergodic user rate and ergodic signal-to-interference-plus-noise ratio, both with closed forms. Then, the semidefinite relaxation is applied to tackle the optimization of beamforming vectors via relaxing the rank constraints. To handle the sum of fractions involved in the optimization objective, a nested iterative algorithm exploiting the quadratic transformation and majorization-minimization method is proposed. Finally, the eigenvalue decomposition is utilized to obtain the beamformers. Simulation results indicate the effectivity of our proposed algorithm in achieving the EE-SE tradeoff and illustrate the superiority of the robust approach compared with the conventional baselines. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is an enabling and key wireless technology for the next generation of mobile communications. Generally, previous works focus on the performance in NOMA networks, based on some assumptions. For instance, the number of users in a NOMA group, is two-category: 1) only two users; 2) any number of users without restriction. And just a certain factor of channel gain, e.g., the propagation path loss or the small scale fading, is subjectively considered to be dominant. Besides, there is lack of the closed-form expression for the probability density function (PDF) of ordered channel gain. In this paper, we investigate a downlink cellular NOMA network with the directional beamforming and the upper limit for user number (ULFUN) in a NOMA group. Meanwhile, the path loss and the Rayleigh fading are taken into account as a whole for ordering channel gains. Based on the derived closed-form PDF of ordered channel gain, we analyze the performance for an arbitrary user in the network and derive closed-form expressions for the outage probability, the approximate meta distribution of conditional success probability (CSP) and the ergodic data rate. The provided simulations demonstrate that the proposed analytical results are accurate and reasonable. Moreover, it illustrates that the existence of ULFUN can have a positive impact on the performance gain in low SINR environments, while in terms of the outage probability, ULFUN would become a non-negligible and negative factor in the improvement of performance for high SINR environments. [ABSTRACT FROM AUTHOR]

The physical layer security (PLS) has become crucial in the fifth generation (5G) wireless technology where non-orthogonal multiple-access (NOMA) plays an essential role for sharing the spectrum among multiple users. Multiple-input multiple-output (MIMO)-NOMA based cognitive radio network (CRN) enhances the spectral-efficiency at the cost of PLS. In this paper, a transmit-zero-forcing beamforming (ZFBF) technique with signal alignment is proposed to improve the PLS for MIMO-NOMA based CRN when perfect channel state information (CSI) is available at the base station (BS). In this study, a generalized cellular model with multi-cell is considered where each cluster is formed with a primary user (PU) and a secondary user (SU) to adopt the CRN. The locations of PU and SU are kept arbitrary within the cells. We also propose an eigen beamforming technique to improve the PLS for MIMO-NOMA based CRN when the imperfect CSI is available at the BS. For both beamforming techniques, optimum power allocation algorithms are developed for further enhancement of the PLS. Finally, a closed-form expression of the secrecy outage probability for the Nakagami- $m$ fading channels is derived and compared with simulation results and other existing techniques. [ABSTRACT FROM AUTHOR]

This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we jointly optimize beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem. Simulation results verify the effectiveness of the IRS, which can significantly improve the system EE as compared to conventional benchmark schemes and also unveil a trade-off between convergence and performance gain for the two proposed algorithms. [ABSTRACT FROM AUTHOR]