Cognitive Radio Wireless Mesh Networks Research Articles

MRCSC: A cross-layer algorithm for joint Multicast Routing, Channel selection, Scheduling, and Call admission control in multi-cell multi-channel multi-radio cognitive radio wireless networks

This paper addresses the problem of joint multicast routing, channel selection, scheduling, and call admission control in multi-cell multi-channel multi-radio cognitive radio wireless mesh networks. The applied model relies on the QoS sessions, where each one is expressed with a bandwidth requirement. We propose a cross-layer algorithm named “Multicast Routing, Channel selection, Scheduling, and Call admission control (MRCSC)”. The proposed scheme, considering wireless broadcast advantage, channel/radio diversity, and spatial reuse, uses both intra- and inter-cell relaying to create multicast trees with a specific bandwidth. MRCSC considers both inter-flow and intra-flow interferences. It adopts an efficient collision-free mechanism to greedily schedule different multicast transmission in the network. As an advantage, considering the multi-radio technology, MRCSC allows the nodes to simultaneously send/receive data on different available non-overlapping channels. Numerical results of our comprehensive simulations confirm the efficiency of the MRCSC algorithm in terms of the number of transmissions, stability, and multicast period.

Cluster-Based Opportunistic Spectrum Allocation in CRWMN’s Using Co-operative Mechanism

With the innovation of Cognitive Radio Wireless Mesh Network’s, Opportunistic Spectrum Allocation can possibly moderate spectrum lack, by letting Secondary User’s a chance to sense and use unused bits of opportunistic licensed spectrum without any unfavourable effect on the Primary User’s. In Cognitive Radio Wireless Mesh Network’s, the Medium-Access-Control protocols characterizes the utilization of spectrum proficiently by allocating the channels among the users. Majority of proposed Medium-Access-Control protocols are utilizing Common-Control-Channel for dealing with the assets of Secondary-Users. The major downsides of these Medium-Access-Control protocols are broadcasting of Control-channel when substantial number of Secondary-User exists. In contrast with these Medium-Access-Control protocols, we develop an algorithm Time-Slotted-Allocation-Medium-Access-Control (TSA-MAC) protocol which is based on Clustered Time-Division-Medium-Access approach, which permits Secondary-User’s to allocate opportunistic spectrum with the help of co-operative decisions by exchange control information. In this approach, we are dividing the channels as different slots on which Secondary-User’s can transfer control and data packets. The TSA-MAC protocol will enhance the throughput for the Secondary-User’s over the communication channel. And also this method will facilitate to decide and allocate free channels for Secondary-User’s without interfering with Primary User’s.

Traffic engineering in cognitive mesh networks: Joint link-channel selection and power allocation

In this paper, we investigate traffic engineering (TE) methods in Cognitive Radio Wireless Mesh Networks (CR-WMNs) through joint selection of link-channel pairs at each forwarding router and allocating powers on those so as to boost up overall network throughput. We first present an optimization framework, Centralized Optimal Traffic Engineering (COTE), aiming at maximizing aggregated network throughput by selecting optimal set of link-channel pairs, power allocation over those and fair traffic splitting. Further, a centralized Suboptimal Traffic Engineering (SOTE) solution is proposed by employing Lagrangian dual decomposition on the COTE problem, to ensure a resolution in polynomial time. Finally, a Distributed Greedy Traffic Engineering (DGTE) method is proposed to ensure fast convergence to the dynamic changing network behavior and to improve scalability. Extensive simulation results are presented to demonstrate the effectiveness of our proposed TE mechanisms compared to the state-of-the-art works.

A model to improve the routing performance of Cognitive Radio Wireless Mesh Networks

The increasing demand for broadband wireless technologies has led to the scarcity, inefficient utilization, and underutilization of the spectrum. The Cognitive Radio (CR) technology has emerged as the promising solution which improves the utilization of the spectrum. However, routing is a challenge due to the dynamic nature of the CR networks. The link quality varies in space and time as nodes join and leave the network. The network connectivity is intermittent due to node mobility and the activities of the primary user. The spectrum aware, spectrum agile, and interference aware routing protocols are vital for the sturdiness of the network and efficient utilization of the resources.
 
