Alternating Optimization Algorithm Research Articles

Zero-Crossing Precoding Techniques for Channels With 1-Bit Temporal Oversampling ADCs

A promising approach to reducing the energy consumption is to consider coarse quantization at the receiver. In this study, we investigate novel precoding techniques in space and time for bandlimited multiuser MIMO downlink channels with 1-bit quantization and oversampling at the receiver, considering zero-crossing modulation. The proposed time-instance zero-crossing modulation conveys the information into the time-instances of zero-crossings. Two design criteria for time-instance zero-crossing modulation are investigated, namely, the minimum distance to the decision threshold and the mean-square error between the received and the desired signal. The maximization of the minimum distance to the decision threshold can be formulated as a quadratically constraint quadratic program. As an alternative, an equivalent problem can be formulated based on power minimization, which reduces computational complexity. Departing from the conventional mean-square error based technique, a more sophisticated algorithm is developed, which implies active constellation extension in order to improve the performance at high SNR. The extended problem is solved with two approaches, namely by formulating the problem as a second-order cone program and by considering an alternating optimization algorithm. Numerical results show that the proposed time-instance zero-crossing precoding methods significantly improve the bit error rate compared to the state-of-the-art methods.

Improved Spectral Efficiency in STAR-RIS Aided Uplink Communication Using Rate Splitting Multiple Access

In this paper, a phase-shift coupled simultaneous transmitting/refracting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided uplink (UL) rate-splitting multiple access (RSMA) system is investigated to achieve improved spectral efficiency. The considered UL RSMA system splits the rate for each user by dividing their message into multiple sub-messages and these sub-messages are transmitted to the base station (BS) via STAR-RIS as direct link between BS and user is absent. In particular, we formulate a resource allocation design problem which aim to maximize the overall rate-throughput of the considered system under the joint optimization of power allocation, decoding order, user-fairness and beamforming design at STAR-RIS for various operating modes of STAR-RIS modes, which is mixed- integer non-linear programming (MINLP). To solve the formulated non-convex complex problem, we first transform the original sum-rate maximization into its simplified form and then solved it using an alternating optimization algorithm where the sub-problems of power allocation and beamforming design under given decoding order are solved alternatively using general convex approximation and fractional programming approaches. Numerical simulation and computational complexity analysis validate that the proposed solution attains fast convergence. Moreover, the proposed RSMA scheme in STAR-RIS aided UL system outperforms the conventional nonorthogonal multiple access and orthogonal multiple access schemes in terms of overall rate-throughput and user-fairness.

Secrecy-Rate Optimization of Double RIS-Aided Space–Ground Networks

The physical layer security (PLS) of a space-ground communication system is examined. To improve the security performance, a pair of reconfigurable intelligent surfaces (RISs) is integrated into the system and benchmarked against a scheme, where there is only a single RIS close to the ground station. As for the double-RIS scenario, we formulate a secrecy rate maximization problem, and then propose an alternating optimization (AO) algorithm for jointly optimizing three vectors, namely the beamformer of the ground station and the reflecting vectors of two different RISs. Similarly, as for the single-RIS case, we also propose another AO algorithm for optimizing a pair of vectors, namely the beamformer of the ground station and the reflecting vector of the single RIS. Both the double-RIS and the single-RIS AO algorithms are developed on the basis of the first-order Taylor expansion and the Dinkelbach’s method, which allow us to approximate non-convex optimization problems by convex ones. Our results demonstrate that the proposed double-RIS scheme outperforms the single-RIS benchmark scheme in terms of its security.

Joint Beamforming and PD Orientation Design for Mobile Visible Light Communications

In this paper, we propose joint beamforming and photo-detector (PD) orientation (BO) optimization schemes for mobile visible light communication (VLC) with the orientation adjustable receiver (OAR). Since VLC is sensitive to line-of-sight propagation, we first establish the OAR model and the human body blockage model for mobile VLC user equipment (UE). To guarantee the quality of service (QoS) of mobile VLC, we jointly optimize BO with minimal UE the power consumption for both fixed and random UE orientation cases. For the fixed UE orientation case, since the transmit beamforming and the PD orientation are mutually coupled, the joint BO optimization problem is nonconvex and intractable. To address this challenge, we propose an alternating optimization algorithm to obtain the transmit beamforming and the PD orientation. For the random UE orientation case, we further propose a robust alternating BO optimization algorithm to ensure the worst-case QoS requirement of the mobile UE. Finally, the performance of joint BO optimization design schemes are evaluated for mobile VLC through numerical experiments.

