Joint Resource Allocation Problem Research Articles

Hierarchical aerial offload computing algorithm based on the Stackelberg-evolutionary game model

Aerial access networks have been envisioned as a promising 6 G technology to enhance the service experience in underserved areas where terrestrial base stations do not exist. In such scenarios, a hierarchical model of high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) is considered to provide aerial computing services for ground Internet of Things (IoT) devices. In this study, we investigate a hierarchical aerial computing system to optimally orchestrate the limited computation resources in both HAPs and UAVs. For offloading services, we formulate a joint resource allocation problem to maximize service satisfaction for terrestrial IoT devices. To solve this problem, we employ the ideas of game theory with centralized decision and decentralized execution. Through the Stackelberg-evolutionary game model, the HAP works as a leader, and selects its price strategy based on the evolutionary learning process. As followers, individual UAVs make decisions to partially offload their computing tasks by considering different objectives. According to the interactive control paradigm, our proposed method can get reciprocal advantages for HAPs, UAVs, and ground IoT devices while adaptively handling dynamic aerial network conditions. Finally, extensive simulation results verify the efficiency of our proposed algorithm to increase the usability of edge servers’ computational resources. Compared with other existing state-of-the-art aerial network offloading protocols, we can improve the profits of system throughput, resource usability and UAV fairness up to 10 %, 10 %, and 15 %, respectively.

Resource allocation for integrated satellite‐terrestrial networks based on RSMA

AbstractTo achieve high data rate transmission of virtual reality (VR) applications globally, a resource allocation algorithm based on Rate‐Splitting Multiple Access (RSMA) in an integrated satellite‐terrestrial network (ISTN) is proposed. In this network, a user selection strategy is proposed. The maximum transmission power budgets of the base station and satellite, as well as the service quality constraints for all users, are considered. Then, a joint resource allocation problem is formulated. To tackle the non‐convex problem, auxiliary variables are introduced and the first‐order Taylor expansion method is used to convert it into a convex one. An iterative algorithm is proposed based on the successive convex approximation (SCA) to allocate rate and power resources. Simulation results demonstrate that the proposed algorithm significantly enhances the transmission rate and effectively satisfies the VR transmission rate requirements, when compared to benchmark algorithms.

Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT

Network energy resources are limited in communication systems, which may cause energy shortages in mobile devices at the user end. Active Reconfigurable Intelligent Surfaces (A-RIS) not only have phase modulation properties but also enhance the signal strength; thus, they are expected to solve the energy shortage problem experience at the user end in 6G communications. In this paper, a resource allocation algorithm for maximizing the sum of harvested energy is proposed for an active RIS-assisted Simultaneous Wireless Information and Power Transfer (SWIPT) system to solve the problem of low performance of harvested energy for users due to multiplicative fading. First, in the active RIS-assisted SWIPT system using a power splitting architecture to achieve information and energy co-transmission, the joint resource allocation problem is constructed with the objective function of maximizing the sum of the collected energy of all users, under the constraints of signal-to-noise ratio, active RIS and base station transmit power, and power splitting factors. Second, the considered non-convex problem can be turned into a standard convex problem by using alternating optimization, semi-definite relaxation, successive convex approximation, penalty function, etc., and then an alternating iterative algorithm for harvesting energy is proposed. The proposed algorithm splits the problem into two sub-problems and then performs iterative optimization separately, and then the whole is alternately optimized to obtain the optimal solution. Simulation results show that the proposed algorithm improves the performance by 45.2% and 103.7% compared to the passive RIS algorithm and the traditional without-RIS algorithm, respectively, at the maximum permissible transmitting power of 45 dBm at the base station.

Energy Minimization for Wireless-Powered Federated Learning Network With NOMA

Limited energy resources remain a challenge for wireless devices in federated learning (FL) networks. To tackle this issue, a joint resource allocation problem is formulated to minimize the total energy consumption for wireless-powered FL networks with non-orthogonal multiple access. A two-dimensional search algorithm (TDSA) is first proposed to obtain the optimal solution for this challenging problem. To lower the computational complexity, the bisection algorithm (BA) is further employed by exploiting the inherent structure of the formulated problem. Numerical results verify the equal performance between the BA and TDSA, and the superiority of the proposed scheme over the benchmark schemes in energy minimization.

