大规模MIMO系统中,相对于其他基于信道矩阵分解的波束成形算法,如迫零、最小均方误差算法等,匹配滤波(Matched filter,MF)具有复杂度极低的优点,从而成为一种极具实用潜力的波束成形算法。鉴于此,本文推导了基站采用MF波束成形算法时,用户端信干噪比(Signal-to-interferenceand-noise ratio,SINR)的近似概率密度函数(Probability density function,PDF)。该函数对于推导与分析系统性能,如和速率、中断概率等至关重要。仿真表明:当基站天线数趋于大规模时,SINR公式的PDF曲线趋近于通过纯仿真得到的PDF曲线。
A pilot pattern across two orthogonal frequency division multiplexing OFDM symbols with a special structure is designed for the channel estimation of OFDM systems with inphase and quadrature IQ imbalances at the receiver.A high-efficiency time-domain TD least square LS channel estimator and a low-complexity frequency-domain Gaussian elimination GE equalizer are proposed to eliminate IQ distortion.The former estimator can significantly suppress channel noise by a factor N/L＋1 over the existing frequency-domain FD LS where N and L＋1 are the total number of subcarriers and the length of cyclic prefix and the proposed GE requires only 2N complex multiplications per OFDM symbol.Simulation results show that by exploiting the TD property of the channel the proposed TD-LS channel estimator obtains a significant signal-to-noise ratio gain over the existing FD-LS one whereas the proposed low-complexity GE compensation achieves the same bit error rate BER performance as the existing LS one.
由于现有的基于最大比合并MRC(Maximum Ratio Combination)的联合搜索定位算法实现复杂度高,为降低其计算复杂度,提出基于网格搜索的加权最大似然代价函数定位算法WMLGS(Weighted ML Grid Search Localization)。仿真结果表明:MRC和WMLGS算法的定位性能近似相等,在无地球表面约束条件下均优于单独时差或频差定位性能,并且逼近克拉美罗联合界,同MRC相比,WMLGS节省了一半左右的计算量,因此更具有实用价值。
针对单用户对双向中继系统中的功率分配问题,提出了一种基于梯度下降法的功率分配方案。该方案在总功率约束的条件下,以最大化和速率为目标函数,通过对中继波束成形矩阵和功率分配矩阵的反复迭代,求解出局部最优的功率分配和中继波束成形矩阵。仿真结果表明:提出方案的误码性能相比于等功率分配有明显提高,在误码率为10-2时,可获得2.5d B^3 d B的信噪比增益;同时,在中高信噪比下,相比于等功率分配,该方案可获得0.3(bit/s)/Hz^0.5(bit/s)/Hz的和速率增益。
在时变多用户MIMO-OFDM系统中,所有子载波整体预编码方案的性能优于单个子载波单独预编码方案。然而前者的复杂度是基站发射天线数J与子载波总数N乘积的函数,显著高于后者,特别NJ>1000时,复杂度极高。为了解决这个问题,我们提出了一种基于最大化信泄噪比的复杂度可调的分组子载波GS-Max-SLNR(Grouped-Subcarrier Maximum Signal-toLeakage-and-Noise Ratio)预编码方案。此外,我们推导了组间干扰公式,该公式在给定多普勒频移和信噪比的条件下,可以根据需要选取合适的分组数。理论建模和仿真表明,通过选取合适的分组数目,提出的GS-Max-SLNR能够实现复杂度和性能的良好折中。
