Subchannel Allocation Research Articles

Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems

The problem of energy optimization for Internet of Things (IoT) devices is crucial for two reasons. Firstly, IoT devices powered by renewable energy sources have limited energy resources. Secondly, the aggregate energy requirement for these small and low-powered devices is translated into significant energy consumption. Existing works show that a significant portion of an IoT device’s energy is consumed by the radio sub-system. With the emerging sixth generation (6G), energy efficiency is a major design criterion for significantly increasing the IoT network’s performance. To solve this issue, this paper focuses on maximizing the energy efficiency of the radio sub-system. In wireless communications, the channel plays a major role in determining energy requirements. Therefore, a mixed-integer nonlinear programming problem is formulated to jointly optimize power allocation, sub-channel allocation, user selection, and the activated remote radio units (RRUs) in a combinatorial approach according to the channel conditions. Although it is an NP-hard problem, the optimization problem is solved through fractional programming properties, converting it into an equivalent tractable and parametric form. The resulting problem is then solved optimally by using the Lagrangian decomposition method and an improved Kuhn–Munkres algorithm. The results show that the proposed technique significantly improves the energy efficiency of IoT systems as compared to the state-of-the-art work.

User Association, Subchannel and Power Allocation in Space-Air-Ground Integrated Vehicular Network With Delay Constraints

The space-air-ground integrated vehicular networks (SAGVN) has significant advantages in satisfying the requirements of the Internet of Vehicles (IoV) business for wide area coverage and long-distance communications. However, the multi-dimensional nature of the SAGVN leads to diverse network resources, it is necessary to provide different services for various scenarios, which makes the problem of network resource allocation becomes extremely challenging. In this paper, we focus on the resource allocation of small cells in SAGVN. Specifically, the user association is considered to optimize connection between base stations and vehicles. On this basis, a subchannel and power allocation method is designed. The proposed subchannel allocation scheme ensures that users can obtain the maximum gain on its subchannel, and the Lagrangian duality theory is introduced to solve the power allocation problem. Meanwhile, the edge computing is applied to the SAGVN, and the vehicles can offload the local tasks to the edge server. In order to reduce the delay in the offloading process, taking the time delay as a constraint condition can further optimize the communication performance. The simulation results show that the proposed scheme can effectively improve the sum rate of small cells in SAGVN, and the performance is better than existing algorithms.

Joint Clustering and Resource Allocation Optimization in Ultra-Dense Networks with Multiple Drones as Small Cells Using Game Theory.

In this study, we consider the combination of clustering and resource allocation based on game theory in ultra-dense networks that consist of multiple macrocells using massive multiple-input multiple-output and a vast number of randomly distributed drones serving as small-cell base stations. In particular, to mitigate the intercell interference, we propose a coalition game for clustering small cells, with the utility function being the ratio of signal to interference. Then, the optimization problem of resource allocation is divided into two subproblems: subchannel allocation and power allocation. We use the Hungarian method, which is efficient for solving binary optimization problems, to assign the subchannels to users in each cluster of small cells. Additionally, a centralized algorithm with low computational complexity and a distributed algorithm based on the Stackelberg game are provided to maximize the network energy efficiency (EE). The numerical results demonstrate that the game-based method outperforms the centralized method in terms of execution time in small cells and is better than traditional clustering in terms of EE.

Spectrum-efficient mode selection and fair resource allocation for D2D-enabled uplink/downlink MC-NOMA networks

The application of device-to-device (D2D) communication and multi-carrier (MC) non-orthogonal multiple access (NOMA) have been considered as promising technologies to enhance energy efficiency, network connectivity, and spectrum efficiency in the next generation of communication systems. This paper investigates a joint optimization problem of spectrum-efficient mode selection, fair sub-channel (SC) allocation, and power allocation of user pairs for a D2D-enabled MC-NOMA network. In the proposed network, user pairs are enabled to transmit and receive data in either underlaid D2D mode or cellular mode, and the NOMA technique is adopted in both downlink and uplink transmission. To maximize the throughput of the system and to manage the co-channel interference, the joint optimization problem is formulated under the constraint of guaranteeing all users with a certain amount of resources while satisfying the minimum signal-to-interference-plus-noise ratio (SINR) requirements of users. As an NP-hard problem, the optimization problem is first transformed into two sub-problems namely 1- the joint SC allocation and mode selection and 2- the power allocation. Then, to jointly solve the SC allocation and mode selection sub-problem, an enhanced three-side many-to-one matching algorithm is proposed and to obtain the solution to the power allocation problem, the known sequential convex programming method is used. Moreover, an iterative algorithm is used to enhance the performance of the system in terms of sum-rate. Finally, a mathematical expression is introduced to check the quality of service (QoS) of the proposed fair resource allocation algorithm and the results of the simulation are stated to indicate improved system performance.

