Minimum Rate Constraints Research Articles

Secure energy efficient power allocation in massive multiple‐input multiple‐output systems with an eavesdropper using cell division technique

SummaryIn this paper, we study energy‐efficient power allocation (PA) to secure the confidential information of massive multiple‐input multiple‐output (MIMO) systems in the presence of an eavesdropper. First a training scheme is presented in the uplink using the MMSE method. Based on the estimated channel state information (CSI), the base station (BS) encrypts the confidential information. Next, the ergodic secret rate is obtained at the downlink. In this paper, in order to maximize the ergodic secrecy rate, the allocation of common power in the uplink and downlink is proposed by considering the constraints such as the transmit power constraint in the uplink, the transmit power constraint in the downlink, and the minimum ergodic secret rate constraint. The objective function in the proposed scenario is a nonconvex problem, which first becomes a convex problem and then, by using the Lagrange dual function method, the problem becomes an unconstraint problem. We also divide the cell into two areas using the cell division method. A practical energy‐efficient PA algorithm is presented which deals with the adverse effect of the eavesdropper. Simulations are carried out to demonstrate the effectiveness of the proposed methods.

Intelligent Reflecting Surface and UAV Assisted Secrecy Communication in Millimeter-Wave Networks

In this paper, we consider the secure transmission problem in an unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) assisted mmWave networks in the presence of an eavesdropper. The UAV base station (UAV-BS) and IRS are deployed to overcome the blockages. Artificial noise (AN) is exploited against the eavesdropper. Aiming to maximize the secrecy rate, we jointly design the positions and beamforming of UAV-BS and IRS, where the positions here represent the deployed position of the UAV-BS and the activation position of the IRS. Meanwhile, the maximum transmits power, minimum height constraint for UAV-BS, and the legitimate receiver minimum rate constraint are required. To obtain the optimal UAV-BS position, the elevation angle-dependent probabilistic LoS air-to-ground channel is exploited in this network. To tackle the formulated non-convex problem, we divide it into two subproblems: (1) design UAV-BS and IRS positions; (2) design UAV-BS and IRS beamforming. Moreover, to reduce the complexity, we propose an ideal beamforming model to find the near-optimal positions of UAV-BS and IRS. Then, semidefinite relaxation (SDR) is utilized to cope with the highly coupled and high-dimensional variable vectors. Finally, we obtain a sub-optimal solution by an efficient and low-complexity alternating optimization algorithm. Numerical results demonstrate the improvement of secrecy rate by jointly designing the positions as well as the significant performance gains achieved by our proposed schemes over benchmark schemes.

Maximizing the latency fairness in UAV‐assisted MEC system

Unmanned aerial vehicles (UAV) assisted edge computing has risen as an assuring technique to accommodate ubiquitous edge computation for resource-limited devices. Thus, this paper proposes an approach to maximize the latency fairness in a UAV-assisted multi-access edge computing (MEC) system. To maximize latency fairness, the authors focus on minimizing the maximum latency experienced among the users. In here, multiple ground users (GUs) offload their tasks to MEC UAV in the absence or unavailability of ground servers due to a disaster or heavy traffic where an iterative algorithm is proposed to minimize the maximum latency among the users subject to minimum control link rate and total power constraints. Sequentially, the UAVs' 3D location, offloading ratio, GUs' transmit power and GUs' computational capacity are optimized. The location of the UAV is optimized by using the novel approach, guided pattern search algorithm while the altitude of the UAV is optimized by analyzing the elevation angle dependant behaviour of the channel gain. A simple approach is utilized for optimizing the offloading ratio of the users by considering the problem as minimizing the point-wise maximum of two convex functions while the bisection method is used to optimize the power allocation. Numerical simulation results illustrate that the proposed approach outperforms other baseline approaches in convergence, minimizing the maximum latency and maximizing and maintaining the fairness among the GUs. Furthermore, it is proved that the guided pattern search algorithm converges at least 3.5 times better while the proposed combined optimization gives 400% fairness gain, in comparison with the baseline approach.

