Showing papers on "Channel allocation schemes published in 2022"

UAV Placement and Resource Allocation for Intelligent Reflecting Surface Assisted UAV-Based Wireless Networks

Abstract: We design an unmanned aerial vehicle (UAV) based wireless network with wireless access and backhaul links leveraging an intelligent reflecting surface (IRS). This design aims to maximize the sum rate achieved by ground users (GUs) through optimizing the UAV placement, IRS phase shifts, and sub-channel assignments considering the wireless backhaul capacity constraint. To tackle the underlying mixed integer non-linear optimization problem (MINLP), we first derive the closed-form IRS phase shift solution; we then optimize the sub-channel assignment and UAV placement by using the alternating optimization method. Specifically, we propose an iterative sub-channel assignment method to efficiently utilize the bandwidth and balance bandwidth allocation for wireless access and backhaul links while maintaining the backhaul capacity constraint. Moreover, we employ the successive convex approximation (SCA) method to solve the UAV placement optimization sub-problem. We show the effectiveness of our proposed design via extensive numerical studies.

Availability-Aware Channel Allocation for Multi-Cell Cognitive Radio 5G Networks

Abstract: In this paper, we propose a channel allocation algorithm for heterogeneous multi-cell cognitive radio 5G networks. The proposed algorithm considers channel-availability and channel-demand heterogeneity across CR cells. We use the theory of continuous-time Markov chains to develop mathematical expressions for channel availability and channel demand. Both expressions are incorporated in a binary integer program to find the optimal channel allocation. Further, we equip the proposed channel allocation algorithm with a novel interference domain identification scheme to detect interference range overlaps between cognitive radio cells. This scheme is essential for channel allocation, as it plays an important role in avoiding inter-cell interference. Our simulation results show significant performance gains compared to existing algorithms overlooking channel-availability. Furthermore, we use the theory of Dantzig-Wolfe decomposition to develop a framework for transforming centralized channel allocation algorithms into distributed ones. Through simulation experiments, we demonstrate the efficacy of the proposed framework.

Deep Learning-Based Resource Allocation for Device-to-Device Communication

Abstract: In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are optimized for maximization of the overall spectral efficiency whilst maintaining the quality-of-service (QoS) of the cellular users. Depending on the availability of channel state information (CSI), two different configurations are considered, namely 1) centralized operation with full CSI and 2) distributed operation with partial CSI, where in the latter case, the CSI is encoded according to the capacity of the feedback channel. Instead of solving the resulting resource allocation problem for each channel realization, a DL framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Furthermore, we propose a new training strategy that combines supervised and unsupervised learning methods and a local CSI sharing strategy to achieve near-optimal performance while enforcing the QoS constraints of the cellular users and efficiently handling the integer optimization variables based on a few ground-truth labels. Our simulation results confirm that near-optimal performance can be attained with low computation time, which underlines the real-time capability of the proposed scheme. Moreover, our results show that not only the resource allocation strategy but also the CSI encoding strategy can be efficiently determined using a DNN. Furthermore, we show that the proposed DL framework can be easily extended to communication systems with different design objectives.

Achieving the Performance Bounds for Sensing and Communications in Perceptive Networks: Optimal Bandwidth Allocation

Abstract: This letter investigates the performance bounds of sensing and communications (S&C) in perceptive networks, where multiple user equipments (UEs) are deployed with both S&C capabilities whose performances are both closely related to the allocated bandwidth. To this end, we develop a novel bandwidth allocation strategy which optimizes the weighted average range resolution for sensing, while guaranteeing the sum-rate among UEs for wireless communications. We first formulate the bandwidth allocation problem into a convex optimization programming, and then develop a highly efficient algorithm to obtain the optimal solution. Through a deeper analysis of the problem structure, we build an initial understanding and provide insights into the performance tradeoff between S&C, which can be achieved by three specifically tailored bandwidth allocation schemes. Numerical results are given to verify the performance of the proposed method, showing that our bandwidth allocation scheme achieves the pareto boundary of S&C performance, with significantly reduced computational complexity.

