Joint Channel Assignment Research Articles

JCARP: Joint Channel Assignment and Routing Protocol for cognitive-radio-based internet of things (CRIoT)

ABSTRACT To deal efficiently with the spectrum sharing between cognitive radio users, we propose an innovative routing protocol involving not only a joint and fully distributed spectrum management, but also a hybridization mechanism, titled as Joint Channel Assignment and Routing Protocol (JCARP). Interestingly, it does not require a central control entity and does not use a common control channel to handle the network's spectrum distribution information. Additionally, in the route discovery phase of the hybrid routing protocol, there are two phases: reactive channel selection and proactive channel selection. Moreover, Secondary Users (SUs) have the option of switching from the reactive to the proactive phase of channel selection by utilizing historical usage information related to their list of available channels that has been stored in their database tables. In particular, carrier sense multiple access with collision avoidance transmission effects (pass or collision) are studied to identify channel availability and utilization, which are then fed into a database table pertaining to each SU. The JCARP protocol's performance is assessed through a Java–based simulator, utilizing throughput as the evaluation metric. The simulation results indicate that the JCARP protocol markedly reduces collisions among SUs during spectrum access opportunities in the transmission process, achieving higher throughput compared to its counterparts' protocols.

Joint Channel and Power Assignment for Underwater Cognitive Acoustic Networks on Marine Mammal-Friendly

When marine animals and underwater acoustic sensor networks (UASNs) share spectrum resources, problems such as serious harm caused to marine animals by underwater acoustic systems and scarcity of underwater spectrum resources are encountered. To address these issues, a mammal-friendly underwater acoustic sensor network channel power allocation algorithm is proposed. Firstly, marine animals are treated as authorized users and sensor nodes as unauthorized users. Considering the interference level of sensor nodes on authorized users, this approach improves network service quality and achieves a mammal-friendly underwater communication mechanism. Secondly, to maximize the utility of unauthorized users, the algorithm incorporates a network interference level and node remaining energy into a game-theoretical framework. Using channel allocation and power control, a game model is constructed with a unique Nash equilibrium point. Finally, through simulation, it can be found that the proposed algorithm can obtain a stable optimal power value, and with the increase of network load, the system capacity of the proposed algorithm is significantly improved than that of the traditional cognitive radio technology and the common spectrum allocation algorithm, and the transmitted power of nodes can be controlled according to the size of the residual energy, so as to comprehensively improve the overall performance of the network.

Joint Channel Assignment and Bandwidth Reservation Using Improved FireFly Algorithm (IFA) in Wireless Mesh Networks (WMN)

Joint Channel Assignment and Bandwidth Reservation Using Improved FireFly Algorithm (IFA) in Wireless Mesh Networks (WMN)

Joint power allocation and channel assignment for device‐to‐device communication using the Hungarian model and enhanced hybrid Red Fox‐Harris Hawks Optimization

SummaryMultihop device‐to‐device (D2D) communication is a promising advancement since it allows wireless users in close proximity to communicate directly with one another without using base stations (BSs). With two nearby users, this technology intends to keep up with the accelerating development of mobile devices and the rising demands of local traffic loads. High‐data‐rate short‐range transmission is made possible by this paradigm, which also lowers network backhaul expenses while improving end‐user experience, spectral efficiency (SE) and network coverage. In order to maximize the cell's overall sum rates, this article examines the problem of selecting the optimal M‐D2D linkages and cellular users (CUs) to form spectrum‐sharing partners while taking quality of service (QoS) and energy efficiency (EE) needs into consideration. This study examines the use of a channel assignment system and a power allocation technique to manage interference in D2D communication scenarios involving many hops (more than one hop). The suggested channel assignment approach is based on the Hungarian method, but the power allocation system is based on the time‐efficient enhanced Harris hawks optimization (HHO) and red fox (RF) algorithms. A genetic algorithm (GA)‐based methodology and various baseline approaches are used to compare the effectiveness of the suggested system. Because it combines exploitation and exploration using a memory‐based local search methodology and a perturbation mechanism, the suggested approach outperforms the GA in simulations. The results clearly demonstrate that the suggested method increases EE by up to 13% by producing the appropriate channel and power assignment for CUs and M‐D2D users.

