Rate Maximization Problem Research Articles

Analysis and optimization of wireless powered untrusted relay system with multiple destinations

In this paper, the physical layer security is investigated in a dual-hop wireless communication scenario, where an energy-constrained relay not only assists the source-to-destination information transmission, but also acts as a potential eavesdropper wiretapping the source information. We take into account both antenna mode switching and destination selection for the amplify and forward relaying network. The untrusted relay (UR) is equipped with two antennas, where one antenna that maximizes the source-to-relay channel gain will be selected for reception and the other one is exploited to forward the processed information. Moreover, the Nth best destination is considered as the legitimate destination, which aims to achieve a better secrecy rate. Specially, in general signal-to-noise ratio regime, a low-complexity Gaussian Q-function is introduced to derive the exact closed-form expression of ergodic secrecy rate (ESR). In addition, when the UR works in a non-linear energy harvesting mode, the achievable secrecy rate maximization problem is formulated and solved by an alternative optimization method. The simulation results reveal the correctness of the theoretical derivation and also proves that much higher secrecy performance can be achieved by the proposed scheme than other benchmark schemes.

PLS-Based Secrecy Transmission for VANETs

Due to the requirement of real time controlling on the vehicular ad-hoc networks (VANETs), the security measure should be fast, lightweight, and strong. In this paper, we investigate the secure communication between the on-board unit and the roadside unit which has strong computing ability to remedy the insufficient computing power of on-board unit. The novel schemes are developed to guarantee both security and energy harvest. Specifically, a muti-antenna roadside unit (PT) conveys a secret message to a muti-antenna primary on-board unit (PR) intercepted by multiple muti-antenna adversaries (Eves). Simultaneously, a secondary roadside unit (ST) adopts simultaneous wireless information and power transfer (SWIPT) strategy to send secret-free data embeded with energy-carrying artificial noise (AN) to its service subscriber (SR), by which secrecy guarantee is provided for the primary on-board unit and the quality of service (QoS) is satisfied, i.e., data traffic and harvested energy. Technically, considering the secondary link's data transmission rate, harvested energy, and transmit power, we propose joint power allocation designs for the nonconvex secrecy rate maximization (SRM) problems under either perfect channel state information (CSI) or norm-bounded channel uncertainty model. Simulation results show that the proposed secondary link-aided secrecy designs can achieve better performance in terms of secrecy rate and energy efficiency.

Fast Adaptive Minorization-Maximization Procedure for Beamforming Design of Downlink NOMA Systems

We develop a novel technique to accelerate minorization-maximization (MM) procedure for the non-orthogonal multiple access (NOMA) weighted sum rate maximization problem. Specifically, we exploit the Lipschitz continuity of the gradient of the objective function to adaptively update the MM algorithm. With fewer additional analysis variables and low complexity second-order cone program (SOCP) to solve in each iteration of the MM algorithm, the proposed approach converges quickly at a small computational cost. By numerical simulation results, our algorithm is shown to greatly outperform known solutions in terms of achieved sum rates and computational complexity.

Hybrid Precoding Design for Wideband THz Massive MIMO-OFDM Systems With Beam Squint

The terahertz (THz) communication has been considered as a promising technique for future 6G wireless networks because of its tens of GHz bandwidth. However, the path direction for THz channel under different subcarrier frequencies is different (i.e., beam squint), which leads to the serious beam gain loss for conventional hybrid precoding design schemes. Thus, the effect of beam squint must be considered for the application of the wideband THz systems. In this paper, we consider the precoding design problem in a wideband THz massive multiple-input-multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) systems with beam squint. We first design two new sparse radio frequency chain antenna structures, including fully connected and subarray. Based on the designed structure, we formulate an average rate maximization problem by jointly designing the hybrid analog/digital precoding at the transmitter and analog combine vector at the receiver. Since it is a challenge to optimize the precoding simultaneously, we propose an alternatively iterative algorithm to solve it under each antenna structure. Finally, the simulation results are conducted to show the effectiveness of the designed antenna structure and proposed precoding schemes.

Secure mmWave UAV-Enabled SWIPT Networks Based on Random Frequency Diverse Arrays

In this article, we investigate physical layer security enhancement methods for millimeter-wave (mmWave) simultaneous wireless information and power transfer (SWIPT) unmanned aerial vehicle (UAV) networks, where the power-limited destination decodes the information and harvests energy from the received radio-frequency signals. Considering the effect of beamforming design and actual 3-D antenna gain, the directional modulation (DM) technique based on random frequency diverse array (RFDA) is adopted to guarantee security. The closed-form expressions of the lower bound of average secrecy rates with uniform linear array (ULA) and uniform planar array (UPA) are derived, respectively. Furthermore, based on the theoretical analysis results, we formulate the secrecy rate maximization problem subject to the energy harvesting constraint at the destination. Then, a suboptimal iterative optimization algorithm is proposed to solve the secrecy rate maximization problem by optimizing the transmit power, power splitting ratio, and UAV trajectory jointly. The simulation results show that the average secrecy rate of RFDA scheme is much larger than the conventional phase array scheme, especially when using ULA. The proposed optimization algorithm can achieve a higher average secrecy rate than other benchmark algorithms.