Resource allocation and network pricing based on double auction in mobile edge computing

By transferring power-hungry huge data centers to lightweight Internet of Things (IoT) mobile devices, mobile edge computing (MEC) has completely changed the IoT. For MEC, to optimize economic gains and motivate profit-oriented entities, the joint resource allocation and network economics problem must be solved, and the joint issue is limited by local constraints, namely, the edge server only serves multiple nearby mobile devices, which is restricted by its available energy. The article studies the jointly issue of network economics and energy allocation in MEC, where mobile device apply for offloading at a purported bid and an edge server supplies its restricted serving at an asking price. In particular, this paper puts forward two dynamic pricing double auction strategies in the MEC system, i.e., a double auction according to the break-even mechanism (DABM) and a more practical double auction based on dynamic pricing mechanism (DADPM) to decide the matching between mobile devices and edge servers, and the pricing strategy for high-priced economic profit in the case of local restricts. Theoretical analysis shows that the proposed two algorithms have properties such as budget balance, individual rationality, economic benefit, authenticity. Extensive simulation experiments evaluate the efficiency of the system, and results verify that the proposed two schemes will greatly make better the economic benefits of MEC.

Average AoI minimization for data collection in UAV-enabled IoT backscatter communication systems with the finite blocklength regime

Thanks to the autonomy of unmanned aerial vehicle (UAV), UAV-enabled data collection in Internet of Things (IoT) networks has become a key application for the next generation communication network. In this paper, we consider a scenario where an UAV is responsible for collecting data from sensor equipments (SEs) one by one and finally carrying the collected data to the computation center for processing. Different from the commonly used assumption that SEs always generate data at the beginning of each time slot, it is assumed that SEs can generate data at any instant during one time slot, which is more practical. Since SEs are energy limited, they upload data to the UAV by adopting backscatter communication technology to reduce energy consumption. Meanwhile, the updated information usually contains a small number of information bits but requires low latency and high reliability, thus the finite blocklength regime in ultra-reliable and low-latency communication is adopted for SEs’ data transmission to the UAV. To keep the freshness of the updated information, a joint resource allocation problem including data collection time allocation, transmission power and trajectory design of the UAV is formulated as an optimization problem to minimize the average age of information (AoI) of all SEs. The formulated problem mixes discrete and continuous variables, which makes it difficult to solve. Thereby, we decompose the optimization problem into data collection time minimization subproblem and UAV trajectory design subproblem, which are solved by the successive convex approximation method, and the backtracking algorithm and the genetic algorithm, respectively. Numerical results show that the backtracking-based algorithm that can obtain the optimal trajectory of the UAV gains the minimal average AoI, and the genetic-based algorithm achieves sub-optimal average AoI with much lower computational complexity. The results also demonstrate that the average AoI of the backtracking-based algorithm and the genetic-based algorithm is reduced by up to 54% and 46% compared with the greedy-based benchmark algorithm, respectively.

ISAC-Accelerated Edge Intelligence: Framework, Optimization, and Analysis

Conventionally, the sensing and communication stages for edge intelligence systems are executed sequentially, leading to an excessive time of dataset generation and uploading. To combat the weakness, this paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC), where the sensing and communication stages are merged to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. For the proposed ISAC-accelerated edge intelligence system, the resource allocation and beamforming should be jointly optimized to exploit the underlying ISAC benefits. We formulate a joint resource allocation and beamforming optimization problem. Despite the non-convexity, we obtain globally optimal solutions assuming that the constant maximal transmits power, and devise an alternating optimization algorithm for the original problem without such assumption. Furthermore, we analyze the ISAC acceleration gain of the proposed system over that of the conventional edge intelligence system. Both theoretic analysis and simulation results show that ISAC accelerates the conventional edge intelligence system when the duration of generating a sample is more than that of uploading a sample. Otherwise, the ISAC acceleration gain vanishes or even is negative. In this case, we derive a sufficient condition for positive ISAC acceleration gain.

Tasks-oriented joint resource allocation scheme for the Internet of vehicles with sensing, communication and computing integration

With the development of artificial intelligence (AI) and 5G technology, the integration of sensing, communication and computing in the Internet of Vehicles (IoV) is becoming a trend. However, the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems. In view of the above challenges, this paper proposes a tasks-oriented joint resource allocation scheme (TOJRAS) in the scenario of IoV. First, this paper proposes a system model with sensing, communication, and computing integration for multiple intelligent tasks with different requirements in the IoV. Secondly, joint resource allocation problems for real-time tasks and delay-tolerant tasks in the IoV are constructed respectively, including communication, computing and caching resources. Thirdly, a distributed deep Q-network (DDQN) based algorithm is proposed to solve the optimization problems, and the convergence and complexity of the algorithm are discussed. Finally, the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme, compared to the existing ones. The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%, and our proposed resource allocation scheme improves the mAP performance by about 0.15 under resource constraints.

