Performance of Cross-layer Design with Multiple Outdated Estimates in Multiuser MIMO System

By combining adaptive modulation (AM) and automatic repeat request (ARQ) protocol as well as user scheduling, the cross-layer design scheme of multiuser MIMO system with imperfect feedback is presented, and multiple outdated estimates method is proposed to improve the system performance. Based on this method and imperfect feedback information, the closed-form expressions of spectral efficiency (SE) and packet error rate (PER) of the system subject to the target PER constraint are respectively derived. With these expressions, the system performance can be effectively evaluated. To mitigate the effect of delayed feedback, the variable thresholds (VTs) are also derived by means of the maximum a posteriori method, and these VTs include the conventional fixed thresholds (FTs) as special cases. Simulation results show that the theoretical SE and PER are in good agreement with the corresponding simulation. The proposed CLD scheme with multiple estimates can obtain higher SE than the existing CLD scheme with single estimate, especially for large delay. Moreover, the CLD scheme with VTs outperforms that with conventional FTs

Using Channel Output Feedback to Increase Throughput in Hybrid-ARQ

Hybrid-ARQ protocols have become common in many packet transmission systems due to their incorporation in various standards. Hybrid-ARQ combines the normal automatic repeat request (ARQ) method with error correction codes to increase reliability and throughput. In this paper, we look at improving upon this performance using feedback information from the receiver, in particular, using a powerful forward error correction (FEC) code in conjunction with a proposed linear feedback code for the Rayleigh block fading channels. The new hybrid-ARQ scheme is initially developed for full received packet feedback in a point-to-point link. It is then extended to various different multiple-antenna scenarios (MISO/MIMO) with varying amounts of packet feedback information. Simulations illustrate gains in throughput.Comment: 30 page

Cross-Layer Combining of Adaptive Modulation and Truncated ARQ in Multichannel Beamforming MIMO Systems

In this study the authors provide a cross-layer design of multiple-input-multiple-output (MIMO) systems, with the aim to maximize spectral efficiency. We consider MIMO systems based on a multichannel beamforming technique that combines an adaptive modulation and truncated automatic repeat request procedures, for the case of Rayleigh fading propagation and imperfect channel state information. Closed-form expressions for the average spectral efficiency and the packet loss rate are derived for arbitrary eigenchannel of multichannel beamforming systems, with any number of receiving and transmitting antennas. An analytical expression for the average time during which a particular constellation is used continuously, is also derived. We propose the method based on the optimization of the target packet error rate and the maximum number of retransmissions that outperforms the existing cross-layer combining procedures. Furthermore, we develop the numerical algorithm for optimization of the eigenchannel power allocation. The proposed cross-layer design results in higher average spectral efficiency, reduced maximum delay and increased energy efficiency. The analytical results are validated by Monte Carlo simulation

Massive MIMO channel prediction using recurrent neural networks

Massive MIMO has been classified as one of the high potential wireless communication technologies due to its unique abilities such as high user capacity, increased spectral density, and diversity among others. Due to the exponential increase of connected devices, these properties are of great importance for the current 5G-IoT era and future telecommunication networks. However, outdated channel state information (CSI) caused by the variations in the channel response due to the presence of highly mobile and rich scattering is a major problem facing massive MIMO systems. Outdated CSI occurs when the information obtained about the channel at the transmitter changes before transmission. This leads to performance degradation of the network. In this work, we demonstrate a low complexity channel prediction method using neural networks. Specifically, we explore the power of recurrent neural network utilizing long-short memory cells in analyzing time series data. We review various neural network-based channel prediction methods available in the literature and compare complexity and performance metrics. Results indicate that the proposed methods outperform conventional systems by tremendously lowering the complexity associated with channel prediction.This work is funded by the scientific and technological research council of Turkey (TÜBITAK) under grand 119E392