Joint Subcarrier Assignment Research Articles

Deep Reinforcement Learning for Computation and Communication Resource Allocation in Multiaccess MEC Assisted Railway IoT Networks

Multi-access mobile edge computing (MEC) is envisioned as a key enabling technology to support compute-intensive and delay-sensitive applications in railway Internet of Things (RIoT) networks. However, the time-varying channel variations in RIoT scenarios make it challenging to achieve efficient resource allocation. The emerging deep reinforcement learning (DRL) is able to respond to the above-mentioned challenge. In this paper, with the aim of reducing the total computational cost (weighted sum of consumed energy and delay), we investigate the dynamic resource management issue of joint subcarrier assignment, offloading ratio, power allocation and computation resource allocation in multi-access MEC assisted RIoT networks. To address this intractable mixed integer nonlinear programming issue, we put forward a hybrid DRL (HDRL) scheme, which is an integration of deep double Q-learning (DDQN) and deep deterministic policy gradient (DDPG). The HDRL algorithm is capable of learning the advisable strategies for actions including discrete-continuous hybrid variables. In HDRL algorithm, DDQN plays the role of making subcarrier assignment decision, and DDPG plays the role of making offloading ratio, power allocation as well as computation resource allocation decisions. Numerical results demonstrate that HDRL scheme can yield much less computational cost than the existing baselines for multi-access MEC assisted RIoT networks. In addition, the HDRL scheme is close to the near-optimal performance with comparatively low execution time.

Joint Energy-Efficient Resource Allocation, Subcarrier Assignment, and SIC ordering for mmWave-enabled NOMA-UAV Networks

Joint Energy-Efficient Resource Allocation, Subcarrier Assignment, and SIC ordering for mmWave-enabled NOMA-UAV Networks

Reliability analysis and ABC based optimization for CoMP-enabled systems over Nakagami-[formula omitted] fading

Providing reliable communications has become one of the major goals for machine-type-oriented applications. In this article, reliable analysis and joint optimization over Nakagami-m fading are investigated for coordinated multi-point (CoMP) assisted multi-cell systems. First, the reliability analysis model for user equipment (UE) served by multiple base stations (BSs) via CoMP technique over Nakagami-m fading is presented. The exact closed-form expression for reliability estimation based on the received signal-to-interference-plus-noise ratio (SINR) value over Nakagami-m fading is derived and verified. Then, the joint sub-carrier assignment and power allocation problem for reliability optimization is formulated. The formulated problem is proved to be NP-hard. Bio-inspired artificial bee colony algorithm (ABC) is thus invoked to tackle this problem, and three ABC based joint optimization frameworks, namely Two-Step ABC Optimization algorithm (TSABC), ABC Combinatorial Optimization algorithm (ABCCO), and Heuristic Two-Step Optimization algorithm (HTSO), are proposed. Simulation results show that the UE reliability can be significantly enhanced by these proposed frameworks. It is also showcased that the proposed ABCCO obtains optimized reliability of 16 nines within 700 generations for most scenarios, which outperforms TSABC, HTSO, and conventional genetic algorithm (GA).

Joint Subcarrier Assignment and Power Allocation for OFDMA Full Duplex Distributed Antenna Systems

In this paper, we investigate the resource allocation scheme for orthogonal frequency division multiple access (OFDMA) full-duplex (FD) distributed antenna systems (DASs), in which both remote access units (RAUs) and users having the full-duplex capability. We aim to maximize the system spectral efficiency through jointly optimizing subcarrier assignment and power allocation, where users’ uplink/downlink quality of service requirements, per-user uplink/downlink transmit power constraints, and subcarrier reuse are considered. The user-centric virtual cell approach is adopted to reduce the computational and signaling overhead. Based on the successive convex approximation method and penalty factor, we propose an efficient overall optimization algorithm to tackle the non-convex optimization problem. Simulation results show that the proposed algorithm can achieve performance close to the exhaustive search algorithm, comparable to the hierarchical decomposition algorithm using the optimal subcarrier allocation scheme, and better than other benchmark algorithms. In addition, the proposed algorithm can guarantee fairness among users at a low cost of system spectral efficiency.

