Channel Allocation Problem Research Articles

Joint optimization of deployment, user association, channel, and resource allocation for fairness‐aware multi‐UAV network

AbstractThis paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework “sense‐as‐you‐fly” based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes.

Self‐attention convolutional neural network optimized with Remora Optimization Algorithm for energy aware resource management in Non‐orthogonal Multiple Access networks

SummaryNon‐orthogonal multiple access (NOMA) is an efficient technology for future wireless communication systems. The resource management on NOMA networks is flattering for improving energy efficiency (EE) of systems. Therefore, self‐attention convolutional neural network (SACNN) optimized with Remora Optimization Algorithm (ROA) for energy aware resource management in NOMA network (SACNN‐ROA‐EE) is proposed in this paper for solving user allocation, channel allocation, and power allocation problems. The Matching‐Coalition approach is considered to face the user allocation issue, and the SACNN is considered to face the channel allocation and power allocation problems. SACNN does not adopt any optimization methods to determine the optimal parameters in channel allocation and power allocation. Therefore, the ROA is applied to optimize the SACNN weight parameters. The proposed technique is activated in MATLAB, and its efficacy is analyzed with under performances metrics, such as minimum user rate, sum rate, energy, and spectral performance. The proposed SACNN‐ROA‐EE attains 24.35%, 27.84%, and 38.58% better sum rate and 27.45%, 43.28%, 30.56% better EE compared with existing methods, such as EE utilizing communication deep neural network optimized with power allocation optimization (PAO‐CDNN‐EE), EE under deep transfer deterministic policy gradient along deep deterministic policy gradient approach (DPPGA‐DTDPPG‐EE), and EE utilizing deep reinforcement learning with baseline algorithms (BA‐DRL‐EE), respectively.

MUSCAT: Distributed multi-agent Q-learning-based minimum span channel allocation technique for UAV-enabled wireless networks

We consider a minimum span channel allocation problem (MS-CAP) to overcome spectrum scarcity and facilitate the efficiency of unmanned aerial vehicle (UAV)-enabled wireless networks. Basically, the MS-CAP minimizes the difference between the maximum and minimum used frequency, i.e., the required total bandwidth, while guaranteeing the quality-of-service (QoS) requirements for each wireless link in the network. The conventional optimal minimum span channel allocation (MS-CA) scheme is based on a centralized approach, assuming that global network information is available at the central controller. In practice, however, this may not be feasible for dynamic environments like UAV-enabled wireless networks since the real-time exchange of network information and channel allocation results with dynamically moving UAVs is formidable. Hence, we propose a novel practical MS-CA algorithm based on distributed multi-agent reinforcement learning (MARL), where each agent independently learns its best strategy with its local observations. To the best of our knowledge, the proposed technique is the first work of designing a distributed MARL for the MS-CAP for multi-UAV-enabled wireless networks in the literature. Numerical results reveal that the proposed distributed MS-CA technique can efficiently save the required total bandwidth while ensuring the QoS requirements of each link, represented by the signal-to-interference plus noise ratio (SINR) threshold, even in dynamic wireless networks. It validates the applicability of the proposed distributed MS-CA framework to dynamic networks.

Heuristics for optimizing minimum interference channel allocation problem in cellular networks

Heuristics for optimizing minimum interference channel allocation problem in cellular networks

Preallocation-Based Combinatorial Auction for Efficient Fair Channel Assignments in Multi-Connectivity Networks

We consider a general multi-connectivity framework, intended for ultra-reliable low-latency communications (URLLC) services, and propose a novel, preallocation-based combinatorial auction approach for the efficient allocation of channels. We compare the performance of the proposed method with several other state-of-the-art and alternative channel-allocation algorithms. The two proposed performance metrics are the capacity-based and the utility-based context. In the first case, every unit of additional capacity is regarded as beneficial for any tenant, independent of the already allocated quantity, and the main measure is the total throughput of the system. In the second case, we assume a minimal and maximal required capacity value for each tenant, and consider the implied utility values accordingly. In addition to the total system performance, we also analyze fairness and computational requirements in both contexts. We conclude that at the cost of higher but still plausible computational time, the fairness-enhanced version of the proposed preallocation based combinatorial auction algorithm outperforms every other considered method when one considers total system performance and fairness simultaneously, and performs especially well in the utility context. Therefore, the proposed algorithm may be regarded as candidate scheme for URLLC channel allocation problems, where minimal and maximal capacity requirements have to be considered.

