Precoding Vectors Research Articles

Secure energy efficiency maximization for joint ITS and IRS assisted satellite downlink communications

AbstractSatellite communications (SatCom) have been viewed as a promising technique to achieve ubiquitous global coverage in next‐generation wireless systems. This article investigates the secure energy efficient downlink transmission for SatCom where an intelligent transmissive surface‐aided transmitter is deployed at the satellite to perform energy efficient beamforming and an intelligent reflecting surface as well as a cooperative jammer are deployed on the ground to enhance the secure performance. The aim is to maximize the secure energy efficiency by jointly designing ITS beamforming, IRS phase shift, jammer precoding vector, and transmit power allocation. To develop a low‐complexity solution,first, an approximated concave lower bound of the non‐concave objective function is derived by utilizing Dinkelbach's method and a novel successive convex approximation technique. Then the reformulated problem is decoupled using alternating optimization algorithm and the subproblems are solved by adopting Riemannian manifold optimization, element‐wise block coordinate descent, Lagrange dual method and Karush–Kuhn–Tucker conditions. At last, simulation results demonstrate the effectiveness of the proposed scheme.

Sum‐rate maximization for downlink multiuser MISO URLLC system aided by IRS with discrete phase shifters

AbstractIntelligent reflecting surface (IRS) has recently been considered as a potential technology for realizing ultra‐reliable and low‐latency (URLLC) in wireless networks. This paper proposes a resource optimization scheme to maximize the sum‐rate for an IRS‐assisted downlink multiuser multi‐input single‐output (MISO) URLLC system. For the perfect CSI scenario, we jointly optimize each user's block‐length and packet‐error probability, the precoding vectors at the base station (BS), and the passive beamforming with discrete phase shifts at the IRS. Given the problem's complexity, we design a computationally efficient iterative algorithm using successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to obtain a locally optimal solution. Specifically, for the imperfect CSI scenario, we construct a robust resource optimization problem model and incorporate the S‐procedure to address the impact of channel uncertainty, proposing an iterative algorithm based on the alternating optimization (AO) method to achieve a locally optimal solution. Simulation results demonstrate that: 1) An IRS equipped with a 2‐bit quantized resolution phase shifter is sufficient to achieve a system sum‐rate comparable to that of an ideal phase shifter; 2) Compared to other Baseline schemes, Algorithm 2 exhibits better robustness and superior performance gains under imperfect CSI.

Antijamming Schemes for the Generalized MIMO Y Channel.

Signal space alignment (SSA) is a promising technique for interference management in wireless networks. However, despite the excellent work done on SSA, its robustness against jamming attacks has not been considered in the literature. In this paper, we propose two antijamming strategies for the SSA scheme applied in the multiple-input-multiple-output (MIMO) Y channel. The first scheme involves projecting the jamming signal into the null space of each source's precoding vectors, effectively eliminating it entirely. The second scheme removes interference originating from the jammer by subtracting the disturbance estimate from the incoming signal. The estimate is derived on the basis of the criterion of minimizing the received signal energy. The block error rate (BLER) performance of the proposed strategies in various channel configurations is verified by link level simulations and is presented to show the efficiency in mitigating jamming signals within the SSA-based MIMO Y channel.

A sum‐rate maximization for IRS‐aided broadcast SWIPT with time‐switching structure and nonlinear energy harvester

AbstractThis study evaluates the sum‐rate gain of the intelligent reflecting surface (IRS) in a broadcast simultaneous wireless information and power transfer network. Users are equipped with a time‐switching (TS) structure for an alternating information decoder and a nonlinear energy harvester. The sum‐rate maximization (SRM) problem is formulated subject to the constraints of the energy‐harvesting threshold required by users and limited transmission power at the base station (BS). To address the SRM problem, we jointly designed a broadcast beamforming precoding vector at the BS, reflective phase shifters at the IRS, and TS factors at the users. Subsequently, after certain manipulations, we propose efficient iterative algorithms that combine alternating optimization, successive convex approximation, semidefinite relaxation, and rank‐one penalty function methods to obtain a suboptimal solution. Finally, the numerical results show the promising sum‐rate gain of the proposed scheme compared with the conventional schemes.

Numtrical Evaluation of Optimal Precoding Algorithm and Zero Forcing Precoding in MU-MISO Channel with Channel Aging

In paper the numerical investigation of the impact of delayed channel state information (CSI) due to user movement and caused channel aging on the performance of multiuser precoder in downlink MISO system. The time variant CSI is obtained by the least squares channel estimation. We consider Zero Forcing algorithm and numerical optimization based solution of calculating precoder vectors maximizimg sum rate of multiuser system. For numerical simulation the QuaDRiGa channel model reflecting the real propagation conditions for moving users is used. The obtained performance of multiuser Zero Forcing and optimization based beamforming in spatially correlated channel are compared based on the empirical cumulative density function of the sum rate of multiple users.