Mean-Field-Aided Multiagent Reinforcement Learning for Resource Allocation in Vehicular Networks

As one technique for autonomous driving, vehicular networks can achieve high efficiency with vehicle-and-infrastructure cooperation, bringing high safety and many value-added services. To achieve higher communication efficiency, much effort has been done to cope with the resource allocation issues for vehicular networks. Nevertheless, due to the strong nonconvexity and nonlinearity, the classical joint resource allocation problem in vehicular networks is typically NP-hard. The multiagent reinforcement learning (MARL) has emerged as a promising solution to tackle this challenge but its stability and scalability are not satisfactory when the amount of vehicles gets increased. In this article, we mainly investigate the issue of joint spectrum and power allocation in vehicular communication networks, and carefully consider the interactions between the vehicles and environment by incorporating the cooperative stochastic game theory with MARL, named complete-game MARL (CG-MARL), to achieve a better convergence and stability with the theoretical computational complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n^{N})$ </tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> denoting the dimension of action space and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> denoting the number of V2X Vehicular. Furthermore, the mean-field game (MFG) theory is employed to further enhance the MARL for decreasing the horrible computing resource consumption caused by the CG-MARL to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n^{2})$ </tex-math></inline-formula> while maintaining an approximate performance. The simulation results demonstrate that the proposed mean-field-aided MARL (MF-MARL) for vehicular network resource allocation can achieve 95% near-optimal performance with much lower complexity, which indicates its significant potentials in the scenarios with massive and dense vehicles.

A Four-Sided Matching Game for Energy-Efficient Scheduling in Narrowband-IoT Networks

This paper studies the uplink resource allocation problem in NB-IoT networks. We aim to maximize the network’s energy efficiency to serve devices in need for long battery lifetime. Also, the network energy efficiency is affected by the associated UEs to candidates, carrying the scheduling information for UEs. For this reason, we investigate the joint resource allocation and Candidate-UE association problem in the uplink (UL) in NB-IoT. The resource allocation problem allocates the UEs with the suitable scheduling delay value and subcarrier indication field. The Candidate-UE association problem selects the appropriate UE for each candidate. The optimization variables involved in the joint problem are divided into binary association variables and binary resource allocation variables. This makes it hard to solve the joint problem since its optimization variables are of combinatorial nature. To this end, we formulate the joint problem as a four-sided matching game that ranks the UEs, candidates, the scheduling delay values and subcarrier indication fields while considering maximizing the energy efficiency as a preference metric. We then describe a four-sided matching game algorithm to solve the problem. The simulation results highlight the proposed algorithm’s effectiveness compared to other algorithms in the obtained network energy efficiency.

Reliability Optimization for Channel Resource Allocation in Multihop Wireless Network: A Multigranularity Deep Reinforcement Learning Approach

This article investigates the high-reliable data transmission for multihop and multichannel wireless sensor networks (WSNs), which jointly optimizes the channel allocation and channel access mechanisms. We propose a novel wireless paradigm empowered by mobile-edge computing (MEC) and deep reinforcement learning (DRL) to improve the data process ability of WSNs and formulate the joint resource allocation problem for reliability maximization as a partially observable Markov decision process (POMDP). Meanwhile, we introduce the distributed decision-making (DDM) framework to decouple channel optimization into two subproblems: 1) channel allocation and 2) channel access. Correspondingly, we present an asynchronous channel allocation algorithm for multiagent scenario and enable the neighbor cooperation to tackle the nonstationary problem, which can significantly improve the network convergence speed. Besides, we present a collision-free channel access algorithm including three submodules that can simultaneously eliminate vanishing nodes, hidden terminal, and exposed terminal problems in large-scale WSNs. Simulation results demonstrate that the proposed algorithm significantly improves network performance in terms of convergence, throughput, collision, and packet delivery ratio (PDR).

