Channel Allocation Policy Research Articles

Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G–4G spectrums. Evaluation reveals SVM’s 98.8% accuracy in detecting spectrum holes and ANFIS’s 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications.

Hyper-graph matching D2D offloading scheme for enhanced computation and communication capacity

As the Internet of Things(IoT) and its intelligent applications continue to proliferate, forthcoming 6G networks will confront the dual challenge of heightened communication and computing capacity demands. To address this, D2D collaborative computing is being explored. However, the current D2D collaborative computing ignores the integrity of computing and communication. For a single-task device, offloading operations intertwine computing and communication, internal coupling causes due to parallel executed between local and D2D offloading. In addition, external coupling arises among devices competing for limited radio and computing resources. Worse, internal coupling and external coupling interact, exacerbating the situation. To address these challenges, a novel D2D offloading framework is proposed based on hyper-graph matching in this paper. Our goal is to minimize both delay and energy costs while ensuring service quality for all users by jointly optimizing task scheduling, offload policies and resource allocation. The original problem is formulated as a nonlinear integer programming problem. Then, by three-stage strategy optimization decomposition, it is separated into several sub-problems. In the first stage, a polynomial-time algorithm has been developed to optimize the task offloading ratio, taking into account both its upper and lower bounds. In the second stage, a geometric programming algorithm has been created to address power allocation. In the third stage, a three-dimensional hyper-graph matching model is employed to derive the optimal offloading and channel allocation policies. This is based on analyzing the conflict graph and applying the claw theorem. Simulation results demonstrate that the proposed scheme outperforms other algorithms by approximately 12%, 20%, 28%, 40%, respectively. Moreover, it enhances both spectral efficiency and computational efficiency.

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

The increasing demand for seamless wireless services coupled with underutilization of limited spectrum due to fixed channel allocation policy have posed numerous challenges to wireless communications. There is need to design appropriate channel allocation strategies for continuous communication by mobile users for higher spectral efficiency. Cognitive radio network (CRN) offers a solution to the spectrum scarcity problem inherent in 4G and other networks through dynamic spectrum access, by allowing unlicensed secondary users (SUs) with cognitive devices to opportunistically access the spectrum holes when the licensed primary users (PUs) are not occupying them. Often times, measurements taken by SUs during the sensing process are uncertain due to multipath fading, shadowing and varying channel conditions. This results in imprecise spectrum detection and selection by SUs during switching decisions causing incessant spectrum handoff and undesirable ping-pong effect. In this paper, a support vector machine (SVM) classifier is used to categorize spectrum into two classes of busy and idle. Then, based on heterogeneous quality of service (QoS) requirements of the SUs, dynamic activities of PUs, and the fluctuating channel state information, a QoS-aware Adaptive Neuro-Fuzzy Inference System (ANFIS) framework is developed for spectrum switching decision using underlay spectrum access model. SVM predicted spectrum holes with 98.8% accuracy. ANFIS model yielded a 91.62% accuracy in the task of allocating spectrum holes to SUs for coexistent with PUs. Results further indicate that the intelligent framework can ensure fairness among SUs, reduce interference, improve throughput, and spectral efficiency. It can be deployed in disaster relief and emergency, public safety, and battlefield environments.

A novel machine learning-based framework for channel bandwidth allocation and optimization in distributed computing environments

Efficient utilization of network resources, particularly channel bandwidth allocation, is critical for optimizing the overall system performance and ensuring fair resource allocation among multiple distributed computing nodes. Traditional methods for channel bandwidth allocation, based on fixed allocation schemes or static heuristics, often need more adaptability to dynamic changes in the network and may not fully exploit the system’s potential. To address these limitations, we employ reinforcement learning algorithms to learn optimal channel allocation policies by intermingling with the environment and getting feedback on the outcomes of their actions. This allows devices to adapt to changing network conditions and optimize resource usage. Our proposed framework is experimentally evaluated through simulation experiments. The results demonstrate that the framework consistently achieves higher system throughput than conventional static allocation methods and state-of-the-art bandwidth allocation techniques. It also exhibits lower latency values, indicating faster data transmission and reduced communication delays. Additionally, the hybrid approach shows improved resource utilization efficiency, efficiently leveraging the strengths of both Q-learning and reinforcement learning for optimized resource allocation and management.

