Sequential Parametric Convex Approximation Research Articles

Large-Scale Rate-Splitting Multiple Access in Uplink UAV Networks: Effective Secrecy Throughput Maximization Under Limited Feedback Channel

Unmanned aerial vehicles (UAVs) are capable of improving the performance of next generation wireless systems. However, their communication performance is prone to both channel estimation errors and potential eavesdropping. Hence, we investigate the effective network secrecy throughput (ENST) of the uplink UAV network, in which rate-splitting multiple access (RSMA) is employed by each legitimate user for secure transmission under the scenario of massive access. To maximize the ENST, the transmission rate versus power allocation relationship is formulated as a max-min optimization problem, relying on realistic imperfect channel state information (CSI) of both the legitimate users and passive eavesdroppers ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eves$</tex-math></inline-formula> ). In the model considered, each user transmits a superposition of two messages to a UAV base-stations (UAV-BS), each having different transmit power and the UAV-BS adopts a successive interference cancellation (SIC) technique to decode the received messages. Given the non-convexity of the problem, it is decoupled into a pair of sub-problems. In particular, we derive a closed form expression for the optimal rate-splitting fraction of each user. Then, given the optimal rate-splitting fraction of each user, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -constrainted transmit power of each user is calculated by harnessing sequential parametric convex approximation (SPCA) programming. Our simulation results confirm that the scheme conceived significantly improves the ENST compared to both the existing orthogonal and non-orthogonal benchmarks.

Performance analysis of uplink optical wireless communications in the presence of a simultaneously transmitting and reflecting reconfigurable intelligent surface

AbstractRecently, reconfigurable intelligent surface (RIS) has gained research and development interests in modifying wireless channel characteristics in order to improve the performance of wireless communications, especially when the quality of the line‐of‐sight channel is not that good. In this work, for the first time in the literature, we have used simultaneously transmitting and reflecting RIS (STAR‐RIS) in a non‐orthogonal multiple‐access visible light communication system to improve the performance of the system. Achievable rates of the users are derived for two data recovery schemes, single‐user detection (SUD) and successive interference cancellation (SIC). Then, the sum‐rate optimisation problem is formulated for two operating modes of STAR‐RIS, namely energy‐splitting and mode‐switching cases. Moreover, a sequential parametric convex approximation method is used to solve the sum‐rate optimisation problems. The authors have also compared energy‐splitting and mode‐switching cases and showed that these two modes have the same performance. Finally, numerical results for SUD and SIC schemes and two benchmarking schemes, time‐sharing and max‐min fairness, are presented, and spectral‐ and energy‐efficiency, number of STAR‐RIS elements, the position of users, and access point are discussed.

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.

Channel Time-Variation Suppression With Optimized Receive Beamforming for High-Mobility OFDM Downlink Transmissions

A channel time variation suppression scheme based on beamforming is proposed for high-mobility orthogonal frequency division multiplexing (OFDM) downlink transmissions. The residual channel time variation after Doppler shift compensation by finite resolution receive beamforming can be measured by the Doppler spread. The interchannel interference (ICI) due to the time-varying channel is then analyzed and the relation between the signal-to-interference ratio (SIR) and Doppler spread is given. Based on the derived Doppler spread and ICI, a beamforming network optimization scheme is proposed to reduce the channel time variation. A closed-form solution is first obtained to minimize the average ICI, which exploits the structure of a tridiagonal Toeplitz matrix. Next, an optimization problem is formulated to minimize the maximum Doppler spread to further verify the impact of average ICI on channel time variation. The sequential parametric convex approximation (SPCA) algorithm is exploited to solve the non-convex min-max problem. Numerical results verify the theoretical analysis.

Performance Analysis and Waveform Optimization of Integrated FD-MIMO Radar-Communication Systems

Multiple-input multiple-output (MIMO) has been used in wireless communications to increase data rates via multiplexing or improve performance via diversity. Additionally, in radar systems, MIMO promises improved parameter identifiability and target resolution. Frequency diverse (FD)-MIMO radar can effectively distinguish targets that are closely spaced in the same angle cell by exploiting its range-angle-dependent transmit beamform. Therefore, in this paper, we first propose an integrated waveform design by embedding weighted, phase-modulated communication signals in the FD-MIMO radar waveform. Then, we derive the Cramer-Rao lower bound (CRLB) of the location estimation and analyze the communication performance achieved when the communication receiver extracts information from the integrated signals. Next, transmit beamforming is optimized to achieve the best tradeoff between the radar and communication performances of the system. Due to the nonconvexity of the optimization problem, we apply sequential parametric convex approximation (SPCA) and semidefinite relaxation (SDR) methods to solve the problem. The simulation results reveal the effects of some parameters, such as the frequency interval, symbol period, and communication symbol sequence, on radar and communication performance and demonstrate the tradeoff between radar and communication obtained by optimizing the transmit beamforming vector.

