Channel Allocation Algorithm Research Articles

Deep Reinforcement Learning based channel allocation (DRLCA) in Cognitive Radio Networks

The Spectrum is a scarce resource in wireless networks, hence these resources need to be perfectly channelized for their better utilization. By the study of the previous decades, it is found that the spectrum is colossally underutilized and the main reason for the underutilization found out to be the policies that are fixed and not dynamic. The dynamic spectrum allocation of frequency bands may overcome this problem. Cognitive radio provides an important concept that can used to solve the problem of underutilization of spectrum. Reinforcement learning is a key technique that is widely used to learn the spectrum allocation behaviour and maximize the system’s efficiency. Therefore, in this work, we have designed and developed a Reinforcement learning-based model to allocate the channels among secondary users. Also, a Deep Reinforcement learning-based channel allocation algorithm (DRLCA) has been proposed. The proposed DRLCA is compared with existing JPCRL [1]. In our algorithm, the Python libraries were used for simulation. From the simulation results and analysis, it is found that the DRLCA outperforms the JPCRL in terms of channel utilization by 5%.

Heterogeneous temporal graph powered DRL algorithm for channel allocation in Maritime IoT Systems

In actual maritime Internet of Things systems, the communication environment is characterized by its time-varying nature and the presence of highly heterogeneous network structures. Those attributes present considerable challenges in devising resource allocation strategies. Given the limited availability of frequency resources, designing a reasonable and flexible channel allocation strategy (CAS) is the primary task for meeting diverse and dynamic communication demands. In this paper, a heterogeneous temporal graph powered deep reinforcement learning algorithm is proposed to optimize the CAS to maximize the channel efficiency in a real-world maritime Internet of Things system. Specifically, we build relation-based heterogeneous edges to connect different types of terminal nodes and adopt a time encoding technology to capture the dynamic evolution of communication scenarios over time. The memory and public mailbox modules are constructed as the implementation entities of the information aggregation method based on the attention mechanism. In addition, we develop a corresponding heterogeneous temporal neural network to estimate the real-time resource requirements of target terminals, and subsequently learn the optimal CAS based on the deep reinforcement learning algorithm from the perspective of maximizing the cumulative channel efficiency. Simulation results prove that the proposed algorithm significantly outperforms the other state-of-the-art algorithms in terms of the channel efficiency and generalization ability.

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.

QoS-Aware Content Dissemination Based on Integrated Social and Physical Attributes Among Cellular and V2V Users

With the recent advancement in intelligent transportation systems (ITS), the vehicular network has become the major force in content dissemination, and it is essential to improve efficiency in content delivery and quality and service (QoS). The rapid growth in vehicular data traffic and limited resources in the vehicular network have created a bottleneck in content dissemination. In addition, the demand of high data rate, low latency and user's satisfaction on content make content dissemination more challenging. To address the above problem which involves vehicle to vehicle (V2V) peer discovery and resource allocation, we propose an efficient content dissemination scheme which integrates both social and physical attributes of the users. V2V users are paired to share content based on the similarity in their preferences and link reliability, while considering the QoS of vehicle to network (V2N) users in both uplink and downlink phases. Moreover, we present an improved matching algorithm for channel allocation, which maximizes the sum rate of V2V pairs weighted by the intensity of their social relationship. Simulation results demonstrate the performance of the proposed scheme in terms of weighted sum rate and user's satisfaction on content.

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.

Design and Analysis of Energy-efficient Algorithm for Wireless Networks

The Industrial, Scientific, and Medical (ISM) frequency spectrum is used by the Wireless Local Area Network (WLAN) for its operation. Due to a limited number of available channels for WLAN operation, frequency channel assignment becomes very difficult. In WLAN, the access points (APs) are widely deployed, and because they share the same frequency, they start interfering with each other and causing more energy consumption. So WLAN frequency channels should be managed and assigned carefully to increase the energy efficiency of the wireless network. This paper proposes an energy-efficient channel assignment algorithm (EECAA) to improve the performance of the network. The suggested approach improves the network's energy efficiency in terms of global delay, packet data loss, and the count of data retransmissions. The extensive simulation study demonstrates that, when compared to the channel handoff scheme and D2MD channel allocation algorithm for various traffic and network conditions, the proposed EECAA method greatly increases network throughput.

VariBan: A Variable Bandwidth channel allocation algorithm for IEEE 802.15.4e-based networks

Among several link-layer protocols, Time-Slotted Channel Hopping (TSCH), defined in the IEEE 802.15.4e standard, offer a good performance improving reliability and throughput for wireless sensor network using in the best way two parameters: time and frequency. Many works in the current literature present scheduling schemes for the protocol. In this scenario, our work is among a small group that aims to explore the improvements of the protocol by exploiting different techniques over the transport layer of the network protocol. Channels with wider bandwidth can improve network performance by increasing the throughput and improving packet delivery. Therefore, this paper proposes a scheduling algorithm to dynamically allocate channels with variable bandwidth to improve the overall throughput and packet delivery. The scheduling and channel allocation happen using the conflict mapping method to identify links in congestion and schedule them. This work proposes a procedure for allocating channels with bandwidth proportional to the demand for packets per link. We implemented the algorithm in the Contiki-NG platform and evaluated the result through simulations. Results show improved time slot allocation, packet delivery ratio, and throughput. Our experiments show that VariBan achieves a throughput of over 99% and improves Packet Delivery Ratio by over 50% in all three scenarios compared to the state-of-the-art algorithm.