Secure Computation Offloading for Device-Collaborative MEC Networks: A DRL-Based Approach

This paper investigates secure computation offloading in device-collaborative mobile edge computing (MEC) networks, where mobile users (MUs) can process a part of their tasks locally and offload the other part to the MEC server or other idle paired MUs. All the transmission links are exposed to a potential eavesdropper. To prevent information leakage, artificial noise (AN) is employed and the secrecy capacity is considered. For such a system, we formulate a weighted latency-energy-aware cost minimization problem by jointly optimizing the subchannel allocation, the offloading proportions, the MUs' transmit powers, the MUs' and the MEC server's CPU frequencies, and the AN power while guaranteeing the secrecy capacities of all transmission links. Since the variables are multi-dimensional and coupled, the formulated problem is challenging to solve with the traditional optimization method. We first propose a swap-matching-based algorithm to achieve the optimal subchannel allocation. We then propose a two-layer asynchronous advantage actor-critic (A3C) algorithm to optimize the rest of the resource variables. Specifically, in the inner layer, by utilizing the Karush-Kuhn-Tucker conditions, we derive the optimal CPU frequency with semi-closed-form expressions. In the outer layer, we propose an A3C-based algorithm to achieve near-optimal solutions with fast convergence speed based on the obtained optimal CPU frequency. Simulation results demonstrate that our proposed scheme can decrease the latency-energy-aware cost compared with other learning-based algorithms.

Dynamic User Resource Allocation for Downlink Multicarrier NOMA with an Actor–Critic Method

Future wireless communication systems require higher performance requirements. Based on this, we study the combinatorial optimization problem of power allocation and dynamic user pairing in a downlink multicarrier non-orthogonal multiple-access (NOMA) system scenario, aiming at maximizing the user sum rate of the overall system. Due to the complex coupling of variables, it is difficult and time-consuming to obtain an optimal solution, making engineering impractical. To circumvent the difficulties and obtain a sub-optimal solution, we decompose this optimization problem into two sub-problems. First, a closed-form expression for the optimal power allocation scheme is obtained for a given subchannel allocation. Then, we provide the optimal user-pairing scheme using the actor–critic (AC) algorithm. As a promising approach to solving the exhaustive problem, deep-reinforcement learning (DRL) possesses higher learning ability and better self-adaptive capability than traditional optimization methods. Simulation results have demonstrated that our method has significant advantages over traditional methods and other deep-learning algorithms, and effectively improves the communication performance of NOMA transmission to some extent.

Subchannel and power optimization for sum rate maximization in downlink multicarrier NOMA networks

In a multicarrier NOMA system, the subchannel allocation (SA) and power allocation (PA) are intricately linked and essential for improving system throughput. Also, for the successful execution of successive interference cancellations (SIC) at the receiver, a minimum power gap is required among users. As a result, this research comes up with optimization of the SA and PA to maximize the sum rate of the NOMA system while sticking to the minimum power gap constraint in addition to minimum user rate, maximum number of users in a subchannel and power budget constraints for downlink transmission in multicarrier NOMA networks. To ensure that the formulated problem can be solved in polynomial time, we propose solving it in two stages; SA followed by PA. To obtain SA, we investigate four algorithms: Greedy, WSA, WCA, and WCF. For PA, we propose a low-complexity algorithm. We compare the performance of the proposed method with benchmark method that does not consider the minimum power gap constraint. We conclude that employing WCF algorithm with the PA algorithm gives the best sum rate performance.

Energy‐efficient semi‐supervised learning framework for subchannel allocation in non‐orthogonal multiple access systems

AbstractNon‐orthogonal multiple access (NOMA) is considered a key candidate technology for next‐generation wireless communication systems due to its high spectral efficiency and massive connectivity. Incorporating the concepts of multiple‐input‐multiple‐output (MIMO) into NOMA can further improve the system efficiency, but the hardware complexity increases. This study develops an energy‐efficient (EE) subchannel assignment framework for MIMO‐NOMA systems under the quality‐of‐service and interference constraints. This framework handles an energy‐efficient co‐training‐based semi‐supervised learning (EE‐CSL) algorithm, which utilizes a small portion of existing labeled data generated by numerical iterative algorithms for training. To improve the learning performance of the proposed EE‐CSL, initial assignment is performed by a many‐to‐one matching (MOM) algorithm. The MOM algorithm helps achieve a low complex solution. Simulation results illustrate that a lower computational complexity of the EE‐CSL algorithm helps significantly minimize the energy consumption in a network. Furthermore, the sum rate of NOMA outperforms conventional orthogonal multiple access.