Secrecy rate maximization for hardware impaired untrusted relaying network with deep learning

This paper investigates the physical layer security (PLS) design of an untrusted relaying network where the source node coexists with a multi-antenna eavesdropper (Eve). While the communication relies on untrustworthy relay nodes to increase reliability, we aim to protect the confidentiality of information against combined eavesdropping attacks performed by both untrusted relay nodes and Eve. Considering the hardware impairments (HIs), both total power budget constraint for the whole network and the individual power constraint at each node, this paper presents a novel approach to jointly optimize relay beamformer and transmit powers aiming at maximizing average secrecy rate (ASR). To safeguard the first cooperative phase, destination-aided cooperative jamming (DACJ) is employed, while for the second phase, the relay beamformer is adjusted. The resultant optimization problem is non-convex, and a suboptimal solution is obtained through the sequential parametric convex approximation (SPCA) method. In order to prevent any failure due to infeasibility, we propose an iterative initialization algorithm to find the feasible initial point of the original problem instead of an arbitrary point, as in the conventional SPCA. To satisfy low-latency as one of the main key performance indicators (KPI) required in beyond 5G (B5G) communications, a computationally efficient data-driven approach is developed exploiting a deep learning model to evaluate the proposed scheme while the computational burden is significantly reduced. Simulation results assess the effect of different system parameters on the ASR performance as well as the effectiveness of the proposed deep learning solution in large-scale cases.

QoE-Aware Resource Allocation for Non-Orthogonal Multiple Access Enhanced HetNets

Non-orthogonal multiple access (NOMA) has drawn significant attention due to its high spectral efficiency. Invoking NOMA in heterogeneous networks (HetNets) can support ubiquitous connectivity and satisfy the growing demand for mobile data traffic. Existing studies on NOMA-enhanced HetNets mainly focus on network’s quality of service (QoS) metrics such as delay, throughput, coverage, etc. However, these parameters are not sufficient for evaluating the quality of experience (QoE) perceived by users. To that end, we propose a QoE-aware resource allocation framework for NOMA-enhanced HetNets under Web browsing and video services. Specifically, a unified QoE-aware joint subchannel and power allocation optimization problem is formulated to maximize the sum mean opinion scores (MOSs) of all users, while guaranteeing the QoE requirement of each user. However, this problem is mixed-integer, non-convex, and intractable. To solve it, a penalty-based iterative algorithm is proposed. In particular, binary constraints on subchannel assignment variables are equivalently transformed into equality constraints via penalty method. Then, subchannel assignment and power allocation are alternately optimized in each iteration by leveraging block coordinate descent method and sequential parametric convex approximation techniques. Extensive numerical results show that the proposed scheme could achieve competitive QoE performance compared to existing NOMA and orthogonal multiple access schemes.

Optimal Energy Beamforming to Minimize Transmit Power in a Multi-Antenna Wireless Powered Communication Network

In this paper, we study the transmit power minimization problem with optimal energy beamforming in a multi-antenna wireless powered communication network (WPCN). The considered network consists of one hybrid access point (H-AP) with multiple antennae and multiple users with a single antenna each. The H-AP broadcasts an energy signal on the downlink, using energy beamforming to enhance the efficiency of the transmit energy. In this paper, we jointly optimize the downlink time allocation for wireless energy transfer (WET), the uplink time allocation for each user to send a wireless information signal to the H-AP, the power allocation to each user on the uplink, and the downlink energy beamforming vectors while controlling the transmit power at the H-AP. It is challenging to solve this non-convex complex optimization problem because it is numerically intractable and involves high computational complexity. We exploit a sequential parametric convex approximation (SPCA)-based iterative method, and propose optimal and sub-optimal solutions for the transmit power minimization problem. All the proposed schemes are verified by numerical simulations. Through the simulation results, we present the performance of the proposed schemes based on the effect of the number of transmit antennae and the number of users in the proposed WPCN. Through the performance evaluation, we show that the SPCA-based joint optimization solution performance is superior to other solutions.