 We propose an interference aware, spectrum aware, and agile extended Weighted Cumulative Expected Transmission Time (xWCETT) routing protocol. The protocol integrates the features of the Ad-hoc On-demand Distance Vector (AODV) and the weighted cumulative expected transmission time (WCETT) routing protocols. The xWCETT was simulated using the Network Simulator 2 and its performance compared with the AODV and the WCETT routing protocols. The xWCETT was evaluated with respect to quality of service related metrics and the results show that it outperformed the AODV and WCETT routing protocols.

Path and Link Aware Routing Algorithm for Cognitive Radio Wireless Mesh Network

Cognitive Radio Wireless Mesh Networks (CRWMNs) are reliable and promising technology to access opportunistically spectrum holes from the licensed bands with intelligible Software Defined Radio. Multi-radio and multichannel CRWMNs are dynamic networks, which creates routing protocol design as a complex. It has a capability of assessing the spectrum availability during routing, which enhance the efficiency of the network. The proposed algorithm is a novel multipath algorithm. The path selected in this algorithm based the minimal interference information, which is useful to make this routing approach as more reliable. Channel selection has the capability to identify more suitable links for data transmission in the network. The proposed algorithm has simulated in network simulator (ns2). The simulation results demonstrate the efficiency of proposed channel selection and path aware routing algorithm.

ATMAC: adaptive token‐based medium access control protocol for cognitive radio wireless mesh network

SummaryIn next generation wireless communication, cognitive radio technology facilitates to utilize underutilized licensed frequency bands that help to enhance the spectrum utilization. Cognitive radio wireless mesh network (CRWMN) is a promising and reliable technology to experience high throughput with low cost. Existing IEEE 802.11 based medium access control (MAC) protocols offer high data rates with decreasing efficiency at the MAC layer. Hence, most of the researchers applied aggregation mechanisms to provide the solution to bandwidth craving applications. In CRWMN, MAC design is significant because stability, efficient resource utilization, and scalability are predominating problems; however, the specified MAC issues are not yet resolved. The proposed MAC is novel, which aims to ensure reliability and scalability for CRWMN. The common control channel is used to exchange handshaking frames between the transmitter and receiver. It helps us to schedule the data transmission as well as reserve the channel in a discrete time interval. It introduces a token‐based channel accessing mechanism with resource‐aware channel assignment, which resolves the problems of efficiency and stability. The proposed MAC simulated using the network simulator (ns‐2), and the simulation results demonstrate that the proposed protocol improved the performance compared with the existing protocols.

An Efficient Mac Protocol for Reducing Channel Interference and Access Delay in Cognitive Radio Wireless Mesh Networks

With the increase of wireless mesh networks, channel access, bandwidth usage and channel interferences are the major key issues of multi-channel WMNs and MAC specifications. The multi-radio, multi-channel wireless mesh networks (MR-MC-WMNs) are a kind of networks that provide high reliable communication for a large number of mesh nodes. These networks have mesh routers and channels that are stationary and each router or node has multiple channels. Although IEEE 802.11 versions support up to 3 and 12 multiple non-overlapping channels, it is the major challenge of the multichannel environment which exploits all channels to communicate a large volume of network data in WMNs. Cognitive radio users has the capability to access unused portion of the multichannel spectrum. The major interference can be created by CR as well as licensed user. Traditional MAC based operations are implemented using common-control-channel to handle cognitive radio end users. We proposed a new Multi-objective based CMR-MC-WMNs framework using TDMA based MAC protocol to optimize the network performance, channel interference and access delay issues in the network. Experimental results prove that, proposed work has less delay and inference rates compared to traditional models.