Energy-Efficient Network Protocols and Resilient Data Transmission Schemes for Wireless Sensor Networks—An Experimental Survey

Wireless sensor networks (WSNs) are considerably used for various environmental sensing applications. The architecture and internal specifications of WSNs have been chosen based on the requirements of particular applications. On this basis, WSNs consist of resource (energy and memory)-limited wireless sensor nodes. WSNs initiate data communication from source to destination via physical layer management principles, channel slot scheduling principles (time division multiple access), wireless medium access control (WMAC) protocols, wireless routing protocols and application protocols. In this environment, the development of WMAC principles, routing protocols and channel allotment schemes play crucial roles in network communication phases. Consequently, these layering functions consume more energy at each sensor node, which leads to minimal network lifetime. Even though the channel management schemes, medium control protocols and routing protocols are functionally suitable, the excessive energy consumption affects the overall network performance. In this situation, energy optimization algorithms are advised to minimize the resource wastage of WSNs during regular operations (medium control and routing process). Many research works struggle to identify the optimal energy-efficient load balancing strategies to improve WSN functions. With this in mind, the proposed article has conducted a detailed literature review and notable experimental comparisons on energy-efficient MAC protocols, channel scheduling policies and energy-efficient routing protocols. To an extent, the detailed analysis over these wireless network operations helps to understand the benefits and limitations of recent research works. In the experimental section of this article, eight existing techniques are evaluated under energy optimization strategies (WMAC, channel allocation, sleep/wake protocols, integrated routing and WMAC policies, balanced routing and cooperative routing). The proposed review and the classified technical observations collected from notable recent works have been recognized as crucial contributions. The results infer the suggestions for feasible WSN communication strategies with optimal channel management policies and routing policies. Notably, the simulation results show that cross-layer or multi-layer energy optimization policies perform better than homogeneous energy optimization models.

Quality ensured non‐orthogonal multiple access based downlink scalable video coding multicasting over cognitive radio network

AbstractThe combination of the cognitive radio network (CRN) and non‐orthogonal multiple access (NOMA) technologies for scalable video coding (SVC) can be considered as a promising approach for quality‐driven video transmission over a fifth‐generation (5G) network. Advanced techniques such as efficient channel allocation, advanced sensing strategy, and optimum power allocation can combinedly improve the overall system performance. In this context, we propose an intergroup content‐aware proportionate channel allocation policy, sensing time optimization strategy and quality of service (QoS) ensured intragroup power allocation scheme for downlink SVC multicasting over CRN using NOMA technology to enhance the quality of experience (QoE) of secondary users (SUs). We propose an optimum power allocation strategy for different NOMA layers to maximize the weighted sum rate of different SVC groups ensuring the individual QoS requirements. Although the power allocation problem seems to be nonconvex, however, it can be transformed into a convex function under some specific conditions which are discussed in this article. Extensive simulation reveals that our proposed power allocation scheme for NOMA with QoS constraint outperforms the other two conventional schemes, that is, NOMA fixed power allocation and NOMA without QoS constraint in terms of average weighted sum rate and average QoE of SUs.

Channel Allocation Policy for Distributed Wireless Network: Derivation and Analysis of Optimal Interference

Distributed wireless networks with smart users (independent and rational) are becoming popular, and researchers are studying distributed equilibrium solutions like Nash Equilibrium (NE) to analyze and predict the convergence of such networks. Our goal is to drive the distributed wireless network to NE with high total throughput. Study of the distribution of network metrics at NE with high total throughput shows that communication links still have significant amount of interference. Adding an interference-received term with an optimal weight (αopt∗) to the link’s payoff can push the distributed network to converge to NE with high total throughput. The channel allocation trend at NE with high total throughput is as follows: each of theC−1links occupies its own channel, and the remainingN−C+1links share the remaining one channel, whereNis the number of links andCis the number of channels in the network. The links (transmitters and receivers) are randomly located andC<N(limited resources). The transmitter of a link has a direct connection with the receiver of the link; hence, several links overlap. This leads to a dense network with considerable amount of interference especially for links sharing channels. A practical application of our work is when smart devices in a room, hall, or concert arena have a direct communication with other smart devices in the area using limited bandwidth. Using best response technique and definitions of NE, we derive and propose an approximate way to mathematically expressαopt∗(referred to asα^opt) along with its probability density function (PDF) for a specific scenario. Then, a generic equation forα^optis inferred for varying network sizes (links) and available resources (channels). Implementing such a policy enhances the total throughput of the distributed wireless network by up to 15%. In a more general setting, our distributed policy can achieve up to 75% of the maximum total throughput (benchmark value reached by centralized solution via exhaustive search) at a fraction of the time and computation resources.

