On prioritised opportunistic spectrum access in cognitive radio cellular networks

Abstract

Cognitive radio (CR) networks allow secondary users (SUs) to opportunistically access licensed spectrum, thus providing efficient use of radio resources. Different studies carried out on CR networks have focused on the procedure of spectrum handoff of SUs such as the data transmission in a communication that can be carried out in different channels. In this paper, we consider cellular networks enhanced with facilities for CR communication where a user with a call in progress moves across neighbouring cells, that is, the so‐called inter‐cell handoff. Our model assumes that the cell gives priority to inter‐cell handoff calls over the calls originating within it. A finite queue is used in every cell for SUs, and a fixed number of buffer slots are reserved for the inter‐handoff SU calls. With these considerations, we study the transmission performance of SUs by using Markov chains‐based modelling approach. The numerical analysis is validated by simulation experiments. We also suggest a fuzzy logic‐based hybrid decision‐making algorithm to select the best solution for inter‐handoff prioritisation. The goal is to optimise the transmission performance of SUs by leveraging their requirements on service completion throughput and waiting time in each cell. Copyright © 2014 John Wiley & Sons, Ltd.

Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks

“…is protocol offers the routing predictions based on the user mobility and previous routing pattern matching, which results in higher channel selection time due to the lower mobility and routing pattern prediction. e limitation of lower routing prediction is managed by proper reinforcement learning mechanism implemented on network layer [33]. However, the problem of channel switching events is still an open challenge due to the limitation of user interference [34].…”

“…User interferences are managed during end-to-end routing decisions using the PU activity On-Off model in RL-based routing protocols. However, the effect of user interferences on channel switching has not been addressed in spectrum mobility approaches [33]. e effects of user interferences were not differentiated into interflow and intraflow interferences during routing decisions to minimize packet collisions.…”

Modeling and performance analysis for mobile cognitive radio cellular networks

“…In [7], by considering a homogeneous cognitive radio multi-cellular system, both intercell handoff rate and forced termination probability of SUs are determined. Reference [20] extends the reported work in [7] by considering that instead of channel reservation, the queueing buffer is used to give both intra-handoff and inter-handoff SU calls priority over the initial SU calls. In [21], the performance of secondary users at the packet level in a cognitive radio cellular network was analyzed, where the transmitter and receiver are not able to communicate directly.…”

“…As in [7,[20][21][22], it is considered that secondary users only use empty channels (resources not being used by primary users) for their data transmission. Since primary users are using a cellular system where co-channel interference is tightly controlled and surveyed in order to guarantee an adequate QoS of cellular users, then cochannel interference is also controlled for the secondary users.…”

“…In [23], it is considered that all the involved time variables are modeled by exponentially negative random variables for both secondary and primary calls. However, no closedform mathematical expressions are obtained and only exponentially distributed CDT and UST are considered in [7,[20][21][22][23]. Furthermore, channel holding times are not analyzed, and Erlang capacity is not addressed in [7,[20][21][22][23].…”