Receiver-Based Channel Allocation in Cognitive Radio Wireless Mesh Networks

In this paper, we study the channel allocation problem in cognitive radio wireless mesh networks (CR-WMNs). We aim at finding an allocation strategy that guarantees quality of service (QoS) (link reliability), maximizes network coverage, and alleviates the need for a common control channel to coordinate the communication process. The allocation of a particular channel to a mesh client (MC) is considered feasible if the MC can establish connectivity with the backbone network in both the upstream and the downstream directions, and has the signal-to-interference-plus-noise ratio (SINR) of the uplink and the downlink with its parent mesh router (MR) within a predetermined threshold. A receiver-based channel allocation (RBA) model that achieves the aforementioned objectives is proposed (channel assignment under this model can be proven to be NP-hard). We then formulate a mixed integer linear program, of the channel allocation problem under the proposed model, and compare its performance to that of two other baseline models, namely, transmitter-based and all-tunable channel allocation strategies. The results prove the superiority of the proposed model. We also developed a heuristic algorithm, which is shown to be an accurate algorithm.

Decentralized robust spectrum allocation for cognitive radio wireless mesh networks

During the last decade we have seen an explosive growth in the deployment of wireless networks in unlicensed frequency bands, mainly driven by the great success of the IEEE 802.11 standard. In addition to its traditional last-hop usage, it has also been widely employed for Internet access infrastructure such as wireless mesh networks (WMNs). A problem that is envisioned in the near future is the spectrum scarcity, which could be a serious threat to cope with the ever increasing demand. Regulators are aware about this problem and they have already started to look for more available spectrum. One of the possibilities that has emerged is to allow secondary assignments in licensed bands, based on the recent cognitive radio networks (CRNs) paradigm. In this context, we focus our work in the analysis of optimum spectrum allocation mechanisms for a cognitive wireless multihop mesh network. We introduce a stochastic model to formulate the problem, considering primary users’ activity and a periodically scheduled assignment scheme. To solve the problem we propose a novel robust solution, for which we develop a decentralized algorithm implementation. Furthermore, we evaluate our proposal through extensive simulations, showing for instance its superiority compared with an expectation based approach.

IJARCCE - Computer and Communication Engineering

QoS (Quality of Service) aware spectrum sensing and channel allocation in cognitive radio wireless mesh networks is a continuous practice due to the divergent scope of communication in wireless mesh networks. Henceforth the current research is moving in a direction to find effective solutions towards QoS aware spectrum sensing and channel allocation. But all of these solutions are specific to one or two QoS factors. According to the real-time practices the QoS assessment by one or two factors is impractical. Moreover majority of current approaches are delivering the computational complexity as O(n2), which due to the magnification of number evolution against the increment in number of channel availability in cognitive radio wireless mesh networks. In this context here we devised a Neighbor Conduct Sensitive QoS variance assessment strategy for cooperative spectrum sensing and channel allocation strategy, which enables to assess the QoS state of a spectrum under possible malicious cooperation that is based on multiple number of QoS factors and also should stabilize the computational complexity to O(n*log(n)). The experiment results are indicating the significance of the proposed model towards scalable and robust QoS variance aware spectrum sensing and channel allocation strategy for cognitive radio wireless mesh networks.

A genetic approach on cross-layer optimization for cognitive radio wireless mesh network under SINR model

Due to the limited spectrum resources and the differences of link loads, how to obtain maximum network throughput through cross-layer design under signal-to-interference-and-noise ratio (SINR) model is recognized as a fundamental but hard problem. For this reason, the throughput maximization problem jointly with power control, channel allocation and routing under SINR model is researched. First, by formulating the optimization model and digging up its special structure, we show that the throughput maximization problem can be decomposed into two sub-problems: a channel allocation and power control sub-problem at the link-physical layer, and a throughput optimization sub-problem at the network layer. As to the link-physical layer sub-problem, since the joint optimization on channel allocation and power control is NP hard, we apply genetic algorithm for searching the optimal solution. As to the network layer sub-problem, we use linear programming technique for throughput optimization. To reflect the interplay property among these three layers, the fitness of each individual in the genetic algorithm is evaluated by solving the network layer sub-problem. Therefore, an effective cross-layer optimization framework based on genetic algorithm is obtained, which can find optimized power control, channel allocation and route selection in polynomial time. In order to enhance the convergence process during evolution, the integer based representation scheme and corresponding genetic operators are well designed with appropriate constraint control mechanisms. Extensive simulation results demonstrate that the proposed scheme obtains higher network throughput compared to the pervious works with comparable computational complexity.