CATECAS: a content-aware transmission efficiency based channel allocation scheme for cognitive radio users with improved QoE

Cognitive radio (CR) is a promising technology for the upcoming 5G communication which addresses opportunistic channel usage for enhanced spectrum utilization. However, Quality of Service (QoS) provisioning is a major challenge for CR Network due to the service interruption and packet error caused by random primary activities. In addition to this, periodic spectrum sensing for primary user protection reduces the effective throughput of the secondary users (SUs). However, to ensure QoS of SUs especially for video application, throughput enhancement is necessary which can be achieved by efficient spectrum sensing and channel allocation policy. As the QoS requirements are different for different secondary applications, we propose a novel content aware channel allocation scheme that enhances the Quality of Experience (QoE) of SUs. At first, the proposed scheme analyzes the QoS requirements of different SUs and prioritizes them. Consequently, the optimum sensing duration is determined to maximize the transmission efficiency and throughput of SUs. Finally, a novel content aware transmission efficiency-based channel assignment scheme (CATECAS) is proposed for SUs, considering the estimated channel quality and QoS requirements concurrently. Extensive performance analysis of CATESCAS on real-time video and file download applications confirms significant QoE improvement for SUs especially for rapid movement type of video application, which is considered as the most critical among different secondary applications.

Game Theory and Lyapunov Optimization for Cloud-Based Content Delivery Networks With Device-to-Device and UAV-Enabled Caching

The paper considers a cloud-based Content Delivery Network (CDN) with Device-to-Device (D2D) and Unmanned Aerial Vehicle (UAV) enabled caching. The network is managed by the operator that offers content transfer services to the set of Content Providers (CPs). A subscriber of any CP can receive its content either by first, cellular content transfer via a terrestrial Base Station (BS) or a UAV, or by second, D2D content transfer from another subscriber of this CP. Each CP aims to maximize its expected payoff defined as a reciprocal of the weighted sum of the expected content transfer cost and delay for its subscribers. The operator's objective is to maximize its long-term average revenue and stabilize the queuing system which represents the content transfer services. To model the interactions between the operator and the CPs, a novel framework is proposed that combines: first, cooperative game that enables the CPs to form coalitions in which all subscribers can exchange content via D2D links, thereby reducing content transfer costs and delays; second, Lyapunov optimization based dynamic channel and UAVs' activity allocation policy of the operator. Through analytical and numerical evaluations, it is proven that if the operator and each CP are rational, the network reaches a state where all the CPs are in the stable coalitional structure, whereas, the dynamic policy of the operator is optimal with the trade-off in the total queue backlog.

Write‐variation aware alternatives to replace SRAM buffers with non‐volatile buffers in on‐chip interconnects

With the advancement in CMOS technology and multiple processors on the chip, communication across these cores is managed by a network-on-chip (NoC). Power and performance of these NoC interconnects have become a significant factor.The authors aim to reduce the leakage power consumption of NoC buffers by the use of non-volatile spin transfer torque random access memory (STT-RAM)-based buffers. STT-RAM technology has the advantages of high density and low leakage but suffers from low endurance. This low endurance has an impact on the lifetime of the router on the whole due to unwanted write-variations governed by virtual channel (VC) allocation policies. Here various VC allocation policies that help the uniform distribution of the writes across the buffers are proposed. Iso-capacity and iso-area-based alternatives to replace SRAM buffers with STT-RAM buffers are also presented. Pure STT-RAM buffers, however, impact the network latency. To mitigate this, a hybrid variant of the proposed policies which uses alternative VCs made of SRAM technology in the case of heavy network traffic is proposed. Experimental evaluation of full system simulation shows that proposed policies reduce the write variation by 99% and improve lifetime by 3.2 times and 1093 times, respectively. Also a 55.5% gain in the energy delay product is obtained.

Vertex Search based Energy-efficient Optimal Resource Allocation in Cognitive Radio ad hoc Networks

Cognitive Radio Ad Hoc Networks (CRAHN) is the infrastructure-less network model of Cognitive radios developed in an ad hoc manner. Regulating resource allocation in CRAHN is considered to be an energy constrained problem. Many researches have been performed for allocating spectrum in an efficient way using various protocols. In this paper, the Spectrum-Map-Empowered Opportunistic Routing (SMOR) model has been utilized as the fundamental model for routing and an energy efficient optimal spectrum allocation solution is provided. In the proposed model, the previously modified SMOR model is enhanced for the main objective of energy efficient and optimal resource allocation using Vertex search algorithm with a gradient-based approximation. Initially, the resource allocation problem is modelled into a non-convex optimization problem. The power allocation, data rate adaptation, channel allocation, and user scheduling policies are optimized for maximization of the energy efficiency during data transmission. The proposed Vortex search algorithm resolves this optimization problem by determining the training interval for the channel estimation and power consumption. The experimental results prove that the proposed Vertex search based modified SMOR (VS-M-SMOR) model provides efficient routing with energy efficient optimal resource allocation.