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Objectives: With increase in number of access points in Cell-Free Massive Multiple Input Multiple Output (CFMM) system, spatial complexity of calculating precoding vector increases with traditional precoding schemes. So, to reduce computational complexity, network cost, and run time, a new deep learning -aided precoder is specially designed for CFMM system. The performance of downlink (DL) Cell-Free Massive Multiple Input Multiple Output (CFMM) system operating under Rayleigh fading channel model is analyze using new deep learning-based precoding scheme. Methods: We introduce new deep learning-aided precoder and an improved version of basic scalable pilot assignment algorithm which enhances system performance. We derive closed- form expression for average DL spectral efficiency (SE) for the proposed scheme, which is then compared with Minimum Mean Square Error Successive Interference Cancellation (MMSESIC), Regularized Zero Forcing (RZF), Least Square (LS) and Maximum Ratio (MR) combining techniques. We analyze the proposed scheme with perfect channel state information (CSI), instantaneous CSI, coherent transmission, non-coherent transmission, different pilot configuration. Findings: The spectral efficiency obtained with CFMM-MMSESIC and the proposed scheme is 3 bits/s/Hz, and 2.5bits/s/Hz respectively, which clearly indicates the difference of 20 % only. So, the proposed scheme gives near optimal results, and we can use simple linear combining techniques instead of complex non-linear combining techniques. Also, the proposed scheme with coherent data transmission gives performance curve, which is very close to MMSESIC scheme as desired. The performance of the proposed system is improved by reducing run time and computational complexity as compared to conventional linear precoding schemes. Novelty: The proposed precoder improves performance by reducing run time and computational complexity. Advanced pilot assignment algorithm enhances performance by reducing pilot contamination. Keywords: Cell-Free Massive Multiple Input Multiple Output, Pilot Contamination, Precoding, Minimum Mean Square Error Successive Interference Cancellation, Maximum Ratio, Regularized Zero Forcing (RZF), Least Square (LS)

QoS-Aware Precoding for Dual-Polarized Downlink Massive MIMO LEO Satellite Communications

Low Earth Orbit (LEO) satellite communications (SATCOM) are important for wireless networks and offer better global wireless access services than higher altitude SATCOM options. SATCOM performance is affected by the number of antennas, which is usually constrained by the satellite space. In this paper, the application of dual-polarized technology is considered to assist LEO SATCOM. Compared with single-polarized antennas, dual-polarized antennas utilize two different polarization orientations to transmit and receive data. This can improve the quality of service (QoS) performance, as it allows for twice the amount of data to be transmitted over the same time–frequency resources. Specifically, we characterize the dual-polarized channel in massive multiple-input multiple-output (MIMO) LEO SATCOM and investigate the precoding design for enhancing the system performance. In particular, a precoding optimization problem is formulated, which aims to jointly optimize the system throughput and the QoS performance. We divide the problem into two stages and develop novel algorithms for each stage to obtain the precoding vectors. The numerical results demonstrate that the dual-polarized technology can improve the system throughput, and the proposed algorithm leads to a higher percentage of users achieving their QoS requirements compared to conventional approaches.

Notice of Violation of IEEE Publication Principles: An Efficient Nonlinear Quantized Constant Envelope Precoding for Massive MU-MIMO Systems