Hypergraph-Based Joint Channel and Power Resource Allocation for Cross-Cell M2M Communication in IIoT

Industrial Internet of Things (IIoT) is the leading application scenario of the fifth generation wireless communication systems (5G) and beyond. Non-orthogonal multiple access (NOMA) has become a key technology for 5G due to its high spectrum efficiency. In this paper, a joint channel and power resource allocation problem is investigated for cross-cell IIoT networks with aim of maximizing sum rate of NOMA-based M2M pairs and cellular Machine Devices (cMDs). Since joint channel and power resource allocation problem is an NP-hard problem, the original problem is transformed into a hypergraph model to optimize channel and power resource allocation. Then, a channel allocation algorithm based on hypergraph colouring theory is proposed, and an alternative power allocation algorithm is presented. Next, some properties of hypergraph colouring and complexities are analysed. Finally, simulation results demonstrate that the proposed algorithm outperforms the graph-based algorithm in terms of sum rate, and also improves the spectrum efficiency significantly.

Joint Optimization Scheme of User Association and Channel Allocation in 6G HetNets

The sixth-generation (6G) wireless cellular network integrates several wireless bands and modes with the objectives of improving quality of service (QoS) and increasing network connectivity. The 6G environment includes asymmetrical heterogeneous networks (HetNets) with the intention of making effective use of the available frequencies. However, selecting a suitable gNB and a communication channel that works for users in the network is an enormous challenge in 6G HetNets. This paper investigates a joint user association (UA) and channel allocation (CA) problem in two-tier HetNets by considering the downlink scenario to improve QoS. Our study presents an innovative scheme for user association and channel allocation, wherein the user can be connected to either the macro base station (MBS) or a possible small base station (SBS) in a direct or relay-assisted link. Furthermore, the proposed scheme identifies the optimal channel to be allocated to each user so that the overall network QoS can be maximized. A symmetric matching game-based user association is proposed to find the optimal association for users. Moreover, a modified auction game is applied to allocate the optimal channel by considering the quota of each gNB. Regarding connection probability, throughput, energy efficiency (EE), and spectrum efficiency (SE), the simulation results show that the proposed approach performs well over the state-of-the-art techniques.

Joint dynamic routing and resource allocation in satellite-terrestrial integrated networks

The Satellite-Terrestrial Integrated Networks (STINs) is considered as a reliable and agile next-generation communication network scheme, because it is flexible to deploy and robust to disasters (e.g., earthquakes, floods, and volcanic eruptions). The routing and resource allocation directly affect the power consumption of satellites in the STINs. However, most of existing research works study these two issues separately, and neglect the impact of path selection on the satellite power consumption. This paper investigates the joint optimization problem of routing, bandwidth allocation, user association, channel allocation, and power allocation of the STINs. We aim at minimizing satellites’ power consumption while satisfying user's Quality of Service (QoS). Because the power consumption of satellites for routing in the space segment is involved with the data rate through satellite gateways in the user segment, this optimization problem is a Multi-Objective Optimization (MOO) problem. To this end, we convert the MOO problem to a Single-Objective Optimization (SOO) problem by linear weighted method. Considering that the SOO problem is a Mixed Integer Nonlinear Programming (MINLP) problem, we decompose it into the resource allocation sub-problem in the user segment and the routing optimization sub-problem in the space segment. And we solve these two sub-problems iteratively. Specifically, we convert the first sub-problem into a convex optimization problem and obtain the current user association and channel allocation. Then we formulate the second sub-problem into a multi-commodity flow problem and solve it based on the obtained user association and channel allocation. The SOO problem is solved by iteratively optimizing these two sub-problems. Experimental results demonstrate that our proposed algorithm is able to get an approximate optimal solution quickly and effectively reduce the power consumption of satellites compared with benchmark algorithms in the STINs.