Joint Channel and Power Assignment for UAV Swarm Communication Based on Multi-Agent DRL

In this manuscript, we propose a joint channel and power assignment algorithm for an unmanned aerial vehicle (UAV) swarm communication system based on multi-agent deep reinforcement learning (DRL). Regarded as an agent, each UAV to UAV (U2U) link can choose the optimal channel and power according to the current situation after training is successfully completed. Further, a mixing network is introduced based on DRL, where Q values of every single agent are non-linearly mapped, and we call it the QMIX algorithm. As it accesses state information, QMIX can learn to enrich the joint action value function. The proposed method can be used for both unicast and multicast scenarios. Experiments show that each U2U link can be trained to meet the constraints of UAV communication and minimize the interference to the system. For unicast communication, the communication rate is increased up to 15.6% and 8.9% using the proposed DRL method compared with the well-known random and adaptive methods, respectively. For multicast communication, the communication rate is increased up to 6.7% using the proposed QMIX method compared with the DRL method and 13.6% using DRL method compared with adaptive method. Besides, the successful transmission probability can maintain a high level.

Distributed Joint Channel Assignment and Power Control for Sum Rate Maximization of D2D-Enabled Massive MIMO System

Device-to-device (D2D) communications underlaid massive multiple-input multiple-output (MIMO) systems have been recognized as a promising candidate technology to achieve the challenging fifth-generation (5G) network requirements. This integration enhances network throughput, improves spectral efficiency, and offloads the traffic load of base stations. However, the co/cross-tier interferences between cellular and D2D communications caused by resource sharing is a significant challenge, especially when dense D2D users exist in an underlay mode. In this paper, we jointly optimize the channel assignment and power allocation to maximize the sum data rate while maintaining the interference constraints of cellular links. Due to the lack of network-wide information in large scale networks, resource management and interference coordination is hard to be implemented in a centralized way. Therefore, we propose a three-stage stable and distributed resource allocation and interference management scheme based on local information and requires little coordination and communication between devices. We model the channel allocation optimization problem in the first stage as a many-to-one matching game. In the second stage, the algorithm adopts a cost charging policy to solve each user’s power control problem as a non-cooperative game. In the third stage, the algorithm search for swap blocking pairs until stable matching exist. It is shown in this paper that the proposed algorithm converges to a stable matching and terminates after finite iterations. Simulation results show that the proposed algorithm can achieve more than 86% of the average transmission rate performance of the optimal matching with lower complexity.

WiMCA: multi-indicator client association in software-defined Wi-Fi networks

In a world with increasing traffic demands, wireless technologies aim to meet them by means of new Radio Access Technologies that provide faster connectivity. Such is the case of 4G and 5G. However, in indoor scenarios, where the capabilities of these technologies are significantly affected by the distance to the base station and the materials used in the construction of buildings, Wi-Fi is still the technology of reference thanks to its low cost and easy deployment. In this context, it is usual to find multi-AP Wi-Fi networks whose deployment has been carefully planned. However, the user-AP association decision procedure is not defined by the IEEE 802.11 standard. As a result, vendors choose selfish approaches based on signal strength. This leads to uneven user distributions and nonoptimal resource utilization. To deal with this, densification has been used over the years, but this is expensive as it needs more infrastructure. Moreover, this results in more APs in the same collision domain. To avoid the need for densification, in this paper we introduce WiMCA, a joint SDN-based user association and channel assignment solution for Wi-Fi networks that considers signal strength, channel occupancy and AP load to make better association decisions. Experimental results have demonstrated that, in terms of aggregated goodput, WiMCA outperforms approaches based on signal strength by 55%, providing better user level fairness and accommodating more users and traffic before reaching the point at which densification is needed.

Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments.

LoRaWAN is a popular internet of things (IoT) solution over the unlicensed radio band. It sustains low-cost, durable, and long range IoT wireless communications. Nonetheless, with over 24 billion connected IoT devices being expected by the end of the year, and over 50 billion by 2025, the concurrent and legacy approaches to spreading factor and channel assignment in LoRaWAN networks can no longer keep up. This is exacerbated with the growing densification of IoT device deployments and, with the increasing requirements for better throughput and packet delivery ratios. In this paper, we propose a proportional fair-based joint optimal formulation for spreading factor and channel assignment in multi-operator LoRaWAN deployments. The objective of this problem is to maximize the total sum of the logarithmic normalized throughput. We split the problem into two subproblems, and propose a game theoretic approach to solving them. We prove that our games converge towards a pure Nash equilibrium and, afterwards, solve the optimization problems using both semi-distributed and completely distributed algorithms. Via simulations, we show that our algorithms greatly improve the total normalized throughput for LoRaWAN as well as the packet success rate, in comparison to the legacy approaches.