Notice of Violation of IEEE Publication Principles <BR><BR>"An Efficient Nonlinear Quantized Constant Envelope Precoding for Massive MU-MIMO Systems," <BR>by R. Liang, H. Li, W. Zhang, C. Liu and Y. Guo, <BR>in IEEE Systems Journal, Early Access <BR><BR>After a careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <BR><BR>This paper contains content from the papers cited below. Credit notices were used, but due to the absence of quotation marks or offset text, the copied content is not clearly referenced or specifically identified. <BR><BR>"VLSI Design of a 3-bit Constant-Modulus Precoder for Massive MU-MIMO," <BR>by O. Castañeda, S. Jacobsson, G. Durisi, T. Goldstein and C. Studer, <BR>in the Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), May 2018 <BR><BR>"Quantized Precoding for Massive MU-MIMO," <BR>by S. Jacobsson, G. Durisi, M. Coldrey, T. Goldstein and C. Studer, <BR>in IEEE Transactions on Communications, vol. 65, no. 11, pp. 4670-4684, November 2017 <BR><BR> <br/> Massive multiuser multiple-input&#x2013;multiple-output (MU-MIMO) systems are foreseen as the key technology in next-generation wireless communication systems. However, many radio-frequency (RF) chains will require high power consumption and lots of hardware cost. One of the practical solutions is using low-resolution discrete phase shifters (PSs) for each antenna and RF chain. This article designs a nonlinear quantized constant envelope precoding algorithm for MU-MIMO systems with low-resolution discrete PS at the base station. In this algorithm, the quantized constant envelope precoding problem is presented as a nonconvex optimization problem. The multiuser interference is minimized by solving the optimal precoded vector and the associated precoding factors. First, we consider relaxing the nonconvex constraint on the 1-bit quantized constant envelope precoding to a continuous set of convex constraints on the unit circle. Afterwards, the semidefinite relaxation method is used to tackle the equivalent 1-bit quantized precoding problem. We further extend the algorithm to provide multi-bit quantized constant envelop precoding. Finally, the simulations are carried out under various conditions and compared with the most advanced precoding methods. The results demonstrate that this algorithm achieves a comparable performance to the existing precoding methods. At the same time, the corresponding performance-complexity tradeoff is more conducive to the expansion of the proposed algorithm to the multibit case.

A Unified Rate-Splitting Framework for Secure Spectrum Sharing via Joint Precoding Optimization

To handle the security problem of cognitive radio (CR) systems, secondary users (SUs) are able to be designed to assist the primary user (PU) in secure transmission. To realize the spectrum sharing among CR users, we adopt the rate-splitting multiple access (RSMA) properly, with nonorthogonal multiple access (NOMA) and space division multiple access (SDMA) as its special cases. In this article, we set up a unified rate-splitting framework to guarantee the safe spectrum sharing through multiple multiaccess strategies, without the knowledge of wiretap channels. The precoding vectors at the transmitter are conjointly designed to boost the transmission of common stream, which is from both PU and SU. Besides, the private stream of the downlink CR system can be well secreted in the high-power common signal. As for NOMA and SDMA, when the message of SU is regarded as the friendly jamming signal, the security transmission requirement of PU can be similarly guaranteed via precoding design. Simulations validate that the rate-splitting framework we established can be effective in ensuring the spectrum sharing security for the secure users through RSMA, and the security for NOMA and SDMA is also able to be promoted through adjusting our designed framework.

Multi-IRS-Aided Millimeter-Wave Multi-User MISO Systems for Power Minimization Using Generalized Benders Decomposition

Difficulties in controlling IRSs to form the optimized passive beamforming have rarely been considered in intelligent reflecting surface (IRS)-aided systems, which are summarized as follows: 1) sending the optimized passive precoding vectors to the IRS controller incurs significant control overheads; 2) implementing the optimized passive precoding needs to set massive modes in the IRS control circuit. To address these issues, we investigate codebook-based passive beamforming for multi-IRS-aided millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) systems, where the control overheads are reduced to several scalars and the number of modes set in the IRS control circuit is reduced to that of codewords. Moreover, we formulate a joint passive and active precoding problem in the multi-IRS-aided mmWave MU-MISO system as a mixed-integer nonlinear programming (MINLP) problem, and then develop a generalized Benders decomposition (GBD)-based joint passive and active precoding algorithm. The proposed algorithm offers near-optimal performance (≥ 99.9%) with significantly-reduced computational complexity. Simulation results show that the proposed algorithm achieves energy savings of up to 50% and 95%, compared to the benchmark by the maximum ratio transmission and that without IRSs, respectively. In addition, the energy savings increase with the number of reflecting elements packed on each IRS as well as that of codewords.

Symbiotic Backscatter Communication Underlying a Cell-Free Massive MIMO System

In ambient backscatter communications, backscatter devices (BDs) utilize ambient “legacy” radio signals as both a harvested energy source and a carrier on which to modulate data. Symbiotic radio, a subtype in which the legacy system assists both its own users and underlaid BDs, has attracted much research interest recently. Furthermore, cell-free massive multiple-input multiple-output (CF mMIMO) systems are promising for beyond-5G networks from both spectral and energy efficiency perspectives. To reap the benefits of both, we consider a primary CF mMIMO system where the access points (APs) aid an underlaid BD layer to both harvest energy and reflect information toward the primary receivers (which receive data from both layers). To acquire separate channel state information (CSI) of the direct and backscattered channels at the APs, a two-phase uplink pilot training method is proposed, with the effects of pilot contamination and spatial correlation between antennas accounted for. However, the receivers are assumed to only have partial CSI (statistical knowledge plus instantaneous phase information for partially-coherent reception). Assuming uplink/downlink radio channel reciprocity, the CSI is used to design downlink precoding vectors for the APs such that channel hardening is enhanced and both primary receivers and BDs benefit. We derive expressions for the average signal-to-interference-plus-noise ratios of both primary and backscatter signals, accounting for the effects of imperfect CSI, spatial correlation, pilot contamination, and channel hardening. Furthermore, the average power harvested in the BDs is derived. Simulation results demonstrate that the performance of the proposed scheme is much more uniform across all devices with CF mMIMO than with conventional co-located mMIMO. The use of CF mMIMO also largely removes the need for special consideration of the BD layer in the symbiotic system, which is not the case with co-located mMIMO.