Resource allocation for downlink non-orthogonal multiple access in joint transmission coordinated multi-point networks

Joint transmission coordinated multi-point (JT-CoMP) and non-orthogonal multiple access (NOMA) are key enabling technologies of 5G ubiquitous broadband infrastructures. These technologies are jointly expected to exploit multi-cell and non-orthogonal resource transmissions; thus, conventional resource allocation schemes that only consider either one of them fail to efficiently exploit resources of 5G networks. In this paper, we bridge this gap by proposing a practical and comprehensive joint sub-carrier assignment and power allocation scheme for network sum-rate maximization in JT-CoMP-enabled NOMA networks. We formulate the problem as a mixed integer non-linear programming (MINLP) problem, which is NP-hard. The problem is decoupled into two sub-problems, where the sub-carrier assignment is modeled as a two-sided many-to-many matching game and the power allocation is formulated as a difference of convex (DC) programming problem. The matching algorithm is proved to converge to a two-sided exchange stable matching. Furthermore, the solution computed by the proposed scheme is verified against a baseline solution computed by a commercial optimization package, and has been shown to achieve 91.38% of the baseline solution for JT-CoMP-NOMA networks. Simulation results illustrate that the proposed scheme enhances cell-edge users’ achievable rates by 0.1−27.7% in JT-CoMP-NOMA over conventional NOMA.

Resource allocation for offloading-efficiency maximization in clustered NOMA-enabled mobile edge computing networks

In this paper, the problem of resource allocation for multi-carrier clustered non-orthogonal multiple-access (NOMA)-enabled mobile edge computing (MEC) networks is considered. In particular, the goal is to maximize the network sum-offloading-efficiency (SOE) via joint subcarrier assignment, power allocation, and computing resource allocation (J-SA-PA-CRA) for all user clusters, under the partial offloading mode, and subject to transmit power, delay and computing resource constraints. However, the formulated J-SA-PA-CRA problem is non-convex, and happens to be NP-hard, and thus is computationally-prohibitive. In turn, a low-complexity solution procedure is proposed, which decouples problem J-SA-PA-CRA into two subproblems: (1) J-PA-CRA per (user cluster, subcarrier) pair, and (2) one-to-one subcarrier assignment. The first subproblem is optimally solved via a successive convex approximation algorithm. As for the second subproblem, the polynomial-time complexity Kuhn–Munkres (KM) algorithm is proposed for optimal subcarrier assignment. Also, the stable marriage matching (SMM) algorithm is devised for a sub-optimal subcarrier assignment solution, but with less complexity than the KM algorithm. Extensive simulation results are provided to validate the proposed solution procedure, illustrating that the KM-based (SMM-based) solution procedure efficiently yields the optimal (a sub-optimal) network SOE, in comparison to the J-SA-PA-CRA scheme (solved via a global optimization package), while being superior to their OMA counterpart schemes (e.g. FDMA and TDMA).

Max Completion Time Optimization for Internet of Things in LEO Satellite-Terrestrial Integrated Networks

In this article, we investigate max completion time optimization for Internet of Things (IoT) in LEO satellite-terrestrial integrated networks (STINs), in which IoT devices use non-orthogonal multiple access (NOMA) scheme to transmit data to central earth stations (CESs), and orthogonal multiple access (OMA) scheme is used for data transmission from CESs to LEO satellite. We decouple this problem into two subproblems: 1) max completion time optimization in terrestrial networks and 2) max completion time optimization among satellite beams. Different from the existing works about NOMA data transmission in terrestrial networks, we propose a cooperative NOMA scheme, and derive the closed expressions of the optimal cooperative data and the optimal transmit power of IoT devices. Based on the closed-form expressions, a joint subcarrier assignment and cooperative NOMA pairing (JSACNP) approach is proposed to minimize the max completion time in terrestrial networks by utilizing matching theory. Then, to minimize the max completion time among satellite beams, the optimal linear receiver expression is derived with fixed transmit power. Convex optimization is utilized to solve transmit power optimization, we propose an algorithm to solve it by CVX tool. An iterative algorithm is proposed for improved performance. Finally, numerical results are provided to evaluate our proposed algorithms, compared with some other proposed approaches or algorithms.

