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.
5G技术的不断发展和普及,使得无线设备对频谱资源的需求越来越大,频谱资源紧张的问题日益突出。为了提高频谱利用率,有效解决频谱资源紧张的问题,通信感知一体化(Integrated Sensing and Communication,ISAC)技术应运而生。在ISAC系统中,通信模块和感知模块共用同一波形和硬件平台,从而提高了频谱和设备利用率。其中,基于正交线性调频分频复用(Orthogonal Chirp-Division Multiplexing,OCDM)的ISAC系统对多普勒频移的抗干扰性能更好,性能优于传统系统。但是OCDM信号的平均峰值功率比(Peak to Average Power Ratio,PAPR)较高,这是由于其需要利用离散菲涅尔逆变换(Inverse Discrete Fresnel Transform,IDFn T)进行从chirp域到时域的转换造成的,过高的PAPR容易造成非线性失真,从而对ISAC系统的表现造成影响。针对上述问题,提出了一种基于chirp保留方法的OCDM通感一体化信号PAPR抑制方法,通过将OCDM信号的全部chirp分为两部分,一部分用来传输降低总体PAPR的信号,另一部分则正常传输通信数据,分别称为优化子载波和通信子载波。将一体化信号的PAPR与其非周期自相关函数建立联系,并利用Gerchberge-Saxton算法对优化子载波上的所得信号进行优化,以降低信号整体的PAPR,同时所有子载波均用于雷达信号处理以保证感知性能。仿真结果表明,分别利用10%、25%的子载波用于优化信号PAPR,且互补累积分布函数值为10-2时,可以使一体化信号的PAPR分别降低2 dB、3 dB左右。
针对传统部分传输序列(Partial Transmit Sequence,PTS)算法在改善正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统峰均功率比(Peak to Average Power Ratio,PAPR)时存在较高计算复杂度问题,文章提出了一种改进PTS算法。该算法利用分治思想对候选相位因子序列进行分组,降低其序列维度;结合相位加权序列的特点,进一步降低需遍历的候选序列个数,从而降低了计算复杂度。与传统的PTS算法对比,所提出的改进算法在改善PAPR性能的同时,较大地降低了计算复杂度。
