Rate Maximization Problem Research Articles

Intelligent Reflect Surface Aided Secure Transmission in MIMO Channel With SWIPT

This paper considers a multiple-input multiple-output (MIMO) secure simultaneous wireless information and power transfer (SWIPT) system aided by the intelligent reflecting surface (IRS), where the transmitter (T), the information receiver (IR) and energy receiver (ER) are all equipped with multiple antennas. Assuming that the ER may be potential eavesdropper (Eve), we aim to maximize the secrecy rate via jointly designing the precoding matrix, the artificial noise (AN) covariance, and the phase shift matrix, subject to the harvested energy and unit modulus reflect coefficient (RC) constraints. The formulated secrecy rate maximization (SRM) problem is non-convex with multiple coupled variables. To tackle it, an inexact block coordinate descent (IBCD) method is proposed, where the unit modulus constraint is handled by the penalty majorization-minimization (MM) and the complex circle manifold (CCM) methods. Finally, simulation results validate the effectiveness of IRS in enhancing the security.

Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey

This paper presents a literature review on recent applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks. Conventionally, the network optimization has been limited to transmission control at two endpoints, i.e., end users and network controller. The fading wireless channel is uncontrollable and becomes one of the main limiting factors for performance improvement. The IRS is composed of a large array of scattering elements, which can be individually configured to generate additional phase shifts to the signal reflections. Hence, it can actively control the signal propagation properties in favor of signal reception, and thus realize the notion of a smart radio environment. As such, the IRS's phase control, combined with the conventional transmission control, can potentially bring performance gain compared to wireless networks without IRS. In this survey, we first introduce basic concepts of the IRS and the realizations of its reconfigurability. Then, we focus on applications of the IRS in wireless communications. We overview different performance metrics and analytical approaches to characterize the performance improvement of IRS-assisted wireless networks. To exploit the performance gain, we discuss the joint optimization of the IRS's phase control and the transceivers' transmission control in different network design problems, e.g.,~rate maximization and power minimization problems. Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging use cases. Finally, we highlight important practical challenges and future research directions for realizing IRS-assisted wireless networks in beyond 5G communications.

User Clustering in mmWave-NOMA Systems With User Decoding Capability Constraints for B5G Networks

This article proposes a millimeter wave-NOMA (mmWave-NOMA) system that takes into account the end-user signal processing capabilities, an important practical consideration. The implementation of NOMA in the downlink (DL) direction requires successive interference cancellation (SIC) to be performed at the user terminals, which comes at the cost of additional complexity. In NOMA, the weakest user only has to decode its own signal, while the strongest user has to decode the signals of all other users in the SIC procedure. Hence, the additional implementation complexity required of the user to perform SIC for DL NOMA depends on its position in the SIC decoding order. Beyond fifth-generation (B5G) communication systems are expected to support a wide variety of end-user devices, each with their own processing capabilities. We envision a system where users report their SIC decoding capability to the base station (BS), i.e., the number of other users signals a user is capable of decoding in the SIC procedure. We investigate the rate maximization problem in such a system, by breaking it down into a user clustering and ordering problem (UCOP), followed by a power allocation problem. We propose a NOMA-minimum exact cover (NOMA-MEC) heuristic algorithm that converts the UCOP into a cluster minimization problem from a derived set of valid cluster combinations after factoring in the SIC decoding capability. The complexity of NOMA-MEC is analyzed for various algorithm and system parameters. For a homogeneous system of users that all have the same decoding capabilities, we show that this equates to a simple maximum number of users per cluster constraint and propose a lower complexity NOMA-best beam (NOMA-BB) algorithm. Simulation results demonstrate the performance superiority in terms of sum rate compared to orthogonal multiple access (OMA) and traditional NOMA clustering schemes that do not incorporate individual users’ SIC decoding capability constraints.