Resource allocation for SWIPT-enabled energy-harvesting downlink/uplink clustered NOMA networks

In this paper, the problem of network sum-rate maximization in energy-harvesting clustered non-orthogonal multiple-access (NOMA) networks with simultaneous wireless information and power transfer (SWIPT) is considered. The aim is to maximize the network sum-rate in the downlink (DL) and uplink (UL) directions via joint subcarrier assignment, time-switching and power allocation (J-SA-TS-PA), while ensuring users’ quality-of-service (QoS). Particularly, each user cluster is to be assigned a subcarrier for data transmission in both link directions. However, problem J-SA-TS-PA happens to be non-convex, and NP-hard, and thus is computationally-intensive. Alternatively, a near-optimal low-complexity iterative solution procedure is devised to solve problem J-SA-TS-PA by splitting it into two sub-problems: (1) joint time-switching and power allocation per (user cluster, subcarrier) pair, and (2) one-to-one subcarrier assignment. Simulation results are presented to evaluate the proposed solution procedure, which is shown to yield comparable network sum-rate to the J-SA-TS-PA scheme (solved via a global optimization package), and superior to its OMA-based counterpart, while satisfying QoS constraints in both link directions.

Resource Allocation for MC MISO-NOMA SWIPT-Enabled HetNets With Non-Linear Energy Harvesting

This article proposes a resource allocation for multi carrier (MC) multiple input single output (MISO) non-orthogonal multiple access (NOMA) enabling simultaneous wireless information and power transfer (SWIPT) where a separated architecture (SA)-based heterogeneous network (HetNet) is considered. More precisely, we focus on designing MC MISO-NOMA scheme in HetNets, where each base stations (BSs) serves multiple information decoding (ID) and energy harvesting (EH) users with a non-linear energy harvesting model which are equipped with multi antennas. For this configuration, we propose a joint subcarrier assignment, beamforming design, and energy beamforming to maximize the total rate of the network. Moreover, we take into account the condition of cross-layer interference constraints to coordinate interference, satisfying a minimum data rate requirement, fulfilling a minimum harvested energy, and respecting the maximum allowed power transfer constraints. We also consider successive interference cancellation (SIC) decoding order to detect the signal of each user due to coupling between SIC order, beamforming design, and subcarrier allocation. As a result of interference in the rate function, our optimization problem is non-convex and thus challenging to solve. Subsequently, first we adopt semi-definite relaxation (SDR) for beamforming design and then an efficient algorithm via the majorization minimization (MM) approach based on the difference of convex (DC) programming is proposed to obtain a locally optimal solution for the original problem. Simulation results reveal the superiority of our proposed scheme as compared to other existing works addressed in the literature.

Joint Subcarrier Assignment and Power Allocation Strategy for Integrated Radar and Communications System Based on Power Minimization

This paper investigates the problem of power minimization-based joint subcarrier assignment and power allocation (PM-JSAPA) for an integrated radar and communications system (IRCS). In the considered system, the integrated transmitter is responsible to fulfill both radar and communications purposes simultaneously. The proposed PM-JSAPA strategy aims to minimize the total radiated power of the IRCS by jointly optimizing the available subcarriers and power resource allocation in order to achieve improved power-saving performance, subject to a predetermined mutual information (MI) for target parameter estimation and a specified data information rate (DIR) for wireless communications. The formulated problem falls into a mixed integer nonlinear programming (MINLP) problem, which is generally NP-hard. Then, an efficient three-step resource allocation framework is designed to solve the resulting optimization problem. Specifically, in Step 1, the subcarrier assignment for both radar and communications purposes is determined. In Step 2 and Step 3, we perform power allocation for different applications with the obtained subcarrier assignment results in Step 1, respectively. Finally, numerical simulation results are presented to demonstrate the superior power-saving performance of the proposed PM-JSAPA strategy and the effectiveness of the proposed solutions.

