Subcarrier Allocation Problem Research Articles

Priority-based subcarrier allocation algorithm for maximal network connectivity in 5G networks

With the widespread adoption of wireless technology, there has been a significant surge in the number of devices seeking wireless connectivity over the past decade. To meet the extensive demand for high-data-rate wireless connectivity, the fifth-generation (5G) cellular network plays a pivotal role. 5G cellular network aims to support a large number of applications with ultra-high data rates by maximizing device connectivity while satisfying quality of service (QoS) requirements. In this paper, we present an innovative priority-based subcarrier allocation (PSA) algorithm to address the challenge of maximizing connectivity in 5G new radio (5G NR) networks. Initially, we formulate the connectivity maximization problem as a subcarrier allocation problem by considering three key parameters: bandwidth requirement, waiting time, and energy level of user devices. The objective of the formulated problem is to optimally allocate subcarriers to multiple users in order to maximize connectivity while maintaining QoS requirements. To address the problem, we propose the PSA algorithm that prioritizes bandwidth, waiting time, and energy parameters using the R-method. To accommodate the network scenarios, we develop three variants of the PSA algorithm—PSA-1, PSA-2, and PSA-3. These variants allocate subcarriers based on the priority-based score of user. We carried out a simulation-based study to illustrate the effectiveness of our proposed algorithm in comparison to traditional methods. The simulation results reveal that our proposed algorithms outperform first come first serve (FCFS) and longest remaining time first (LRTF), and achieves comparable or superior results compared to priority and fairness-based resource allocation with 5G new radio numerology (PFRA-0N) in terms of the number of user allocations, average user allocation ratio, user drop ratios and average connectivity rate. Compared to the shortest job first (SJF) technique, our proposed PSA algorithm performance is slightly inferior in terms of the number of user allocations, average allocation ratio and average connectivity rate; however, it shows superior performance in drop ratios. Further, the proposed algorithms show significant improvements in execution time compared to the optimal exhaustive search solution.

Traffic-Aware Energy-Efficient Resource Allocation for RSMA Based UAV Communications

Traffic measurement will play an essential role in future networks to reveal the traffic requirements of the users, which will support network operations like resource allocation. In this paper, we study the traffic-aware resource allocation problem for downlink rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) communications. Specifically, with the help of traffic measurement, user requirements on achievable rate are known as prior knowledge to the UAV. Considering user requirements, we maximize the energy efficiency of the UAV by jointly optimizing the UAV deployment, beamforming, rate allocation, and subcarrier allocation. To overcome the non-convexity of the above problem, a joint optimization is developed to solve it iteratively. First, a heuristic approach is proposed to find the three-dimensional location of the UAV. Then, successive convex approximation approach is utilized to optimize RSMA parameters. Moreover, we formulate the subcarrier allocation problem as a many-to-one two-sided matching game, which is tackled by the swap matching algorithm. Numerical results suggest that the proposed subcarrier allocation scheme outperforms benchmark schemes, and RSMA based schemes perform close to that based on non-orthogonal multiple access, which all outperform orthogonal frequency division multiple access based scheme.

Joint Communication and Computation Design in Transmissive RMS Transceiver Enabled Multi-Tier Computing Networks

In this paper, a novel transmissive reconfigurable meta-surface (RMS) transceiver enabled multi-tier computing network architecture is proposed for improving computing capability, decreasing computing delay and reducing base station (BS) deployment cost, in which transmissive RMS equipped with a feed antenna can be regarded as a new type of multi-antenna system. We formulate a total energy consumption minimization problem by a joint optimization of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient while taking into account the constraints of communication resources and computing resources. This formulated problem is a non-convex optimization problem due to the high coupling of optimization variables, which is NP-hard to obtain its optimal solution. To address the above challenging problems, block coordinate descent (BCD) technique is employed to decouple the optimization variables to solve the problem. Specifically, the joint optimization problem of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient is divided into three subproblems to solve by applying BCD. Then, the decoupled three subproblems are optimized alternately by using successive convex approximation (SCA) and difference-convex (DC) programming until the convergence is achieved. Numerical results verify that our proposed algorithm is superior in reducing total energy consumption compared to other benchmarks.