Fixed Region Beamforming Using Frequency Diverse Subarray for Secure mmWave Wireless Communications

Millimeter-wave (mmWave) using conventional phased array (CPA) enables highly directional and fixed angular beamforming (FAB), therefore enhancing physical layer security (PLS) in the angular domain. However, as the eavesdropper is located in the direction pointed by the mainlobe of the information-carrying beam, information leakage is inevitable and FAB cannot guarantee PLS performance. To address this threat, we propose a novel fixed region beamforming (FRB) by employing a frequency diverse subarray (FDSA) architecture to enhance the PLS performance for mmWave directional communications. In particular, we carefully introduce multiple frequency offset increments (FOIs) across subarrays to achieve a sophisticated beampattern synthesis that ensures a confined information transmission only within the desired angle-range region (DARR) in close vicinity of the target user. More specifically, we formulate the secrecy rate maximization problem with FRB over possible subarray FOIs, and consider two cases of interests, i.e., without/with the location information of eavesdropping, both turn out to be NP-hard. For the unknown eavesdropping location case, we propose a seeker optimization algorithm to minimize the maximum sidelobe peak of the beampattern outside the DARR. As for the known eavesdropping location case, a block coordinate descend linear approximation algorithm is proposed to minimize the sidelobe level in the eavesdropping region. By using the proposed FRB, the mainlobes of all subarrays are constructively superimposed in the DARR while the sidelobes are destructively overlayed outside the DARR. Therefore, FRB takes prominent effect on confining information transmission within the DARR. Numerical simulations demonstrate that the proposed FDSA-based FRB can provide superior PLS performance over the CPA-based FAB.

On the Max-Min Fairness of Beamspace MIMO-NOMA

Non-orthogonal multiple access (NOMA) based beamspace multiple-input multiple-output (MIMO) is a MIMO-NOMA scheme for millimeter-wave (mmWave) communications to improve the number of connections with increased sum rate. However, most of existing works only aim at maximizing the sum rate, which may cause an unbearable rate loss to weak users. To guarantee the rate performance for all served users, we maximize the minimal rate of the system from the max-min fairness perspective, where the NOMA users in the same beam share the same digital precoding vector, i.e., the beam-specific digital precoding, is adopted. The challenge is that, both the inter-beam and intra-beam interferences exist in the system, which makes the minimal rate maximization problem non-convex and thus hard to solve. To cope with this challenge, we propose an alternating optimization method to optimize the power allocation for each user and the digital precoding vector for each beam. Moreover, we break the commonly adopted beam-specific digital precoding scheme by using the user-specific digital precoding, i.e., each user is assigned with a unique digital precoding vector, to further improve the max-min rate. This can be achieved by the proposed two-stage optimization method, where the user-specific digital precoding vectors are firstly designed, and then the power allocation for all users is finetuned. Simulation results verify the proposed two methods. Moreover, the two-stage optimization method for the user-specific digital precoding outperforms the alternating optimization method for the beam-specific digital precoding, since the former can provide more degrees of freedom for designing the digital precoding vectors.

Cooperative Jamming for Secure UAV-Enabled Mobile Relay System

Due to the air-to-ground line-of-sight communication links, it is challenging to deal with the security threats in the wireless system with unmanned aerial vehicles (UAVs) integrated. In a UAV-enabled mobile relay system, the paper proposes a novel cooperative communication scheme from a physical layer security perspective to address the security issue. Specifically, in a mobile relay system where a UAV relay is employed to forward confidential information between two ground users, an eavesdropper exists on the ground wiretapping the relay UAV. To enhance the security performance of the system, we introduce a friendly UAV jammer transmitting interference signals and confusing the eavesdropper. A secrecy rate maximization problem is then formulated, subject to mobility constraints, power constraints on both UAVs and the information-causality constraint on the relay UAV. To solve the non-convex optimization problem with closely coupled variables, we decouple the problem into more tractable subproblems. An iterative algorithm is thus proposed to optimize the transmit power and flight trajectory of both UAVs alternately via the update-rate-assisted block coordinate descent and successive convex approximation techniques. Simulation results demonstrate that proposed cooperative design can significantly improve the secrecy rate of the UAV-enabled mobile relay system compared to benchmark schemes.