A Performance Model for the HSDPA User Equipment and its Validation

A new queuing model is proposed for the performance evaluation of the High Speed Downlink Packet Access (HSDPA) protocol, with respect to a specified user, in UMTS networks. This analytical model is an integrated one capable of capturing many complicated features of HSDPA operation such as correlated and bursty traffic, channel fading, channel allocation policy and packet-losses in the air interface. The validation of the model for comparing the user terminal categories is performed with a detailed simulation of HSDPA terminals with "real" traffic traces. It is shown that the model is quite accurate to predict and compare the throughput of the HSDPA terminal categories.

Learning Temporal–Spatial Spectrum Reuse

We formulate and study a multi-user multi-armed bandit problem that exploits the temporal–spatial opportunistic spectrum access (OSA) of primary user (PU) channels, so that secondary users (SUs) who do not interfere with each other can make use of the same PU channel. We first propose a centralized channel allocation policy that has logarithmic regret, but requires a central processor to solve an NP-complete optimization problem at exponentially increasing time intervals. To overcome the high computation complexity at the central processor, we also propose heuristic distributed policies that, however, have linear regrets. Our first distributed policy utilizes a distributed graph coloring and consensus algorithm to determine SUs’ channel access ranks, while our second distributed policy incorporates channel access rank learning in a local procedure at each SU at the cost of a higher regret. We compare the performance of our proposed policies with other distributed policies recently proposed for temporal (but not spatial) OSA. We show that all these policies have linear regrets in our temporal–spatial OSA framework. Simulations suggest that our proposed policies have significantly smaller regrets than the other policies when spectrum temporal–spatial reuse is allowed.

A game theory‐inspired channel allocation policy for multi‐radio pervasive ad hoc communications

AbstractThe impressive increase of innovative wireless communication technologies and applications represents nowadays a key approach to enable pervasive communications environments. In particular, the emerging paradigms of the Internet of Things and capillary networks offer effective ways to make devices connected. This has triggered the development of effective methodologies and procedures to allow an unlimited number of devices to exchange information mainly in an autonomous mode. However, it is a well‐known concept that wireless networks capacity usually decreases with the number of nodes. In particular, in order to improve the throughput scalability, a promising approach is that of resorting to the use of multiple radio interfaces at each node, in order to exploit spatial reuse of frequencies. Towards this end, game theory methodologies offer efficient approaches to solve the complex radio interfaces selection and allocation problems. This paper proposes a game theory‐inspired approach to efficiently select the number of radio interfaces to be used at each node site in order to lower the energy consumption and maximise the end‐to‐end throughput of any communication on which the node is involved in. The good behaviour of the proposed approach is validated by provided theoretical framework and numerical results derived by considering different data packets wireless forwarding schemes. Copyright © 2015 John Wiley & Sons, Ltd.

Performance Analysis of Finite Population Cellular System Using Channel Sub-rating Policy

This paper illustrates a channel sub-rating allocation policy for finite population cellular system. In this policy, certain channels are permitted to tran- siently divide into two channels at half the original rate to adapt handoff calls. As a result, one half can be employed to serve the existing call and the other half to serve the handoff request so that we can get rid of forced termination of calls. We propose two system models based on fractional guard channel and sub-rating channel allocation policies with and without queueing of handoff calls which give a significant improvement in the QoS of the cellular network. New call blocking probability, handoff call forced termination probability and the degraded call quality are also investigated to examine the effects of the sub-rating on the QoS. Our policies have the least forced termination probability and the call incompletion probability when compared with the other policies.