Performance of an Adaptive Aggregation Mechanism in a Noisy WLAN Downlink MU-MIMO Channel

Abstract: This paper investigates an adaptive frame aggregation technique in the medium access control (MAC) layer for the Wireless Local Area Network (WALN) downlink Multi-User–Multiple-In Multiple-Out (MU-MIMO) channel. In tackling the challenges of heterogeneous traffic demand among spatial streams, we proposed a new adaptive aggregation algorithm which has a superior performance over the baseline First-in–First-Out (FIFO) scheme in terms of system throughput performance and channel utilization. However, this earlier work does not consider the effects of wireless channel error. In addressing the limitations of this work, this study contributes an enhanced version of the earlier model considering the effect of channel error. In this approach, a dynamic adaptive aggregation selection scheme is proposed by employing novel criteria for selecting the optimal aggregation policy in WLAN downlink MU-MIMO channel. Two simulation setups are conducted to achieve this approach. The simulation setup in Step 1 performs the dynamic optimal aggregation policy selection strategy as per the channel condition, traffic pattern, and number of stations in the network. Step 2 then performed the optimal wireless frame construction that would be transmitted in the wireless channel in adopting the optimal aggregation policy obtained from Step 1 that maximizes the system performance. The proposed adaptive algorithm not only achieve the optimal system throughput in minimizing wasted space channel time but also provide a good performance under the effects of different channel conditions, different traffic models such as Pareto, Weibull, and fBM, and number of users using the traffic mix of VoIP and video data. Through system-level simulation, our results again show the superior performance of our proposed aggregation mechanism in terms of system throughput performance and space channel time compared to the baseline FIFO aggregation approach.

Delay-Sensitive Task Offloading With D2D Service-Sharing in Mobile Edge Computing Networks

Abstract: This letter considers a mobile edge computing (MEC) system where some mobile users (MUs) have not pre-installed the service program required to process their task data. Depending on the availability of the service program, and thus the ability to process data locally, users are tagged as eligible users (EUs) and ineligible users (IUs), respectively. Other than offloading all the task data to the edge server under a stringent spectrum, we consider a device-to-device (D2D) enabled service sharing method that allows IUs to obtain the service program from its adjacent EUs, such that some tasks can be processed locally at the user devices. To fully utilize the stringent wireless spectrum, we propose a spectrum-aggregation scheme that an EU who shares its service program to an IU can occupy the bandwidth originally allocated to both users. We aim to minimize the execution latency of all the MUs by jointly optimizing the service sharing, computation offloading, and bandwidth allocation. We formulate the problem as a mixed integer non-linear programming (MINLP), where the major difficulties are the combinatorial service sharing and task offloading decisions at UEs and the strong coupling between the integer decisions and system bandwidth allocation. To deal with this problem, we propose an efficient algorithm named MOP that first obtains the combinatorial decisions by formulating a service matching game, and accordingly derives the closed-form solution of the optimal bandwidth allocation. Simulation results show that MOP achieves less than 2.8% optimality gap compared to exhaustive search method, and offers substantial performance gain over the considered benchmark algorithms.

A Robust Power Allocation Algorithm for Cognitive Radio Networks Based on Hybrid PSO

Abstract: The use of a cognitive radio power allocation algorithm is an effective method to improve spectral utilization. However, there are three problems with traditional cognitive radio power allocation algorithms: (1) based on the ideal channel model analysis, channel fluctuation is not considered; (2) they do not consider fairness among cognitive users; and (3) some algorithms are complex and locating the optimal power allocation scheme is not an easy task. For the above problems, this study establishes a robust model which adds the cognitive user transmission rate variance constraint to solve the maximum channel capacity time power allocation scheme by considering the worst-case channel transmission model, and finally solves this complex non-convex optimization problem by using the hybrid particle swarm algorithm. Simulation results show that the algorithm has good robustness, improves the fairness among the cognitive users, makes full use of the channel resources under the constraints, and has a simple algorithm, fast convergence, and good optimization results.