Deep Learning–Based Energy Beamforming With Transmit Power Control in Wireless Powered Communication Networks

In this paper, we propose deep learning–based energy beamforming in a multi-antennae wireless powered communication network (WPCN). We consider a WPCN where a hybrid access point (HAP) equipped with multiple antennae broadcasts an energy-bearing signal to wireless devices using energy beamforming. We investigate the joint optimization of the time allocation for wireless energy transfer (WET) and wireless information transfer (WIT) with the design for energy beams while minimizing the transmit power at the HAP for efficient use of its available resources. However, this is a non-convex problem, and it is numerically intractable to solve it. In the literature, the traditional approach to solving this problem is based on an iterative algorithm that incurs high computational and time complexity, which is not feasible for real-time applications. We study and analyze a deep neural network (DNN)-based scheme and propose a faster and more efficient approach for the fair approximation of a near-optimal solution to this problem. To train the proposed DNN, we acquire training data samples from a sequential parametric convex approximation (SPCA)-based iterative algorithm. Instead of acquiring data samples and training the DNN, which is highly complex, we use offline training for the DNN to provide a faster solution to the real-time resource allocation optimization problem. Through the simulation results, we show the proposed DNN scheme provides a fair approximation of the traditional SPCA method with low computational and time complexity.

On the Physical Layer Security of the Cooperative Rate-Splitting-Aided Downlink in UAV Networks

Unmanned Aerial Vehicles (UAVs) have found compelling applications in intelligent logistics, search and rescue as well as in air-borne Base Station (BS). However, their communications are prone to both channel errors and eavesdropping. Hence, we investigate the max-min secrecy fairness of UAV-aided cellular networks, in which Cooperative Rate-Splitting (CRS) aided downlink transmissions are employed by each multi-antenna UAV Base Station (UAV-BS) to safeguard the downlink of a two-user Multi-Input Single-Output (MISO) system against an external multi-antenna Eavesdropper ( $Eve$ ). Realistically, only Imperfect Channel State Information (ICSI) is assumed to be available at the transmitter. Additionally, we consider a realistic total power constraint and guarantee the specific Quality of Service (QoS) requirements of the legitimate users. To handle the worst-case channel uncertainty of the legitimate users and an external $Eve$ , we conceive a robust secure resource allocation algorithm, which maximizes the minimum worst-case secrecy rate of the legitimate users. Based on the CRS principle, the transmitter splits and encodes the messages of legitimate users into common as well as private streams and the user having stronger CSI is asked to help the cell-edge user by opportunistically forwarding its decoded common message. In contrast to the existing schemes adopted in the literature for ensuring secure transmission of the first cooperative phase only, in our proposed solution the common message has a twin-fold mission. Explicitly, apart from serving as the desired message, it also acts as Artificial Noise (AN) for drowning out $Eve$ without consuming extra power. This is in stark contrast to the conventional AN designs. In the second phase, the pure AN is directed towards the $Eve$ , deploying a robust Maximum Ratio Transmitter (MRT) beamformer at the UAV-BS. To solve the resultant non-convex optimization problem we resort to the Sequential Parametric Convex Approximation (SPCA) method together with a bespoke initialization algorithm to avoid any failure due to infeasibility. Our simulation results confirm that the proposed secure transmission scheme outperforms the existing cooperative benchmarkers.

Joint Beamwidth and Power Optimization in MmWave Hybrid Beamforming-NOMA Systems

The use of directional transmission in millimeter-Wave (mmWave) frequencies results in limited channel coherence time. In this paper, we take the limited channel coherence time into account for non-orthogonal multiple access (NOMA) in mmWave hybrid beamforming systems. Due to the limited coherence time, the beamwidth of the hybrid beamformer affects the beam-training time, which in turn directly impacts the data transmission rate. To investigate this trade-off, we utilize a combined beam-training algorithm. Then, we formulate a sum-rate expression which considers the channel coherence time and beam-training time as well as users' power and other system parameters. Further, a joint power and beamwidth optimization problem is solved by iterating between the power allocation and the beamwidth optimization. When allocating the power, we use the log-exponential reformulation and the sequential parametric convex approximation (SPCA) methods to solve the non-convex problem. Since beamwidth optimization involves too many variables, we propose an algorithm which iterates between clusters of users. Numerical results show that the optimized mmWave hybrid beamforming-NOMA system can achieve much higher sum-rates compared to NOMA with analog beamforming and traditional multiple access techniques.