Joint HAP deployment and resource allocation for HAP‐UAV‐terrestrial integrated networks

While the terrestrial base stations (TBSs) in the fifth-generation (5G) network provide high throughput for the conventional terrestrial users (TUs), it is still challenging for the network to support massive TUs and unmanned aerial vehicles (UAVs) simultaneously due to the complicated air–ground channel and severe interference. In this paper, the deployment of a high-altitude platform (HAP) as a supplement for the terrestrial networks, in which the HAP and TBSs serve TUs and UAVs simultaneously in a joint manner, is studied. The novel network has two challenges. First, the deployment of the HAP, which is a new degree of freedom, should be optimized considering the terrestrial network. Second, the channel of the joint HAP and TBS network that serves multiple TUs and UAVs concurrently is complicated, and the resource allocation of the network should be designed. To tackle the above two challenges, a joint resource allocation and HAP deployment problem are formulated, and a gradient-and-matching-based algorithm is proposed to solve it efficiently. Simulation results show that the HAP and the proposed algorithm enhance the sum-rate of the network by over 30%, and the average data rate of both the TUs and the UAVs can be effectively improved.

Coordinated Resource Allocations for eMBB and URLLC in 5G Communication Networks

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two core services of the emerging fifth-generation (5G) systems. URLLC services require lower latency and high reliability, whereas eMBB services expect maximum data rates. Since 5G new radio (NR) aims to support heterogeneous services, the 3rd generation partnership project (3GPP) supports a highly flexible frame structure by introducing mixed numerology and mini-slot approaches. In these systems, the time slot is further divided into mini-slots. The coexistence of these two heterogeneous services leads to a challenging resource allocation problem. In this paper, we study the joint resource allocation problem of eMBB and URLLC schedulers to maximize the minimum expected achieved rate (MEAR) of eMBB users while satisfying URLLC users’ quality of service (QoS) constraints. The resource allocation of eMBB traffic occurs at time slot boundaries. In contrast, URLLC traffic is overlapped at the mini-slot timescale by adopting the non-orthogonal multiple access (NOMA) superposition technique. The suitable pairs of eMBB, URLLC users are evaluated using the one-one matching theory by inculcating fairness among eMBB users while satisfying the QoS parameters of URLCC users. The significance of the proposed approach over the baseline approaches is evaluated through numerical simulations in terms of the MEAR, mixed numerologies, and fairness scores among eMBB users.

Heterogeneous Markov Decision Process Model for Joint Resource Allocation and Task Scheduling in Network Slicing Enabled Internet of Vehicles

In the Internet of Vehicles (IoV) with network slicing functions, both the inter-slice resource allocation and the intra-slice task scheduling have received extensive attention. However, the joint optimization of these two problems has not been fully studied. This letter focuses on the complexity issue of the joint resource allocation and task scheduling problem in the IoV with both ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) slices. A heterogeneous Markov decision process (HMDP) which models the resource allocation process and the task scheduling process in two different layers is proposed. Benefiting from the two-layer structure of the HMDP, the size of action space is significantly reduced, and the asynchronous decisions of different sub-MDPs are enabled. The corresponding layered deep reinforcement learning (DRL) architecture is also designed to solve the HMDP-based optimization problem. Simulation results show that the layered DRL-based algorithm outperforms other methods.

Enhanced Resource Allocation in D2D Communications With NOMA and Unlicensed Spectrum

Device-to-device (D2D) communications with non- orthogonal multiple access (NOMA) have been proved in enhancing the network capacity and reducing the burden of the traditional cellular network. Due to reusing the same resources, the underlying D2D communication may cause high interference to cellular users, and thus reducing the network throughput. To alleviate this issue, we consider moving D2D users to the unlicensed band. In this case, D2D users can reuse the resources in cellular networks or share the unlicensed band with WiFi users. However, a critical question is how to effectively perform NOMA user clustering and determine D2D pairs that should use unlicensed band and how to guarantee the quality service of users at both networks. Our objective is to maximize the network throughput by investigating a joint resource allocation problem of user clustering, power control, and D2D mode selection. Since the formulated problem is a mixed-integer nonlinear programming problem, finding an optimal solution is a great challenge. To solve this problem, we propose to use a swarm intelligence approach, namely whale optimization algorithm in solving our optimization problem with an efficient solution. Through simulation results, we show that our proposed scheme attains a better performance than other alternative schemes.