Energy Efficient Resource Allocation for Uplink MC-NOMA Based Heterogeneous Small Cell Networks With Wireless Backhaul

In this paper, we investigate energy efficient resource allocation for uplink multi-carrier non orthogonal multiple access (MC-NOMA) based two-tier heterogeneous networks (HetNets) with wireless backhaul. We have exploited channel diversity by assigning multiple subchannels to various MC-NOMA users (NUs), while allowing multiple NUs to use the same subchannel. We formulate a joint NU clustering, and power and subchannel allocation problem for system's energy efficiency (EE) maximization under the constraints of minimum quality of service (QoS) requirement, maximum allowable transmit power for each NU, and successive interference cancelation. The formulated problem is non-convex with fractional objective function, and therefore, to convert it into a tractable from, it is divided into two subproblems, i.e., subchannel allocation and NU clustering subproblem, and power allocation subproblem. The subchannel allocation and NU clustering subproblem is solved through a suboptimal algorithm, which exploits the channel diversity by maintaining maximum possible difference in transmit power levels of clustered NUs. Whereas for the power allocation subproblem, the EE optimization problem is convexified through Dinkelbach and successive convex approximation techniques and a closed-form suboptimal solution is obtained. The simulation results show that the proposed energy efficient algorithm outperforms the existing NOMA and orthogonal multiple access (OMA) algorithms. The proposed approach also guarantees user fairness by giving priority to the NUs with unfulfilled throughput requirements.

Deep reinforcement learning-based collaborative routing algorithm for clustered MANETs

Flexible adaptation to differentiated quality of service (QoS) is quite important for future 6G network with a variety of services. Mobile ad hoc networks (MANETs) are able to provide flexible communication services to users through self-configuration and rapid deployment. However, the dynamic wireless environment, the limited resources, and complex QoS requirements have presented great challenges for network routing problems. Motivated by the development of artificial intelligence, a deep reinforcement learning-based collaborative routing (DRLCR) algorithm is proposed. Both routing policy and subchannel allocation are considered jointly, aiming at minimizing the end-to-end (E2E) delay and improving the network capacity. After sufficient training by the cluster head node, the Q-network can be synchronized to each member node to select the next hop based on local observation. Moreover, we improve the performance of training by considering historical observations, which can improve the adaptability of routing policies to dynamic environments. Simulation results show that the proposed DRLCR algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid congestion. In addition, the effectiveness of the routing policy in a dynamic environment is verified.

Fair Computation Efficiency for OFDMA-Based Multiaccess Edge Computing Systems

This letter investigates the joint optimization of task offloading and resource allocation for OFDMA-based multi-access edge computing systems. This joint optimization aims at maximizing the minimum computation efficiency, which is a ratio between the task data size to energy consumption, of all users to ensure fairness via adjusting transmit power, computation capacity and sub-channel allocations under the concrete delay, data size and energy consumption requirements. The formulated minimum computation efficiency maximization problem is a non-convex mixed-integer problem. To solve this problem, an iterative algorithm is proposed by alternatively solving the continuous sub-problem and the discrete sub-problem. For the continuous sub-problem about joint transmit power and computation capacity, a low-complexity bisection method is proposed to obtain the optimal solution by solving a series of feasibility problems. Then, the dual method is adopted to solve the discrete sub-problem about sub-channel association. Moreover, we acquire the closed-form optimal solution at each iteration. Simulation results verify that the proposed algorithm is superior over baseline schemes in terms of minimum computation efficiency.