Robust Secure Beamforming and Power Splitting for Millimeter-Wave Cognitive Satellite–Terrestrial Networks With SWIPT

In this article, we investigate robust secure beamforming and power splitting for multiuser secondary terrestrial networks with simultaneous wireless information and power transfer, which coexist with primary satellite networks in the same millimeter-wave bands. In particular, a base station is equipped with a uniform planar array, and a secondary user (SU) employs a power-splitting-type receiver. Under an angle-based channel state information (CSI) error model, we formulate an optimization problem with the aim to maximize the minimal worst-case achievable secrecy rate among all SUs, while satisfying the quality-of-service (QoS) constraints for each SU, the interference limit for a primary satellite earth station, and the power consumption limit for the base station. To handle the intractable problem, we propose an iterative algorithm based on a series of approximations and a concept of the convex hull. Specifically, the former is for jointly designing secure beamformers and power splitting ratios, and the latter is for ensuring robustness to the angle-based CSI uncertainty. Furthermore, we analyze the computational complexity of this algorithm and extend the proposed scheme for solving a weighted sum secrecy rate maximization problem. Numerical results validate the effectiveness and superiority of the proposed scheme compared to the existing approaches, and obtain an insight into different robust designs.

Non-Orthogonal Multiple Access in Two-Hop Wireless Powered Communication Networks

This letter studies the resource allocation problem of sum rate maximization in wireless powered communication networks (WPCNs) with non-orthogonal multiple access (NOMA) that satisfy minimum data rate requirements per user. Firstly, we derive closed-form solutions for the time-splitting ratio and power allocation coefficients that maximize the approximated sum rate NOMA expression. Then, we use the solution of the approximated problem as the starting point for solving the original problem through Newton’s method. Simulation results show the effectiveness of the proposed approximation as well as the fast convergence of the optimal solution.

Secure Millimeter Wave Cloud Radio Access Networks Relying on Microwave Multicast Fronthaul

In this paper, we investigate the downlink secure beamforming (BF) design problem of cloud radio access networks (C-RANs) relying on multicast fronthaul, where millimeter-wave and microwave carriers are used for the access links and fronthaul links, respectively. The base stations (BSs) jointly serve users through cooperating hybrid analog/digital BF. We first develop an analog BF for cooperating BSs. On this basis, we formulate a secrecy rate maximization (SRM) problem subject both to a realistic limited fronthaul capacity and to the total BS transmit power constraint. Due to the intractability of the non-convex problem formulated, advanced convex approximated techniques, constrained concave convex procedures and semi-definite programming (SDP) relaxation are applied to transform it into a convex one. Subsequently, an iterative algorithm of jointly optimizing multicast BF, cooperative digital BF and the artificial noise (AN) covariance is proposed. Next, we construct the solution of the original problem by exploiting both the primal and the dual optimal solution of the SDP-relaxed problem. Furthermore, a per-BS transmit power constraint is considered, necessitating the reformulation of the SRM problem, which can be solved by an efficient iterative algorithm. We then eliminate the idealized simplifying assumption of having perfect channel state information (CSI) for the eavesdropper links and invoke realistic imperfect CSI. Furthermore, a worst-case SRM problem is investigated. Finally, by combining the so-called $\mathcal {S}$ -Procedure and convex approximated techniques, we design an efficient iterative algorithm to solve it. Simulation results are presented to evaluate the secrecy rate and demonstrate the effectiveness of the proposed algorithms.

Joint beamforming and time switching designs for energy-constrained cognitive two-way relay networks

In this paper, we investigate a joint beamforming and time switching (TS) design for an energy-constrained cognitive two-way relay (TWR) network. In the network, the energy-constrained secondary user (SU) relay employs TS protocol to harvest energy from the signals sent by the circuit-powered primary user (PU) transmitter, and then exploits the harvested energy to perform information forwarding. Our aim is to maximize the sum rate of SUs under the constraints of the data rate of PU, the energy harvesting and the transmit power of the SU relay. To determine the beamforming matrix and TS ratio, we decouple the original non-convex problem into two subproblems which can be solved by semidefinite relaxation and successive convex optimization methods. Furthermore, we derive closed form expressions of the optimal solutions for each subproblem, which facilitates the design of a suboptimal iterative algorithm to handle the original sum rate maximization problem. Simulation results are presented to illustrate the effectiveness and superior performance of the proposed joint design against other conventional schemes in the literature.