Energy-Efficient Resource Allocation for Short Packet Transmission in MISO Multicarrier NOMA

In this article, we investigate energy efficiency (EE) maximization of short packet transmission (SPT) in a downlink multiple-input single-output (MISO) multi-carrier non-orthogonal multiple access (MC-NOMA) communication system. We use the ratio of the effective throughput to the power as a performance metric to formulate a non-convex mixed-integer nonlinear programming (MINLP) problem. Then, a low-complexity three-step optimization framework is proposed for MINLP optimization. Under a given maximum transmit power and channel blocklength of each subcarrier, a block coordinate descent (BCD) based method is proposed to jointly optimize the power allocation coefficient, transmission rate, and transmit power within a single subcarrier. Then, we transform the subcarrier allocation problem into a weighted bipartite graph matching problem and obtain the optimal subcarrier allocation scheme using the Kuhn-Munkres (KM) algorithm. Finally, a dynamic programming (DP) method is proposed to obtain the optimal channel blocklength allocation scheme. The simulation results validate the effectiveness of the proposed three-step optimization framework and indicate that the MC-NOMA system achieves higher EE and higher effective throughput than the orthogonal frequency-division multiple access (OFDMA) system in SPT.

A NOMA-Enabled Framework for Relay Deployment and Network Optimization in Double-Layer Airborne Access VANETs

A non-orthogonal multiple access (NOMA)-enabled double-layer airborne access vehicular ad hoc networks (DLAA-VANETs) architecture is designed in this paper, which consists of a high-altitude platform (HAP), multiple unmanned aerial vehicles (UAVs) and vehicles. For the designed DLAA-VANETs, we investigate the UAV deployment and network optimization problems. In particular, a UAV deployment scheme based on particle swarm optimization is presented. Then, the NOMA technique is introduced into the designed architecture, which can improve the transmission rate. Afterward, we take the information security into account and formulate a downlink total transmission rate maximization problem by optimizing UAV height and subcarrier allocation. For tackling this non-convex problem, we decouple this downlink total transmission rate maximization problem as two subproblems, where UAV height and subcarrier allocation problems are solved in turn. Moreover, the transmission performance of the designed DLAA-VANETs is analyzed, based on which the security outage probability (SOP) is derived. Finally, simulation results demonstrate that the presented UAV deployment scheme can maximize the relay coverage ratio. In addition, the proposed can achieve a higher downlink total transmission rate in comparison with the current works.

Resource Allocation for OFDM-NOMA Systems under Multiuser Scenarios with Different Speeds

In this paper, we consider an OFDM-NOMA (orthogonal frequency division multiplexing-nonorthogonal multiple access) downlink system, which has multiple subcarriers and multiple users at different locations and speeds from the base station. The goal is to maximize energy efficiency (EE), which is the bit rate per unit of energy consumption, while meeting the user’s quality of service and power constraints. Since the optimization problem of joint user subcarrier allocation and power allocation is a mixed-integer nonlinear programming (MINLP) problem, the use of traversal search will produce an unacceptable amount of calculation. It should be noted that the optimization problem includes the variables of user and subcarrier allocation and power allocation coefficient. In order to effectively solve this problem, we divide the problem into two subproblems: the problem of user and subcarrier allocation and the problem of power allocation between users. We use a matching algorithm to solve the problem of user and subcarrier assignment. An iterative algorithm to ensure convergence is proposed to obtain the power budget between subcarriers. Finally, the effective binary search is used to obtain the power allocation among users. The simulation results show that the proposed algorithm has a faster convergence rate. At the same time, as the number of users and subcarriers increases, EE improves significantly. As the user speed increases, the EE gradually decreases; especially when the number of users is larger, the effect is more obvious. Compared with random matching, the proposed algorithm has higher EE under the same number of users, number of subcarriers, and speed.