Power Allocation and Beam Scheduling for Multi-User Massive MIMO Secret Key Generation

Secret key generation in multi-user massive multiple-input multiple-output (MIMO) communication suffers from high computation complexity, pilot overhead, and inter-user interference caused by the large dimension of the massive MIMO channel. To address these problems, this paper proposes a novel key generation approach that exploits the beam domain channel for channel feature extraction. The proposed approach can effectively reduce the channel feature dimension and mitigate the inter-user interference, via the well-designed power allocation and beam scheduling algorithms. Specifically, the multi-user key generation model is built and the design problem of channel feature extraction matrices is formulated as a sum key rate maximization problem. To tackle this problem, we derive an upper bound of the secret key rate and prove that the upper bound maximization is a convex optimization. After solving the upper bound maximization, the base station (BS) obtains the sum beams for all the users. To allocate the selected beams to each user, an efficient beam scheduling algorithm is proposed, which converges to a locally optimal solution with low computational complexity. Numerical results illustrate that in the proposed scheme, the bit disagreement ratio (BDR) of legitimate users achieves 10 -2 , while that of the eavesdropper tends to 0.5, confirming the feasibility and security of the proposed scheme.

Sum-Rate Maximization for UAV Aided Wireless Power Transfer in Space-Air-Ground Networks

The Internet of things (IoT) has become a prominent platform which bridges diverse technologies in order to meet the ever-increasing application requirements of various industries. However, the IoT devices, especially in remote areas that lack infrastructures, are featured by the restricted energy and pose great challenges on network access and sustainable communication. In this paper, we investigate the unmanned aerial vehicle (UAV) aided wireless power transfer under a space-air-ground (SAG) network, where the UAV is exploited as an aerial relay to assist in uploading information generated by ground nodes (GNs), and mounted with energy transmitter to deliver wireless energy for GNs. The goal is to maximize the system sum rate while satisfying the proportional rate for GNs and the sustainability of the ground network. To this end, by adopting decode and forward (DF) and amplify and forward (AF) protocols, two sum rate maximization problems are formulated via jointly optimizing power control, time allocation as well as UAV trajectory. The resource allocation problems are both nonconvex, which are difficult to solve directly. To tackle them, two near-optimal iterative algorithms are proposed by leveraging the successive convex approximation technology and the alternating optimization method. Extensive simulations are provided to demonstrate the effectiveness of the proposed algorithms and evaluate the impacts of various parameters on DF and AF relays.

Joint Channel Estimation and Data Rate Maximization for Intelligent Reflecting Surface Assisted Terahertz MIMO Communication Systems

Terahertz (THz) communications recently attract significant attention and become an emerging technology pillar for sixth generation (6G) wireless systems. Due to the serious path attenuation of THz signals, THz communication is applicable for the short-distance indoor scenarios. However, the THz waves are easily blocked by obstacles, leading to a communication interruption. To this end, an intelligent reflecting surface (IRS), which interacts with incident THz waves in a controlled manner by adjusting the discrete phase shifts of the IRS elements, is considered as a promising technology to mitigate blockage vulnerability and enhance coverage capability for indoor scenarios. In light of graphene-enabled hardware structure of an IRS, the IRS-assisted THz multiple-input multiple-output (MIMO) system model is developed. Moreover, an iterative atom pruning based subspace pursuit (IAP-SP) scheme is developed for channel estimation. Compared to the classical subspace pursuit (SP) scheme, the proposed IAP-SP algorithm can substantially reduce the computational complexity while maintaining accurate channel recovery. With the estimated channel, a data rate maximization problem is formulated, which can be converted to a discrete phase shift search problem. The exhaustive search method is firstly proposed to obtain the optimal transmission rate but endure extremely high computational burden. Then, a local search method is proposed to decrease the number of possible discrete phase candidates of IRS while undergoes obvious performance loss. Interestingly, a novel feedforward fully connected structure based deep neural network (DNN) scheme is put forward, which has the ability to learn how to output the optimal phase shift configurations by inputting the features of estimated channel. Simulation results demonstrate that, in contrast with the exhaustive search scheme and the local search scheme, the proposed DNN-based scheme achieves a near-optimal communication rate performance. Meanwhile, the DNN-based scheme enormously alleviates the computational complexity and allows for dynamic parameter adaption in rapid-varying channel conditions.