Joint Caching and Routing in Cache Networks With Arbitrary Topology

In-network caching and flexible routing are two of the most celebrated advantages of next generation network infrastructures. Yet few solutions are available for jointly optimizing caching and routing that provide performance guarantees for networks with arbitrary topology. We take a holistic approach towards this fundamental problem by analyzing its complexity in all the cases and developing polynomial-time algorithms with approximation guarantees in important special cases. We also reveal the fundamental challenge in achieving guaranteed approximation in the general case and propose an alternating optimization algorithm with good empirical performance and fast convergence. Our algorithms have demonstrated superior performance in both routing cost and congestion compared to the state-of-the-art solutions in evaluations based on real topology and request traces.

Collaborative Management of Resource Allocation and Precoding for Dual-Mode Networks

A sub-6GHz (μWave) and millimeter wave (mmWave) dual-mode network can deliver high data rates while maintaining dependable coverage. This paper proposes a novel resource allocation and precoding algorithm based on collaborative sensing in the dual-mode network. We formulated the collaborative optimization problem of resource allocation and precoding, which maximizes the sum of data rates while meeting minimum acceptable rate requirements and ensuring proportional fairness. Considering the spatial similarity between mmWave channel and μWave channel, the prior information of mmWave channel predicted by μWave band is generated to process sensing weight (i.e., subchannel allocation weight of Markov algorithm), which realizes fast dual-mode network resource allocation. Furthermore, a low complexity two-stage precoding scheme for dual-mode networks is proposed based on μWave spatial information. Low-complexity analog precoding is realized in the first stage based on μWave spatial information. In the second stage, inspired by the correlation between minimum mean square error (MMSE) and mutual information, this paper proposes an alternating optimization algorithm to find a local weighted sum-rate (WSR) optimum with low complexity. Compared with other heuristic algorithms, simulation results demonstrate the apparent advantages of the proposed collaborative sensing optimization algorithm. Most importantly, it can be seen that we can achieve a performance that approaches an upper bound.

Double RIS-Assisted MIMO Systems Over Spatially Correlated Rician Fading Channels and Finite Scatterers

This paper investigates double RIS-assisted MIMO communication systems over Rician fading channels with finite scatterers, spatial correlation, and the existence of a double-scattering link between the transceiver. First, the statistical information is driven in closed form for the aggregated channels, unveiling various influences of the system and environment on the average channel power gains. Next, we study two active and passive beamforming designs corresponding to two objectives. The first problem maximizes channel capacity by jointly optimizing the active precoding and combining matrices at the transceivers and passive beamforming at the double RISs subject to the transmitting power constraint. In order to tackle the inherently non-convex issue, we propose an efficient alternating optimization algorithm (AO) based on the alternating direction method of multipliers (ADMM). The second problem enhances communication reliability by jointly training the encoder and decoder at the transceivers and the phase shifters at the RISs. Each neural network representing a system entity in an end-to-end learning framework is proposed to minimize the symbol error rate of the detected symbols by controlling the transceiver and the RISs’ phase shifts. Numerical results verify our analysis and demonstrate the superior improvements of phase shift designs to boost system performance.

Integrated Sensing and Communication for RIS-Assisted Backscatter Systems

To facilitate the development of Internet of Things (IoT) services, future networks are expected to simultaneously provide sensing functionality and support low-power communications. In this paper, we investigate the system sum rate maximization problem in an integrated sensing and RIS backscatter communication system, where the base station (BS) simultaneously detects backscattered signals from multiple IoT devices and senses targets based on the echo signals. We formulate a joint transmit beamforming, RIS phase shifts and receive beamforming design problem under the Cramér-Rao bound (CRB) constraint for target angle estimation. To solve the non-convex problem, we then propose a fractional programming based alternating optimization algorithm. In particular, the fractional programming technique is firstly employed to transform the formulated problem into a more tractable form, and the exact penalty method and manifold optimization are then utilized to address the CRB constraint and constant-modulus constraint, respectively. Numerical results have shown that the proposed design significantly improves the system sum rate and illustrate the trade-off between the communication and sensing performance.