Joint optimization on resource allocation with coordinated scheduling-based transmission for interference handling in Femtocell networks

Background: The most challenging problem in femtocell networks is alleviating the Inter-Cell Interference (ICI) between the macro and femtocells while broadcasting the video streams in the Heterogeneous Networks (HetNets). It degrades the spectral efficacy and throughput significantly. To alleviate this problem, a Joint dynamic Layer, Channel and Power assignment with Further enhanced ICI Coordination (JLCP-FeICIC) method was recommended to assign the resources within every User Equipments (UEs) and enhance the throughput in a two-tier HetNets. Nonetheless, it schedules only the Cell Range Expansion (CRE) UEs of femtocells while the center UEs are not assigned and so the fairness of Resource Block (RB) allocation is reduced. Objective: Therefore in this study, JLCP-FeICIC is further enhanced with the Coordinated Multipoint Transmission (CMT) method (JLCP-FeICIC-CMT) for maximizing the efficiency of the HetNetcenter UEs. Methods: In this method, RBs are assigned by a primary scheduler. Also, the UE is detected in every eNodeBs (eNBs) according to the determination of the interference from the adjacent cells. Then, the Modulation and Coding Scheme (MCS) level is selected in all eNBs for UEs with and without the support of CMT according to the interference-free RB allocation. So, a higher level of spectral efficiency is achieved for corresponding RBs. Findings: Finally, the experimental results exhibit the JLCP-FeICIC-CMT achieves an increased efficacy than the JLCP-FeICIC.The efficiency is analyzed based on the average of Peak Singal-to-Noise Ratio (PSNR) per Femtocell UE (FUE), utility, monetary cost, and Playback Interruption Rate (PIR). Keywords: Femtocell network; HetNet; JLCPFeICIC; coordinated multipoint transmission; modulation and coding scheme

A Robust Game-Based Algorithm for Downlink Joint Resource Allocation in Hierarchical OFDMA Femtocell Network System

Femtocell is a promising technology for wireless service networks to facilitate sustainable and efficient services for users. This paper deals with a downlink joint channel assignment and power allocation problem with multiple channels, users, constraints, and uncertainties in the orthogonal frequency division of a multiple-access hierarchical femtocell network system. Specifically, a hierarchical robust Stackelberg game, which aims to achieve robust equilibrium, is first proposed for resource allocation with uncertainties. Then, a low-complexity, low-interference, high-efficiency, and high-performance algorithm is presented to handle the complex robust joint allocation problem. Considering the demand capacity of macro-base stations, an efficient fitness function in conjunction with particle swarm optimization-constriction factor, is utilized to yield the best response in the upper game to satisfy multiple constraints. Meanwhile, an iterative waterfilling algorithm is exploited to achieve the best response of femto-base stations in the lower game. Lastly, a stop protocol is established for two-tier users to accomplish an efficient robust Stackelberg equilibrium. Comparative results demonstrated that the proposed algorithm is superior to the existing game-based algorithms.

Joint Channel Assignment and Adaptive Mode Selection in MIMO-Based Cognitive Radio Networks

In this paper, we develop a joint channel assignment and adaptive Multi-Input–Multi-Output (MIMO) mode selection protocol for MIMO-based multi-channel cognitive radio networks. The proposed protocol utilizes the dynamic access capabilities of cognitive radio devices along with the multiplexing and/or diversity gains offered by MIMO systems in order to improve network throughput and communication reliability. The proposed protocol attempts at enhancing system performance by selecting the best MIMO operating modes (multiplexing, diversity, or hybrid multiplexing-diversity mode) for each idle channel according to the surrounding RF environment, and then selects the channel that provides the highest throughput. The network performance of our protocol (the overall network throughput and MIMO efficiency) is investigated under various traffic and network conditions through simulations. Compared to diversity-mode channel assignment, multiplexing-mode channel assignment, and adaptive mode selection with random channel selection protocols, our proposed protocol reveals significant performance improvement in network throughput under different traffic and network conditions.

Resources Allocation in Underlay Device-to-Device Communications Networks: A Reduced-Constraints Approach

Device-to-Device (D2D) communications underlaying cellular networks have emerged as a necessity for a substantial increase in the system throughput and the number of active devices for the future cellular networks. In underlay D2D networks, it is conventional to use different interference management (IM) techniques to allow D2D transmitters to reuse the cellular users’ subcarriers. Conventionally, those IM techniques pair a specific number (one or more) of D2D transmitters to each subcarrier and/or allow each D2D transmitter to transmit on a specific number of subcarriers simultaneously in order to achieve the target rates. Due to the mixed-integer nature of those IM techniques, convex optimization techniques can not be used, and usually complex heuristic or game-theoretic approaches are exploited. In this paper, we introduce a reduced-constraints approach to seek sub-optimal joint power allocation and channel assignment solutions for two non-convex, mixed-integer, and non-linear programs (MINLP). Specifically, via the reduced-constraints approach and variable transformation techniques, we can exploit primal-dual algorithms to solve system power minimization and energy-efficiency maximization problems. Extensive numerical simulation results show that the proposed approach outperforms state-of-the-art techniques.

