A Path-Centric Channel Assignment Framework for Cognitive Radio Wireless Networks

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

“…(r) Path-centric CA: A path-centric channel assignment algorithm for the CRAHNs is proposed in [132]. The proposed algorithm takes a different perspective from the traditional channel assignment algorithms in multi-channel wireless networks, by integrating the routing and channel assignments.…”

“…Channel Selection Characteristics and Requirement-based Comparison Table III shows comparison of the channel assignment algorithms based on the channel selection characteristics and requirements. The parameters Segment-based CA [122] n/a n/a ACO-based CA [126] n/a n/a Centralized and distributed CA [123] n/a n/a MRF-based CA [124] RMCA [118] Path-centric CA [132] Cross-layer design [117] Risk-based CA [44] Dynamic source routing [128] Joint power CA [129] Maximum flow segment-based CA [131] Throughput-oriented CA [42] Effective CA [127] Genetic algorithm-based CA [130] Distance-and traffic-aware CA [34] Spectrum-aware CA [35] RIMCA [125] Probabilistic CA [133] Receiver-centric CA [33] Access contention resolution [116] CCAR [134] Joint link & CA routing optimization [135] Cross-layer optimization framework [136] Transmission opportunity-based CA [137] n/a n/a n/a n/a CA for CSS [43] Downlink CA [138] QoS-aware CA [57] n/a n/a Residual energy-aware CA [139] Dynamic CA [140] Cognitive dynamic fair CA [142] n/a n/a Q-learning-based CA [56] n/a n/a Uplink CA [146] n/a n/a ZAP [147] CoSAP [143] Unified CA [148] CA and routing [145] n/a n/a Route tree-based CA [144] n/a n/a CARD …”

CRP: A Routing Protocol for Cognitive Radio Ad Hoc Networks

“…Such an approach is seen in [7], wherein the edge weights represent the wireless capacity, and are calculated probabilistically based on interference from the PUs, the received signal strength, among others. A path-centric spectrum assignment framework (CogNet) is proposed in [16] that constructs a multi-layered graph of the network at each node such that the edge weights of the graph represent the spectrum availability between the nodes. In either case, a Dijsktra or Bellman Ford-like algorithm is run over the topology graph to find the optimal path.…”

“…However, these approaches are not suited for CR operation as there is no support for simultaneously choosing the spectrum band (each of which may have several channels), or considering the effect routes may have on other licensed devices sharing the spectrum. To address these issues, several works have been recently proposed for CR networks [7][9] [13] [16]. In our proposed approach, we consider several key CR-specific performance metrics that are not incorporated in these related works.…”

A Critical Review of the Routing Protocols for Cognitive Radio Networks and a Proposal for Load Balancing Local Spectrum Knowledgebased Routing

“…Graph theoretic algorithms optimizing the overall cost of a path between every source-destination pair, or trees connecting a group of nodes (for multicasting) or all nodes (for broadcasting) could then be run on such a weighted layered graph. For example, in [11], the authors represent the horizontal edge weights to be proportional to the traffic load and interference, and propose a centralized heuristic algorithm to calculate shortest paths. The main weaknesses of the layered graph model presented above are that it requires a networkwide signaling to generate such a global graph at each node and it may not scale well as the network dimensions increase.…”