A Non-Stationary IMT-Advanced MIMO Channel Model for High-Mobility Wireless Communication Systems

Abstract

Abstract-With the recent developments of high-mobility wireless communication systems, e.g., high-speed train (HST) and vehicle-to-vehicle (V2V) communication systems, the ability of conventional stationary channel models to mimic the underlying channel characteristics has widely been challenged. Measurements have demonstrated that the current standardized channel models, like IMT-Advanced (IMT-A) and WINNER II channel models, offer stationary intervals that are noticeably longer than those in measured HST channels. In this paper, we propose a non-stationary channel model with time-varying parameters including the number of clusters, the powers and the delays of the clusters, the angles of departure (AoDs), and the angles of arrival (AoAs). Based on the proposed non-stationary IMT-A channel model, important statistical properties, i.e., the local spatial crosscorrelation function (CCF) and local temporal autocorrelation function (ACF) are derived and analyzed. Simulation results demonstrate that the statistical properties vary with time due to the non-stationarity of the proposed channel model. An excellent agreement is achieved between the stationary interval of the developed non-stationary IMT-A channel model and that of relevant HST measurement data, demonstrating the utility of the proposed channel model.

A Novel Simulation Model for Nonstationary Rice Fading Channels

“…The parameters are configured as follows, = 10 ms, = 8, and = 2. The frequencies and boundaries can be calculated by using (10)- (13) and are also shown in Figure 1. It is clear that the frequencies are continuous and change linearly within the upper and lower boundaries.…”

“…There are a number of research papers on designing accurate and efficient simulators [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. For the good fitting with plan-wave propagation channels, the sum-of-sinusoids (SoS) [1,2] or sum-ofcisoids (SoC) [3] based simulators and their derivatives [4][5][6][7][8] have gained the most widespread acceptance.…”

“…However, measurement campaigns have proved that the WSS assumption is only valid for a short time interval [20]. Therefore, several modified SoC-based simulators were proposed in [9][10][11][12][13][14][15][16][17][18][19] to generate nonstationary fading channels. For example, the WINNER+ model [9] was simulated by generating several independent channel segments with different simulation parameters.…”

“…For example, the WINNER+ model [9] was simulated by generating several independent channel segments with different simulation parameters. Simulation parameters were updated according to the fixed trajectories of transceivers and clusters in [10,11,13,14], which improves the continuity of adjacent channel states.…”

A Blind Handover Technique for the Intelligent Transportation Network

“…Unlike traditional handover methods, the proposed technique does not require handover measurements, greatly reduces the total handover latency, and improves the handover success rate and service quality. 如图1所示，传统的切换流程分为切换测量(Handover Measurement)、切换判决(Handover Decision)、切换执行(Handover Execution) 三步 [4]- [5] 。所谓切换测量是指用户端设备UE(User Equipment)对附近的若干个相邻小区的信号质量进行测量，基站根据测量报告选择信号质量 最好的小区作为切换目标小区。这种测量方式需要大量的测量时间，以WCDMA和LTE为例， 整个切换测量时间需要200 ms以上 [6] 。在切换判决前，为了防止乒乓效应，还必须有一段切 换触发时延（TTT, Time to Trigger） [7] ，LTE的TTT默认值为256 ms. 相比于切换测量和切换 判决所花费的时间，LTE切换执行的时间仅需100 ms. …”