Sum-rate Optimization for IRS-Aided D2D Communication Underlay Cellular Networks

Abstract: The intelligent reflective surface (IRS)-assisted communication is recognized as a promising technology to enhance capacity and coverage of the network by controlling propagation. However, in IRS-aided device-to-device (D2D) communication underlaying cellular networks, co-channel interference is more complicated and challenging than traditional D2D communication networks due to the addition of extra reflection paths. To alleviate interference and improve sum-rate, we establish a multivariable resource optimization function based on IRS-assisted D2D communication in this paper. Then, a hybrid sub-optimal algorithm is proposed, which includes channel allocation based on game theory, power allocation and phase shift optimization. Specifically, a sub-channel allocation algorithm is introduced to maximize system sum-rate, where the problem of sub-channel allocation is formulated as a two-stage combinatorial auction model, and the approximate optimal solution is obtained by bidding and adjustment. Then, a sub-optimal solution for transmission power and phase shifts is obtained through local search and iterative optimization. Simulation results show that the proposed hybrid sub-optimal algorithm is feasible and effective. Compared to the other 5 schemes, it has best sum-rate performance and lowest power consumption.

Bandwidth Allocation of Stream-Reservation Traffic in TSN

Abstract: Time-Sensitive Networking (TSN) evolves from Ethernet AVB in which the Credit-Based Shaping (CBS) is employed to guarantee the deterministic transmission of traffic. In CBS, the real-time performance of traffic associated with the credits can practicably settle the uncertainty of queueing and forwarding for stream-reservation traffic. Hence, the optimization of bandwidth allocation under the CBS becomes a key point to ensure guaranteed performance in the network. In this paper, a bandwidth allocation method is proposed for stream-reservation traffic while the real-time requirements are satisfied. We first illustrate the significance of appropriate bandwidth allocation for traffic under the CBS and construct a general schema for its bandwidth allocation. Also, the influences of the protected window for Control Data Traffic (CDT) on stream-reservation traffic are considered in our method. Further, mathematical models are constructed to analyze the delay bounds and backlogs of traffic to form the feedback for the optimal bandwidth allocation process. Finally, two node-level cases with different bandwidth utilization and a synthetic industrial networking scenario are carried out to demonstrate our method. The results confirm that the excessive reserved bandwidth does not necessarily decrease the delay bound, especially under the high traffic loads scenario. A more desirable bandwidth allocation strategy under the CBS mechanism is that the reserved bandwidth should be just enough according to the traffic loads to ensure the deterministic transmission of traffic.

Decentralized Learning for Channel Allocation in IoT Networks Over Unlicensed Bandwidth as a Contextual Multi-Player Multi-Armed Bandit Game

Abstract: We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network. In the considered network, the impoverished channel sensing/probing capability and computational resource on the IoT devices make them difficult to acquire the detailed Channel State Information (CSI) for the shared multiple channels. In practice, the unknown patterns of the primary users’ transmission activities and the time-varying CSI (e.g., due to small-scale fading or device mobility) also cause stochastic changes in the channel quality. Decentralized IoT links are thus expected to learn channel conditions online based on partial observations, while acquiring no information about the channels that they are not operating on. They also have to reach an efficient, collision-free solution of channel allocation with limited coordination. Our study maps this problem into a contextual multi-player, multi-armed bandit game, and proposes a purely decentralized, three-stage policy learning algorithm through trial-and-error. Theoretical analyses shows that the proposed scheme guarantees the IoT links to jointly converge to the socially optimal channel allocation with a sub-linear (i.e., polylogarithmic) regret with respect to the operational time. Simulations demonstrate that it strikes a good balance between efficiency and network scalability when compared with the other state-of-the-art decentralized bandit algorithms.