Optimized Design for IRS-Assisted Integrated Sensing and Communication Systems in Clutter Environments

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system design in a clutter environment. Assisted by an IRS equipped with a uniform planar array (UPA), a multi-antenna base station (BS) is targeted for simultaneously sensing multiple targets in the non-light-of-sight (NLoS) region and communicating with multiple communication users (CUs). We consider the joint IRS-assisted ISAC design in the case with Type-I or Type-II CUs, where each Type-I CU and Type-II CU can and cannot cancel the interference from sensing signals, respectively. Under the perfect communication/sensing channel state information assumption, we aim to maximize the minimum sensing beampattern gain among multiple targets, where the sensing beampattern gain qualifies the achieved illumination signal power at the given location of the target of interest. We jointly optimize the BS’s communication-sensing beamformers and the IRS’s phase shifting matrix, subject to the signal-to-interference-plus-noise ratio (SINR) constraint for each Type-I/Type-II CU, the interference power constraint per clutter, the transmission power constraint at the BS, and the cross-correlation pattern constraint. Due to the design variable coupling, the joint IRS-assisted ISAC design problem is shown to be non-convex in the case with Type-I or Type-II CUs. To circumvent the non-convexity dilemma, we propose semidefinite relaxation (SDR) based alternating optimization algorithms in both cases, for which the computational complexity and convergency behavior are analyzed. In the case with Type-I CUs, we show that the dedicated sensing signal at the BS can help enhance the sensing performance gain. By contrast, the dedicated sensing signal at the BS is not required for the IRS-assisted ISAC designs in the case with Type-II CUs. Numerical results are provided to show that the proposed IRS-assisted ISAC design schemes achieve a significant gain over the existing benchmark schemes.

Joint Information-Theoretic Secrecy and Covertness for UAV-Assisted Wireless Transmission With Finite Blocklength

In this paper, we study a novel joint information-theoretic secrecy and covertness model for an unmanned aerial vehicle (UAV)-assisted finite blocklength transmission system, in which an UAV delivers classified information to a ground user (Bob) in the presence of two unauthorized users (Eve and Willie). Specifically, Eve tries to decode the legitimate information while Willie tries to detect if the UAV's transmission is present or not. Firstly, we assume that the perfect location knowledge of both Eve and Willie is known at the UAV. In this scenario, to guarantee security and covertness, the average secrecy rate maximization problem is formulated via joint design of the UAV's transmit power and three dimensional (3D) trajectory under Willie's covertness constraint. Technically speaking, the optimization problem is non-convex and mathematically intractable to tackle owing to the coupling of multiple variables. To address this non-convex problem, an iterative alternating optimization (AO) algorithm is developed with the assistance of the successive convex approximation (SCA) technique. After that, we extend the system model to both Eve's and Willie's imperfect location knowledge case, which is more sophisticated compared to the perfect location knowledge case due to the uncertain location constraints. We still utilize the iterative AO algorithm combined with SCA and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {S}$</tex-math></inline-formula> -procedure techniques to cope with the nonconvex problem. Numerical results indicate that our proposed 3D optimization algorithm performs better than the conventional two dimensional (2D) scheme and the benchmark scheme for both perfect and imperfect location knowledge cases.

A secure physical layer transport scheme with illegal reconfigurable intelligent surface

As a passive programmable reflecting device, Reconfigurable Intelligent Surface (RIS) can change signal transmission conditions very well and is now widely used in physical layer security (PLS) communications. However, due to the low cost of RIS, eavesdroppers (Eves) may equip themselves with illegal RIS in the future to reduce the secrecy rate (SR) of the system. In this correspondence, we design a maximum–minimum problem for the interplay between legal and illegal RISs on the SR, and propose two schemes to improve the SR. Scheme I designs an alternating optimization algorithm based on Riemannian manifolds to determine the degree of the illegal RIS on the system SR and tries to offset its impact by increasing the number of reflected elements of the legal RIS. Scheme II replaces the legal passive RIS with the legal active RIS and designs an alternating optimization scheme based on semi-positive definite relaxation (SDR) and Lemma I for the non-convex problem caused by the legal active RIS. The final simulation results show that both proposed schemes can effectively ensure PLS in the presence of illegal RIS.

Beamforming design for multi-antenna multi-tag symbiotic radio backscatter systems

In this paper, we consider a multi-tag symbiotic radio backscatter system, where a multi-antenna source node transmits its own message to a multi-antenna destination, while multiple backscatter tags transmit information to the destination by directly reflecting the incident signals from the source. We investigate the optimal beamforming design to minimize the transmit power at the source, subject to the signal-to-interference-plus-noise ratio (SINR) constraints for the signals of the source and the tags, and the harvested power constraints at the tags. The formulated min-power optimization problem is non-convex, and we propose a semi-definite programming (SDP) based alternating optimization algorithm to optimize the transmit beamforming vector at the source and the receive beamforming vector at the destination. Numerical results show that the proposed beamforming design algorithm converges fast and outperforms the counterpart scheme significantly.