Traffic-Demand-Aware Collision-Free Channel Assignment for Multi-Channel Multi-Radio Wireless Mesh Networks

In multi-channel multi-radio wireless mesh networks (MCMR WMNs), assigning each radio with an appropriate channel to maximize the performance is a challenging problem. In this optimization, the primal concern lays on how to mitigate effects of interference to avoid performance degradation. However, collision-freedom for a given traffic demand under the limitation of precious channel resources has not been achieved yet. In this paper, we present a collision-free joint channel assignment and routing scheme called TACCA (Traffic-demand-Aware Collision-free Channel Assignment) for MCMR WMNs. To reduce the required number of channels, TACCA incorporates a property of CSMA (Carrier Sense Multiple Access), i.e., it adopts CSMA-aware interference model and a CSMA-aware shared link capacity model. We formulate a mixed integer linear programming (MILP) to optimize the network utility and enhance the practical usefulness under the given traffic demands. The evaluation results with a MILP solver show that TACCA achieves collision-freedom in both grid and random topology networks with 3-5 orthogonal channels, and exhibits good network utilization performance. In addition, the network simulation results show that TACCA achieves mostly collision-free communications under up-to-date PHY and MAC models, and presents excellent communication performance.

Joint Channel Assignment and Occupancy Time Optimization in Frame-Based Listen-Before-Talk

We study the performance optimization problem of the long term evolution (LTE) network operating in the unlicensed band and sharing it with an existing WiFi network. We consider the LTE network to be based on the frame-based listen-before-talk protocol. We formulate the joint channel assignment and channel occupancy time optimization problem using a stochastic integer programming (IP) model. By applying the Lyapunov drift-plus-penalty theorem, we develop an asymptotically optimal solution with polynomial time-complexity for the stochastic IP problem. Numerical results are presented and the performance of the proposed solution is compared to the performance of two alternative approaches in which the channel assignment and channel occupancy time are determined sequentially.

Joint D2D Group Association and Channel Assignment in Uplink Multi-Cell NOMA Networks: A Matching-Theoretic Approach

This paper studies joint device-to-device (D2D) group association and channel assignment in uplink multi-cell non-orthogonal multiple-access (NOMA) networks. Particularly, the goal is to assign D2D groups to cellular user channels at each base-station, while accounting for negative network externality due to the interference caused by pairing a user with a D2D group. To that end, a multi-objective signal-to-interference-plus-noise ratio (SINR)-maximizing power allocation solution procedure is proposed to determine the optimal power allocation for each (D2D group, user) pair, while meeting quality-of-service (QoS) requirements. After that, the joint D2D group association and channel assignment problem is modeled as a student-project allocation with preferences over (student, project) pairs matching problem. More specifically, two polynomial-time complexity stable matching algorithms are proposed to pair D2D groups with users, and associate them with base-stations. Simulation results are presented to evaluate the proposed matching algorithms when combined with the devised solution procedure, and compare them to a joint D2D group association, channel assignment and power allocation (J-GA-CA-PA) scheme. More importantly, the proposed algorithms are shown to efficiently yield comparable SINR-per user and D2D receiver-to the J-GA-CA-PA scheme, while maintaining QoS requirements.

Joint Power Allocation and Channel Assignment for NOMA With Deep Reinforcement Learning

Non-orthogonal multiple access (NOMA) has been considered as a significant candidate technique for the next generation wireless communication to support high throughput and massive connectivity. It allows different users to be multiplexed on one channel through applying superposition coding at the transmitter and successive interference cancellation (SIC) at the receiver. To fully utilize the benefit of the NOMA technique, the key problem is how to optimally allocate resources, such as power and channels, to users to maximize the system performance. There have been some existing works on the power allocation for the single-carrier NOMA system. However, how to optimally assign channels in the multi-carrier NOMA system is still unclear. In this paper, we propose a deep reinforcement learning framework to allocate resources to users in a near optimal way. Specifically, we exploit an attention-based neural network (ANN) to perform the channel assignment. Simulation results show that the proposed framework can achieve better system performance, compared with the state-of-the-art approaches.