Joint Channel Allocation and Data Delivery for UAV-Assisted Cooperative Transportation Communications in Post-Disaster Networks

Abstract: As the natural disasters may destroy the ground communication infrastructures for the transportation systems, the communication relief in post-disaster networks is more crucial to reduce risk loss. The growing application of unmanned aerial vehicles (UAVs) holds great potential for disaster communication relief due to its flexibility and functionalities. In this paper, we investigate the channel allocation and data delivery problems for UAV-assisted cooperative transportation communications in post-disaster networks to provide communication and data delivery services for affected users. Specifically, we first introduce the UAV-assisted communication relief system, in which UAVs equipped with the communication and caching functionalities are deployed as the aerial base stations in post-disaster regions. Then, we propose the channel allocation scheme between UAVs and users by taking the interferences into consideration, and obtain the channel allocation strategy to improve the network throughput. Based on the optimal channel allocation strategy, users can deliver their data to UAVs for backup. Next, we propose the data delivery scheme to cope with the pricing problem for UAVs and the data delivery strategy for users to improve the efficiency of data delivery, with the objective of maximizing the utilities of both UAVs and users. The optimal strategy for both UAVs and users are derived according to the analysis of Stackelberg game. Finally, we conduct simulations to evaluate the performance of the proposed channel allocation and data delivery scheme, and the numerical results demonstrate that the proposed scheme can significantly improve the efficiency and effectiveness of channel allocation and data delivery in post-disaster networks, compared with benchmark schemes.

Effective Utilization of Channel Spacing in Elastic Optical Network for 400 Gb/s Transmission

Abstract: This work focusses on flexi grid elastic optical network (EON) and demonstrates high bit rate transmission. Traditional wavelength division multiplexing (WDM) technology uses fixed grid spectral spacing. Underutilization channels are avoided in flexi grid and caters to traffic demand at a high bit rate (1 Tb/s). To overcome this problem, flexi grid channel spacing is used and transmitted 400 Gb/s data rate in EON. In the proposed EON architecture, 12.5 GHz channel spacing granularity is used for channel allocation. Finally, EON channel spacing is compared with a fixed grid network.

Integrated UAV Trajectory Control and Resource Allocation for UAV-Based Wireless Networks With Co-Channel Interference Management

Abstract: In this article, we study the trajectory control, subchannel assignment, and user association design for unmanned aerial vehicles (UAVs)-based wireless networks. We propose a method to optimize the max-min average rate subject to data demand constraints of ground users (GUs) where spectrum reuse and co-channel interference management are considered. The mathematical model is a mixed-integer nonlinear optimization problem which we solve by using the alternating optimization approach where we iteratively optimize the user association, subchannel assignment, and UAV trajectory control until convergence. For the subchannel assignment subproblem, we propose an iterative subchannel assignment (ISA) algorithm to obtain an efficient solution. Moreover, the successive convex approximation (SCA) is used to convexify and solve the nonconvex UAV trajectory control subproblem. Via extensive numerical studies, we illustrate the effectiveness of our proposed design considering different UAV flight periods and number of subchannels and GUs as compared with a simple heuristic.

Distributed reinforcement learning for dynamic spectrum allocation in cognitive radio-based internet of things

Abstract: Cognitive Radio (CR) with other advancements such as the Internet of things and machine learning has recently emerged as the main involved technique to use spectrum in an efficient manner. It can access the spectrum in a fully dynamic way and exploit the unused spectrum resources without creating any harm to cognitive users. In this paper, the authors develop a CR access strategy founded on the implementation of an efficient Deep Multi-user Reinforcement Learning algorithm based on a combination of a Deep neural network, Q-learning, and cooperative multi-agent systems. The proposed approach consists of two stages: the user choice algorithm to set up an agent's activation order, and the frequency choice method to select the optimal channel on the appropriate bandwidth. Reasonable implementation is proposed, and the obtained results demonstrate that the authors’ approach can improve wireless communication for all CR terminals. It shows satisfactory performances in terms of user satisfaction degree and the number of used channels and can keep the channel allocation plan always in the appropriate state.