Matrix-variate data analysis by two-way factor model with replicated observations

Motivated by recent work on matrix-variate data analysis in various scientific domains, we propose a two-way factor model (2wFMs) to capture the separable effects of row and column attributes. This paper studies the identification conditions of 2wFMs and develops a block alternative optimization algorithm for maximum likelihood estimation (MLE). The asymptotic theories for the maximum likelihood estimators are established. Monte Carlo simulations show that the method we propose is effective and robust.

Joint Resource and Trajectory Optimization for Heterogeneous-UAVs Enabled Aerial-Ground Cooperative Computing Networks

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) is a promising paradigm for providing extensive coverage and additional computation services to the Internet of Things (IoT) devices with smart applications, which are normally computation-intensive and delay-critical, especially in some remote areas or emerging scenarios without available infrastructure. However, considering the restricted computation capabilities of UAVs, even multiple UAVs may not satisfy the quality of service (QoS) requirement of IoT users with heavy-computation applications. In this paper, a heterogenous-multiple-UAVs enabled collaborative aerial-ground MEC framework is proposed. The total computation bits maximization problem under the time-division multiple access (TDMA) scheme and partial offloading mode is formulated by jointly optimizing the user association, the CPU-cycle frequencies of users and computing UAVs, the transmit power of users and UAVs, the offloading time of users and UAVs, as well as the UAVs' trajectories. Due to the non-convexity of the original problem with the nonlinear coupling of different variables, a two-layered alternative optimization (TLAO) algorithm is proposed, where the outer layer optimizes the user association and resource allocation, while the inner layer optimizes the UAV trajectory scheduling. Simulation results demonstrate that our proposed TLAO scheme can improve the total computation bits compared with other benchmark schemes, and achieve fast convergence. Moreover, comparative results also verify that our proposed strategy is flexible to apply to homogeneous multi-UAVs and single-UAV scenarios.

RIS-Aided Integrated Sensing and Communication: Joint Beamforming and Reflection Design

Integrated sensing and communication (ISAC) has been envisioned as a promising technique to alleviate the spectrum congestion problem. Inspired by the applications of reconfigurable intelligent surface (RIS) in dynamically manipulating wireless propagation environment, in this paper, we investigate to deploy a RIS in an ISAC system to pursue performance improvement. Particularly, we consider a RIS-assisted ISAC system where a multi-antenna base station (BS) performs multi-target detection and multi-user communication with the assistance of a RIS. Our goal is maximizing the weighted summation of target detection signal-to-noise ratios (SNRs) by jointly optimizing the transmit beamforming and the RIS reflection coefficients, while satisfying the communication quality-of-service (QoS) requirement, the total transmit power budget, and the restriction of RIS phase-shift. An efficient alternating optimization algorithm combining the majorization-minimization (MM), penalty-based, and manifold optimization methods is developed to solve the resulting complicated non-convex optimization problem. Simulation results illustrate the advantages of deploying RIS in ISAC systems and the effectiveness of our proposed algorithm.

Joint Receive and Passive Beamforming Optimization for RIS-Assisted Uplink RSMA Systems

In this letter, we investigate the joint optimization of transmit power allocation and receive/passive beamforming in a reconfigurable intelligent surface (RIS)-assisted uplink two-user rate-splitting multiple access (RSMA) system. Aiming at maximizing the achievable rate of one user subject to another user’s quality of service (QoS) requirements, RIS is deployed to assist the uplink transmissions from both users to a multi-antenna base station (BS). We decouple the non-convex rate maximization problem into three sub-problems and propose an alternating optimization (AO) algorithm to optimize the receive/passive beamforming and transmit power allocation. A closed-form expression is derived for the sub-problem of transmit power allocation by using the optimal decoding order and the concave-convex fractional programming beamforming problems are converted to convex ones by using the Charnes-Cooper transform. Then, a penalty term approach is applied to effectively solve the rank-1 constrained beamforming subproblems. Simulation results verify that the proposed AO algorithm attains a higher achievable rate than the existing benchmark schemes.