Joint Allocations of Radio and Computational Resource for User Energy Consumption Minimization Under Latency Constraints in Multi-Cell MEC Systems

This paper investigates the joint allocation of radio resources (i.e., base stations (BSs), sub-channels, and uplink transmission powers) and computational resources for a multicell mobile edge computing (MEC) system, aimed at minimizing the sum energy consumption of all mobile terminals under strict delay and signal-to-interference-plus-noise ratio (SINR) constraints. The problem is challenging to solve due to the highly coupled mixed-integer variables and the complicated expressions of the constraints. To tackle the difficulty, we decompose the problem into two sub-problems: the power and computational resource allocation sub-problem and the user-BS-subchannel association sub-problem. For the first sub-problem, we derive the optimal computational resource as a function of the optimal transmission power. Then the power optimization problem is distributed to each cell, and the local-optimal power solution within each cell is derived in closed-form expressions. For the second sub-problem, we design an optimal modified-cutting-plane (MCP) algorithm with exponential time-complexity. A polynomial-time near-optimal pivoting-and-subgradient (PS) algorithm is further proposed to reduce the complexity. Simulation results demonstrate that the PS algorithm achieves comparable performance to the optimal MCP algorithm, while they both outperform the benchmark schemes in most cases.

Sum-rate maximization for cognitive relay NOMA Systems with channel uncertainty

In this paper, we study the sum-rate maximization problem for relay cognitive network based on decoding and forwarding (DF), where the primary network is implemented with the orthogonal frequency division multiple access (OFDMA) technology and the secondary users are in the underlying accessing mode. In the system, multiple primary users are included and one of them plays a role of relay. The relay forwards information to secondary users using the non-orthogonal multiple access (NOMA) technology. We attempt to improve the transmission rate of both primary and secondary users. The block power control strategy is developed by addressing interference of primary users caused by the channel sharing with secondary users. To tackle the tricky issue caused by the channel uncertainty, the worst-case method is adopted when solving the proposed sum-rate maximization problem. Considering the nonlinear objective function and constraints, the successive convex approximation (SCA) method is introduced to determine a suboptimal solution. Also, the Lagrangian dual algorithm is utilized to obtain the closed-form solutions of the transmission power and subchannel allocation. Numerical results verify that the proposed scheme significantly improves the sum-rate under the uncertainty channel gains compared to benchmarks.

An Interference-Aware Resource-Allocation Scheme for Non-Cooperative Multi-Cell Environment

Inter-cell interference cancellation has been investigated for several decades and has become an elementary technique for modern wireless networks. However, the existing interference cancellation mechanism rarely considers the historical channel variations and interference characteristics. In this paper, we propose an interference-aware prediction-based resource-allocation strategy to deal with multi-cell interference, where the historical noisy channel state and the acknowledgment feedback are fully utilized. Together with the predicted interference patterns, our proposed joint sub-channel allocation and rate selection mechanism can achieve better average throughput performance. Through the numerical as well as the prototyping results, we show that our proposed scheme is able to provide more than 9.7% and 8% average throughput improvement compared with many existing baselines.

On the application of uplink/downlink decoupled access in heterogeneous mobile edge computing

Mobile edge computing (MEC) is a key player in low latency 5G networks with the task to resolve the conflict between computationally-intensive mobile applications and resource-limited mobile devices (MDs). As such, there has been intense interest in this topic, especially in multi-user single-server and homogeneous multi-server scenarios. However, the research in the heterogeneous multi-server scenario is limited, where the servers are located at small base-stations (SBSs), macro base-stations (MBSs), or the cloud with different computing and communication capabilities. On the other hand, computational-tasks offloading is limited by the type of MD-BS association with almost all previous works focusing on offloading the MD’s computational tasks to the MEC servers/cloudlets at its serving BS. However, in multi-BS association, or downlink/uplink decoupled (DUDe) scenarios, an MD can be served by multiple BSs and hence has multiple offloading choices. Motivated by this, we proposed a joint BS association and subchannel allocation algorithm based on a student-project allocation (SPA) matching approach to minimize the network sum-latency, which breaks the constraint that one MD must connect to the same BS in the UL and DL, and jointly consider the communication and computational disparity of SBS and MBS cloudlets in heterogeneous MEC networks. Moreover, an optimal power allocation scheme is proposed to optimize the system performance subject to the predefined quality of service constraints. Our results show that the proposed scheme is superior to benchmark techniques in enabling the effective use of computational and communication resources in heterogeneous MEC networks.