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.

Heterogeneous VLC/RF multi-hop cluster V2V channel allocation algorithm based on equivalent SINR.

To address low communication quality and limited transmission rate between vehicle nodes in the vehicularad hoc network (VANET), this paper builds a heterogeneous visible light communication (VLC) and radio frequency (RF) communication multi-hop communication model based on vehicle node clustering, and then a heterogeneous VLC/RF multi-hop cluster vehicle-to-vehicle (V2V) channel allocation algorithm based on equivalent signal to interference plus noise ratio (SINR) (NCAABES) is presented. This algorithm is based on the clustering of vehicle nodes, which introduces the concept of equivalent SINR. The equivalent SINR of the VLC channel between the cluster head (CH) and cluster member (CM) is used as the condition for channel allocation. When the channel between CH and CM is blocked or low quality, the neighboring vehicle between two vehicles is used as a relay node to communicate in a multi-hop way, and the channel with the best SINR is chosen as the current CH-CM or CM-CM communication method. The simulation results show that the SINR of NCAABES in this paper increases by 21.73%, 30.23%, and 70.96% compared to the novel multi-hop clustering scheme based on the weighted virtual distance detection (MCSVDD), the VLC network (VLCnet), and the RF network (RFnet), respectively. And the NCAABES's bit error rate (BER) is always the lowest compared to MCSVDD, VLCnet, and RFnet, even when the number of vehicles and transmission power change. This algorithm can improve the quality of communication between vehicle nodes, make VANET more efficient, and get a higher transmission rate.

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.

Load-Aware Channel Allocation for IEEE 802.11ah-Based Networks

Most wireless communication technologies addressed to the Internet of Things (IoT) applications face the bottleneck of dense and large-scale use cases. One solution to this problem is a periodic channel reservation strategy, in which only a small group of stations can compete for channel access during a given period. The IEEE 802.11ah standard, a.k.a. Wi-Fi HaLow, deploys this idea in its channel access protocol, named Restricted Access Window (RAW). A single RAW consists of one or more RAW slots during which only designated stations can contend for channel access. This paper considers an IEEE 802.11ah-based network with randomly distributed stations around the Access Point (AP), operating under a Rayleigh-fading channel with capture enabled. We develop an analytical model to evaluate the contention of a group of stations and propose a Load-Aware Channel Allocation (LACA) algorithm for the RAW slot period. The LACA algorithm ensures the delivery of all packets that designated stations carry, allowing the allocation of load-aware RAW slots, which is effective in enhancing the Age of Information (AoI). We evaluate the Packet Delivery Ratio (PDR) and channel usage within a pre-allocated RAW slot to prove the effectiveness of our proposal. We further study the impact of the spatial distribution of the stations around the AP and the capture effect under a Rayleigh channel on the performance of the proposed LACA algorithm. Extensive simulations are used to validate our analytical results. Our proposal provides a load-aware and adaptive channel allocation scheme based on the dynamic conditions of the network. Our model can be implemented in a global configuration scheme for the RAW mechanism in heterogeneous networks or for alternative communication technologies that address dense scenarios with the integration of periodic channel reservations.

Comparison of Different Frequency Channel Allocation Methods in ultra-dense networks

The impact of information transmission on production and life under dense networking is self-evident. Dense networking application scenarios include airports, dense residential buildings, dense business districts and blocks, campuses, large gatherings, stadiums, subways, etc. However, with the increase of 5G frequency band, the 5G macro base station signal attenuates more when it penetrates the wall than 4G, which makes it difficult to cover indoor signals. The outdoor 5G macro base station signal can only provide shallow indoor coverage after penetrating obstacles such as brick walls, glass and cement, which cannot guarantee the good experience required for indoor deep coverage. This paper compares and discusses various channel allocation algorithms based on user scenarios in ultra-dense networks. By adjusting some parameters of 19 units, the integer frequency reuse (IFR) method (N=1,3), fractional frequency reuse (FFR) method, simultaneous water injection (SWF) method, and transmission interference temperature limit (TX-ITL) method are compared. In order to increase the capacity, a combination of several algorithms will also be used.

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.

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

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

Design of Ideological and Political Communication Path of Curriculum under the Background of Intelligent Information of New Media

In order to improve the effectiveness of the safe, high-quality, and efficient communication mechanism of ideological and political courses in the new media perspective, the ideological and political communication path of courses in the new media perspective based on blockchain technology is designed. Mainly through the organic combination of online communication, offline-online dynamic feedback, and offline communication, based on the blockchain structure, an online information transmission path of online ideological and political courses leading to offline and offline feedback online is formed. In this path, the new media network platform uses the method based on bad information screening to screen out bad information, obtain a relatively safe ideological and political course information and cultural source, and then spread the information. Through the optimization strategy of PBFT consensus protocol based on a C4.5 decision tree, the trust environment for the dissemination of ideological and political courses is constructed. The network load balancing channel allocation algorithm based on the maximum flow of curriculum ideological and political communication path is adopted to ensure the network fluency and realize the efficient dissemination of curriculum ideological and political information. The experimental results show that the designed path can realize information security, high quality, and efficient sharing of ideological and political courses and has good performance.

