基于回归分析算法的5G室分变频双路功率差与速率关系研究

目前在实现5G深度覆盖的同时,5G室分双路变频策略可最大化利用现网4G单路DAS,使之快速、低成本地升级改造成具备MIMO能力的5G室分方案。通过场景选取、测试方案等策略寻优,对新型5G室分变频双路系统进行双路功率差与下载速率的测试采样,并采用MATLAB编程技巧、经典函数使用等研究方法,研究分析其双路功率差与速率的量化关系,根据一元线性及一元多项式回归分析算法计算出当双路功率差约在4.72 dB以内时,下载速率降幅可控制在5%范围内。

PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.

Shaped Offset Quadrature Phase Shift Keying Based Waveform for Fifth Generation Communication

Fifth generation(5G)wireless networks must meet the needs of emerging technologies like the Internet of Things(IoT),Vehicle-to-everything(V2X),Video on Demand(VoD)services,Device to Device communication(D2D)and many other bandwidth-hungry multimedia applications that connect a huge number of devices.5G wireless networks demand better bandwidth efficiency,high data rates,low latency,and reduced spectral leakage.To meet these requirements,a suitable 5G waveform must be designed.In this work,a waveform namely Shaped Offset Quadrature Phase Shift Keying based Orthogonal Frequency Division Multiplexing(SOQPSK-OFDM)is proposed for 5G to provide bandwidth efficiency,reduced spectral leakage,and Bit Error Rate(BER).The proposed work is evaluated using a real-time Software Defined Radio(SDR)testbed-Wireless open Access Research Platform(WARP).Experimental and simulation results show that the proposed 5G waveform exhibits better BER performance and reduced Out of Band(OOB)radia-tion when compared with other waveforms like Offset Quadrature Phase Shift Key-ing(OQPSK)and Quadrature Phase Shift Keying(QPSK)based OFDM and a 5G waveform candidate Generalized Frequency Division Multiplexing(GFDM).BER analysis shows that the proposed SOQPSK-OFDM waveform attains a Signal to Noise Ratio(SNR)gain of 7.2 dB at a BER of 10�3,when compared with GFDM in a real-time indoor environment.An SNR gain of 8 and 6 dB is achieved by the proposed work for a BER of 10�4 when compared with QPSK-OFDM and OQPSK-OFDM signals,respectively.A significant reduction in OOB of nearly 15 dB is achieved by the proposed work SOQPSK-OFDM when compared to 16 Quadrature Amplitude Modulation(QAM)mapped OFDM.