On the Design of RD-MIMO: Spatial Multiplexing Versus Opportunistic Transmission Schemes

In this work, we focus on the implementation of reconfigurable distributed multiple-input multiple-output (RD-MIMO) schemes in practical communication environment. Considering RD-MIMO schemes, we raise an original research question, i.e., under what scenario whether we should recruit-then-transmit to multiple mobile receivers simultaneously or just recruit-then-transmit to one best selected mobile receiver. To address this question, we design a spatial multiplexing RD-MIMO (SM-RD-MIMO) scheme and an opportunistic RD-MIMO (O-RD-MIMO) scheme for the scenario of multiuser communication and that of one single best user communication, respectively. Aiming at the practical implementation of the RD-MIMO schemes, we choose zero-forcing (ZF) and maximal ratio transmitting (MRT) as beamforming strategies for the SM-RD-MIMO scheme and the O-RD-MIMO scheme, respectively. To evaluate the performance of the proposed schemes, we formulate sum rate maximization problems and solve the formulated problems analytically under some specific network settings. Through analytical and numerical examples, we show that the SM-RD-MIMO scheme outperforms the O-RD-MIMO scheme at moderate and high transmit signal-to-noise ratio (SNR) regimes, and vice versa at low transmit SNR regime. In addition, via numerical results, we provide a new finding that the ZF precoding under dynamic transmit power threshold-based per transmitter power constraint (PTPC) gives the same performance as that of the ZF precoding under sum power constraint (SPC).

Dynamic Rotated Angular Beamforming Using Frequency Diverse Phased-Array for Secure MmWave Wireless Communications

In this paper, we propose a new secure millimeter-wave wireless communication architecture called dynamic rotated angular beamforming (DRAB), to address the physical layer security (PLS) challenge for aligned transmitter, eavesdropper and target receiver in the mainlobe path, where the conventional angular beamforming (CAB) fails to provide satisfactory PLS performance. The proposed DRAB consists of a conventional phased-array and a set of frequency offset modulator. A frequency offset increment (FOI) set is intentionally introduced to control the dynamic rotation of DRAB’s mainlobe around the target user in the angle-range space. Thus, the average sidelobe of DRAB outside the target region is suppressed dramatically while the mainlobe inside the target region is a constant. We consider two cases of interests, i.e., with/without the location information of eavesdropping. For the known eavesdropping case, DRAB steer the zero gain sidelobes towards eavesdropper. The secrecy rate maximization problem is simplified to a form only depending on the FOI. As for the unknown eavesdropping case, we mainly depends on the beam rotating dynamically and randomly to hide the mainlobe path. Moreover, we propose inverted antenna subset technique to further randomize the sidelobes against sensitive eavesdropping. Numerical simulations demonstrate that the proposed DRAB can provide superior PLS performance over the existing CAB.

Low-Complexity Analog Beamforming for mmWave Large-Scale MISOME Wiretap Channel

With the popularization of large-scale antenna arrays in wireless communication, the conventional fully digital beamforming, which requires dedicated radio frequency (RF) chain per antenna element, is not viable for large-scale antenna systems due to its prohibitively high hardware cost. On the contrary, the analog beamforming design only requires one RF chain for all antenna elements, and has been widely investigated in recent years. In this letter, we consider the secure analog beamforming design that maximizes the secrecy rate in the large-scale multi-input single-output multi-eavesdropper (MISOME) wiretap channel. However, the associated secrecy rate maximization (SRM) problem is naturally non-convex due to the constant envelope constraint induced by the analog beamformer. To handle the problem, a low-complexity algorithm that combines the Dinkelbach approach and iterative coordinate ascent (ICA) algorithm is proposed to obtain a high-quality suboptimal solution. Numerical results illustrate that the proposed algorithm achieves both higher secrecy rate performance and lower complexity as compared to the existing schemes.