Sum Rate Maximization for Cooperative NOMA with Hardware Impairments

This paper investigates the sum rate (SR) maximization problem for downlink cooperative non-orthogonal multiple access (C-NOMA) systems with hardware impairments (HIs). The source node communicates with users via a half-duplex amplified-and-forward (HD-AF) relay with HIs. First, we derive the SR expression of the systems under HIs. Then, SR maximization problem is formulated under maximum power of the source, relay, and the minimum rate constraint of each user. As the original SR maximization problem is a non-convex problem, it is difficult to find the optimal resource allocation directly by tractional convex optimization method. We use variable substitution method to convert the non-convex SR maximization problem to an equivalent convex optimization problem. Finally, a joint power and rate allocation based on interior point method is proposed to maximize the SR of the systems. Simulation results show that the algorithm can improve the SR of the C-NOMA compared with the cooperative orthogonal multiple access (C-OMA) scheme.

Energy efficient resource allocation in delay-aware UAV-based cognitive radio networks with energy harvesting

In this paper, we study an UAV-based cognitive radio network with energy harvesting (EH-UAV-CRN) in delay-aware scenarios. First, based on the sensing results of the primary user, the energy harvesting-enabled UAV dynamically adapts the transmission power to obtain spectrum efficiency for data transmission. In addition, the process of UAV collecting renewable energy is described by compound Poisson distribution approximately. Our goal is to obtain the maximum energy efficiency (EE) of EH-UAV-CRN under the constraints of average energy, multiple minimum rate outage probability (MMROP), average transmission power, and average interference power. As the EE maximization is a non-tractable optimal problem with multiple complex constraints, one so-called delay-related approximate optimization strategy (De-AOS) is proposed. Specifically, the primitive optimal problem is firstly transformed into a tractable counterpart by the proposed De-AOS. Moreover, the optimal resource allocation solution is obtained by taking advantage of the Lagrange duality method and one-dimensional linear programming. Simulation results demonstrate that our strategy can be used flexibly in various delay-related scenarios by setting different MMROP thresholds. Moreover, compared with the traditional quality of service-based minimum rate constraint scheme, our algorithm can obtain significant EE gains under the same constraints of energy, interference and transmission power.

Game Theoretic Approach for a Service Function Chain Routing in NFV With Coupled Constraints

In this brief, the service function chain (SFC) routing in network function virtualization (NFV) with server capacity limitation and user minimum target rate constraint is investigated simultaneously. After relaxing the constraints by the addition of extra cost terms, an extended NFV game incorporating the coupled constraints is formulated. By using semi-tensor product of matrices, we prove that the formulated game is a potential game which possesses at least one Nash equilibrium. We then present the equilibrium point algorithm for solving the SFC routes problem. The numerical simulation of two cases with constraints and without constraints illustrates the influence of coupled limitations on the SFC routing.

Energy-Efficient Resource Allocation for IRS-Assisted Multi-Antenna Uplink Systems

In this letter, we study the resource allocation for an intelligent reflecting surface (IRS)-assisted uplink system, where the base station is equipped with multiple antennas. We propose to jointly optimize the transmit power of the users, active beamforming at the base station, and passive beamforming at the IRS to maximize the overall system energy efficiency while maintaining users' minimum rate constraints. This problem belongs to a highly intractable non-convex bi-quadratic programming problem, for which an iterative solution based on block coordinate descent is proposed. Extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme and the benefits of having more elements at the IRS.

Energy Efficient Subchannel and Power Allocation in Cooperative VLC Systems

This letter studies the energy efficiency (EE) optimization of cooperative visible light communication (VLC) systems while considering the inter-cell interference and the lineof-sight blockage problems. Specifically, the optimization problem of subchannel and power allocation to maximize EE under transmit power budgets and users' minimum rate constraints is considered. The formulated problem turns out to be a difficult nonlinear fractional program for which a low-complexity iterative solution based on fractional programming theory and the quadratic transform approach is proposed. Extensive simulations are conducted to show the efficacy of the proposed scheme over conventional approaches. In addition, the outage analysis and impacts of varying the transmit power and the subchannel bandwidth on the EE performance are investigated.

Non-Orthogonal Multiple Access in Distributed Antenna Systems for Max-Min Fairness and Max-Sum-Rate

