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.
In this work, we consider an amplify-and-forward two-way multi-relay system for wireless communication and mvesngate me effect of channel estimation error on the error rate performance. With the derivation of effective signal-to-noise ratio at the transceiver and its probability density function, we can get approximate expression for average bit error rate. Simulation results are performed to verify the analytical results.
WANG Si-ye XU Wen-jun HE Zhi-qiang NIU Kai WU Wei-ling
In this paper, a frequency domain decision feedback equalizer is proposed for single carrier transmission with time-reversal space-time block coding （TR-STBC）. It is shown that the diagonal decision feedback equalizer matrix can be calculated from the frequency domain channel response. Under the perfect feedback assumption, the proposed equalizer can approach matched filter bound （MFB）. Compared with the existing time domain decision feedback equalizer, the proposed equalizer exhibits better performance with the same equalization complexity.
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 tradeoff between energy-efficiency capacity and spectrum sensing under hybrid spectrum sharing model, where the spectrum sharing method is based on sensing results of secondary user （SU）. The metric ＇bits per joule＇, which captures the effect of energy overhead in spectrum sensing, is adopted to evaluate energy-efficiency capacity. We first formulize the tradeoff between energy-efficiency capacity and spectrum sensing as an optimization problem with mixture constraint of sensing time and detection threshold. Under some certain condition on the domain of detection threshold, i.e. in which we can＇t improve energy-efficiency capacity through increasing the detection probability, the original optimization problem can be reduced to a new unconstrained one, which only relates to sensing time. Then the existence and uniqueness of optimal sensing time to achieve maximum energy-efficiency capacity are discussed and a low-complexity algorithm is proposed to obtain the optimal solution. Finally, numerical simulation is performed to verify the theoretical analysis results. The simulation results indicate that hybrid spectrum sharing is remarkably beneficial to energy-efficient transmission in cognitive radio networks （CRN）. And the proposed algorithm can quickly converge to the optimal solution.
This paper studies the problem of effective resource allocation for multi-radio access technologies （Multi-RAT） nodes in heterogeneous cognitive wireless networks （HCWNs）. End-to-end utility, which is defined as the delay of end-to-end communication, is taken into account in this paper. In the scenario of HCWNs, it is assumed that the cognitive radio nodes have the ability of Multi-RAT and can communicate with each other through different paths simultaneously by splitting the arrival packets. In this paper, the problem is formulated as the optimization of split ratio and power allocation of the source cognitive radio node to minimize the delay of end-to-end communication, and a low complexity step-by-step iterative algorithm is proposed. Numerical results show good performance of the proposed algorithm over two other conventional algorithms.