Resource Allocation in Millimeter-Wave Multicarrier-Division Duplex Systems With Hybrid Beamforming

In-band full-duplex (IBFD) systems require promising resource allocation (RA) strategies to fully exploit the available time-frequency resources. Furthermore, the acquisition of channel state information and signal reception in IBFD systems are significantly impacted by insufficient self-interference cancellation (SIC), impeding the applications of IBFD in practical wireless systems. Multicarrier-division duplex (MDD), which benefits low-budget SI mitigation in digital domain and flexible subcarrier assignment, is expected to be a promising transitional technique from half-duplex (HD) to IBFD. To demonstrate the advantages of MDD over HD, this paper first compares the upper-bound performance of MDD and HD by applying unfair greedy RA. Then, considering the millimeter-wave (mmWave) with hybrid beamforming, we propose the RA optimization with the quality-of-service constraints on both downlink (DL) and uplink (UL) mobile-stations (MSs). To solve this non-convex RA problem, we divide it into a suboptimal subcarrier allocation problem solved by the proposed improved fair greedy (IFG) algorithm, and a convex power allocation problem. Furthermore, we design two general hybrid precoder based on matrix factorization and direct approach, and a combiner having high SIC capability. Our results show that the proposed RA algorithm can achieve the performance near the upper-bound achieved by the unfair greedy algorithm, while guaranteeing the proportional fairness among all DL/UL MSs. The performance of the two precoding schemes is depended on the number of radio frequency chains supported. Finally, the proposed SIC algorithm is able to provide sufficient SI mitigation, which can be implemented without impacting the RA operation.

Secure communication with joint resource allocation, relay and jammer selection in OFDM‐based cooperative networks

SummaryThis paper considers an orthogonal frequency division multiplexing (OFDM) based cooperative network, where a source node communicates with a destination node with the help of multiple intermediate nodes, in the presence of an eavesdropper. Each intermediate node is assumed to be able to act as a relay or a jammer to improve the physical layer security. The problem of joint subcarrier allocation, power allocation, and mode selection to maximize the sum secrecy rate is investigated. We first reformulate the problem of subcarrier allocation and intermediate node mode selection as a three‐sided matching game and propose an algorithm that achieves a stable matching. Then, the sum secrecy rate is further improved by proposing a heuristic algorithm to switch the modes of the intermediate nodes. Finally, an iterative power allocation algorithm is proposed. It is verified by simulation results that the proposed algorithm outperforms various benchmark algorithms such as the equal subcarrier allocation algorithm, the pure relay selection algorithm, and the random relay selection algorithm, especially when the transmit power limit is high and the number of intermediate nodes is large.

Impairments-Aware Resource Allocation for FD Massive MIMO Relay Networks: Sum Rate and Delivery-Time Optimization Perspectives

In this paper, we investigate the resource allocation problem for a full-duplex (FD) massive multiple-input-multiple-output (mMIMO) multi-carrier (MC) decode and forward (DF) relay system which serves multiple MC single-antenna half-duplex (HD) nodes. In addition to the prior studies focusing on maximizing the sum-rate and energy efficiency, we focus on minimizing the overall delivery time for a given set of communication tasks to the user terminals. As our system is an FD MC system, we consider the impact of hardware distortions resulting in residual self-interference and inter-carrier leakage. We also consider that only limited channel state information is available. A joint power and sub-carrier allocation problem to maximize the sum-rate of the system is then formulated. Due to the intractable nature of the underlying problem, an iterative solution is proposed, employing the successive inner approximation (SIA) framework, with guaranteed convergence to the point that satisfies the Karush-Kuhn-Tucker (KKT) conditions. For the energy efficiency maximization problem, a two-stage iterative algorithm which follows the SIA and Dinkelbach algorithm is proposed. The operation of an FD mMIMO MC DF relay system is evaluated for different system parameters using numerical simulations. We also show the importance of considering delivery time minimization rather than sum-rate maximization, i.e., maximizing the sum-rate of the system does not necessarily minimize the overall delivery time. Numerical results show the significance of distortion-aware design for such systems and also the significant gain in terms of different objectives such as sum-rate, energy efficiency, and delivery time compared to its HD counterpart.