The distributed antenna systems (DAS) and non-orthogonal multiple access (NOMA) are the key technologies to boost the data rate of the cellular system in terms of small cell and multiple access, respectively. To meet the high data rate requirements for 5G and beyond, we suggest a framework of using NOMA in DAS. In the proposed scheme, RRUs which are geographically distributed in the cell serve the cell-edge users within their own coverage. Meanwhile, the macro BS, which covers the entire cell region with relatively high transmit power, supports both the cell-center and the cell-edge users with identical resources by using NOMA. Compared to the conventional DAS where the macro BS and the RRU serve the cell-center and the cell-edge users, respectively, the proposed framework also boosts the data rate of the cell-center user by improving the reliability of successive interference cancellation (SIC) at the cell-center user. In the proposed framework, this paper also proposes the optimal power allocation rules maximizing the user fairness in two different cases of instantaneous channel gain information (CGI) and channel distribution information (CDI) known at the transmitter. Also, the power allocation methods maximizing the sum-rate with a minimum rate constraint and the weighted sum-rate are presented in the case of CGI known at the transmitter. Simulation results show that the proposed framework of using NOMA in DAS can boost data rates more in a variety of system environments compared to the conventional NOMA or DAS.

Minimum-Delay Routing for Integrated Aeronautical Ad Hoc Networks Relying on Real Flight Data in the North-Atlantic Region

Relying on multi-hop communication techniques, aeronautical ad hoc networks (AANETs) seamlessly integrate ground base stations (BSs) and satellites into aircraft communications for enhancing the on-demand connectivity of planes in the air. The goal of the paper is to assess the performance of the classic shortest-path routing algorithm in the context of the real flight data collected in the North-Atlantic Region. Specifically, in this integrated AANET context we investigate the shortest-path routing problem with the objective of minimizing the total delay of the in-flight connection from the ground BS subject to certain minimum-rate constraints for all selected links in support of low-latency and high-speed services. Inspired by the best-first search and priority queue concepts, we model the problem formulated by a weighted digraph and find the optimal route based on the shortest-path algorithm. Our simulation results demonstrate that aircraft-aided multi-hop communications are capable of reducing the total delay of satellite communications, when relying on real historical flight data.

Energy Efficiency Maximization Under Minimum Rate Constraints in Multi-Cell MIMO Systems With Finite Buffers

Having recognized the dramatic increase in the number of mobile devices and infrastructure nodes, standards organizations and regulatory bodies have adopted energy efficiency (EE) as a key performance metric for fifth-generation networks. Recent works on multiple input multiple output (MIMO) systems have suggested that it is important to use finite-buffer models, because they may lead to better transceiver designs and more accurate performance analyses than full-buffer traffic models. Therefore, this article addresses the MIMO transceiver design problem for EE maximization in the downlink of finite-buffer multicell systems. Unlike previous works, our problem formulation takes into account per-user minimum rate requirements. We arrive at a nonconvex fractional optimization problem, which is hard to tackle. By exploiting the properties of fractional programming, and using Dinkelbach's method, the resulting fractional form optimization problem is transformed to an equivalent optimization problem in subtractive form. Next, the nonconvexity of this problem is handled using successive convex approximation, leading to iterative centralized and decentralized resource allocation solutions. Finally, considering a realistic channel model with space, frequency and time correlations, numerical results confirm the effectiveness of the proposed algorithms and indicate significant performance gains in terms of achieved EE over existing solutions for full and finite-buffer models.

A Coverage-Aware Resource Provisioning Method for Network Slicing

With network slicing in 5G networks, Mobile Network Operators can create various slices for Service Providers (SPs) to accommodate customized services. Usually, the various Service Function Chains (SFCs) belonging to a slice are deployed on a best-effort basis. Nothing ensures that the Infrastructure Provider (InP) will be able to allocate enough resources to cope with the increasing demands of some SP. Moreover, in many situations, slices have to be deployed over some geographical area: coverage as well as minimum per-user rate constraints have then to be taken into account. This paper takes the InP perspective and proposes a slice resource provisioning approach to cope with multiple slice demands in terms of computing, storage, coverage, and rate constraints. The resource requirements of the various SFCs within a slice are aggregated within a graph of Slice Resource Demands (SRD). Infrastructure nodes and links have then to be provisioned so as to satisfy all SRDs. This problem leads to a Mixed Integer Linear Programming formulation. A two-step approach is considered, with several variants, depending on whether the constraints of each slice to be provisioned are taken into account sequentially or jointly. Once provisioning has been performed, any slice deployment strategy may be considered on the reduced-size infrastructure graph on which resources have been provisioned. Simulation results demonstrate the effectiveness of the proposed approach compared to a more classical direct slice embedding approach.

Simultaneous dose and dose rate optimization (SDDRO) for FLASH proton therapy.