User Association and Resource Allocation in Full-Duplex Relay Aided NOMA Systems

To support ubiquitous connectivity and the rising demand of tele-traffic in the Internet of Things (IoT), we amalgamate nonorthogonal multiple access (NOMA) and full-duplex (FD) techniques to propose a new hybrid NOMA (FDH-NOMA) framework. We formulate an ergodic sum rate maximization problem under statistic channel state information (CSI). To simplify optimization, we derive upper and lower bounds to the ergodic sum rate and decompose the rate upper bound maximization problem into two subproblems, namely, user association and resource allocation. Since these two subproblems are coupled, we design an iterative algorithm for successively optimizing ergodic sum rate. In user association, we propose the mode selection criterion and NOMA mode pairing scheme, whereas for resource allocation, we optimize both interpair and intrapair resource allocation. Our numerical results demonstrate the strength and the effectiveness of our proposed FDH-NOMA framework and optimization algorithm.

Joint Communication and Computation Optimization for Wireless Powered Mobile Edge Computing with D2D Offloading

This paper studies a wireless powered mobile edge computing (MEC) system with device-to-device (D2D)-enabled task offloading. In this system, a set of distributed multi-antenna energy transmitters (ETs) use collaborative energy beamforming to wirelessly charge multiple users. By using the harvested energy, the actively computing user nodes can offload their computation tasks to nearby idle users (as helper nodes) via D2D communication links for self-sustainable remote computing. We consider the frequency division multiple access (FDMA) protocol, such that the D2D communications of different user-helper pairs are implemented over orthogonal frequency bands. Furthermore, we focus on a particular time block for task execution, which is divided into three slots for computation task offloading, remote computing, and result downloading, respectively, at different user-helper pairs. Under this setup, we jointly optimize the collaborative energy beamforming at ETs, the communication and computation resource allocation at users and helpers, and the user-helper pairing, so as to maximize the sum computation rate (i.e., the number of task input-bits executed over this block) of the users, subject to individual energy neutrality constraints at both users and helpers. First, we consider the computation rate maximization problem under any given user-helper pairs, for which an efficient solution is proposed by using the techniques of alternating optimization and convex optimization. Next, we develop the optimal user-helper pairing scheme based on exhaustive search and a low-complexity scheme based on greedy selection. Numerical results show that the proposed design significantly improves the sum computation rate at users, as compared to benchmark schemes without such joint optimization.

C-RAN With Hybrid RF/FSO Fronthaul Links: Joint Optimization of Fronthaul Compression and RF Time Allocation

This paper considers the uplink of a cloud radio access network (C-RAN) comprised of several multi-antenna remote radio units (RUs) which compress the signals that they receive from multiple mobile users (MUs) and forward them to a CU via wireless fronthaul links. To enable reliable high rate fronthaul links, we employ a hybrid radio frequency (RF)/free space optical (FSO) system for fronthauling. Moreover, to strike a balance between complexity and performance, we consider three different quantization schemes at the RUs, namely per-antenna vector quantization (AVQ), per-RU vector quantization (RVQ), and distributed source coding (DSC), two different RF fronthaul transmission modes, namely orthogonal transmission and non-orthogonal transmission, and two different detectors at the CU, namely the linear minimum mean square error detector and the optimal successive interference cancellation detector. For this network architecture, we investigate the joint optimization of the quantization noise covariance matrices at the RUs and the RF time allocation to the multiple-access and fronthaul links for rate region maximization. To this end, we formulate a unified weighted sum rate maximization problem valid for each possible combination of the considered quantization, RF fronthaul transmission, and detection schemes. To handle the non-convexity of the unified problem, we transform it into a bi-convex problem which facilitates the derivation of an efficient suboptimal solution using alternating convex optimization and golden section search. Moreover, by introducing a backoff parameter to reduce the probability of infeasibility, we generalize the proposed optimization framework to account for imperfect channel estimation. Our simulation results show that for each combination of the considered quantization, RF fronthaul transmission, and detection schemes, C-RAN with hybrid RF/FSO fronthauling can achieve a considerable sum rate gain compared to conventional systems employing pure FSO fronthauling, especially under unfavorable atmospheric conditions. In addition, employing a more sophisticated quantization scheme can significantly improve the system performance under adverse atmospheric conditions. In contrast, in clear weather conditions, when the FSO link capacity is high, the simple AVQ scheme performs close to the optimal DSC scheme. Furthermore, our simulation results suggest that the proposed algorithm can be adapted to the quality of the channel estimates by tuning the backoff parameter.

