针对现有信道估计方案导致正交时频空间(Orthogonal Time Frequency Space,OTFS)调制系统峰均功率比(Peak-to-Average Power Ratio,PAPR)高或频谱效率(Spectral Efficiency,SE)低的问题,提出一种多叠加导频的低PAPR、高SE信道估计方法。发送端利用时域正交性和离散傅里叶域相位的随机性,在时延多普勒域中嵌入与数据相叠加的5导频符号的导频图案实现低PAPR,提高SE。接收端以数据符号与噪声之和的能量均值为基准,实现导频信号检测,同时根据每个导频的不同位置信息恢复出存在相位旋转的数据信号。基于能量准则,利用多个独立的接收信号进行联合信道估计,以降低数据符号的干扰,并采用消息传递算法进行数据恢复。仿真结果表明,该方法比单叠加导频信道估计的PAPR低,同时较嵌入式导频信道估计的SE提高约14.4%。
OFDM是5G物理层关键技术之一,其缺点是PAPR过高,容易导致功放效率下降并造成信号失真。如何抑制OFDM信号的PAPR对低功耗的物联网终端来说是一个重要问题。本文提出了一种联合深度学习与FDSS的抑制PAPR算法。仿真结果表明,所提算法对于多种调制方式及子载波个数配置均有很好的PAPR抑制效果。在峰值功率受限的条件下,采用所提算法能使信道的传输增益提升6 dB左右。OFDM, one of the key techniques of the 5G physical layer, has the disadvantage of excessively high PAPR. The excessively high PAPR will lead to a decrease in power amplifier efficiency and cause signal distortion. How to suppress the PAPR of OFDM signals is an important problem for low-power Internet of Things terminals. This paper proposes a joint method combining deep learning and FDSS for PAPR suppression based on the PAPR suppression scheme of FDSS, and conducts simulation verification. The results show that the proposed joint method achieves excellent PAPR suppression performance in different modulation scenarios and different subcarrier numbers. Under the condition of peak power constraint, the proposed joint method can improve the transmission gain of the channel by about 6 dB.
In this paper,we formulate the precoding problem of integrated sensing and communication(ISAC)waveform as a non-convex quadratically constrained quadratic programming(QCQP),in which the weighted sum of communication multi-user interference(MUI)and the gap between dual-use waveform and ideal radar waveform is minimized with peak-toaverage power ratio(PAPR)constraints.We propose an efficient algorithm based on alternating direction method of multipliers(ADMM),which is able to decouple multiple variables and provide a closed-form solution for each subproblem.In addition,to improve the sensing performance in both spatial and temporal domains,we propose a new criteria to design the ideal radar waveform,in which the beam pattern is made similar to the ideal one and the integrated sidelobe level of the ambiguity function in each target direction is minimized in the region of interest.The limited memory Broyden-Fletcher-Goldfarb-Shanno(LBFGS)algorithm is applied to the design of the ideal radar waveform which works as a reference in the design of the dual-function waveform.Numerical results indicate that the designed dual-function waveform is capable of offering good communication quality of service(QoS)and sensing performance.
