The resource allocation scheme for the multiple description coding multicast （MDCM） in orthogonal frequency division multiplexing （OFDM-based） cognitive radio network （CRN） is studied in this paper, aiming at maximizing the total throughput of cognitive radio （CR） users, with constraints on sum transmit power, the maximal receiving rate of each CR user and the maximal total interference introduced to each primary user. With the analysis of the model, an algorithm, which consists of subcarrier assignment and power allocation using the sub-gradient updating method, is proposed. Meanwhile, to reduce the complexity, a suboptimal algorithm is also proposed, which divides the total transmit power into small slices and allocates them one by one. Moreover, the suboptimal algorithm is modified by adding an advanced water-filling process to improve the performance. The simulation results obtained in this paper show that the system throughput using the MDCM scheme is much higher than that using the conventional multicast （CM） scheme and the performance of the proposed suboptimal algorithms can approximate the MDCM scheme very well.
A scenario where one ＇dumb＇ radio and multiple cognitive radios communicating simultaneously with a common receiver is considered. In this paper, we derive an achievable rate region of the multiple-user cognitive multiple-access channel （MUCMAC） under both additive white Gaussian noise （AWGN） channel and rayleigh fading channel, by using a combination of multiple user dirty paper coding （DPC） and superposition coding. Through cognition, it is assumed that the secondary users （SUs） are able to obtain the message of the primary user （PU） non-causally beforehand. Using this side information, the SUs can perform multiple user DPC to avoid the interference from the SU. Besides, the SUs can also allocate part of their transmit power to aid the PU, using superposition coding. Therefore, the capacity region of traditional multiple-access channel （MAC） can be enlarged. Moreover, some asymptotic results are shown as the number of SUs increases. In the AWGN case, it is illustrated that the maximum achievable rate of the PU grows logarithmically with the increase of the number of SUs, whereas in the Rayleigh case, we show that the cognitive gain will increase with the decreasing of the channel signal to noise ratio （SNR）.
This paper investigates the resource allocation problem for the cluster-based cooperative multicast in orthogonal frequency division multiplexing （OFDM）-based cognitive radio （CR） systems. Aiming at maximizing the system sum rate, an efficient clustering scheme is proposed. It begins with the clustering phase where secondary users （SUs） with good channel conditions are selected as cluster heads, while others decide to which cluster they belong. When the clusters are organized, it turns to a two-stage data transmission phase： in stage 1, the secondary＇ base station （BS） transmits data to the cluster heads; in stage 2, the cluster heads forward the received data to their cluster members. Based on this scheme, a joint subcarrier and power allocation algorithm is proposed. Simulation results show that the proposed scheme significantly outperforms the conventional multicast （CM） as well as the multiple description coding multicast 0VIDCM） in terms of the system sum rate.