Energy Efficiency Maximization via Joint Sub-Carrier Assignment and Power Control for OFDMA Full Duplex Networks

In this paper, we develop an energy efficient resource allocation scheme for orthogonal frequency division multiple access (OFDMA) networks with in-band full-duplex (IBFD) communication between the base station and user equipments (UEs) considering a realistic self-interference (SI) model. Our primary aim is to maximize the system energy efficiency (EE) through a joint power control and sub-carrier assignment in both the downlink (DL) and uplink (UL), where the quality of service requirements of the UEs in DL and UL are guaranteed. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle this difficulty, we first use fractional programming to transform the fractional objective function to the subtractive form. Then, by employing Dinkelbach method, we propose an iterative algorithm in which an inner problem is solved in each iteration. Applying majorization-minimization approximation, we make the inner problem convex. Also, by introducing a penalty function to handle integer sub-carrier assignment variables, we propose an iterative algorithm for addressing the inner problem. We show that our proposed algorithm converges to the locally optimal solution which is also demonstrated by our simulation results. In addition, simulation results show that by applying the IBFD capability in OFDMA networks with efficient SI cancellation techniques, our proposed resource allocation algorithm attains a 75% increase in the EE as compared to the half-duplex system.

Joint subcarrier assignment and weighted-sum energy-efficient power allocation in multi-carrier uplink NOMA relay networks

In this paper, the problem of joint subcarrier assignment and weighted-sum energy-efficient power allocation (J-SA-WSEE-PA) in multi-carrier uplink NOMA relay networks is considered. Specifically, the aim is to assign subcarriers to network users, while maximizing the weighted-sum energy-efficiency, subject to quality-of-service (QoS) constraints. The J-SA-WSEE-PA problem is shown to be non-convex, and thus is computationally-expensive. In turn, it is split into two sub-problems: (1) WSEE maximizing power allocation per subcarrier, and (2) matching-theoretic subcarrier assignment. For the first sub-problem, a low-complexity iterative solution procedure is devised to optimally solve the WSEE-maximizing power allocation problem per subcarrier. As for the second sub-problem, two linear time-complexity stable matching algorithms based on the hospitals–residents matching problem are proposed to assign subcarriers to network users. Simulation results are presented to validate the devised solution procedure and the proposed stable matching algorithms, where it has been shown that they yield comparable network sum energy-efficiency to the J-SA-WSEE-PA scheme, while satisfying QoS constraints, and with lower computational complexity.

Joint Subcarrier Assignment With Power Allocation for Sum Rate Maximization of D2D Communications in Wireless Cellular Networks

Device-to-device (D2D) communications underlaying cellular networks have been recognized as a promising technology to improve the system capacity and spectral efficiency of 5G networks. In this paper, we investigate the problem of designing the joint uplink subcarrier assignment and power allocation scheme to optimize the sum data rate of D2D pairs, under a general “many-to-many” scenario in which a subcarrier could be shared by multiple D2D pairs and a D2D pair could also reuse multiple subcarriers. In particular, the optimization problem is to maximize the sum data rate of all D2D pairs under the constraints of minimum data rate requirements of cellular user equipments (CUEs). The formulated problem falls into a mixed integer nonlinear programming, which is generally NP-hard. To solve the problem, we decompose it into two subproblems: the subcarrier assignment subproblem and the power allocation subproblem. More specifically, we first propose an effective algorithm to assign subcarriers for both D2D pairs and CUEs under the assumption that the transmit power of each user is equally allocated among all the subcarriers. After that, we apply the successive convex approximation approach to transform the nonconvex power allocation problem into a sequence of convex problems, based on which an effective power allocation algorithm is designed. Simulations validate that the proposed scheme can significantly improve the achievable sum data rate of D2D pairs, compared with the other existing schemes.