Decentralized Throughput Maximization in Cognitive Radio Wireless Mesh Networks

Scheduling and spectrum allocation are tasks affecting the performance of cognitive radio wireless networks, where heterogeneity in channel availability limits the performance and poses a great challenge on protocol design. In this paper, we present a distributed algorithm for scheduling and spectrum allocation with the objective of maximizing the network’s throughout subject to a delay constraint. During each time slot, the scheduling and spectrum allocation problems involve selecting a subset of links to be activated, and based on spectrum sensing outcomes, allocate the available resources to these links. This problem is addressed as an aggregate utility maximization problem. Since the throughput of any data flow is limited by the throughput of the weakest link along its end-to-end path, the utility of each flow is chosen as a function of this weakest link’s throughput. The throughput and delay performance of the network are characterized using a queueing theoretic analysis, and throughput is maximized via the application of Lagrangian duality theory. The dual decomposition framework decouples the problem into a set of subproblems that can be solved locally, hence, it allows us to develop a scalable distributed algorithm. Numerical results demonstrate the fast convergence rates of the proposed algorithm, as well as significant performance gains compared to conventional design methods.

Exploiting Multichannel Diversity for Cooperative Multicast in Cognitive Radio Mesh Networks

Cognitive radio networks (CRNs) have emerged as a promising, yet challenging, solution to enhance spectrum utilization, thanks to the technology of cognitive radios. A well-known property of CRNs is the potential heterogeneity in channel availability among secondary users. Therefore, multicast throughput in CRNs may suffer from significant degradation because of this property since a link-level broadcast of a frame may only reach a small subset of destinations that are able to receive on the same channel. This may necessitate multiple sequential transmissions of the same frame by the source on different channels to guarantee delivery to all receivers in the destination set. In case of high data generation rate, delivery delay will be high due to the repeated transmissions by the source. In this paper, we propose an assistance strategy to reduce the effect of the channel heterogeneity property on the multicast throughput in cognitive radio wireless mesh networks (CR-WMNs). This assistance strategy is composed of two main activities: first, allowing multicast receivers to assist the source in delivering the data, and second, allowing the transmission of coded packets so that multicast receivers belonging to different multicast groups can decode and extract their data concurrently. Results show that the proposed assistance paradigm reduces multicast time and increases throughput significantly.

Throughput maximization in distributed cognitive radio wireless mesh networks using lagrangian duality theory

Throughput maximization in distributed cognitive radio wireless mesh networks using lagrangian duality theory

Interference Aware Routing Using Network Formation Game in Cognitive Radio Mesh Networks

In cognitive radio (CR) wireless mesh networks (WMNs), wireless mesh routers work as secondary users (SUs) that can opportunistically and intelligently access the idle licensed spectrum. When developing efficient routing techniques in CR networks, the aggregate interference from the SUs to the primary users (PUs) should be considered. Though the interference from a single SU that is outside the PUs' footprints is small, the aggregate interference from a great number of SUs transmitting at the same time may be significant, and this will greatly influence the PUs' performance. In this paper, a distributed routing algorithm is proposed using the network formation game to minimize the aggregate interference from the SUs to the PUs. The proposed distributed algorithm can avoid the problems in the centralized approach, such as the high cost for building the centralized coordinate nodes, high information-gathering delay, and system breakdown caused by the possible failures in the centralized nodes. From the simulation results, we can observe that the proposed algorithm can find the routes with lower interference to the PUs compared to the Dijkstra's algorithm. Compared to an upper bound, the distributed solution shows near optimum.

Link Capacity Based Channel Assignment (LCCA) for Cognitive Radio Wireless Mesh Networks

Cognitive radio wireless mesh network (CRWMN) is expected as an upcoming technology with the potential advantages of both cognitive radio (CR) and the wireless mesh networks (WMN). In CRWMN, co-channel interference is one of the key limiting factors that affect the reception capabilities of the client and reduce the achievable transmission rate. Furthermore, it increases the frame loss rate and results in underutilization of resources. To maximize the performance of such networks, interference related issues need to be considered. Channel assignment (CA) is one of the key techniques to overcome the performance degradation of a network caused by the interferences. To counter the interference issues, we propose a novel CA technique which is based on link capacity, primary user activity and secondary user activity. These three parameters are fed to the proposed weightage decision engine to get the weight for each of the stated parameters. Thus, the link capacity based channel assignment (LCCA) algorithm is based on the weightage decision engine. The end-to-end delay, packet delivery ratio and the throughput is used to estimate the performance of the proposed algorithm. The numerical results demonstrate that the proposed algorithm is closer to the optimum resource utilization.