Genetic Based Approach for Optimal Power and Channel Allocation to Enhance D2D Underlaied Cellular Network Capacity in 5G

Abstract: With the obvious throughput shortage in traditional cellular radio networks, Device-to-Device (D2D) communications has gained a lot of attention to improve the utilization, capacity and channel performance of next-generation networks. In this paper, we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular networks. The genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently, aiming to maximize the overall system throughput of D2D underlaied cellular network with minimum interference level, while satisfying the required quality of service QoS of each user. The simulation results show that our proposed approach has an advantage in terms of maximizing the overall system utilization than fixed, random, BAT algorithm (BA) and Particle Swarm Optimization (PSO) based power allocation schemes.

Dynamic Channel Allocation Algorithm for Heterogeneous Cellular Networks in Power Communication

Abstract: In order to solve the problems of low efficiency and high noise in the application of modern channel resource allocation methods, an anti-interference dynamic channel allocation algorithm for heterogeneous cellular networks in power communication is proposed. First, the heterogeneous cellular network model of power communication and the architecture of power multi-channel transmission platform are constructed. Then, according to the receiving and noise characteristics of communication resources, the anti-interference algorithm of power communication resources under multi-channel transmission is designed to obtain the communication index of power communication heterogeneous cellular network, and the anti-interference dynamic allocation algorithm of power communication heterogeneous cellular network resources is realized, Finally, the comparison method is used to prove the practicability of the method in this paper. The experimental results show that the average value of communication resources after interference suppression is 950 Mb, the average signal to noise ratio is 10.5 dB, and the resource allocation time is less than 3 min after 5000 iterations, which is superior to the comparison method. The method has good channel anti-interference ability and channel resource allocation efficiency, and has certain application value.

High Bandwidth Utilization DWBA Algorithm for Upstream Channel in NG-EPON

Abstract: In this paper, an (ONU-load and RTTs)-based (OLR) dynamic wavelength and bandwidth allocation (DWBA) algorithm for upstream channel in next-generational Ethernet passive optical network (NG-EPON) is proposed. By proposing adaptive threshold grouping algorithm, the waste of bandwidth resources caused by massive guard timeslots and mismatch between assigned transmission window and frame size is reduced. By adjusting the Optical Network Unit (ONU) scheduling order, the idle time caused by Round-Trip Times (RTTs) is reduced. By proposing joint ONU grouping and RTT scheduling mechanism, load balance among wavelengths is achieved. By proposing a fair allocation scheme, the fairness of bandwidth granting for each ONU is ensured. Finally, by the simulation, the effectiveness of proposed algorithm is demonstrated. The simulation indicates that the rate of bandwidth utilization, the average package delay, the scheduling cycle and the network throughput in the system based on proposed algorithm have better performance.

Effective Channel Planning for IEEE 802.11 Networks as a Plane Tessellation Problem. Part 2. Method of Best Channel Configuration Selection and Solutions for a Low Number of Channels

Abstract: The assignation a particular channel to an access point in large, distributed IEEE 802.11 networks can present a complex challenge. Although the channel can be assigned automatically by the network controller in some cases based on specified settings, it may require human attention when this is not possible. In order to select a frequency plan, it is necessary to understand the advantages of a particular channel configuration and evaluate the resulting effects of adjacent-channel interference at the design stage. A similar problem may arise during WLAN troubleshooting. In this paper, we consider distributed flat wireless networks as regular structures in plane tessellation and propose a method for finding the best channel configurations for the most efficient channel planning of IEEE 802.11 networks, which take the specifics of spectrum use in these networks into account. In addition, we consider the simplest possible solutions for three- and four- channel frequency plans.

Channel Allocation Strategy based on Traffic Distribution in Ultra-Dense Networks

Abstract: In ultra-dense network (UDN), although the capacity of the system can be increased by network densification, it leads to severe inter-cell interference, which has become a very important factor that affects the performances of the ultra-dense networks. Channel allocation is a common and effective means to mitigate inter-cell interference. Different from the previous channel allocation strategy, this paper considers the sub-channel allocation based on the traffic distribution under the network coverage area to improve the system throughput, and reduce the interference between SBSs. A graph theory-based clustering method is proposed to cluster small cells and assign sub-channels on this basis. Simulation shows that this scheme can reduce inter-cell interference, improve system throughput and the network performance effectively.