Robust Beamforming Designs in Secure MIMO SWIPT IoT Networks With a Nonlinear Channel Model

In this article, we study a robust beamforming design for multiuser multiple-input–multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet-of-Things (IoT) devices under the nonlinear energy harvesting (EH) model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve EH fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, the original robust problem is transformed into a two-layer optimization problem, where the inner layer problem is reformulated as semidefinite programming (SDP) and the outer layer problem is solved by a one-dimensional (1-D) line search algorithm. In addition, in order to reduce computational complexity, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method achieves the same performance as the two-layer algorithm with much lower complexity.

Total transmit power minimization with physical layer security in multiuser peer-to-peer two-way relay networks

In this paper, we investigate a multiuser peer-to-peer (MUP2P) two-way relay network (TWRN) in the presence of an eavesdropper. In this network, only the k∗th pair of users demand secure communication. Also, a multi-antenna eavesdropper aim to wiretap the confidential data of the k∗th pair in both cooperative phases. Our goal is to minimize the total transmit power provided that the secrecy rate for two secure users and the quality of service constraints of each user are met. For simplifying the problem and also omitting the confidential information leakage in the second phase, a null space beamforming (NSBF) scheme is employed. Despite this, the problem is still non-convex. We divided the problem into two subproblems to solve beamforming and users’ power formulated in two iterations, respectively. Three different methods are proposed to solve the first subproblem, semi-definite programming (SDP), second-order cone programming (SOCP), and sequential quadratic programming (SQP). We employ a sequential parametric convex approximation (SPCA) method to convert the users’ power subproblem into inequality form a linear programming (LP) problem to solve it. In the following, we evaluate the computational complexity of these methods. Simulation results confirmed the validity of the proposed methods.

Exploiting a Deep Neural Network for Efficient Transmit Power Minimization in a Wireless Powered Communication Network

In this paper, we propose a learning-based solution for resource allocation in a wireless powered communication network (WPCN). We provide a study and analysis of a deep neural network (DNN) which can reasonably effectively approximate the iterative optimization algorithm for resource allocation in the WPCN. In this scheme, the deep neural network provides an optimized solution for transmitting power with different channel coefficients. The proposed deep neural network accepts the channel coefficient as an input and outputs minimized power for this channel in the WPCN. The DNN learns the relationship between input and output and gives a fairly accurate approximation for the transmit power optimization iterative algorithm. We exploit the sequential parametric convex approximation (SPCA) iterative algorithm to solve the optimization problem for transmit power in the WPCN. The proposed approach ensures the quality of service (QoS) of the WPCN by managing user throughput and by keeping harvested energy levels above a defined threshold. Through numerical results and simulations, it is verified that the proposed scheme can best approximate the SPCA iterative algorithms with low computational time consumption.

Joint Sparse Beamforming and Power Control for a Large-Scale DAS With Network-Assisted Full Duplex

In this paper, we studied the joint sparse beamforming and power control for network-assisted full duplex (NAFD) in large-scale distributed antenna system (L-DAS), where the remote antenna units either be operating in half-duplex mode or full-duplex mode are all connected to the central processing unit (CPU) via high-speed backhaul links. With joint processing at CPU, NAFD could achieve truly flexible duplex, including flexible half-duplex and full-duplex. Cross-link interference and the finite-capacity backhaul are the main problems of NAFD in L-DAS. To solve these problems, we aim to maximize the aggregated rate of uplink and downlink subject to quality of service constraints and backhaul constraints. Two approaches have been proposed to solve the optimization problem, where the first approach converts the object function to the difference of two convex functions with semi-definite relax (SDR) and then an iterative SDR-block coordinate descent method is applied to solve the problem. The second method is based on sequential parametric convex approximation. Simulation results have shown that the proposed algorithms yield a higher rate gain compared to the traditional time-division duplex scheme.

