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Cooperative spectrum sensing (CSS) is used in cognitive radio (CR) networks to improve the spectrum sensing performance in shadow fading environments. Moreover, clustering in CR networks is used to reduce reporting time and bandwidth over-head during CSS. Thus, cluster based cooperative spectrum sensing (CBCSS) has manifested satisfactory spectrum sensing results in harsh environments under processing constraints. On the other hand, the antenna diversity of multiple input multiple output (MIMO) CR systems can be exploited to further improve the spectrum sensing performance. This paper presents the CBCSS performance in a CR network which is comprised of single as well as multiple antenna CR systems. We give theoretical analysis of CBCSS for orthogonal frequency division multiplexing (OFDM) signal sensing and propose a novel fusion scheme at the fusion center which takes into account the receiver antenna diversity of the CRs present in the network. We introduce the concept of weighted data fusion in which the sensing results of different CRs are weighted proportional to the number of receiving antennas they are equipped with. Thus, the receiver diversity is used to the advantage of improving spectrum sensing performance in a CR cluster. Simulation results show that the proposed scheme outperforms the conventional CBCSS scheme.Keywords Cognitive Radio · cooperative spectrum sensing · cluster based sensing · weighted data fusion.

In next-generation wireless networks, relay-based packet forwarding, emerged as an appealing technique to extend network coverage while maintaining the required service quality. The incorporation of multiple frequency bands, ranging from MHz/GHz to THz frequencies, and their opportunistic and/or simultaneous exploitation by relay nodes can significantly improve system capacity, however at the risk of increased packet latency. Since a relay node can use different bands to send and receive packets, there is a pressing need to design an efficient channel allocation algorithm without a central oracle. While existing greedy heuristics and game-theoretic techniques, which were developed for multi-band channel assignment to relay nodes, achieve minimum packet latency, their performance drops significantly when network dynamism (i.e., user mobility, non-quasi-static channel conditions) is introduced. Since this problem involves multiple relay nodes, we model it as a Markov Decision Process (MDP) involving various stages, which essentially means that achieving an optimal and stable solution is a computationally hard problem. Since solving the MDP, traditionally, consumes a great deal of time and is intractable for relay nodes, we explore how to approximate the optimal solution in a distributed manner by reformulating a reinforcement learning-based, smart channel adaptation problem in the considered multi-band relay network. By customizing a Q-Learning algorithm that adopts an epsilon-greedy policy, we can solve this re-formulated reinforcement learning problem. Extensive computer-based simulation results demonstrate that the proposed reinforcement learning algorithm outperforms the existing methods in terms of transmission time, buffer overflow, and effective throughput. We also provide the convergence analysis of the proposed model by systematically finding and setting the appropriate parameters.