User association and channel assignment in downlink multi-cell NOMA networks: A matching-theoretic approach

This paper studies the problem of stable user association and channel assignment in downlink multi-cell non-orthogonal multiple-access (NOMA) networks. To be specific, the goal is to assign network users to the channels at each base station, while accounting for inter-user interference and maintaining quality of service (QoS) per user. To that end, a low-complexity iterative solution procedure is devised to obtain the optimal power allocation for proportional fairness signal-to-interference-plus-noise ratio (SINR)-based maximization, which is then utilized to determine the preferences of network users over the channels available at each base station and the preferences of base stations over the network users. In turn, a many-to-one matching-theoretic model based on the student-project allocation problem is applied. Particularly, two polynomial-time complexity stable matching algorithms are proposed to associate users with base stations and perform channel assignment, such that no user or base station would deviate and change its association or channel assignment unilaterally. To validate the efficacy of the proposed solution procedure and stable matching algorithms, extensive simulation results are presented to compare them to a centralized joint user association, channel assignment, and power allocation (C-J-UA-CA-PA) scheme. It is demonstrated that the proposed algorithms efficiently associate users with base stations and assign them to channels as well as efficiently yielding comparable SINR per user to the C-J-UA-CA-PA scheme, while maximizing proportional fairness and satisfying QoS constraints.

Device vs Edge Computing for Mobile Services: Delay-Aware Decision Making to Minimize Power Consumption

A promising technique to provide mobile applications with high computation resources is to offload the processing task to the cloud. Utilizing the abundant processing capabilities of the clouds, mobile edge computing enables mobile devices with limited batteries to run resource hungry applications and to save power. However, it is not always true that edge computing consumes less power compared to device computing. It may take more power for the mobile device to transmit a file to the cloud than running the task itself. This paper investigates the power minimization problem for the mobile devices by data offloading in multi-cell multi-user OFDMA mobile edge computing networks. We consider the maximum acceptable delay as QoS metric to be satisfied in our network. We formulate the problem as a mixed integer nonlinear problem which is converted into a convex form using D.C. approximation. To solve the converted optimization problem, we have proposed centralized and distributed algorithms for joint power allocation and channel assignment together with decision-making. Simulation results illustrate that by utilizing the proposed algorithms, considerable power savings can be achieved, e.g., about 60 percent for large bit stream size compared to local computing baseline.

Joint Computation Offloading, Power Allocation, and Channel Assignment for 5G-Enabled Traffic Management Systems

Due to the ever-increasing requirements of delay-sensitive and mission-critical applications in 5G, mobile edge computing is promising to react and support real-time interactive systems. However, it is still challenging to construct a 5G-enabled traffic management system, owing to the qualification of ultra-low latency and ubiquitous connectivity. Furthermore, the computing resources and storage capacities of edge nodes are limited, thus computation offloading is a fundamental issue for real-time traffic management. This paper puts forward a hybrid computation offloading framework for real-time traffic management in 5G networks. Specially, we consider both nonorthogonal-multiple-access-enabled and vehicle-to-vehicle-based traffic offloading. The investigated problem is formulated as a joint task distribution, subchannel assignment, and power allocation problem, with the objective of maximizing the sum offloading rate. After that, we prove its NP-hardness and decompose it into three subproblems, which can be solved iteratively. Performance evaluations illustrate the effectiveness of our framework.

Resource Allocation for Real-Time D2D Communications Underlaying Cellular Networks

Real-time device to device (D2D) communications are important for applications of intelligent transportation, Internet of Things, etc. Most recently, researchers have focused on improving the throughput of the D2D communications underlaying cellular networks, while they have ignored the real-time requirements of packet transmissions. In this paper, we investigate the resource allocation problems for real-time D2D communications aiming to maximize the total utility of packets meeting their deadlines. First, we adopt the Markov Decision Process (MDP) to model the problem. Based on this model, we propose an optimal offline channel and slot allocation algorithm. Considering the high time complexity of the optimal offline algorithm, we then propose an online joint packet admission control, channel, and slot assignment algorithm. The online algorithm is $O(\log _2(\mu))$ -competitive, where $\mu$ is related to the deadlines of packets. We have proved the optimality of the online algorithm in terms of the competitive ratio among all of the online algorithms. Additionally, we have proposed a method to reduce the pessimism of the online algorithm. Simulation results show that the optimal offline algorithm achieves better performance than the online algorithm. The online algorithm outperforms the well-known real-time task scheduling algorithm EDF in terms of the total utility.