Intelligent Computing Collaboration for the Security of the Fog Internet of Things

The application of fog Internet of Things (IoT) technology helps solve the problem of weak computing power faced by IoT terminals. Due to asymmetric differences in communication methods, sensing data offloading from IoT terminals to fog and cloud layers faces different security issues, and both processes should be protected through certain data transmission protection measures. To take advantage of the relative asymmetry between cloud, fog, and sensing layers, this paper considers using physical layer security technology and encryption technology to ensure the security of the sensing data unloading process. An efficient resource allocation method based on deep reinforcement learning is proposed to solve the problem of channel and power allocation in fog IoT scenarios, as well as the selection of unloading destinations. This problem, which is NP-hard, belongs to the attribute of mixed integer nonlinear programming. Meanwhile, the supporting parameters of the method, including state space, action space, and rewards, are all adaptively designed based on scene characteristics and optimization goals. The simulation and analysis show that the proposed method possesses good convergence characteristics. Compared to several heuristic methods, the proposed method reduces latency by at least 18.7% on the premise that the transmission of sensing data is securely protected.

Channel Allocation Algorithm Based on Swarm Intelligence for a Wireless Monitoring Network

In wireless networks, multiple monitoring nodes are used to collect users’ transmission data in real time, which can be used for fault diagnosis and analytical feedback of the wireless network. Due to the limited number of monitoring nodes, key issues include how to optimize and schedule the channel resources of each node to cover more users, obtain more network data, and maximize the quality of network monitoring. In this paper, a channel allocation algorithm based on swarm intelligence—“discrete bacterial foraging optimization”—is proposed based on the classic bacterial foraging optimization algorithm. The position of each dimension in the iterative process is discretized to binary 0 or 1 to encode and express the channel allocation problem of wireless monitoring networks, and the channel allocation scheme is optimized by location updates guided by bacterial foraging. Many simulation and practical experiments have proved the effectiveness of the algorithm, and it also has low complexity and provable convergence. Compared with similar algorithms, this algorithm improves monitoring quality by 1.428% while boosting speed by up to 32.602%. The algorithm has lower complexity, higher performance, and can converge to the optimal solution at a faster rate.

Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

In Beyond the Fifth Generation (B5G) heterogeneous edge networks, numerous users are multiplexed on a channel or served on the same frequency resource block, in which case the transmitter applies coding and the receiver uses interference cancellation. Unfortunately, uncoordinated radio resource allocation can reduce system throughput and lead to user inequity, for this reason, in this paper, channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate. Since the construction model is non-convex and the response variables are high-dimensional, a distributed Deep Reinforcement Learning (DRL) framework called distributed Proximal Policy Optimization (PPO) is proposed to allocate or assign resources. Specifically, several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation. Moreover, agents in the collection stage slow down, which hinders the learning of other agents. Therefore, a preemption strategy is further proposed in this paper to optimize the distributed PPO, form DP-PPO and successfully mitigate the straggler problem. The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

Channel Allocation for Mega LEO Satellite Constellations in the MEO–LEO Networked Telemetry System

Recently, numerous mega LEO satellite constellation plans have emerged as an indispensable part supporting the future 6G satellite communications. Since the traditional telemetry systems are inappropriate for monitoring the operation status of all satellites, a networked telemetry system is adopted to achieve full time, low delay telemetry for mega LEO satellite constellations, which is a significant scenario of satellite Internet of Things. Furthermore, this article formulates a channel allocation problem to maximize the overall transmitted data amount. Adopting the dual decomposition method, this problem can be decomposed and transformed into a dual problem and multiple path scheduling subproblems for each LEO satellite. This article develops an optimal dynamic programming algorithm to solve the path scheduling subproblems and an iterative algorithm to solve the channel allocation problem. Numerical simulations show that the proposed channel allocation algorithm improves the transmitted data amount and approximates the upper bound performance.

Graph coloring-based multichannel MAC protocol in distributed underwater acoustic sensor networks

In this paper, the multichannel medium access control (MAC) problem in distributed underwater acoustic sensor networks (UASNs) were investigated. Compared with single-channel MAC protocols in terrestrial radio networks, there exist multichannel hidden terminal problem and long-delay hidden terminal problem in multichannel MAC protocol due to long propagation delay in UASNs. In addition, energy constraint makes channel allocation a challenging problem in distributed UASNs. To solve these aforementioned problems, a new multichannel MAC protocol, called graph coloring-based multichannel MAC protocol (GCMAC) is present. The protocol GCMAC is a synchronized MAC protocol which splits the time into three phases, namely, channel negotiation phase, channel selecting phase and data transmission phase. Specially, the rule for selecting channel is carefully designed based on graph coloring theory to avoid collision and maximize the utilization rate of channels in channel selecting phase. Simulation results show that GCMAC can greatly improve the system throughput and energy efficiency by effectively solving the hidden terminal problems and channel allocation problem.

