Distributed Channel Allocation Research Articles

Multi-Agent Deep Reinforcement Learning-Empowered Channel Allocation in Vehicular Networks

Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is appealing to devise a blended strategy to perform effective resource sharing. In this paper, we exploit deep learning techniques predict vehicles’ mobility patterns. Then we propose an architecture consisting of centralized decision making and distributed channel allocation to maximize the spectrum efficiency of all vehicles involved. To achieve this, we leverage two deep reinforcement learning techniques, namely deep Q-network (DQN) and advantage actor-critic (A2C) techniques. In addition, given the time varying nature of the user mobility, we further incorporate the long short-term memory (LSTM) into DQN and A2C techniques. The combined system tracks user mobility, varying demands and channel conditions and adapt resource allocation dynamically. We verify the performance of the proposed methods through extensive simulations and prove the effectiveness of the proposed LSTM-DQN and LSTM-A2C algorithms using real data obtained from California state transportation department.

Trial and Error Learning for Dynamic Distributed Channel Allocation in Random Medium

This paper considers the problem of fully distributed channel allocation in clustered wireless networks when the propagation medium is random. We extend here the existing Trial and Error (TE) framework developed in the deterministic case and for which strong convergence properties hold. We prove that using directly this solution in the random context leads to unsatisfactory solutions. Then we propose an adaptation of the original Trial and Error Learning (TEL) algorithm, called Robust TEL (RTEL), assuming that the random channel effects translate into a bounded stochastic disturbance of the utility function. The solution consists in introducing thresholds in the transitions of the TEL’s Finite State Controller (FSC). We prove that this new solution restores the good convergence property inherited from the TEL. Furthermore, we provide analysis of the stochastic utilities in the Rayleigh fading case in order to check the bounded assumption. Finally, we develop an online algorithm that dynamically estimates the optimal threshold values to adapt to the instantaneous disturbance. Numerical results corroborate our theoretical claims.

Centralized Versus Heuristic-Based Distributed Channel Allocation to Minimize Packet Transmission Delay for Multiband Relay Networks

To address the high capacity versus limited coverage tradeoff in Fifth Generation (5G) networks, in this letter, we introduce a relay-assisted network leveraging multiple frequency bands simultaneously. First, we formulate an optimal heterogeneous band/channel allocation strategy to multiple relays. Then, we elucidate the infeasibility of this centralized approach resulting in increased latency. As a solution, we propose a heuristic-based, distributed channel allocation approach whereby each relay node is able to make localized decision regarding the best channel allocation over heterogeneous bands. Adaptability analysis and computer-based simulation results demonstrate that our proposal outperforms the conventional method with much reduced relay latency.

QoS aware distributed dynamic channel allocation for V2V communication in TVWS spectrum

Vehicles use Dedicated Short Range Communication (DSRC) standard for communicating among themselves. But, with the increased number of on-road vehicles, DSRC channels get congested. So, an alternative to DSRC needs to be introduced, to meet the diverse demand for various vehicular applications. Federal Communication Commission released TV White Space (TVWS) for dynamic spectrum access. These TVWS channels has the potential to be used for Vehicle-to-Vehicle (V2V) communication. However, the vehicles are highly mobile and communicate in an adhoc manner. Thus, employing TVWS in V2V communication requires efficient optimized channel allocation technique. In this work, a distributed joint channel and power allocation technique is devised, that considers both spatio-temporal change of TVWS availability and vehicular mobility. The channels are allocated based on user's transmission requirement with an adjusted power level such that significant interference is not caused to neighbouring primary users and other secondary users, transmitting in the same channel. Multiple vehicles are assigned a single channel using the proposed optimization technique to increase the system throughput. Vehicles are enabled with cognitive facility that allows them to sense the spectrum availability instead of relying on the geo-location TVWS database. The optimized channel and power allocation problem is a Mixed Integer Non-Linear Programming model which is solved using Lagrange's dual method. Simulation analysis shows that proposed optimization technique achieves high throughput while maintaining the network's QoS.

Maximizing network coverage in a multichannel short-range underwater acoustic sensor network

In this paper, a Media Access Control (MAC) protocol is investigated for multichannel underwater acoustic sensor networks and a distributed channel allocation scheme is proposed for acoustic nodes equipped with a multichannel bi-directional transceiver. To ensure that a minimum of one channel is allocated for each transmitter-receiver pair, a novel distributed channel allocation scheme, the High Coverage High Fairness (HCHF) algorithm, is proposed. The algorithm requires the exchange of channel sensing information among neighbor nodes at the beginning of each transmission time slot. To compare HCHF and existing schemes, various performance metrics are assessed including spectrum utilization, coverage, fairness, and control packet overhead. Simulation results indicate that the HCHF scheme can improve the coverage and fairness in comparison to other schemes without sacrificing much on the spectrum utilization. The performance improvement of HCHF is constrained by a higher control message overhead, since local packet exchange between neighbor nodes is required to share spectrum sensing information.