FLASH radiotherapy (RT) can potentially reduce normal tissue toxicity while preserving tumoricidal effectiveness to improve the therapeutic ratio. The key of FLASH for sparing normal tissues is to irradiate tissues with an ultra-high dose rate (i.e., ≥40Gy/s), for which proton RT can be used. However, currently available treatment plan optimization method only optimizes the dose distribution and does not directly optimize the dose rate. The contribution of this work to FLASH proton RT is the development of a novel treatment optimization method, that is, simultaneous dose and dose rate optimization (SDDRO), to optimize tissue-receiving dose rate distribution as well as dose distribution. Distinguished from existing methods, SDDRO accounts for dose rate constraint and optimizes dose rate distribution. In terms of mathematical formulation, SDDRO is a constrained optimization problem with dose-volume constraint on dose distribution, minimum dose rate constraint on dose-averaged tissue-receiving dose rates, minimum monitor unit constraint on spot weight, and maximum intensity constraint on beam intensity. In terms of optimization algorithm, SDDRO is solved by iterative convex relaxation and alternating direction method of multipliers. SDDRO algorithms are presented for both scenarios with either constant or variable beam intensity. SDDRO was compared with intensity modulated proton therapy (IMPT) (dose optimization alone, and no dose rate optimization) using three lung cases. SDDRO substantially improved the dose rate distribution compared to IMPT, for example, increasing of the region-of-interest (ROI) volume (ROI=CTV_10mm: the ring sandwiched by 10mm outer and inner expansion of CTV boundary) receiving at least 40Gy/s from ~30-50% to at least 98%, and the lung volume receiving at least 40Gy/s from ~30-40% to ~70-90%. Moreover, both dose and dose rate distributions from SDDRO were further considerably improved via the combined use of hypofractionation and multiple beams. We have developed a joint dose and dose rate optimization method for FLASH proton RT, namely SDDRO, which is first-of-its-kind to the best of our knowledge. The results suggest that (a) SDDRO can substantially improve the FLASH-dose rate coverage (e.g., in terms of dose rate volume histogram) compared to IMPT for the purpose of normal tissue sparing while preserving the dose distribution and (b) the combination of hypofractionation and multiple beams can further considerably improve the SDDRO plan quality in terms of both dose and dose rate distribution.

Remote Radio Head Activation and User Association in Dense C-RANs

Cloud radio access networks (C-RANs) is a promising technology for improving the performance of future mobile networks. In this paper, we focus on the joint remote radio head (RRH) activation and user association problem in dense C-RANs. Considering that a certain required minimum data rate may not be satisfied universally for all users in some situation, we aim to first maximize the number of users whose minimum data rate constraints can be guaranteed, and then maximize the system profit, which is a combination of the system throughput and the total power consumption. Due to the NP-hard feature of the problem, the conventional algorithms are impractical when the network size is large due to large computational complexity. Therefore, we adopt the max-sum algorithm to efficiently solve the joint RRH activation and user association problem, which can be implemented efficiently in a distributed manner. The signaling overhead of the max-sum algorithm can be reduced further by simplifying message updating rules and by shortening messages exchanged between nodes. Considering the fact that the high density of RRH deployment may hinder the convergence rate of the max-sum algorithm, we also propose a simplified algorithm by decomposing the original problem into two sub-problems, and each can be solved by the max-sum algorithm individually. Simulation results prove the efficiencies of the proposed algorithms.

Achievable Rate Maximization in Orbital Angular Momentum-Based Downlink Non-Orthogonal Multiple Access Systems

Orbital angular momentum (OAM)-based communication system is promising to increase the spectrum efficiency by using orthogonal OAM-modes to transmit different signals simultaneously. To further increase the spectrum efficiency, incorporating the non-orthogonal multiple access (NOMA) scheme with the OAM-based communication system is proposed. For an OAM-based downlink NOMA system which consists of a base station, a near user, and a far user, the achievable rate maximization problem is studied, which maximizes the achievable rate of the near user under the minimum transmission rate constraint at the far user. To solve the non-convex problem, a majorization-minimization based iterative algorithm is proposed, where in each iteration, a low-complexity Lagrange dual method is employed. It is shown from simulation results that the proposed NOMA system is superior than the conventional orthogonal multiple access system.