SecBoost: Secrecy-Aware Deep Reinforcement Learning Based Energy-Efficient Scheme for 5G HetNets

In this paper, we propose a secrecy-aware energy-efficient scheme for a two-tier heterogeneous network (HetNet), consisting of a sub-6 GHz macrocell and multiple millimeter wave (mmWave) picocells. Each picocell is assumed to have several users and an eavesdropper (Eve) which intercepts the signal of the picocell users. In the proposed scheme, firstly, to maximize the secrecy energy-efficiency (SEE) of picocell users, a joint optimization problem of power control, channel allocation, and beamforming is formulated by considering the minimum secrecy rate and signal-to-interference-plus-noise ratio (SINR) constraints. Due to the non-convex nature of the aforementioned optimization problem in a highly dynamic HetNet environment, we transform it into a reinforcement learning (RL) problem using the Markov decision process (MDP). Then, a multi-agent reinforcement learning (MARL) technique is used to obtain the maximum long-term reward. Moreover, we propose a multi-agent cooperative deep reinforcement learning (DRL) scheme known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> to solve the MDP with large number of action and state spaces. It uses the dueling and double-Q architecture of dueling double deep Q-network (D3QN) to optimize power control, channel allocation, and beamforming vectors to maximize the SEE of picocells. Also, prioritized experience replay is used to increase the sampling efficiency of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> . The SEE performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> is compared with MARL, multi-agent deep Q-network (MA-DQN), state-of-the-art joint beamforming based secrecy energy efficiency maximization (JBF-SEEM) scheme, and one-time pad based encrypted data transmission (O-EDT). Simulation results demonstrated that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> scheme achieves 14.7%, 8.33%, 30%, and 69% better average SEE in comparison to MARL, MA-DQN, JBF-SEEM, and O-EDT schemes, respectively, which reveals its effectiveness in improving SEE of picocells.

Mobility aware resource allocation for millimeter-wave D2D communications in presence of obstacles

The device-to-device (D2D) communication is envisioned as the solution to the bandwidth scarcity problem in the era of exponentially growing smart handheld devices. Although D2D communication in high frequency millimeter wave (mmWave) band offers high data rate, it requires obstacle free line-of-sight (LOS) communication path due to high propagation and penetration losses. The relay selection problem in D2D communication deals with careful selection of relay devices in order to establish a communication link when the direct LOS path is blocked or suffers from high interference from neighboring devices. With the limited number of available frequency channels, the channel allocation problem aims to judicially allocate the frequency channels to the activated D2D communication links. Due to the interdependence of the aforesaid two problems, it is a challenging task to optimally allocate frequency channels to the requesting D2D pairs along with proper selection of relay devices, specially in the presence of obstacles and user mobility. In this paper, we have proposed a mobility and obstacle aware base-station controlled centralized framework which jointly deals the channel allocation and relay selection problems for mmWave D2D communication. After proving the hardness of this joint problem, we provide a greedy solution along with its approximation bound. Our proposed algorithm has been shown to outperform an existing work through extensive simulations.

Network Lifetime Optimization in Multi-hop Industrial Cognitive Radio Sensor Networks

Industrial cognitive radio sensor networks (ICRSNs) extend channel resources by occupying the vacant licensed channels in the absence of licensed users. In ICRSNs, industrial devices should switch to a common available channel to set up a communication link. However, channel switching leads to severe energy consumption. As the energy resources of battery-powered industrial devices are limited, it is crucial to carefully allocate channels to prolong the network lifetime of multi-hop ICRSNs. This paper is the first work that studies the channel allocation problem to optimize the network lifetime by considering the channel-switching (CS) energy consumption and the time-critical requirements of industrial applications. The problem is formulated to maximize the minimum residual energy at each round of data transmission, which is linearized as integer linear programming. As the channel allocation results will affect the residual energy at subsequent rounds, we propose a switching distance-optimized channel allocation (SDOCA) scheme that shortens the CS distances to improve the residual energy of each device. Moreover, we analyze the characteristics of SDOCA, i.e., convergent CS distance and guaranteed end-to-end delay. Extensive simulation results show that SDOCA can adaptively allocate channels according to the end-to-end delay requirement and significantly prolong the network lifetime.