A game-theoretic learning approach to QoE-driven resource allocation scheme in 5G-enabled IoT

To significantly promote Internet of Things (IoT) development, 5G network is enabled for supporting IoT communications without the limitation of distance and location. This paper investigates the channel allocation problem for IoT uplink communications in the 5G network, with the aim of improving the quality of experience (QoE) of smart objects (SOs). To begin with, we define a mean opinion score (MOS) function of transmission delay to measure QoE of each SO. For the sum-MOS maximization problem, we leverage a game-theoretic learning approach to solve it. Specifically, the original optimization problem is equivalently transformed into a tractable form. Then, we formulate the converted problem as a game-theoretical framework and define a potential function which has a near-optimum as the optimization objective. To optimize the potential function, a distributed channel allocation algorithm is proposed to converge to the best Nash equilibrium solution which is the global optimum of maximizing the potential function. Finally, numerical results verify the effectiveness of the proposed scheme.

Distributed Channel Allocation for D2D-Enabled 5G Networks Using Potential Games

Frequency channel allocation is a key technique for improving the performance of cellular networks. In this paper, we address the channel allocation problem for a 5G multi-cell system. We consider ...

Game Theoretic Dynamic Channel Allocation for Frequency-Selective Interference Channels

We consider the problem of distributed channel allocation in large networks under the frequency-selective interference channel. Performance is measured by the weighted sum of achievable rates. Our proposed algorithm is a modified Fictitious Play algorithm that can be implemented distributedly, and its stable points are the pure Nash equilibria of a given game. Our goal is to design a utility function for a non-cooperative game, such that all of its pure Nash equilibria have close to optimal global performance. This will make the algorithm close to optimal while requiring no communication between users. We propose a novel technique to analyze the Nash equilibria of a random interference game, determined by the random channel gains. Our analysis is asymptotic in the number of users. First, we present a natural non-cooperative game where the utility of each user is his achievable rate. It is shown that, asymptotically in the number of users and for strong enough interference, this game exhibits many bad equilibria. Then, we propose a novel non-cooperative M frequency-selective interference channel game as a slight modification of the former, where the utility of each user is artificially limited. We prove that even its worst equilibrium has asymptotically optimal weighted sum rate for any interference regime and even for correlated channels. This is based on an order statistics analysis of the fading channels that is valid for a broad class of fading distributions (including Rayleigh, Rician, m-Nakagami, and more). We carry out simulations that show fast convergence of our algorithm to the proven asymptotically optimal pure Nash equilibria.

Distributed Resource Allocation for D2D-Assisted Small Cell Networks With Heterogeneous Spectrum

Device-to-device (D2D) communication with increased spectral efficiency and reduced communication delay has undoubtedly become a general trend in future wireless networks. However, when D2D communication is incorporated into small cell networks (SCNs) with large number of randomly overlapped small cells, the co-channel interference between small cell users (SUEs) and D2D users is an inevitable challenge, especially with the heterogeneous spectrum, i.e., licensed spectrum bands and unlicensed spectrum bands. In this paper, we study the downlink channel allocation in D2D-assisted small cell networks with heterogeneous spectrum bands. By taking the required data rate of users and the interference constraint of SUEs into account, we formulate a channel allocation problem integrating channel selection and channel sharing to maximize the network utility, which is the service satisfaction of all users. To derive the solution, we decompose the optimization problem into two games: a potential game and a coalition game. Then, a potential game-based scheme using an interference graph and a coalition scheme with D2D user transferring is proposed to solve these two games, respectively. Based on these schemes, a two-stage distributed channel allocation algorithm is proposed and can converge with low computational complexity. Moreover, the simulation results reveal that the proposed algorithm could achieve high system throughput and network utility.

A Simple Distributed Channel Allocation Algorithm for D2D Communication Pairs

Device-to-device (D2D) communication is a promising technology to achieve high spectrum efficiency, low energy consumption, and enhanced system capacity. In D2D communications, the channel allocation plays a crucial role to avoid interference, which usually involves a centralized coordination. With the increasing number of devices or terminals as well as the dynamic network environment, the computation and information exchange during the centralized channel allocation would be a big burden for wireless networks. We tackle this problem by proposing a distributed channel allocation method with blind rendezvous to enable collision-free concurrent transmissions over multiple channels. Specifically, we first present a receiver-oriented channel allocation algorithm to reduce interference and then propose a sender-jump blind channel rendezvous algorithm based on channel hopping techniques. The proposed channel allocation method allows each sender–receiver pair to obtain an appropriate channel for collision-free transmissions requires no centralized coordination. Theoretical analysis, simulation results and test-bed experiments validate the proposed solution. Our findings reported in this paper may help network designers to contribute in the development of high-density Internet of Things.

