Designing an efficient spectrum assignment (SA) mechanism is a key issue for realizing dynamic spectrum access in cognitive radio network. In multi-channel selection based SA schemes, secondary users (SUs) are able to utilize multiple channels simultaneously to enhance the network throughput. However, a fairness problem may happen if few SUs utilize too many idle data channels that other SUs are left with no idle channels, thus increasing the blocking probability and reducing the fairness. Aiming at improving the network throughput with multi-channel selection capability while maintaining fairness among the SUs, in this paper, we propose a fair multi-channel assignment scheme (FMCA) for distributed cognitive radio networks. For the FMCA scheme, we design a new MAC framework for sensing and access contention resolution, which is integrated into the FMCA scheme. Channel-aggregation (CA) technique is used in each SU to enable the multi-channel selection ability. Considering both of the idle data channel utilization efficiency and the transmit power budget constrained CA ability of each SU, we analytically formulate a channel assignment problem according to the well-known Jain's fairness criterion. Our objective is to find a channel assignment with maximal fairness index for all SUs. The optimization problem is turned out to be a quadratic integer programming (QIP). According to the definition of Jain's fairness criterion, we design an algorithm to get the optimal solution of the QIP. With the optimal channel assignment solution, the FMCA scheme is realized in the channel assignment phase of the proposed MAC protocol. Extensive simulation results show that the proposed FMCA scheme gets a good tradeoff between throughput and fairness compared with the existing SA schemes. INDEX TERMSFair multi-channel assignment, channel-aggregation, cognitive radio, distributed cognitive radio networks, medium access control (MAC) protocol, fairness, throughput.
The output of the network in a deep learning (DL) based single-user signal detector, which is a normalized 2 × 1 class score vector, needs to be transmitted to the fusion center (FC) by occupying a large amount of the communication channel (CCH) bandwidth in the cooperative spectrum sensing (CSS). Obviously, in cognitive radio for vehicle to everything (CR-V2X), it is particularly important to propose a method that makes full use of the bandwidth-constrained CCH to obtain the optimal detection performance. In this paper, we firstly propose a novel single-user spectrum sensing method based on modified-ResNeXt in CR-V2X. The simulation results show that our proposed method performs better than two advanced DL based spectrum sensing methods with shorter inference time. We then introduce a quantization-based cooperative spectrum sensing (QBCSS) algorithm based on DL in CR-V2X, and the impact of the number of reported bits on the sensing results is also discussed. Through the experimental results, we conclude that the QBCSS algorithm reaches the optimal detection performance when the number of bits for quantizing local sensing data is 4. Finally, according to the conclusion, a bandwidth-constrained QBCSS scheme based on DL is proposed to make full use of the CCH with limited capacity to achieve the optimal detection performance.
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