Learning Task-Oriented Channel Allocation for Multi-Agent Communication

Benefiting from the rapid progress of wireless communication and artificial intelligence, multi-agent collaboration opens up new opportunities for various fields. To facilitate multi-agent acting as a group, effective communication plays a crucial role. Recently, many efforts based on multi-agent reinforcement learning have been made to enable effective multi-agent communication under limited bandwidth or noisy channel. However, current methods do not explore wireless resource allocation strategy explicitly. Moreover, due to ignoring task-relevant significance of information, traditional wireless resource allocation schemes may fail to guarantee the transmission efficiency and reliability for multi-agent communication. To this end, in this paper, we propose a task-oriented communication principle for multi-agent communication. We model the task-oriented channel allocation problem as a decentralized partially observable Markov decision process and propose a multi-agent reinforcement learning framework as a solution. Specifically, we design a novel variational information bottleneck to extract task-relevant information from local observation. Furthermore, a task-oriented channel allocation mechanism is developed to choose the allocation pattern with maximum expected gain. Finally, a double attention mechanism is developed to motivate the efficient utilization of task-relevant information. Experimental results show that our method can improve the effectiveness and efficiency of multi-agent communication, enhancing collaboration performance compared to baselines.

An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks

Low Earth orbit (LEO) satellites that can be used as computing nodes are an important part of future communication networks. However, growing user demands, scarce channel resources and unstable satellite–ground links result in the challenge to design an efficient channel allocation algorithm for the LEO satellite network. Edge computing (EC) provides sufficient computing power for LEO satellite networks and makes the application of reinforcement learning possible. In this paper, an adaptive dynamic channel allocation algorithm based on a temporal–spatial correlation analysis for LEO satellite networks is proposed. First, according to the user mobility model, the temporal–spatial correlation of handoff calls is analyzed. Second, the dynamic channel allocation process in the LEO satellite network is formally described as a Markov decision process. Third, according to the temporal–spatial correlation, a policy for different call events is designed and online reinforcement learning is used to solve the channel allocation problem. Finally, the simulation results under different traffic distributions and different traffic intensities show that the proposed algorithm can greatly reduce the rejection probability of the handoff call and then improve the total performance of the LEO satellite network.

Binary Jaya‐based channel allocation in cognitive radio networks

SummaryCognitive radio is a key solution for wireless networks, as it offers the possibility to cope with spectrum scarcity problem. In this work, we are interested in the channel allocation problem (CA) in cognitive radio networks which is a well‐known NP‐hard optimization problem. CA has been resolved using classic evolutionary approaches such as genetic algorithms (GAs) and particle swarm optimization (PSO). However, the performance of these latters depends strongly on the parameters tuning which is a hard time‐consuming task. Under this umbrella, in this article, we propose a new solution based on Jaya algorithm which is a recent free parameters approach. The performance of our proposed algorithm is compared against GA and PSO under different network topologies, and this in terms of max‐sum‐reward, max‐min‐reward, and max‐proportional‐fair. The obtained simulation results confirm the superiority of our proposed approach under all the tested scenarios.

A single-channel single-winner auction model for homogeneous channel allocation in CRNs

Cognitive radio (CR) is a novel intelligent technology which enables opportunistic access to temporarily unused licensed frequency bands. A key functionality of CR is to distribute free channels efficiently amongst Secondary Users (SUs) boosting spectrum usage to assist the escalating wireless applications world wide. In this context, this paper introduces a channel allocation mechanism which enables SUs (CR enabled unlicensed users) to dynamically access unused spectrum bands to fulfill their spectrum needs. We model the channel allocation problem as a sealed-bid single-sided auction which primarily aims at maximizing the overall spectrum utilization. Market based spectrum auctions in CR networks motivate licensed users to participate and lease their under utilized radio resources to gain monetary benefits. Sequential bidding is applied to this model for auctioning homogeneous channels, which reduces communication overhead. Bid submission takes into account two major CR constraints, namely, dynamics in spectrum opportunities and differences in channel availability time, which on incorporation provide disruption free data transmission to the SUs. We reduce resource wastage in this model by performing multiple auction rounds. Application of second price auction determines winning bidders and their respective payments to auctioneer. The design of our auction mechanism is supported with the proofs of truthfulness and individually rational properties. Furthermore, experimental results indicate that our model outperforms an existing auction method. Spectrum utilization values show 22 to 75% improvement in our model with changing number of SUs, and 23 to 93% improvement in our model with changing number of channels.