【摘要】 针对传统信道估计算法对稀疏性约束不强，导致信道估计性能下降，进而影响通信质量等问题，着重对车到车(vehicle to vehicle V2V)信道估计进行研究，提出了基于基扩展模型(base expansion model, BEM)的稀疏度自适应匹配追踪(sparsity adaptive matching pursuit, SAMP)信道估计算法。该算法将信道估计问题转变为对BEM系数的稀疏重构，通过SAMP获得BEM的系数，再利用反馈结果进行迭代，进而实现最优的信道估计。仿真结果表明，与最小二乘(least square, LS)、线性最小均方误差(linear minimum mean square error, LMMSE)和正交匹配追踪(orthogonal matching pursuit, OMP)信道估计算法比较，该算法在V2V信道下可以显著提高正交频分复用(orthogonal frequency division multiplexing, OFDM)系统的均方误差和误码率性能。更多还原

【Abstract】 Due to the conventional channel estimation algorithms do not have strong constraints on sparse characteristics, which leads to the degradation of channel estimation performance and affects the communication efficiency, this paper focuses on the sparsity of vehicle-to-vehicle(V2V) channel estimation, and proposes a sparse adaptive matching pursuit(SAMP) channel estimation algorithm based on basis extended model(BEM). The algorithm transforms the problem of channel estimation into sparse reconstruction of BEM coefficients. The coefficients of BEM are obtained by SAMP, and then iterated using the feedback results to achieve the optimal channel estimation. Simulation results show that, compared with LS, LMMSE and orthogonal matching pursuit(OMP) channel estimation algorithms, this algorithm can significantly improve the mean square error(MSE) and bit error rate(BER) performance of OFDM systems in V2V channel.更多还原