How Much Can Radio Resource Allocation Help to Improve Secrecy Capacity of V2V Underlay Cellular Networks?

Vehicle-to-vehicle (V2V) underlay cellular communication plays an important role in the fifth-generation (5G) and beyond communications and its security issues attract a lot of attention. Physical layer security, which explores channel characteristics to protect wireless communications, is very attractive due to its low latency. This paper focuses on secrecy capacity maximization in V2V underlay cellular communications, where the secrecy capacity of vehicular user equipments (VUEs) is optimized first, followed by sum proportional fairness function optimization for both VUEs and cellular user equipments (CUEs). The optimization problems can be solved in two sub-problems, e.g., power and subcarrier allocation problems. The power allocation can be done with the first strategy using a one-dimensional searching algorithm, whereas a genetic algorithm (GA) is used to solve the second optimization problem. In addition, as VUEs share uplink resources with CUEs, the co-channel interference exists and the subcarrier allocation sub-problem is a 3D search problem, which is solved by a two-step iterative Hungarian algorithm (IHA). Finally, simulations are performed to validate the performance of the proposed schemes.

Online Task Scheduling and Resource Allocation for Intelligent NOMA-Based Industrial Internet of Things

Fog computing (FC) has the potential to process computation-intensive tasks in Industrial Internet of Things (IIoT) systems. In parallel with the development of FC, non-orthogonal multiple access (NOMA) has been recognized as a promising technique to significantly improve the spectrum efficiency. In this paper, a NOMA-based FC framework for IIoT systems is considered, where multiple task nodes offload their tasks via NOMA to multiple nearby helper nodes for execution. We formulate a joint task scheduling and subcarrier allocation problem, with an objective to minimize the total cost in terms of the delay and energy consumption, while taking into account the practical communication and computation constraints. Note that the task scheduling includes task, computation resource, and power allocations. Since the task and subcarrier allocations involve binary variables, it is challenging to obtain an optimal solution for such a combinatorial problem. To this end, we solve the task scheduling and subcarrier allocation problem in an online learning fashion. During the online learning process, we propose an iterative algorithm to jointly optimize the subcarrier allocation and task scheduling in each time episode. Simulation results show that the proposed scheme can significantly reduce the sum cost compared to the baseline schemes.

Resource allocation algorithm for symmetrical services in OFDMA systems

<p><span>The widespread acceptance of symmetrical services has urged for performance betterment techniques in wireless communication systems. In this paper, we propose an algorithm for resource allocation in MIMO-OFDMA system for applications that demand similar quality in uplink and downlink direction. The problem is formulated as multiobjective optimization problem with objectives to maximize the bidirectional data rates for individual users and to minimize the difference between the uplink and downlink data rate for each user. Fairness has been considered as a constraint in the optimization problem. The power allocation for each subcarrier in the OFDMA system is carried out using Linear Programming (LP) techniques, while the subcarrier allocation problem has been undertaken using an innovative multiobjective optimization technique that employs the concept of non-dominance in evolutionary algorithms. The results are extremely encouraging as they outperform the algorithms reported in literature using linear programming techniques or evolutionary algorithms solely. </span></p>

Online Computation Offloading in NOMA-Based Multi-Access Edge Computing: A Deep Reinforcement Learning Approach

One of the missions of fifth generation (5G) wireless networks is to provide massive connectivity of the fast growing number of Internet of Things (IoT) devices. To satisfy this mission, non-orthogonal multiple access (NOMA) has been recognized as a promising solution for 5G networks to significantly improve the network capacity. Considered as a booster of IoT devices, and in parallel with the development of NOMA techniques, multi-access edge computing (MEC) is also becoming one of the key emerging technologies for 5G networks. In this paper, with an objective of maximizing the computation rate of an MEC system, we investigate the computation offloading and subcarrier allocation problem in Multi-carrier (MC) NOMA based MEC systems and address it using Deep Reinforcement Learning for Online Computation Offloading (DRLOCO-MNM) algorithm. In particular, the DRLOCO-MNM helps each of the user equipments (UEs) decides between local and remote computation modes, and also assigns the appropriate subcarrier to the UEs in the case of remote computation mode. The DRLOCO-MNM algorithm is especially advantageous over the other machine learning techniques applied on NOMA because it does not require labeled data for training or a complete definition of the channel environment. The DRLOCO-MNM also does avoid the complexity found in many optimization algorithms used to solve channel allocation in existing NOMA related studies. Numerical simulations and comparison with other algorithms show that our proposed module and its algorithm considerably improve the computation rates of MEC systems.

