Abstract
In the existing network-layered architectural stack of Cognitive Radio Ad Hoc Network (CRAHN), channel selection is performed at the Medium Access Control (MAC) layer. However, routing is done on the network layer. Due to this limitation, the Secondary/Unlicensed Users (SUs) need to access the channel information from the MAC layer whenever the channel switching event occurred during the data transmission. This issue delayed the channel selection process during the immediate routing decision for the channel switching event to continue the transmission. In this paper, a protocol is proposed to implement the channel selection decisions at the network layer during the routing process. The decision is based on past and expected future routing decisions of Primary Users (PUs). A learning agent operating in the cross-layer mode of the network-layered architectural stack is implemented in the spectrum mobility manager to pass the channel information to the network layer. This information is originated at the MAC layer. The channel selection is performed on the basis of reinforcement learning algorithms such as No-External Regret Learning, Q-Learning, and Learning Automata. This leads to minimizing the channel switching events and user interferences in the Reinforcement Learning- (RL-) based routing protocol. Simulations are conducted using Cognitive Radio Cognitive Network simulator based on Network Simulator (NS-2). The simulation results showed that the proposed routing protocol performed better than all the other comparative routing protocols in terms of number of channel switching events, average data rate, packet collision, packet loss, and end-to-end delay. The proposed routing protocol implies the improved Quality of Service (QoS) of the delay sensitive and real-time networks such as Cellular and Tele Vision (TV) networks.
Highlights
Cognitive Radio (CR) was first coined by Mitola et al in 2002 [1]
We propose a new channel selection routing protocol for Cognitive Radio Ad Hoc Network (CRAHN), which is implemented in the network layer with multiple disjoint Primary Users (PUs) operating on the same frequency channels. e proposed routing protocol is able to minimize the number of channel switching events by minimizing user interferences through Reinforcement Learning (RL) techniques such as No External Regret learning, Qlearning and Learning Automata. e proposed protocol adds the channel information through the header of Route Request (RREQ) and Route Reply (RREP) as the List of Available Channels (LAC), Channel Assigned (CA), Channel Access Duration (CAD), and Path Identifier (PI)
The Coolest Path (CP) routing protocol was chosen in this simulation study since it emerges as the optimal approach for minimizing the Secondary Users (SUs)’s interference to the PU. e route having the minimum accumulated amount of PU’s activities will be selected by CP routing; that means, the least number of PUs is encountered by the particular route
Summary
CR technology is yet different from conventional wireless radios since it can opportunistically detect the available channels of wireless spectrum [2]. It is the foundation for CR Network establishment. Is is made possible through its network layer capability that controls communication and the spectrum awareness between layers. In this case, the layers are Medium Access Control (MAC) and Network layers. E difference in control from local to end-to-end enables easier operation for CR network across all network protocol stack layers [3]. SUs in CRAHNs can communicate with each other in a peer to peer fashion [5]. e ultimate goal of the CRAHN is to choose and assign channels to SUs that are currently not being utilized by the incumbent PUs [6]
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