Nondata-Aided Error Vector Magnitude Analysis of $\eta-\mu$ Fading Channels in Device-to-Device Communications

Abstract

Transmission in non-line-of-sight (NLOS) conditions has a poor throughput in device-to-device (D2D) communications. In order to achieve high throughput, adaptive modulation has been selected as a spectrally-efficient transmission technology. Moreover, quantifying the performance of a fading channel in NLOS conditions becomes a core issue. In general, the η − µ distribution can be employed to characterize and model the NLOS fading channel. Effective evaluating the quality of η − µ channels can provide an efficient theoretical reference for determining the optimum switching thresholds in adaptive modulation. However, due to the sensitivity of D2D transmission to fading channels, providing an efficient theoretical benchmark for evaluating, the quality of η − µ channels is still a challenge in transmission design. In this paper, the nondata-aided error vector magnitude (NDA-EVM) is considered as a novel metric to evaluate the quality of wireless fading channels. Specifically, the NDA-EVM of the multilevel quadrature amplitude modulation (MQAM) signals over η−µ fading channels and its lower bound is analytically derived. This can be used to further determine the optimum switching thresholds in adaptive modulation in NLOS conditions. The numerical results validate the effectiveness of the proposed formulation and also reveal the influence of the channel parameters on the lower bound of the NDA-EVM. INDEX TERMS η − µ fading channels, D2D, NLOS, nondata-aided error vector magnitude, lower bound. I. INTRODUCTION Device-to-device (D2D) communication is one of the key techniques in the 5th generation (5G) wireless communication systems that allows direct communication between mobile nodes [1]. However, D2D transmission schemes are sensitive to channel conditions and provide poor throughput in Non-line-of sight (NLOS) scenarios [2]. Adaptive modulation can select the optimal modulation parameters relative to the channel characteristics, which can provide an effective leverage to achieve high throughput [3]. Hence, quantifying the performance of a fading channel in NLOS conditions is a core issue of adaptive modulation in D2D communications. The associate editor coordinating the review of this manuscript and approving it for publication was Rui Wang.

Multi-Parameter Estimation Method and Closed-Form Solution Study for k-µ Channel Model

“…Rayleigh distribution [6] 3 Rician distribution 2 [7] 4 Nakagami-m distribution 2 [8] 5 Hoyt (Nakagami-q) distribution [9] 6 k-µ distribution 1, 2, 3, 4 [10] 7 k-µ shadowed fading 1, 2, 3, 4, 5, 6, 7 [4] In recent years, there have been many studies on channel performance analysis and system performance optimization of typical fading channel models both domestically and internationally, as reported in [1,[11][12][13]. However, there have been relatively few studies on parameter estimation of the k-µ distribution and k-µ shadowing distribution models [14][15][16][17]. In [14], the authors analyzed the error vector magnitude (EVM) performance of the k-µ fading channel, where the EVM parameter was used to evaluate the variation of channel quality, and the results could be used to predict the lower bound of channel quality based on k-µ fading channels.…”

“…However, there have been relatively few studies on parameter estimation of the k-µ distribution and k-µ shadowing distribution models [ 14 , 15 , 16 , 17 ]. In [ 14 ], the authors analyzed the error vector magnitude (EVM) performance of the k-µ fading channel, where the EVM parameter was used to evaluate the variation of channel quality, and the results could be used to predict the lower bound of channel quality based on k-µ fading channels. In [ 15 ], the authors assumed QAM modulation channels passing through k-µ shadow fading channels and proposed the average symbol error rate (ASER) expressions for orthogonal amplitude modulation (RQAM) and cross-orthogonal amplitude modulation (XQAM) in single-input single-output (SISO) and multiple-input multiple-output (MIMO) channel systems.…”

Co-Existence With IEEE 802.11 Networks in the ISM Band Without Channel Estimation

“…When the number of subcarriers is large, the real and imaginary parts of x p and x c can be assumed to be independent and identically distributed Gaussian random variables with zero mean and variance of σ 2 xp and σ 2 xc , respectively [13]. Then, the conditional probability density function of the received QAM signal is given by:…”

Low-complexity EVM estimation based on artificial neural networks for coherent optical systems

“…performance evaluation measured by error vector magnitude (EVM), is crucial for achieving reliable data transmission [3,4]. EVM is an indicator of signal error statistics for higherorder MFs, which can be connected to bit error rate (BER) [5] and overall signal-to-noise ratio (OSNR) [6,7] within a limited range of actual defects. Tracking EVM has the ability to expand the functional capabilities of network modules by supplying network analytics functions with understandable error information about the system.…”