Joint Subcarrier and Power Allocation in NOMA: Optimal and Approximate Algorithms

Non-orthogonal multiple access (NOMA) is a promising technology to increase the spectral efficiency and enable massive connectivity in 5G and future wireless networks. In contrast to orthogonal schemes, such as OFDMA, NOMA multiplexes several users on the same frequency and time resource. Joint subcarrier and power allocation problems (JSPA) in NOMA are NP-hard to solve in general. In this family of problems, we consider the weighted sum-rate (WSR) objective function as it can achieve various tradeoffs between sum-rate performance and user fairness. Because of JSPA's intractability, a common approach in the literature is to solve separately the power control and subcarrier allocation (also known as user selection) problems, therefore achieving sub-optimal result. In this work, we first improve the computational complexity of existing single-carrier power control and user selection schemes. These improved procedures are then used as basic building blocks to design new algorithms, namely Opt-JSPA, $\varepsilon$-JSPA and Grad-JSPA. Opt-JSPA computes an optimal solution with lower complexity than current optimal schemes in the literature. It can be used as a benchmark for optimal WSR performance in simulations. However, its pseudo-polynomial time complexity remains impractical for real-world systems with low latency requirements. To further reduce the complexity, we propose a fully polynomial-time approximation scheme called $\varepsilon$-JSPA. Since, no approximation has been studied in the literature, $\varepsilon$-JSPA stands out by allowing to control a tight trade-off between performance guarantee and complexity. Finally, Grad-JSPA is a heuristic based on gradient descent. Numerical results show that it achieves near-optimal WSR with much lower complexity than existing optimal methods.

Secure resource allocation for green two‐way relay networks with mutually untrusted relay and mobile users

The authors investigate secure resource allocation for an orthogonal frequency division multiple access two-way relay network, which has several pairs of mobile users (MUs) and one energy harvesting (EH) relay. In this network, the relay is untrusted and each MU pair may also overhear the other MU pairs' confidential information. In particular, to forward information for each MU, the relay adopts power splitting technique to harvest energy from radio frequency signals emitted by each MU. For this scenario, which has hardly been considered, the joint problem of subcarrier allocation, power control, and power splitting ratio selection is formulated, with the aim to maximise the total secrecy rate of all MUs. A suboptimal algorithm with low complexity and satisfactory performance is proposed to solve the formulated non-convex problem.

A Novel Genetic Resource Allocation Algorithm for Symmetrical Services in OFDMA Systems

Performance enhancement of symmetrical services has been very essential today owing to the widespread acceptance and demand of these services in the present generation communication systems. An algorithm with reduced complexity for subcarrier allocation in OFDMA/SC-FDMA system for specific applications that demand similar bidirectional quality is proposed in this paper. The resource allocation problem devised is a multiobjective optimization problem with objectives to maximize bidirectional data rates and minimize the difference in bidirectional data rates, with fairness as a significant constraint. The original problem is mathematically intractable due to non-convexity and therefore linear programming techniques fail to find an optimal solution. The subcarrier allocation problem has been undertaken using an innovative multiobjective optimization technique that employs the concept of non-dominance in evolutionary algorithms. The results are extremely encouraging, while significantly reducing the complexity involved in the processing of algorithm.

Interference-Aware Subcarrier Allocation for Massive Machine-Type Communication in 5G-Enabled Internet of Things.