Joint Pilot and Payload Power Allocation for Massive-MIMO-Enabled URLLC IIoT Networks

The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way the factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, and suffers from transmission rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of achievable uplink data rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low-complexity algorithms to solve the achievable data rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

Energy-Constrained UAV-Assisted Secure Communications With Position Optimization and Cooperative Jamming

In this paper, we consider an energy-constrained unmanned aerial vehicle (UAV)-enabled mobile relay assisted secure communication system in the presence of a legitimate source-destination pair and multiple eavesdroppers with imperfect locations. The energy-constrained UAV employs the power splitting (PS) scheme to simultaneously receive information and harvest energy from the source, and then exploits the time switching (TS) protocol to perform information relaying. Furthermore, we consider a full-duplex destination node which can simultaneously receive confidential signals from the UAV and cooperatively transmit artificial noise (AN) signals to confuse malicious eavesdroppers. To further enhance the reliability and security of this system, we formulate a worst case secrecy rate maximization problem, which jointly optimizes the position of the UAV, the AN transmit power, as well as the PS and TS ratios. The formulated problem is non-convex and generally intractable. In order to circumvent the non-convexity, we decouple the original optimization problem into three subproblems; this facilitates the design of a suboptimal iterative algorithm. In each iteration, we propose a multi-dimensional search and numerical method to handle the subproblem. Numerical simulation results are provided to demonstrate the effectiveness and superior performance of the proposed joint design versus the conventional schemes in the literature.

Joint Resource Optimization for Orthogonal Frequency Division Multiplexing Based Cognitive Amplify and Forward Relaying Networks.

This paper investigates two resource allocation problems in cognitive relaying networks where both secondary network and primary network coexist in the same frequency band and adopt orthogonal frequency division multiplexing (OFDM) technology. The first one is the sum rate maximization problem of a secondary network under total power budget of a secondary network and tolerable interference constraint of a primary network. The second one is the sum rate maximization problem of a secondary network under separate power budgets of a secondary network and tolerable interference constraint of a primary network. These two optimization problems are completely different from those in traditional cooperative communication due to interference management constraint condition. A joint optimization algorithm is proposed, where power allocation and subcarrier pairing are decomposed into two subproblems with reasonable cost. The first one is a closed form solution of power allocation of the secondary network while managing the interference to a primary network under a constraint condition. The other is optimal subcarrier pairing at given power allocation. Simulation results reveal aspects of average signal to noise ratio (SNR), interference level, relay position, and power ratio on the sum rate of a secondary network.

Secure transmission for energy harvesting relay networks with the destination self-protection mechanism

In this paper, we investigate the physical layer security (PLS) of an energy harvesting (EH) cooperative network with the destination self-protection mechanism. More specifically, we consider a cooperative transmission scenario in which a source communicates with a destination through a wireless-powered relay using power splitting (PS) protocol in the presence of an eavesdropper. To degrade the wiretap channel, the destination is operated in full-duplex (FD) to achieve simultaneous information reception and jamming transmission. Taking into account the residual self-interference (SI) at the destination, the non-convex secrecy rate maximization problem is formulated to jointly optimize the PS factor and power allocation of source and destination. Due to the non-convexity of the joint optimization, a low complexity iterative algorithm based on the alternative search method and difference of convex functions (DC) programming is proposed. The original problem is first decomposed into two sub-problems with the fixed transmission power or PS factor. Since the decomposed two sub-problems are still non-convex, we utilize the DC programming to obtain the local solutions of the two sub-problems. Then the global solutions can be obtained by the alternative search method. Simulation results present that the proposed scheme has fast convergence. Comparing with other conventional schemes, the proposed scheme has the best security performance.

Robust Rate-Maximization Precoder Design for VFDM System

The new radio (NR) system is recently proposed to deploy alongside the long term evolution (LTE) system to improve the wireless communication capacity. The Vandermonde-subspace frequency division multiplexing (VFDM) is an efficient technique for such a two tier network, whereby the NR transmitter can reuse the frequency spectrum of LTE system meanwhile guaranteeing interference free to LTE. In this paper, we first investigate the robust rate-maximization problem in the VFDM system. In the condition of imperfect channel state information (CSI), the precoder is optimized to satisfy both non-interference to LTE system and rate-maximization in the NR system. To tackle the challenge in solving such a non-convex problem, an iterative algorithm is proposed based on the block coordinate descent (BCD) and penalty dual decomposition (PDD) methods. Finally, simulation results demonstrate the superiority of the proposed algorithm in terms of robustness and achievable rate over the conventional SVD-based VFDM technique.