Multichannel Resource Allocation for Smooth Streaming: Non-Convexity and Bandits

User dissatisfaction due to buffering pause during streaming is a significant cost to the system, which we model as a non-decreasing function of the frequency of buffering pause. Minimization of the total user dissatisfaction in a multi-channel cellular network leads to a non-convex problem. By utilizing a combinatorial structure in the problem, we first propose a polynomial time joint admission control and channel allocation algorithm which is provably (almost) optimal. This scheme assumes that the base station (BS) knows the multimedia frame statistics of the streams. In a more practical setting, where these statistics are not available a priori at the BS, a learning based scheme with provable guarantees is developed. This learning based scheme is related to regret minimization in multi-armed bandits with non-i.i.d. and delayed reward (cost). All these algorithms require none to minimal feedback from the user equipment to the base station regarding the states of the media player buffer at the application layer, and hence, are of practical interest.

Cellular Communications Over Unlicensed mmWave Bands With Hybrid Beamforming

In this paper, we study the cellular communications over unlicensed millimeter-wave (mmWave) bands to satisfy the extra-high transmission rate demands. To guarantee the harmonious coexistence of cellular and WiGig users over 60 GHz bands, we design a frame structure for the unlicensed mmWave spectrum sharing to reduce the interference caused by cellular data transmission to WiGig networks. By joint frequency and spatial resource allocation, an iterative channel allocation and hybrid beamforming algorithm is designed to maximize the sum rate of all cellular users while minimizing the interference to WiGig networks. Simulation results show that our proposed scheme outperforms the SVD-based hybrid beamforming method. The fairness between the cellular and the WiGig users is achieved by setting an optimal transmit power of the gNB for unlicensed mmWave spectrum sharing. The influence of the beamwidth and beam direction of the gNB is evaluated in terms of the system performances of both the unlicensed cellular users and WiGig users.

Coupled-Theoretical-Model-Based on-Demand Quantum Secured Future Fronthaul Architecture Over Hybrid Core Fibers

In this paper, we propose a coupled theoretical model used for noise processing at nodes of hybrid core fiber with single core fiber (SMF)-multicore fiber (MCF), and intra-core noises and inter-core noises are considered in the model. Then, we propose an on-demand quantum secured future fronthaul architecture and a channel allocation algorithm over hybrid core fibers, and the proposed coupled theoretical model is used to calculate the noises in architecture. For fronthaul optical signals, wavelength modes and the number of transceiver modules in the distributed unit (DU) can be dynamically adjusted according to requirements, and the function of load aggregation is adopted. For quantum key distribution (QKD), quantum signals are transmitted in a dedicated core, and wavelength-time multiplexing is adopted to increase the number of quantum secured base station (BSs) while improving wavelength utilization. Finally, simulation results show that proposed channel allocation algorithm can suppress noise about 60% compared to First-Fit. The architecture can save up to 83.3% modules in 7-core feeder fiber, and it is feasible to support 128 BSs. The experimental and simulation results show that the proposed coupled theoretical model matches well with the experimental results, and the quantum bit error rates in experiment are both lower than 2.5%. The results show that the architecture has the advantages of allocating time-space-frequency resources on-demand, saving power consumption, and expanding the number of users. Therefore, it provides a feasible solution for future optical fronthaul networks secured by QKD.

Outage probability minimization based power control and channel allocation in underlay D2D‐NOMA for IoT networks

AbstractThe emergence of Internet‐of‐Things (IoT) aims to improve modern life by inter‐connecting many smart devices, technologies, and applications. It, however, drastically increases mobile traffic and requires large wireless spectrum. Therefore, for a better spectrum utilization to support this massive connectivity along with low latency and close proximity based communication, non‐orthogonal multiple access enabled underlay device‐to‐device (D2D‐NOMA) is envisioned as a key technology for IoT deployment. Hence, in this work, we consider multiple IoT clusters communicating via underlay D2D‐NOMA in an uplink cellular network, with the objective of minimizing the outage probability (OP) while allocating resources to the clusters. Specifically, for each cluster, we formulate the optimization problem of OP‐based power control with fixed channel allocation (PCCA) and solve it using particle swarm optimization (PSO). Additionally, we prove the convexity of PCCA problem and obtain closed‐form expressions at high SNR. Further, we propose a novel OP‐aware channel allocation (OPCA) algorithm to enhance the overall D2D network outage performance. The results are numerically validated and an average performance improvement of 31% and 51% is observed over the two conventional schemes namely fixed power allocation and orthogonal multiple access enabled D2D, respectively.

Dynamic Channel Allocation Algorithm for Heterogeneous Cellular Networks in Power Communication

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.