Joint Trajectory Plan and Resource Allocation for UAV-Enabled C-NOMA in Air-Ground Integrated 6 G Heterogeneous Network

Leveraging unmanned aerial vehicles (UAVs) for access and high altitude platform stations (HAPSs) for data backhaul to construct the Air-Ground Integrated Network (AGIN), is a feasible solution to achieve seamless network coverage for remote IoT devices in future 6 G era. However, since the number of terminals increases exponentially, it is essential to improve spectrum efficiency and throughput of the system. In addition, the limited on-board energy storage of UAVs and finite battery of IoT terminals make the system energy efficiency (EE) a new concern. To cope with the above mentioned challenges, in this study we first put forward a clustered-NOMA (C-NOMA)-enabled heterogeneous AGIN model for remote areas including one HAPS for backhaul and multiple UAVs for access, where C-NOMA is used to obtain both improved system throughput and terminal complexity. Then, we study the joint UAV trajectory plan and resource allocation problem in order to maximize the system EE. Since it is a mixed integer nonlinear programming (MINLP) issue coupled with transmission power, subchannel allocation, UAV trajectory and speed control, this problem is decoupled into two subproblems and solved iteratively. For the first one, the optimal channel and power strategy is obtained by our subchannel allocation and power control for AGIN EE (SAPAE) maximum algorithm according to Lagrange dual decomposition method. For the second, for transforming the nonconvex problem into a convex one, we provide the successive convex approximation-based UAV trajectory and speed optimization for AGIN EE (SUTSAE) maximum algorithm, and then the near-optimal UAV trajectory and flight speed are obtained. We conduct extensive experiments to compare with other benchmark methods. It is shown that the proposed approach is better in EE and spectrum efficiency.

A Hypergraph Matching-Based Subchannel Allocation for Multi-Platoon’s Communications

A Hypergraph Matching-Based Subchannel Allocation for Multi-Platoon’s Communications

Joint Mode Selection and Resource Allocation for D2D and Femtocell Users in Dense Heterogeneous Networks with Full Frequency Reuse

We consider the problem of joint mode selection and resource allocation for D2D and femtocell users in a three-tier dense heterogeneous network in which all the users can reuse the spectrum. The goal is to maximize their total weighted sum rate, subject to minimum rate requirements and maximum tolerable interference on the cellular system. Since the spectrum can be fully reused, the co-tier and cross-tier interferences among the users result in a mixed integer non-linear, non-convex program that is difficult to solve directly. Using insights into the structure of the interference, we derive a close, conservative approximation of the joint problem that has a convex relaxation that is tight. That enables good solutions to the joint problem to be obtained using a customized iterative algorithm employing the Lagrange dual decomposition method. Since the proposed iterative scheme is semi-distributed, the signaling overhead is mitigated. To further reduce the computational complexity, a low-complexity (primal) decomposition-based method is also introduced, in which we select the transmission mode heuristically based on the traffic level in the network, and then sequentially perform admission control, power control, and sub-channel allocation for the femtocell users and D2D users. Our simulation results indicate that the proposed iterative and heuristic algorithms, on average, achieve around 94% and 82% of the sum rate of the optimal Branch & Bound method, respectively, and do so at much lower computational costs.

Downlink Resource Allocation With Pilot Length Optimization for User-Centric Cell-Free MIMO Networks

Pilot length is a crucial parameter in cell-free multiple-input multiple-output (MIMO) networks, which could significantly affect the downlink throughput. In this letter, we jointly optimize the user scheduling, power allocation and pilot length for user-centric (UC) cell-free MIMO networks. We consider the frequency band is uniformly split into several sub-channels. As the number of users scheduled on one sub-channel is commonly small, orthogonal pilot sequences are adopted among users on each sub-channel to avoid pilot contamination. To achieve user fairness, we aim to maximize the minimum ergodic user rate in the downlink transmission, yielding a challenging mix-integer non-convex optimization. Inspired by the weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm approximation in compressive sensing together with successive convex approximation (SCA), an iterative scheme is then proposed to solve the problem. Simulation results demonstrate that the proposed scheme outperforms its state-of-the-art counterpart, which is a three-step algorithm including user grouping, sub-channel allocation, and power optimization.

Resource Allocation for the Backhaul of NOMA-Based Cellular UAV Network

A cellular unmanned-aerial-vehicle (UAV)-enabled system is studied in this research, where multiple UAVs work cooperatively to upload the collected sensory data to the base station (BS). The UAVs form a two-tier network with two kinds of communication modes, i.e., UAV-to-Network (U2N) and UAV-to-UAV (UAV) communications. We investigate the communication resource allocation for UAVs to maximize the aggregate energy efficiency of the system. The formulated problem is mixed-integer and NP-hard. To make the formulated problem tractable, an efficient matching-based algorithm is proposed to complete the transmission mode selection and subchannel allocation, respectively. Then the transmission power is optimized based on game theory. The simulation results reveal that the uplink energy efficiency can be improved with the proposed scheme, compared with the maximum power strategy.