Hybrid Offline-Online Design for UAV-Enabled Data Harvesting in Probabilistic LoS Channels

This paper considers an unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) in urban areas, where a UAV is deployed to collect data from distributed sensor nodes (SNs) within a given duration. To characterize the occasional building blockage between the UAV and SNs, we construct the probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using the combined simulation and data regression method, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. We assume that only the knowledge of SNs’ locations and the probabilistic LoS channel model is known a priori , while the UAV can obtain the instantaneous LoS/Non-LoS channel state information (CSI) with the SNs in real time along its flight. Our objective is to maximize the minimum (average) data collection rate from all the SNs for the UAV. To this end, we formulate a new rate maximization problem by jointly optimizing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Although the optimal solution is intractable due to the lack of complete UAV-SNs CSI, we propose in this paper a novel and general design method, called hybrid offline-online optimization, to obtain a suboptimal solution to it, by leveraging both the statistical and real-time CSI. Essentially, our proposed method decouples the joint design of UAV trajectory and communication scheduling into two phases: namely, an offline phase that determines the UAV path prior to its flight based on the probabilistic LoS channel model, followed by an online phase that adaptively adjusts the UAV flying speeds along the offline optimized path as well as communication scheduling based on the instantaneous UAV-SNs CSI and SNs’ individual amounts of data received accumulatively. Extensive simulation results are provided to show the significant rate performance improvement of our proposed design as compared to various benchmark schemes.

A Deep Learning Framework for Optimization of MISO Downlink Beamforming

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

Joint Beamforming and Power Splitting Design for C-RAN With Multicast Fronthaul

In this paper, we investigate the joint beamforming and power splitting design problem in a base station (BS) cluster-based cloud radio access network (C-RAN) with multicast fronthaul, where users are jointly served by BSs within each cluster. Meanwhile, each user can simultaneously obtain information and energy from received signals. On this basis, under predefined minimum harvested energy of each user and maximum transmit power of each BS and central processor, we formulate a sum rate maximization problem by jointly optimizing multicast fronthaul beamforming, cooperative access beamforming and power split ratios. Due to the difficulty in solving the formulated problem, we first transform it into a convex one by successive convex approximate and semidefinite program (SDP) relaxation techniques, and then propose an effective iterative algorithm. Moreover, we design a randomization method that can always obtain the rank-one solution. Finally, numerical results are conducted to validate the effectiveness of our proposed algorithm.

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.

Choice Modeling and Assortment Optimization in the Presence of Context Effects

In the presence of context effects, the perceived attractiveness of individual items is not fixed and depends on other items that are offered beside them. While context effects are well explored in the marketing and psychology literature, very little work has been done on incorporating these effects in discrete choice and investigating revenue management problems in their presence. In this paper, we introduce a new Random Utility Maximization choice model, the Contextual MNL model, that incorporates these effects. In our model, the utility of a presented item to the customer depends on what other items are offered beside it in an assortment. We compare the prediction power of our choice model and some of the other widely used choice models in the literature on a real data set and show that incorporating the context effects may significantly enhance the prediction scores and our understanding of the patterns of choice selection. We show the NP-hardness of the assortment optimization problem and the click through rate optimization problem, under our choice model. Several polynomially solvable special cases of the model are identified that also perform well in our empirical validation for our data set. In addition, to derive approximation results, we obtain some conditions for monotonicity and submodularity of the objective function of the assortment optimization and click through rate maximization problems. We also develop fast heuristics for solving the assortment optimization problem which provide near-optimal solutions according to our test results.

Resource Allocation for Multicarrier Rate-Splitting Multiple Access System

In this article, we investigate the resource allocation problem for the multicarrier rate-splitting multiple access (RSMA) systems. On each subcarrier, messages are non-orthogonal superimposed on the power domain through the one-layer RSMA scheme. A novel three-step resource allocation algorithm is proposed to deal with the non-convex problem of sum rate maximization. In step 1, assuming average power allocation among subcarriers, we obtain the power distribution factors of the users in a single subcarrier by converting this problem into a difference of convex program (DCP), and approximate it by its first-order Taylor expansion. In step 2, we convert the user-subcarrier matching problem into an assignment problem and use the Hungarian algorithm to solve it. In step 3, the optimized power allocation algorithm is used to calculate the power allocation among the subcarriers, and then updates the power vector for each user. Numerical results show that our proposed three-step resource allocation algorithm could achieve comparable sum rate performance to the existing near-optimal solution with much lower computational complexity and outperforms orthogonal multiple access (OMA) scheme.