A dynamic programming approximation for downlink channel allocation in cognitive femtocell networks

Both femtocells and cognitive radio (CR) are envisioned as promising technologies for the NeXt Generation (xG) cellular networks. Cognitive femtocell networks (CogFem) incorporate CR technology into femtocell deployment to reduce its demand for more spectrum bands, thereby improving the spectrum utilization. In this paper, we focus on the channel allocation problem in CogFem, and formulate it as a stochastic dynamic programming (SDP) problem aiming at optimizing the long-term cumulative system throughput of individual femtocells. However, the multi-dimensional state variables resulted from complex exogenous stochastic information make the SDP problem computationally intractable using standard value iteration algorithms. To address this issue, we propose an approximate dynamic programming (ADP) algorithm in pursuit of an approximate solution to the SDP problem. The proposed ADP algorithm relies on an efficient value function approximation (VFA) architecture that we design and a stochastic gradient learning strategy to function, enabling each femtocell to learn and improve its own channel allocation policy. The algorithm is computationally attractive for large-scale downlink channel allocation problems in CogFem since its time complexity does not grow exponentially with the number of femtocells. Simulation results have shown that the proposed ADP algorithm exhibits great advantages: (1) it is feasible for online implementation with a fair rate of convergence and adaptability to both long-term and short-term network dynamics; and (2) it produces high-quality solutions fast, reaching approximately 80% of the upper bounds provided by optimal backward dynamic programming (DP) solutions to a set of deterministic counterparts of the formulated SDP problem.

A Markovian queue with varying number of servers and applications to the performance comparison of HSDPA user equipment

Inspired by the need for performability models for HSDPA user equipment, a Markovian queue with varying number of servers is conceived. The arrival and the service processes, the number of allocated or active servers of the queue are inherently, and independently (or jointly) Markov modulated. Batch arrivals, batch services, autocorrelation of inter-arrival times, and autocorrelation of batch sizes can be accommodated in the queue, by a suitable use of Markov modulation and generalized exponential distribution. The queue has a provision for negative customers too. Transformations of the balance equations into a computable form are proposed in order to obtain the steady state probabilities with the Spectral Expansion method. This queue is used to model the High Speed Downlink Packet Access (HSDPA) wireless networks. The model is an integrated one with respect to HSDPA, capable of accommodating many of the intricate aspects of HSDPA such as, channel allocation policy, loss of packets due to channel fading, bursty and correlated traffic. Good agreement is observed between the numerical results of the proposed analytical model and those of an independent simulator of real HSDPA and radio channel behaviors. The comparison of the terminal categories specified by the 3rd Generation Partnership Project (3GPP) is also presented.

Virtual channel load-balanced routing algorithm for Torus networks

In order to improve the network performance furthermore, a routing algorithm for 2D-Torus is investigated from the standpoint of load balance for virtual channels. The 2D-Torus network is divided into two virtual networks and each physical channel is split into three virtual channels. A novel virtual channel allocation policy and a routing algorithm are proposed, in which traffic load is distributed to those three virtual channels in a more load-balanced manner by introducing a random parameter. Simulations of the proposed algorithm are developed with a SystemC-based test bench. The results show that compared with the negative first for Torus networks (NF-T) algorithm, the proposed algorithm can achieve better performance in terms of network latency and throughput under different traffic patterns. It also shows that a routing algorithm with load balance for virtual channels can significantly improve the network performance furthermore.

Adaptive Admission-Control and Channel-Allocation Policy in Cooperative Ad Hoc Opportunistic Spectrum Networks

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, we propose an admission-control scheme and channel-allocation policy for ad hoc opportunistic spectrum networks (OSNs) based on user dynamic IDs. By dividing the secondary users (SUs) into sensing and accessing groups, they can cooperate to exploit as many unused channels as possible. However, the number of sensing users and the number of accessing users are balanced to efficiently identify and then utilize the available channels. We then study the secondary network behavior to investigate the parameters that can be used to control the number of admitted users. The secondary network controller in each time slot adjusts the controlling parameters to admit only the number of users that meet the quality-of-service (QoS) requirements. Moreover, the admitted SUs use their dynamic IDs and the order of the available channels to determine which channels can be allocated to each of them. Extensive numerical and simulation results are provided to demonstrate the effectiveness of the proposed admission-control and channel-allocation policy. </para>

Christian Radio in a New Millennium

There is no segment of the broadcasting industry growing as rapidly as Christian Radio has since 1990. The number of radio stations with Christian formats has nearly tripled in this time frame, surpassing all other formats. This remarkable growth is due to a combination of factors, including changes in FCC channel allocation and licensing policies, innovations in software to identify available frequencies and plot coverage areas, the popularity of Contemporary Christian Music, the growth of Evangelical Christianity and conservative political organizations, and the dedicated efforts of many broadcasters who want Christian Radio to happen now.