Cluster based Routing Protocol for Cognitive Radio Wireless Mesh Networks

Cognitive wireless mesh networks (WMN’s) are an emerged technology has made rapid changes in communication world. A wireless mesh network consists of mesh routers and mesh clients, in which mesh clients are highly mobile; due to this it should transmit continuous signaling messages to the gateway for their location registration. By transmitting signaling messages continuously more bandwidth is utilized so the overall performance of the network will be degraded. To overcome this problem we are introducing a new concept called clustering, which includes both static and dynamic clustering for mesh routers and mesh clients. By using these clustering algorithms we can reduce the signaling messages, so the overall network performance will be increased.

Joint Optimization for Congestion Avoidance in Cognitive Radio WMNs under SINR Model

Due to limited spectrum resources and differences in link loads, network congestion is one of the key issues in cognitive radio wireless mesh networks. In this letter, a congestion avoidance model with power control, channel allocation, and routing under the signal-to-interference-and-noise ratio is presented. As a contribution, a nested optimization scheme combined with a genetic algorithm and linear programming solver is proposed. Extensive simulation results are presented to demonstrate the effectiveness of our algorithm.

Optimal Frequency-time Allocation in Cognitive Radio Wireless Mesh Networks

The open spectrum allocation scheme is an effective approach to provide enhanced-capacity, flexible and reliable wireless networks. However, due to interference and coexistence with primary users, new spectrum management and network coordination technologies are required. Wireless mesh networks (WMNs) are currently the key technologies for providing Internet access as well as effective networking services in local and metropolitan areas. However, because a large number of nodes are involved in wireless networks and there is limited bandwidth, traffic congestion limits performance. Dynamic spectrum access and cognitive radio (CR) are enabling technologies to solve performance degradation problems in WMNs. In this paper, we suggest a cross-layer optimization scheme for opportunistic spectrum and time sharing in CR-based WMNs. The problem was formulated using linear programming and solved by the simplex-II method. The flow optimization problem was first developed for grid topology and random topology of CR-based WMNs. Results showed optimized flow of the CR-based WMNs with ideal interference free link scheduling. Second, a practical interference free link scheduling algorithm has been suggested. Simulation results showed that the normalized cost of the optimized algorithm was close to 1 and offered a cross-layer optimized solution between scheduling and network flow. The throughput of the CR-based WMNs using the suggested algo rithm was optimally enhanced. Finally, throughput performance was shown depending on the occupied area of the primary users in CR-based WMNs.

Supporting Differentiated Service in Cognitive Radio Wireless Mesh Networks

The MAC layer protocols utilizing enhanced distributed channel access (EDCA) in the recently published IEEE 802.11-2007 are able to provide differentiated QoS for different traffic types in wireless networks through varying the Arbitration Inter-Frame Spaces (AIFS) and contention window sizes. However, the performance of high priority traffic can be seriously degraded in the presence of strong noise over the wireless channels. The noise problem is further aggravated in wireless mesh networks when packets traverse multiple-hops from source to destination. The noise problem can be mitigated by distributing network traffic across multiple vacant channels to reduce the node density per transmission channel. Although multiple non-overlapped channels exist in the 2.4GHz and 5GHz spectrum, most IEEE 802.11-based multi-hop ad hoc networks today use only a single channel at anytime. As a result, these networks cannot fully exploit the aggregate bandwidth available in the radio spectrum provisioned by the standards. In this paper, we propose the Power-Controlled Rate-Adaptive MAC (CPCRA) protocol for single transceiver based Cognitive Radio Networks (CRNs) which combines adaptive modulation and coding with dynamic spectrum access. Simulation results demonstrate that CPCRA can achieve better performance in terms of lower delay and higher throughput.