Joint Subcarrier Assignment and Global Energy-Efficient Power Allocation for Energy-Harvesting Two-Tier Downlink NOMA Hetnets

In this paper, the problem of joint subcarrier assignment and global energy-efficient power allocation (J-SA-GEE-PA) for energy-harvesting (EH) two-tier downlink non-orthogonal multiple-access (NOMA) heterogeneous networks (HetNets) is considered. Particularly, the HetNet consists of a macro base-station (MBS) and a number of small base-stations (SBSs), which are solely powered via renewable-energy sources. The aim is to solve the J-SA-GEE-PA maximization problem subject to quality-of-service (QoS) per user as well as other practical constraints. However, the formulated J-SA-GEE-PA problem happens to be non-convex and NP-hard, and thus is computationally-prohibitive. In turn, problem J-SA-GEE-PA is split into two sub-problems: (1) subcarrier assignment via many-to-many matching, and (2) GEE-maximizing power allocation. In the first sub-problem, the subcarriers are assigned to users via the Gale-Shapley deferred acceptance mechanism. As for the second sub-problem, the GEE-PA problem is solved optimally via a low-complexity algorithm. After that, a two-stage solution procedure is devised to efficiently solve the J-SA-GEE-PA problem, while ensuring stability. Simulation results are presented to validate the proposed solution procedure, where it is shown to efficiently yield comparable network global energy-efficiency to the J-SA-GEE-PA scheme, and superior to that of OFDMA; however, with lower computational-complexity. The algorithmic designs presented in this work constitute a step towards filling the gap for computationally-efficient and effective resource allocation solutions to guarantee a fully autonomous and grid-independent operation of EH two-tier downlink NOMA HetNets.

Joint power allocation and subcarrier assignment for device to device in 5G time division duplex networks

The explosive demands for mobile broadband service bring a major challenge to 5G wireless networks. Device-to-device communication, adopting side links for user-direct communication, is regarded as a main technical source for offloading large volume of mobile traffic from cellular base station. This article investigates the joint power and subcarrier allocation scheme for device-to-device communication in 5G time division duplex systems. In time division duplex system, instead of utilizing an exclusive portion of the precious cellular spectrum, device-to-device pairs reuse the subcarriers occupied by cellular users, thus producing harmful interference to cellular users in both uplink and downlink communication, and strongly limiting the spectrum efficiency of the system. To this end, we focus on the maximization of device-to-device throughput while guaranteeing both uplink and downlink channel quality of service of cellular users as well as device-to-device pairs. The problem is formulated as a mixed integer non-linear programming (MINLP) problem. To make it tractable, we separate the original MINLP problem into two sub problems: power allocation and sub-carrier reusing. The former is to develop optimal power allocation for each device-to-device pair and each cellular user, with the constraints of maximum power and quality of service. It is solved by geometric programming technique in convex optimization method. The latter is derived as a one-to-many matching problem for scheduling multiple subcarriers occupied by cellulars to device-to-device pairs. It is solved by Hungarian method. Simulation results show that the proposed scheme significantly improves system capacity of the device-to-device underlay network, with quality of service of both device-to-device users and cellular users guaranteed.

Secure Communications in NOMA System: Subcarrier Assignment and Power Allocation

Secure communication is a promising technology for wireless networks because it ensures secure transmission of information. In this paper, we investigate the joint subcarrier (SC) assignment and power allocation problem for non-orthogonal multiple access (NOMA) amplify-and-forward two-way relay wireless networks, in the presence of eavesdroppers. By exploiting cooperative jamming (CJ) to enhance the security of the communication link, we aim to maximize the achievable secrecy energy efficiency by jointly designing the SC assignment, user pair scheduling and power allocation. Assuming the perfect knowledge of the channel state information (CSI) at the relay station, we propose a low-complexity subcarrier assignment scheme (SCAS-1), which is equivalent to many-to-many matching games, and then SCAS-2 is formulated as a secrecy energy efficiency maximization problem. The secure power allocation problem is modeled as a convex geometric programming problem, and then solved by interior point methods. Simulation results demonstrate that the effectiveness of the proposed SSPA algorithms under scenarios of using and not using CJ, respectively.