Distributed Channel Allocation and Time Slot Optimization for Green Internet of Things.

In sustainable smart cities, power saving is a severe challenge in the energy-constrained Internet of Things (IoT). Efficient utilization of limited multiple non-overlap channels and time resources is a promising solution to reduce the network interference and save energy consumption. In this paper, we propose a joint channel allocation and time slot optimization solution for IoT. First, we propose a channel ranking algorithm which enables each node to rank its available channels based on the channel properties. Then, we propose a distributed channel allocation algorithm so that each node can choose a proper channel based on the channel ranking and its own residual energy. Finally, the sleeping duration and spectrum sensing duration are jointly optimized to maximize the normalized throughput and satisfy energy consumption constraints simultaneously. Different from the former approaches, our proposed solution requires no central coordination or any global information that each node can operate based on its own local information in a total distributed manner. Also, theoretical analysis and extensive simulations have validated that when applying our solution in the network of IoT: (i) each node can be allocated to a proper channel based on the residual energy to balance the lifetime; (ii) the network can rapidly converge to a collision-free transmission through each node’s learning ability in the process of the distributed channel allocation; and (iii) the network throughput is further improved via the dynamic time slot optimization.

Distributed ON/OFF switching and dynamic channel allocation: Decreasing complexity and improving energy efficiency

AbstractHeterogeneous network (HetNet) has emerged as a promising and viable approach to deliver higher‐rate data services. In HetNets, switching OFF base stations (BSs) is known as an effective solution to reduce the energy consumption of networks and thus enables the “greener” networks concept. In this paper, we investigate the impact of the number of energy‐saving modes for BSs on improving energy efficiency. We assume that BSs are able to switch among several modes, ie, different transmission power levels. We apply a no‐regret learning algorithm to choose the BS's operation mode while modeling the BS's operation under dynamic traffic with a Markov process. As a result, we face a trade‐off between energy savings and computational complexity. Furthermore, we use a distributed channel allocation algorithm based on the average cochannel interference power. Simulation results show that energy consumption reduces with increased number of modes. However, increasing the number of modes beyond a certain limit will not have a significant effect on energy efficiency.

Distributed Channel Allocation and Rate Control for Hybrid FSO/RF Vehicular Ad Hoc Networks

We address joint channel allocation and rate control to maximize the network throughput in vehicular ad hoc networks (VANET). Considering the limited channel resources and quality of service (QoS) requirements of vehicular communication, a mixed-integer linear programming (MILP) problem is formulated. However, it is unrealistic and inefficient to choose a center node to perform global optimization in VANET. Hence, we propose a distributed channel allocation and rate control approach to solve the cross-layer design problem. Applying the carrier sense multiple access with collision avoidance protocol, the proposed channel allocation decides the channel usages by measuring the channel demands of links and transforms the MILP problem to a corresponding linear programming problem. We adopt the alternating direction method of multipliers (ADMM) to optimize the flow rates. Since imbalanced rates may be allocated to paths passing the same link, rate adjustment is proposed to ensure the throughput requirement of each path. Simulation results demonstrate the fast convergence of the proposed distributed algorithm and the improvement of ADMM with rate adjustment over the ADMM. While the performance is sensitive to the outage probability of the FSO link, increasing the single FSO link rate does not substantially improve the probability of finding a feasible solution. In addition, a small radio frequency communication range is desirable for a network with dense node distribution due to the dominated interference.

Distributed Channel Fast Allocation in Cognitive Wireless Sensor Networks

For the feature of zero delay in continuous relay transmission, wireless sensor networks supporting multiple communication interfaces is much suitable for the data collection and transmission backbone network of coal mine safety monitor and control system which has high demand of reliability in real-time data transmission. Based on in-depth analysis of the existing distributed channel allocation algorithms, this paper proposes an innovative fast distributed channel allocation scheme based on largest edges of network topology (CALENT) with the combination of distributed and architecture features. By comparing with the existing distributed channel allocation Dis-Link algorithm, CALENT has better performance in co-channel interference index and convergence velocity.