User Clustering and Power Allocation for Hybrid Non-Orthogonal Multiple Access Systems

In this article, a comprehensive strategy of user clustering and power allocation is investigated in downlink hybrid non-orthogonal multiple access (NOMA) networks. More particularly, users in the same cluster can receive signals simultaneously by using NOMA techniques, while time-division multiple access (TDMA) schemes are utilized among different clusters. By employing the weight factor, a weighted sum rate maximization problem is formulated and decoupled into user clustering and power allocation problems, where two different schemes for time slot allocation are proposed. The formulated user clustering problems with user-based and cluster-based time slot allocation schemes are respectively considered as coalitional games in characteristic and partition formations, and solved by two different algorithms, where both low-complexity and global optimal methods are proposed. The properties, including complexity, convergence, stability and optimality, are analyzed. To further improve the system performance, the formulated power allocation problem is solved by a successive convex approximation (SCA) based iterative algorithm. Simulation results reveal that: i) the proposed hybrid NOMA system is capable of achieving promising gains over centralized NOMA-based and conventional TDMA-based frameworks; and ii) the developed algorithms can significantly improve the weighted sum rate compared with the random user structure and the fixed power allocation.

UAV-Aided Wireless Communication Designs With Propulsion Energy Limitations

This paper studies unmanned aerial vehicle (UAV) aided wireless communication systems where a UAV supports uplink communications of multiple ground nodes (GNs) while flying over the area of the interest. In this system, the propulsion energy consumption at the UAV is taken into account so that the UAV's velocity and acceleration should not exceed a certain threshold. We formulate the minimum average rate maximization problem and the energy efficiency (EE) maximization problem by jointly optimizing the trajectory, velocity, and acceleration of the UAV and the uplink transmit power at the GNs. As these problems are non-convex in general, we employ the successive convex approximation (SCA) techniques. To this end, proper convex approximations for the non-convex constraints are derived, and iterative algorithms are proposed which converge to a local optimal point. Numerical results demonstrate that the proposed algorithms outperform baseline schemes for both problems. Especially for the EE maximization problem, the proposed algorithm exhibits about 109 % gain over the baseline scheme.

Correlation-Based Secure Transmission for Correlated MISO Wiretap Channels

This letter designs a correlation-based secure beamformer for correlated multiple-input-single-output wiretap channels with multiple noncollaborating passive single-antenna eavesdroppers. The outage-probability-constrained secrecy rate maximization problem is investigated under the assumption that the source has accurate main channel information and statistical wiretap channel information. As the correlation provides some useful information, a more precise statistical distribution of the wiretap channel can be obtained, with which a closed-form expression for the secrecy outage probability is derived and a specific secure beamformer is designed. Simulation results show that the secrecy rate under the transmit power and secrecy outage constraints is improved.

UAV-Aided Two-Way Mobile Relaying Systems

This letter investigates a two-way mobile relaying strategy where a relay mounted on a unmanned aerial vehicle (UAV) helps bidirectional communication of a pair of ground nodes (GNs). In order to deal with the minimum average rate maximization problem between two GNs, UAV trajectory, transmit power, and bandwidth are jointly optimized by fully utilizing the mobility of UAV and communication resources. To solve this problem, we apply a successive convex approximation technique for non-convex constraints by introducing new surrogate functions. Since all variables are jointly updated at the same iteration, we can yield a Karush-Kuhn-Tucker (KKT) point at the convergence point from the proposed algorithm. The effectiveness of the proposed algorithm is demonstrated in numerical results.

Outage Constrained Robust Beamforming for Sum Rate Maximization in Multi-Beam Satellite Systems

This letter investigates robust beamforming for multi-beam satellite communications in face of a phase error of channel state information. From the perspective of a practical system, we first formulate a sum rate maximization problem under the outage probability constraint of each user and the power consumption constraint of each individual feed. To handle the challenging problem, we characterize the Pareto frontier of the outage constrained achievable rate region based on a rate-profile method. In particular, a conservative approximation of the probabilistic constraint is achieved by means of the Taylor series expansion and the large deviation inequality. Numerical results validate the effectiveness and superiority of our proposed scheme compared to the existing approaches and give an insight into the impact of on-board payloads on robust beamforming.