Joint User Scheduling and Precoding for XL-MIMO Systems With Imperfect CSI

We propose an algorithm for joint precoding and user scheduling in multiple-input multiple-output systems with extremely-large aperture arrays, assuming realistic channel conditions and imperfect channel estimates. The use of statistical channel state information (CSI) for user scheduling, and a proper selection of the set of users for which CSI is updated allow for obtaining an improved achievable sum spectral efficiency. We also confirm that the effect of imperfect CSI in the precoding vector design and the cost of training must be taken into consideration for realistic performance prediction.

Semantic-Based Precoding Design for Multi-User MISO Networks

We consider the precoding design to reduce uninformative data transmissions in multi-user multiple-input singleoutput (MISO) networks. By considering the informativeness as well as the freshness of the data, we first present the peak age of incorrect information (AoII) at each physical machine (PM). We then propose the generalized power iteration (GPI) precoding algorithm to provide the precoding vector of the base station (BS) that minimizes the sum peak AoII of PMs. We show the proposed algorithm outperforms baseline methods by scheduling more for informative data updates.

Robust WMMSE Precoder With Deep Learning Design for Massive MIMO

In this paper, we investigate the downlink robust precoding with imperfect channel state information (CSI) for massive multiple-input-multiple-output (MIMO) communications. With the estimated channel and channel error statistics, the general design of the robust precoder is to maximize the ergodic sum rate subject to the total transmit power constraint. To make the problem more tractable, we find a lower bound of the ergodic sum rate and propose the robust weighted minimum mean-squared-error (WMMSE) precoder to maximize the bound. We characterize the structure of the precoding vectors by low-dimensional parameters, which are learned directly from the available CSI through a neural network. As such, the precoding vectors can be immediately computed without iterations. To extend the deep learning design to multi-antennas users, we present a flexible approach that allows the various antenna configurations at the user side to be handled. Simulation results show that the deep learning design can significantly reduce the computational complexity compared with the existing precoder designs while achieving near optimal performance.

QoS-Aware Precoding in Downlink Massive MIMO LEO Satellite Communications

Satellite communications (SATCOM) are essential parts of future wireless networks and are moving towards standardization in 6G. Low Earth Orbit (LEO) SATCOM show significant performance gain in global wireless access services, due to their minor path loss and short propagation delay, compared with other SATCOM counterparts. A quality of service (QoS)-aware precoding design is presented in this work to enhance the performance of massive multiple-input multiple-output (MIMO) LEO SATCOM. In particular, we set up a multi-objective optimization problem to balance the system energy efficiency (EE) and the percentage of users achieving their QoS requirements. An efficient algorithm is developed for obtaining the precoding vectors. Numerical results show that the proposed algorithm leads to a larger percentage of users achieving their QoS requirements and brings improvements in EE performance, compared with conventional approaches.

Beam Domain MIMO-OFDM Optical Wireless Communications With PAPR Reduction

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), confirmed as an efficient technique, has been applied in optical wireless communications (OWCs) to improve the transmission rate. However, the disadvantage of high peak-to-average power ratio (PAPR) also brings into the OWC systems, especially for multi-user communications, resulting in low power efficiency. This paper proposes beam domain MIMO-OFDM OWCs with PAPR reduction. We describe the MIMO-OFDM OWC system architecture and derive the achievable sum rate and PAPR for multi-user OFDM systems, which are dominated by the precoding vectors. To reduce the PAPR, we design the precoding vectors to maximize the sum rate under the PAPR constraint. We propose an iterative process to obtain a candidate optimal solution by the sequential parametric convex approximation (SPCA) method. Furthermore, we prove that the PAPR increases with the number of users and propose a low complexity beam scheduling and power allocation algorithm. Numerical results validate that the proposed schemes can efficiently improve the transmission rate and reduce the PAPR.