Distributed fast channel allocation in cognitive wireless sensor networks

For the feature of zero delay in continuous relay transmission, wireless sensor networks (WSNs) supporting multiple communication interfaces is much suitable for data collection and transmission backbone network of coal mine safety monitor and control system which has high demand of reliability in real-time data transmission. Based on in-depth analysis of the existing distributed channel allocation algorithms, this study proposes an innovative fast distributed channel allocation scheme based on largest edges of network topology (CALENT) with the combination of wireless network architecture features. By comparing with existing distributed channel allocation dis-link algorithm, CALENT has better performance in co-channel interference index and convergence velocity.

Distributed Channel Allocation for Ad-hoc Multichannel Cognitive Radio Networks

Cognitive radio network was proposed to use the spectrum resource more efficiently. In a multichannel ad-hoc cognitive radio network, there is no central station and requires distributed channel scheduling. An important requirement of the cognitive radio network is to avoid interference to primary networks. In this paper, the authors propose the distributed packet scheduling scheme for ad-hoc multichannel cognitive radio networks. Each secondary user calculates independently its own priority with probabilistic manner and selects one of multi channels considering the primary user’s traffic pattern to minimize interference to the primary networks.

Self-organized Spectrum Access in Small-cell Networks with Dynamic Loads

This paper investigates the problem of co-tier interference mitigation for dynamic small- cell networks, in which the load of each small-cell varies with the number of active associated small-cell users (SUs). Due to the fact that most small-cell base stations (SBSs) are deployed in an ad-hoc manner, the problem of reducing co-tier interference caused by dynamic loads in a distributed fashion is quite challenging. First, we propose a new distributed channel allocation method for small-cells with dynamic loads and define a dynamic interference graph. Based on this approach, we formulate the problem as a dynamic interference graph game and prove that the game is a potential game and has at least one pure strategy Nash equilibrium (NE) point. Moreover, we show that the best pure strategy NE point minimizes the expectation of the aggregate dynamic co-tier interference in the small-cell network. A distributed dynamic learning algorithm is then designed to achieve NE of the game, in which each SBS is unaware of the probability distributions of its own and other SBSs’ dynamic loads. Simulation results show that the proposed approach can mitigate dynamic co-tier interference effectively and significantly outperform random channel selection.

Distributed Channel Allocation Protocols for Wireless Sensor Networks

Interference between concurrent transmissions can cause severe performance degradation in wireless sensor networks (WSNs). While multiple channels available in WSN technology such as IEEE 802.15.4 can be exploited to mitigate interference, channel allocation can have a significant impact on the performance of multi-channel communication. This paper proposes a set of distributed protocols for channel allocation in WSNs with theoretical bounds. We first consider the problem of minimizing the number of channels needed to remove interference in a WSN, and propose both receiver-based and link-based distributed channel allocation protocols. Then, for WSNs with an insufficient number of channels, we formulate a fair channel allocation problem whose objective is to minimize the maximum interference (MinMax) experienced by any transmission link in the network. We prove that MinMax channel allocation is NP-hard, and propose a distributed link-based MinMax channel allocation protocol. Finally, we propose a distributed protocol for link scheduling based on MinMax channel allocation that creates a conflict-free schedule for transmissions. The proposed decentralized protocols are efficient, scalable, and adaptive to channel condition and network dynamics. Simulations based on the topologies and data traces collected from a WSN testbed of 74 TelosB motes have shown that our channel allocation protocols significantly outperform a state-of-the-art channel allocation protocol.

Interference Minimization and Dynamic Sub-Carrier Allocation in Broadband Wireless Networks

Efficient channel allocation is the key to fully exploit the signal diversity presented in the multi-carrier physical link in today's broadband wireless access networks. There are mounting evidences that the 4G and future-generation systems will take advantage of two opposite types of access methods, one using centralized control method and the other using a distributed approach. The authors study the distributed channel allocation problem in this article, formulated as a non-linear optimization problem, in broadband wireless networks. The signal properties of the multi-carrier radio interface in 3G and 4G networks are discussed to comprehend the complexity of the channel allocation problem. The authors propose a novel distributed heuristic algorithm based on the particle-swarm searching method. The distributed approach allows user stations to quickly switch sub-carriers with minimum intervention from the base station. The work presented in this paper shows an effective method to allocate a large number of channels while minimizing the possible interference. Extensive numerical experiments are conducted to evaluate several versions of distributed channel allocation algorithm for these new problem settings. The authors' results show that PSO-based method converges quickly in all our numerical experiments despite the high-dimensional searching space. The proposed technologies will eventually allow the true mobile steaming video/audio experience anywhere and anytime, which will have the huge impact to business, entertainment and people's quality of life.

A Distributed Channel Allocation Algorithm for Multi-channel Wireless Networks

A Distributed Channel Allocation Algorithm for Multi-channel Wireless Networks