Interference Channels With Full-Duplex Amplify-and-Forward Receiver Cooperations

This paper considers a two-transmitter and two-receiver interference channel (IC), where each transmitter sends a message to its desired receiver. In particular, the full-duplex (FD) amplify-and-forward (AF) protocol is adopted to build up the receiver cooperations: each receiver can receive the signals from the two transmitters and two receivers, and after self-interference (SI) cancellation and message decoding, it forwards them to its counterpart. With the above scheme, the equivalent channel model is analyzed, and the statistics of the accumulated residual interference and noise (ARIN), generated by the imperfect SI cancellation and the AF scheme at the receivers, is calculated by mathematical induction. Then, the achievable rate regions of both the single-user and joint decoding schemes are derived. It is proved that one-side cooperation, i.e., only one of the two receivers forwards its counterpart’s signal, is optimal to achieve the best system performance. Next, to characterize the obtained rate regions, the rate maximization problems are formulated and approximately solved by a sequential parametric convex approximation (SPCA) method. Simulation results show that the proposed scheme can improve the achievable rate compared to the conventional non-cooperative scheme in several typical scenarios.

Joint Channel Estimation and Power Allocation for the CRS‐NOMA

We study the channel estimation and power allocation method for the Cooperative relaying system using Non-orthogonal multiple access (CRS-NOMA). Our object is maximizing the Average effective signal-to-interference-and-noise ratio (AESINR) of the weak user while guaranteeing bounded AESINR for the strong user. We transform the nonconvex optimization problem into a Difference of convex (DC) programming and propose a Sequential parametric convex approximation (SPCA) based iterative algorithm to obtain a local optimum. For comparison, we consider a similar optimization problem for a Non-cooperative NOMA (NC-NOMA) system. Simulation results show that the AESINR performance of the CRS-NOMA outperforms signicantly that of the NCNOMA system.

User-centric harvested energy-efficiency maximisation for secure SWIPT transmissions

ABSTRACTIn this paper, simultaneous wireless information and power transfer (SWIPT) transmissions with multiple-input single-output transceiver pairs are considered. In each pair, a multi-antenna transmitter sends data and energy to a single-antenna receiver with a signal-splitting structure and a non-linear energy harvesting model. Each transceiver pair tries to achieve high individual harvested energy efficiency, and uses artificial noise (AN) to protect the information from being overheard by other users that cooperate like a virtual eavesdropper. The weighted sum user-centric harvested energy-efficiency maximisation problem is formulated under constraints on the required signal-to-interference-plus-noise ratio (SINR) at each receiver, plus a limited SINR for virtual eavesdroppers and limited transmit power at each transmitter. The optimal precoding beamforming, AN, and signal-splitting factors are obtained by two proposed algorithms. The first solution exploits semidefinite relaxation and sequential parametric convex approximation techniques to convert the non-convex problem to a series of convex subproblems with matrix variables. We obtain optimal beamforming vectors along with an optimal covariance matrix for AN. The second solution directly utilizes beamforming approximation and obtains a low complexity solution for the convex subproblem. Moreover, we need to apply the feasible point pursuit method. Finally, numerical evaluations are provided in comparison with some benchmark schemes.

Distributed ADMM-based approach for total harvested power maximization in non-linear SWIPT system

In this paper, the distributed alternating direction method of multipliers (ADMM)-based approach is investigated for total harvested power maximization (THPM) in multiple-input single-output transceiver pairs with simultaneous wireless information and power transfer. The power-splitting architecture and practical non-linear energy harvesting are utilized at each receiver. The highly non-convex THPM problem is formulated under requirements for the data rate, the amount of harvested power, and with limited power at the transmitters. First, semidefinite relaxation (SDR) and sequential parametric convex approximation are exploited to convert the non-convex THPM to a series of convex subproblems. Then, slack variables replacing interference are introduced to allow decomposing into distributed subproblems via ADMM. The interesting point is that optimal precoding matrices of the local subproblems satisfy the rank-1 constraints of SDR. Finally, the numerical experiments present the convergence of the proposed algorithm, obtaining results similar to the centralized approach, as well as comparing it with some baseline schemes.

Radiation Efficiency Aware High-Mobility Massive MIMO With Antenna Selection

High-mobility wireless communications have received a lot of attentions in the past few years. In this paper, we consider angle-domain Doppler shifts compensation scheme to reduce the channel time variation for the high-mobility uplink from a high-speed terminal (HST) to a base station (BS). We propose to minimize Doppler spread by antenna weighting under the constraint of maintaining radiation efficiency. The sequential parametric convex approximation (SPCA) algorithm is exploited to solve the above non-convex problem. Moreover, in order to save the RF chains, we further exploit the idea of antenna selection for high-mobility wireless communications. Simulations verify the effectiveness of the proposed studies.