Massive machine-type communication (mMTC) is investigated as one of three typical scenes of the 5th-generation (5G) network. In this paper, we propose a 5G-enabled internet of things (IoT) in which some enhanced mobile broadband devices transmit video stream to a centralized controller and some mMTC devices exchange short packet data with adjacent devices via D2D communication to promote inter-device cooperation. Since massive MTC devices have data transmission requirements in 5G-enabled IoT with limited spectrum resources, the subcarrier allocation problem is investigated to maximize the connectivity of mMTC devices subject to the quality of service (QoS) requirement of enhanced Mobile Broadband (eMBB) devices and mMTC devices. To solve the formulated mixed-integer non-linear programming (MINLP) problem, which is NP-hard, an interference-aware subcarrier allocation algorithm for mMTC communication (IASA) is developed to maximize the number of active mMTC devices. Finally, the performance of the proposed algorithm is evaluated by simulation. Numerical results demonstrate that the proposed algorithm outperforms the three traditional benchmark methods, which significantly improves the utilization of the uplink spectrum. This indicates that the proposed IASA algorithm provides a better solution for IoT application.

Resource Allocation for SC-FDMA Femtocell Networks

We consider joint subcarrier and power allocation problem for uplink femtocell networks. The air interface is single carrier frequency division multiple access. We consider zero forcing and minimum mean square error frequency domain equalizers. To formulate the resource allocation problem, we derive a closed-form expression for the received signal-to-interference-plus-noise ratio. Since the cross-tier interference can deteriorate the macrocell data rate, we impose a temperature limit on the interference arising from femtousers to the macro-base station. This constraint protects the data rate of the macrousers. Moreover, we consider the quality of service requirement constraint for the high data rate femtousers, and the adjacency of the assigned subcarriers to a user. We divide the optimization procedure into two steps: the power allocation and the subcarrier assignment. In the first step, we use the dual problem to obtain the optimal powers. In the second step, we formulate the problem of subcarrier allocation as binary integer programming (BIP). The BIP problem is NP-hard and cannot be solved in polynomial time. To reduce the complexity, we propose a practical suboptimal algorithm for the subcarrier assignment, which is significantly simpler than the BIP approach.

Optimal radio resource allocation to achieve a low BER in PD‐NOMA–based heterogeneous cellular networks

AbstractIn this paper, the problem of downlink radio resource allocation for a power‐domain nonorthogonal multiple‐access–based heterogeneous cellular network is investigated. We propose two scenarios: (1) resource allocation with exclusive subcarrier allocation and (2) resource allocation considering intercell interference. The aim of this paper is to obtain joint subcarrier and power allocation for the two considered system models by addressing the maximization of the users' sum rate subject to the bit error rate constraint, the successive interference cancellation constraint, subcarrier constraints, and the transmit power restriction of each base station. To solve the corresponding optimization problems, an alternating method is applied by decoupling the proposed problems into two subproblems, namely, subcarrier allocation problem and power allocation problem, where they are solved by using the mesh adaptive direct search algorithm and different successive convex approximation methods, respectively. Moreover, to evaluate the optimality gap of the alternating method, the proposed problems are solved using an optimal method, namely, monotonic optimization with high complexity as a benchmark. As the results show, with the bit error rate constraint, the link‐level performance, at the cost of a decreasing system sum rate, is improved.

Novel Joint Subcarrier and Power Allocation Method in SWIPT for WSNs Employing OFDM System

In recent research trends, simultaneous wireless information and power transfer (SWIPT) has proved to be an innovative technique to deal with limited energy problems in energy harvesting (EH) technologies for wireless sensor networks (WSNs). In this paper, a method of subcarrier and power allocation for both EH and information decoding (ID) operations is proposed under orthogonal frequency division multiplexing (OFDM) systems, with an improved the quality of service (QoS) parameters. This proposed method assigns one group of subcarriers for ID and remaining group of subcarriers is assigned for EH, despite of applying any splitting schemes. We achieved maximum EH under the ID and power constraints with an effective algorithm for the first time incorporating the dual decomposition technique which deals with power and subcarrier allocation problem jointly. The obtained simulation outcomes in relation to power allocation ratio, subcarrier allocation ratio and energy harvested (EH) at the destination node proved better when compared to the schemes that contain water filling, time switching (TS) or power splitting (PS) approaches under different target transmission rates and transmitter and receiver distances.