Distributed Energy Efficient Channel Allocation in Underlay Multicast D2D Communications

In this paper, we address the optimization of the energy efficiency of underlay multicast device-to-device (D2MD) communications on cellular networks. In particular, we maximize the energy efficiency of both the global network and the individual users considering various fairness factors such as maximum power and minimum rate constraints. For this, we employ a canonical mixed-integer non-linear formulation of the joint power control and resource allocation problem. To cope with its NP-hard nature, we propose a two-stage semi-distributed solution. In the first stage, we find a stable, yet sub-optimal, channel allocation for D2MD groups using a cooperative coalitional game framework that allows co-channel transmission over a set of shared resource blocks and/or transmission over several different channels per D2MD group. In the second stage, a central entity determines the optimal transmission power for each user in the system via fractional programming. We performed extensive simulations to analyze the resulting energy efficiency and attainable transmission rates. The results show that the performance of our semi-distributed approach is very close to that obtained with a pure optimal centralized one.

Legitimate eavesdropping of wireless powered suspicious communication networks with a monitoring power station

This paper investigates the legitimate eavesdropping of a wireless powered suspicious communication network. The considered network consists of a wireless powered suspicious transmitter, a suspicious receiver, and a power station. The power station is assumed to be also a monitor to legitimately eavesdrop the suspicious communication with the help from a wireless powered jammer. The problem for maximizing the average eavesdropping rate under the minimum suspicious communication rate constraint and the transmit power constraint at the monitor is investigated, where the time allocation for energy transferring and information transmission, and the power controls of the monitoring power station and the jammer are jointly optimized. By using the Lagrange duality method and exploring the particular problem structure, the closed-form optimal solution is derived. Some valuable insights are also obtained from the optimal solution. Extensive simulations are provided to confirm the superiority of the proposed optimal solution compared to various benchmark algorithms.

Secrecy sum rate maximisation in multi‐antenna two‐way relay networks

This study investigates the achievable secrecy rate of two-way relay-assisted networks. It is assumed that there is no direct link between transceiver ends and the communication is carried out in two hops through the use of multi-antenna relays in the middle of transmission, where the relays employ amplify-and-forward strategies. To increase the bandwidth efficiency, intending nodes exchange their confidential messages in two hops. First, both nodes simultaneously send their messages to the relay nodes. Then, throughout the second hop, the relays make use of cooperative beamforming to send their incoming information in such a way that the achievable secrecy sum rate is maximised, considering each node is subject to a minimum secrecy rate constraint. Accordingly, two beamforming strategies, named as null-space beamforming and information leakage alignment beamforming are proposed, where their achievable secrecy sum rates are investigated and compared to existing works addressed in the literature.

User Clustering Scheme for Downlink Hybrid NOMA Systems Based on Genetic Algorithm

Non-orthogonal multiple access (NOMA) is considered to be a promising technology for improving bandwidth utilization efficiency and reducing power consumption. In this paper, we consider the optimization of user clustering in the NOMA scenario, where the goal is to maximize the system total throughput under minimum rate constraints. Different from most existing literatures, there is no limit on the number of users in each cluster. Simulation results show that the proposed scheme can significantly reduce computational complexity and have a better performance compared with schemes based on the other heuristic algorithms and random user clustering with greedy strategy.

A RAN Resource Slicing Mechanism for Multiplexing of eMBB and URLLC Services in OFDMA Based 5G Wireless Networks

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two services on the same wireless infrastructure leads to a challenging resource allocation problem due to their heterogeneous specifications. To address this problem, slicing has emerged as an architecture that enables a logical network with specific radio access functionality to each of the supported services on the same network infrastructure. The allocation of radio resources to each slice according to their requirements is a fundamental part of the network slicing that is usually executed at the radio access network (RAN). In this work, we formulate the RAN resource allocation problem as a sum-rate maximization problem subject to the orthogonality constraint (i.e., service isolation), latency-related constraint and minimum rate constraint while maintaining the reliability constraint with the incorporation of adaptive modulation and coding (AMC). However, the formulated problem is not mathematically tractable due to the presence of a step-wise function associated with the AMC and a binary assignment variable. Therefore, to solve the proposed optimization problem, first, we relax the mathematical intractability of AMC by using an approximation of the non-linear AMC achievable throughput, and next, the binary constraint is relaxed to a box constraint by using the penalized reformulation of the problem. The result of the above two-step procedure provides a close-to-optimal solution to the original optimization problem. Furthermore, to ease the complexity of the optimization-based scheduling algorithm, a low-complexity heuristic scheduling scheme is proposed for the efficient multiplexing of URLLC and eMBB services. Finally, the effectiveness of the proposed optimization and heuristic schemes is illustrated through extensive numerical simulations.