Subcarrier‐pair based joint resource allocation for secure cooperative OFDM system with multiple untrusted AF relays

In this paper, a cooperative orthogonal frequency division multiplexing system with multiple untrusted amplify-and-forward relays is considered. Since there exists no direct link between source and user because of the shadowing effect, a positive system secrecy rate cannot be obtained. Addressing this issue, a cooperative user-aided jamming technique to improve the secrecy performance is employed. With the aim of maximising the system secrecy rate, a joint resource allocation problem of power allocation, relay assignment and subcarrier pairing is formulated. By using the signal-to-noise ratio based approach and the Lagrange dual method, this nonconvex problem is solved efficiently. To reduce the calculation complexity, a suboptimal algorithm, which decouples the power allocation with the relay assignment and subcarrier pairing is further proposed. Numerical results are presented to demonstrate the advantage of the two proposed algorithms and the impact of relays' location and number of subcarriers on the secrecy performance. Moreover, it is shown that the secrecy performance of the proposed scheme is decreased with respect to the number of untrusted relays.

Joint Resource Allocation in Secure OFDM Two-Way Untrusted Relay System.

The security issue of wireless communication is a common concern because of its broadcast nature, especially when the relay becomes an eavesdropper. In the orthogonal frequency division multiplexing (OFDM) relay system, when the relay is untrusted, the security of the system faces serious threats. Although there exist some resource allocation schemes in a single-carrier system with untrusted relaying, it is difficult to apply them to the multi-carrier system. Hence, a resource allocation scheme for the multi-carrier system is needed. Compared to the one-way relay system, a two-way relay system can improve the data transmission efficiency. In this paper, we consider joint secure resource allocation for a two-way cooperative OFDM system with an untrusted relay. The joint resource allocation problem of power allocation and subcarrier pairing is formulated to maximize the sum secrecy rate of the system under individual power constraints. To solve the non-convex problem efficiently, we propose an algorithm based on the alternative optimization method. The proposed algorithm is evaluated by simulation results and compared with the benchmarks in the literature. According to the numerical results, in a high signal-to-noise ratio (SNR) scenario, the proposed algorithm improves the achievable sum secrecy rate of the system by more than 15% over conventional algorithms.

Virtual Resource Allocation for Wireless Virtualized Heterogeneous Network With Hybrid Energy Supply

In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.

Multi-Hop Task Offloading With On-the-Fly Computation for Multi-UAV Remote Edge Computing

The dramatic growth in computing capability and the inherent mobility of the unmanned aerial vehicles (UAVs) foster the recent surge of interests in incorporating UAVs into edge computing systems to facilitate on-demand deployment and extended coverage. Nonetheless, due to the limited communication capability of the UAVs, single-UAV edge computing systems may still be incompetent when serving remote users. Although the traditional multi-UAV relay network can be a viable solution, it fails to exploit the computing capability of the UAVs. With this consideration, a multi-hop task offloading with on-the-fly computation scheme is proposed in this work to enable a more powerful multi-UAV remote edge computing network. To solve the corresponding joint resource allocation and deployment problem, two efficient algorithms are proposed. One of them can find the global optimal strategy in a special case, while the other can obtain a good local optimal strategy in the general cases. Both algorithms have a complexity only linear in the number of UAVs and admit distributed implementation. In addition to analysis, numerical results are provided to corroborate the effectiveness of the proposed scheme.

Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning

Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated by the recent advances in deep reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic channel access and power control in a wireless interference network. We first propose a multi-agent DRL algorithm with centralized training (DRL-CT) to tackle the joint resource allocation problem. In this case, the training is performed at the central unit (CU) and after training, the users make autonomous decisions on their transmission strategies with only local information. We demonstrate that with limited information exchange and faster convergence, DRL-CT algorithm can achieve 90% of the performance achieved by the combination of weighted minimum mean square error (WMMSE) algorithm for power control and exhaustive search for dynamic channel access. In the second part of this paper, we consider distributed multi-agent DRL scenario in which each user conducts its own training and makes its decisions individually, acting as a DRL agent. Finally, as a compromise between centralized and fully distributed scenarios, we consider federated DRL (FDRL) to approach the performance of DRL-CT with the use of a central unit in training while limiting the information exchange and preserving privacy of the users in the wireless system. Via simulation results, we show that proposed learning frameworks lead to efficient adaptive channel access and power control policies in dynamic environments.