Robust Beamforming Design for Cache-Enabled C-RAN With Fronthaul Multicast

In this article, we study a robust beamforming design problem in the cached-enable cloud radio access network, where the imperfect channel state information of the access links are considered. Two transmission schemes, nonsimultaneous transmission (NST) scheme and simultaneous transmission (ST) scheme, are developed. To improve the system performance, the cooperative transmission technique is applied. In NST scheme, the base stations (BSs) obtain the uncached required files from the central processor (CP) and then transmit all required files to users. Based on this, we design an effective transmission model based on cache files and the qualities of the fronthaul link channels, and formulate a transmission delay minimization problem. Then, an alternative optimization algorithm is developed to solve it. In ST scheme, the BSs obtain the uncached files from the CP and transmit the cached files to users simultaneously. We divide two cases according to the access link transmission rate and cached files. For each case, we formulate an optimization problem by jointly designing the fronthaul multicast beamforming and access link transmission beamforming. Next, an iterative algorithm is developed via the L0 norm approximation, successive convex approximation and semidefinite relaxation techniques. Finally, simulation results show the proposed schemes can reduce the transmission delay better than the conventional multicast schemes.

Robust Beamforming Design for IRS-Aided Secure Communication Systems Under Complete Imperfect CSI

We investigate the performance of an intelligent reflecting surface (IRS) aided secure communication system which operates in a complete imperfect channel state information (CSI) regime. By jointly considering the imperfect CSI of both IRS-Bob and IRS-Eve channels, we propose a robust transmission scheme which is based upon a joint optimization of the continuous transmit beamforming at Alice and the discrete phase shifts at the IRS. To deal with this mixed integer non-convex optimization problem, novel lower- and upper-bounding techniques are used to transform it in a more tractable form for which a solution based upon the derivation of a quantization-based alternative optimization (AO) algorithm is presented. More specifically, on the one hand, a successive convex approximation (SCA) algorithm is proposed to optimize the continuous transmit beamforming. On the other hand, by applying a SCA, penalty convex-concave procedure (PCCP), and quantization-based algorithm, the discrete phase shifts are obtained. Various performance evaluation results obtained by means of computer simulations have shown that the proposed robust transmission scheme guarantees a minimum quality of service (QoS) while simultaneously limits Eve's maximum eavesdropping rate.

Robust Beamforming Design for RIS-Aided NOMA Secure Networks With Transceiver Hardware Impairments

Integrating reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) provides a promising solution to support large-scale secure communications. However, reducing the impact of transceiver hardware impairments (HWI) on the performance of RIS-aided NOMA secure networks remains a challenge. In this paper, we propose a robust transmission scheme for RIS-aided NOMA secure networks with transceiver HWI under two proposed eavesdropping scenarios and two artificial noise methods. A closed-form expression for the distortion noise power caused by transceiver HWI in NOMA networks is derived to quantify transceiver HWI. The sum secrecy rate of NOMA users with imperfect successive interference cancellation (SIC) is maximized under distortion noise for more robust security. Further, to tackle the resulting non-convex problem, we decompose it into two sub-problems. Both of them are converted into convex problems using the multi-dimensional quadratic transform (MDQT) method, semi-definite relaxation (SDR) technique, and the successive convex approximation (SCA) algorithm, and then employing the alternate optimization (AO) algorithm solve them. Numerical results demonstrate that the proposed scheme has superior security performance over orthogonal multiple access (OMA) networks, time division multiple access (TDMA) networks, and traditional NOMA networks, achieving more robust performance than the conventional scheme that ignores HWI and imperfect SIC.

RIS-Aided Cell-Free Massive MIMO System: Joint Design of Transmit Beamforming and Phase Shifts

This article studies a reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output system and formulate the max&#x2013;min fairness problem that maximizes the minimum achievable rate among all the users by jointly optimizing the transmit beamforming at access points and the phase shifts at RISs. To address such a challenging problem, we first study the special single-user scenario and propose an algorithm that can transform the optimization problem into a semidefinite program (SDP) or an integer linear program for the cases of continuous or discrete phase shifts, respectively. Then, in order to solve the optimization problem for the multiuser scenario with continuous phase shifts, we propose an alternating optimization algorithm, which can alternately transform the problem into a second-order-cone program and an SDP. Finally, for the multiuser scenario with discrete phase shifts, we design a zero-forcing-based successive refinement algorithm, which can find the suboptimal transmit beamforming and phase shifts by means of alternating optimization. Numerical results show that compared with the benchmark schemes of random phase shifts and without using the RIS, the proposed algorithms can significantly increase the minimum achievable rate. It is also demonstrated that, compared with the case of programming continuous phase shifts, using 2-bit discrete phase shifts can practically achieve the same performance.