Joint Subcarrier and Power Allocation in the Energy-Harvesting-Aided D2D Communication

Device-to-device (D2D)-enabled Internet-of-things promises higher spectral efficiency and system capacity by sharing the cellular spectrum and offloading the cellular traffic. However, it poses new challenges in terms of resource allocation because of interference from D2D to base station and vice versa in the downlink. Moreover, energy efficient operation of the network becomes a major challenge because of unattended operation of devices. In this study, we investigate resource allocation for the energy-harvesting-aided D2D communication underlaying cellular network. We formulate a joint subcarrier assignment and power allocation problem for multiple cellular and energy harvesting direct D2D links to maximize the overall sum rate subject to quality of service, subcarrier reuse, power, and energy harvesting constraints. The problem is formulated as a mixed integer nonlinear programming and is difficult to be solved in polynomial time. Therefore, a low complexity algorithm named energy harvesting and gain-based resource allocation (EHGRA) is proposed, which determines reuse partners considering interference among D2D and cellular links, and then, allocates power optimally to them such that the sum rate is maximized. Our performance evaluation demonstrates that energy harvesting can boost up the system performance and at the same time can achieve higher sum rate over short frame duration. Comparison shows that EHGRA performs better than the existing algorithms in terms of overall sum rate.

Dynamic resource allocation for uplink non‐orthogonal multiple access systems

This study investigates a problem of resource allocation for an uplink multi-carrier non-orthogonal multiple access (NOMA) system. To provide a general resource allocation problem, the order of successive interference cancellation (SIC), subcarrier assignment and power allocation are all considered as the optimisation variables of interest, with the objective of maximising the weighted sum rate under the transmit power constraint of each user equipment (UE). Firstly, a novel optimal SIC order is proposed based on the inherent structure of the uplink NOMA system. Then, with the optimal SIC order, the Lagrange dual method is employed to solve the joint subcarrier assignment and power allocation problem in an asymptotically optimal manner. In addition, an optimal solution of power allocation with a fixed subcarrier assignment is presented. In light of the optimal power allocation, a tight upper bound of the uplink NOMA system is developed by relaxing the number of multiplexed UEs on each subcarrier. Simulation results show that the proposed algorithm outperforms conventional orthogonal multiple access schemes and other static NOMA schemes, and as a benchmark, the upper bound reveals that multiplexing at most two UEs on each subcarrier is sufficient for the performance improvement.

A Joint Subcarrier Assignment and Power Allocation with Statistical Delay QoS Provisioning in Full-Duplex OFDMA Systems

In this paper, we explore a joint subcarrier assignment and power allocation with statistical delay QoS guarantees for full-duplex OFDMA systems. Effective capacity theory which evaluates wireless channel capacity from a novel view of link layer provides a theory basis for modeling the system capacity under the conditions of delay QoS constrains. After modeling the aggregate effective capacity of the system, we formulate joint subcarrier assignment and power allocation problem as a mixed integer programming problem, whose target is to maximize the aggregate effective capacity. To solve this integer programming problem, we allocate initial power on each subcarrier and decompose it into two subproblems, which are subcarrier allocation problem and power allocation problem respectively. Further, we propose an alternative algorithm to achieve joint subcarrier assignment and power allocation, which can meet the “subcarrier continuity” constraint of uplink resource allocation. Simulations have shown that the performance of our proposed scheme is favorite. Additionally, we investigate the effect of the residual selfinterference, inter-node interference (INI) and statistical delay QoS exponent on the performance of systems.

Energy-Efficient Heterogeneous Cellular Networks With Spectrum Underlay and Overlay Access

In this paper, we provide joint subcarrier assignment and power allocation schemes for quality-of-service-constrained energy-efficiency (EE) optimization in the downlink of an orthogonal frequency division multiple access based two-tier heterogeneous cellular network. Considering underlay transmission, where spectrum efficiency (SE) is fully exploited, the EE solution involves tackling a complex mixed-combinatorial and nonconvex optimization problem. With appropriate decomposition of the original problem and leveraging on the quasi-concavity of the EE function, we propose a dual-layer resource allocation approach and provide a complete solution using difference of two concave functions approximation, successive convex approximation, and gradient-search method. On the other hand, the inherent intertier interference from spectrum underlay access may degrade EE particularly under dense small-cell deployment and large bandwidth utilization. Therefore, we develop a novel resource allocation approach based on the concepts of spectrum overlay access and resource efficiency (RE) (normalized EE–SE tradeoff). Specifically, the optimization procedure is separated where the macro-cell optimal RE and the corresponding bandwidth is first determined, then the EE of small cells utilizing the remaining spectrum is maximized. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation schemes can approach the optimal EE with each strategy being superior under certain system settings.