Efficient Channel Assignment under Dynamic Source Routing in Cognitive Radio Networks

Abstract

The channel assignment problem is one of the most important issues in cognitive radio networks (CRNs). Under a SINR-driven model, we consider channel assignments in a network using dynamic source routing (DSR). In this unicasting model, channel assignments are conducted in a relatively small scale of nodes, which are on the chosen route. In addition, we can make use of the route reply (RREP) message in DSR to estimate the SINR and the maximum data transmission rate of nodes on the chosen route. In this way, the source node can conduct the channel assignment in a more efficient way. We propose two algorithms for the single route and multi-route channel assignments, where the multi-route scheme uses alternative nodes to help transmitting. We give a complexity analysis of two algorithms and an extension of reducing complexity for the multi-route channel assignment algorithm. Finally, we conduct simulations of our two algorithms under networks with different densities and show that the performance of our algorithms is efficient.Index Terms-channel assignment, cognitive radio networks, dynamic source routing, piggyback, SINR estimation.

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

“…(m) Dynamic source routing: Dai et al [128] propose a channel assignment algorithm for the CRAHNs based on the SINR. The channel is selected based on the SINR values and is then assigned to the communicating nodes.…”

“…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 …”

A Distributed Dynamic Channel Assignment and Routing Framework for Cognitive Sensor Systems

“…As a result, they are not suitable for sensor applications in which the sink node is always the traffic source or destination. Dai and Wu [17] have proposed an approach to assign channel under dynamic source routing in cognitive radio networks. Since the channel assignment is based on the established route information, it incurs frequent channel switches when the number of data flows is large.…”

“…However, these protocols do not consider channel assignment and switching issue. Some previous works have been done for combining dynamic channel assignment and routing [14]- [17]. Since [14]- [16] assign channels based on each source destination pair, they are not suitable for sensor applications.…”

“…Since [14]- [16] assign channels based on each source destination pair, they are not suitable for sensor applications. In [17], channel assignment is based on the established route information. This is not suitable for a sensor system in which many sensors may need to send data to a gateway at the same time.…”