Optimized channel prediction and auction‐based channel allocation for personal cognitive networks

Abstract: Cognitive networks are stands out as intelligent technology which evolved to enhance spectrum utilization. Secondary users are allowed to utilize the primary user's frequency bands on idling times. Identifying the idle licensed spectrum is achieved through spectrum sensing. The spectrum holes should be explored such that a suitable spectrum can be selected and allocated to the secondary users. Existing spectrum sensing and selection schemes have limitations due to interferences. Thus, an optimization algorithm based on bio‐inspired improved weed optimization was presented in this research work for enhanced channel utilization. The optimization model explores the channel characteristics and reduces the primary network interferences through its optimal solution. Further, Markov greedy‐based auction scheme was presented for channel allocation. Considering the channel capacity, delay, and switching rates the allocation is performed to enhance the overall system performance. Simulation analysis demonstrates the superior performance of the proposed model over existing techniques like particle swarm optimization and genetic algorithm.

Capacity Performance for Full-Duplex Multihop Wireless Networks Using Channel Interference Balancing Allocation Scheme

Abstract: Full-duplex (FD) communication has been attractive as one of the research interests related to spectrum utilization for wireless networks from the previous evolution of communication systems. Previous studies discuss the realization of the FD system by focusing on self-interference cancellation and transmit power control in low-power wireless network scenarios. Today, capacity maximization is a key challenge in FD multihop wireless networks, in which the multi-channel allocation may lead to imbalance interference power due to the different number of simultaneous transmissions and its group selection that occurred on the same sub-channels. In this paper, we focus on the capacity maximization of the FD system by considering the influence of total interference power on each sub-channel and how to balance by selecting the different number of simultaneous transmissions to form a group that leads to a minimum difference in the total interference power on those sub-channels. Therefore, a channel interference balancing allocation (CIBA) scheme for balancing the total interference power in the multi-channel multihop wireless networks is proposed and further investigated by the idea of cooperative transmission. We also adopt the concept of interference distance to overcome the interference balancing problem of the proposed CIBA scheme. Performance evaluation results reveal that the proposed CIBA scheme achieves lesser total interference power and higher achievable capacity than other fixed channel allocation schemes.

Optimized Resource Management over Connected Vehicles in RF-CRN Environment

Abstract: The increasing number of vehicles and the shrinking supply of spectrum resources are major roadblocks to the establishment of a Web that could be utilised in vehicles. In this research, we present a game-theoretic framework for the re - allocation of available spectrum. It is necessary to implement such a plan in the congested case. This approach offers users the cognitive capability to actualize dynamic and smart effective use of spectrum, in contrast to the standard technique of assigning spectral efficiency. There is no need for a centralised hub or for individuals to share channel data with one another to achieve this result. Inside a situation when there are more users than wifi resources, the technique may effectively filter out customers who have better communication circumstances. This contributes to the system's improved capacity for communication as a whole. The simulation results show that the algorithm is very effective because it can autonomously choose the channels with the best conditions and it can also efficiently prevent the interfering that may arise among various users.

Channel Allocation Scheme to Mitigate Interference in 5G Backhaul Wireless Mesh Networks

Abstract: Fifth-generation (5G) is the next cellular generation and is expected to quench the growing thirst for taxing data rates and to enable the internet of things. The challenge for next-generation wireless networks is efficient channel management to meet the increasing bandwidth demand and transmission rate requirements. In this paper, we study the channel allocation problem for joint uplink/downlink non-orthogonal multiple access (NOMA) network, where the base station has the facility to allocate spectrum and power resources to a set of users. We propose a scheduling approach for 5G backhaul mesh networks based on the traffic demands in small cells that maximize the small cell utility. This scheduling approach is intended to work with any transmission scheme’s physical layer. We aim to jointly optimize the uplink/downlink channel assignment to maximize user fairness. Here the channel allocation problem is formulated as a traveling salesman problem (TSP) which is then associated with a many-to-many two-sided user channel allocation. We introduce a decreasing coefficient to the updating principle so that modified particle swarm optimization (MPSO) can be viewed as a standard stochastic approximation algorithm. A random velocity is added to improve exploration ability in order to balance exploration behavior and convergence rate with respect to different optimization problems. With these features base stations (BSs) guarantee the end-user’s quality of service by committing the necessary resources. This algorithm investigates the maximization of network capacity and the fairness among stations. The performance of the proposed scheme is evaluated through simulation in terms of throughput and spectral efficiency.