Multigateway precoded NOMA in multibeam satellite multicast systems

In this paper, we consider multigateway-based multibeam satellite non-orthogonal multiple access (NOMA) multicast systems. We investigate the improvement in spectral efficiency and accessibility by superimposing multiple signals on each beam at the same frequency and time resource employing NOMA, where multiple gateways transmit precoded signals to alleviate inter-beam interference caused by full frequency reuse. We formulate an optimization problem of maximizing the sum rate while satisfying the gateways and satellite transmission power constraints, where the precoding vector and power allocation for superposition coding as well as the decoding order for successive interference cancellation are optimized. This optimization problem is challenging to solve due to its non-convex mixed-integer nonlinear programming. However, a suboptimal solution can be obtained using a block coordinate descent algorithm. The simulation results of the proposed NOMA technique are compared with those of the orthogonal multiple access (OMA) technique. The proposed technique outperforms the OMA technique. We also investigate the impact of channel imperfection and decoding capability of the proposed algorithm through some selected simulation results.

Downlink NOMA for Short-Packet Internet of Things Communications With Low-Resolution ADCs

In this article, we propose a precoding design to maximize the sum achievable rate in downlink nonorthogonal multiple access (NOMA)-aided short-packet Internet of Things (IoT) communications, wherein each IoT device is equipped with low-resolution analog-to-digital converters (ADCs). Due to intertwined effects caused from low-resolution ADCs, short packets, and NOMA, it is challenging to find efficient precoding vectors. To resolve the difficulties, we first linearize quantization distortion by adopting an additive quantization noise model. Thereafter, we approximate nonsmooth functions by using a LogSumExp technique. With the transformed problem, we derive a first-order optimality condition and propose a novel precoding algorithm which identifies an efficient local optimal solution with low complexity. Based on the proposed algorithm, we also investigate an efficient NOMA decoding ordering method for the considered system. Via simulations, we demonstrate that the proposed method outperforms other baseline methods.

Vector Coded Caching Multiplicatively Increases the Throughput of Realistic Downlink Systems

The recent introduction of vector coded caching has revealed that multi-rank transmissions in the presence of receiver-side cache content can dramatically ameliorate the file-size bottleneck of coded caching and substantially boost performance in error-free wire-like channels. In this work, we employ large-matrix analysis to explore the effect of vector coded caching in realistic wireless multi-antenna downlink systems. For a given downlink MISO system already optimized to exploit both multiplexing and beamforming gains, and for a fixed set of antenna and SNR resources, our analysis answers a simple question: What is the multiplicative throughput boost obtained from introducing reasonably-sized receiver-side caches that can pre-store information content? The derived closed-form expressions capture various linear precoders, and a variety of practical considerations such as power dissemination across signals, realistic SNR values, as well as feedback costs. The schemes are very simple (we simply collapse precoding vectors into a single vector), and the recorded gains are notable. For example, for 32 transmit antennas, a received SNR of 20 dB, a coherence bandwidth of 300 kHz, a coherence period of 40 ms, and under realistic file-size and cache-size constraints, vector coded caching is here shown to offer a multiplicative throughput boost of about 310% with ZF/RZF precoding and a 430% boost in the performance of already optimized MF-based (cacheless) systems. Interestingly, vector coded caching also accelerates channel hardening to the benefit of feedback acquisition, often surpassing 540% gains over traditional hardening-constrained cacheless downlink systems.

Fair multigroup multicast precoding design based on traffic demands for multibeam satellite systems

Onboard resources are limited for multibeam satellite communication systems, while differences exist in traffic demands among users. Since conventional multigroup multicast precoding methods do not take traffic demands for users into consideration, it is difficult to flexibly adjust the offered throughput to users’ demands. Users with higher demand may obtain lower throughput while users with lower demand may be over-satisfied, which results in the requested-offered throughput mismatch and resource waste. This paper proposes a fair multigroup multicast precoding design based on traffic demands. To obtain the precoding design, the optimization problem aimed at maximizing the minimum throughput satisfaction ratio, which is defined as the ratio of the offered throughput to the traffic demand, is formulated, so as to provide fair service for all users while satisfying per-feed power constraints and traffic demand constraints. To address this problem, an auxiliary variable is first introduced to equivalently simplify it. Then, the semidefinite relaxation and the bisection search strategies are adopted to further handle the problem. Finally, the optimal precoding vectors are obtained by employing eigenvalue decomposition or Gaussian randomization. Simulation results indicate the effectiveness of the proposed precoding algorithm and demonstrate that it can better adapt to the scenarios with different traffic demands.