D2D resource allocation algorithm based on joint interference control in 5G heterogeneous networks

Abstract: (D2D) communication is a promising technique to improve the spectral efficiency of cellular networks, by means of reusing the spectrum resource of cellular users (CUEs), but it also causes interference control and resource allocation problems. To address this problem, in this paper, we propose an interference management and resource allocation algorithm for D2D communication in heterogeneous networks, with the objective of maximising the sum throughput of the CUEs and the D2D users (DUEs). Firstly, according to the principle of interference minimisation, DUEs are grouped based on fuzzy clustering algorithm. Secondly, the channel allocation problem is modelled as an iterative combinatorial auction process. Under the premise of ensuring the quality of service (QoS) for both CUEs and DUEs, then allocate the channel to DUEs with the largest multiplexing throughput. Simulation results show that our proposed algorithm can significantly improve the system performance.

Optimization Models for Routing and Frequency Assignment in Wireless Mesh Networks

Abstract: Starting with the first mobile networks developed, the frequency channel assignment has become a significant problem due to the limited number of licensed frequencies and cost-related concerns. The minimization of the number of frequencies assigned has become the main objective of the frequency channel assignment problems, and today this problem is applicable and relevant for wireless networks as well. In this study, we focus on routing and frequency assignment models for wireless mesh networks and propose an integrated approach that combines these two aspects of frequency assignment problems. We modify our approach with respect to different interference models such as protocol-based or SIR-based interference. The integrated model is run for different sizes of randomly generated networks, and the results are compared with the sequential approach proposed in the literature. The impact of the size of the network and the interference model on the number of frequencies assigned are investigated.

Adaptive Channel Allocation for Wi-Fi HaLow Networks

Abstract: The Restricted Access Window (RAW) introduced in IEEE 802.11ah (marketed as Wi-Fi HaLow) is a key feature to address large-scale and dense IoT applications, where stations (STAs) are split into groups and access the channel periodically. In this work, we propose an Adaptive Channel Allocation (ACA) scheme for the RAW mechanism given the assigned STAs, their load, and their locations. Our proposal yields the successful delivery of all STAs’ packets with an improvement in channel usage of up to 80%.

Multi-Agent Q-Learning for Power Allocation in Interference Channel

Abstract: Signal transmission in wireless networks suffers from unwanted interference. To maximize signal to interference plus noise ratio, transmit power of each transmitter needs to be optimally allocated. Here, we propose to use multi-agent Q-learning to optimize such transmit power within interference channel. Our simulation indicated that multi-agent Q-Iearning resulted in better sum-rate than the traditional methods such as the maximum power allocation and the random power allocation. Our work offers a novel and practical computational approach to optimizing signal transmission in wireless networks.

Learning-Based Algorithms for Channel Allocations in Wireless Mesh Network

Abstract: Many studies have been devoted to channel allocation for backhaul links in wireless mesh networks. Among them, a game-theoretic approach proposed by Yen and Dai is promising for the ability to self-stabilize to a valid solution in a decentralized manner. However, game-based solutions are generally not optimal. Furthermore, Yen and Dai's approach did not fully utilize all available channels, wasting scarce bandwidth resource. In this paper, we propose two learning-based approaches to enhance the prior work. One uses Spatial Adaptive Play (SAP) for agents to learn best probability distributions on their possible channel selections. The other based on multi-agent reinforcement learning (MARL) algorithm allows each agent to find out its best selection over time. Simulation results reveal that the proposed approaches do improve the game-based solutions in terms of the number of operative links after channel allocations.