Nuts and Bolts of a Realistic Stochastic Geometric Analysis of mmWave HetNets: Hardware Impairments and Channel Aging

© 2019 IEEE.Motivated by heterogeneous network (HetNet) design in improving coverage and by millimeter-wave (mmWave) transmission offering an abundance of extra spectrum, we present a general analytical framework shedding light on the downlink of realistic mmWave HetNets consisting of K tiers of randomly located base stations. Specifically, we model, by virtue of stochastic geometry tools, the multi-Tier multi-user (MU) multiple-input multiple-output (MIMO) mmWave network degraded by the inevitable residual additive transceiver hardware impairments (RATHIs) and channel aging. Given this setting, we derive the coverage probability and the area spectral efficiency (ASE), and we subsequently evaluate the impact of residual transceiver hardware impairments and channel aging on these metrics. Different path-loss laws for line-of-sight and non-line-of-sight are accounted for the analysis, which are among the distinguishing features of mmWave systems. Among the findings, we show that the RATHIs have a meaningful impact at the high-signal-To-noise-ratio regime, while the transmit additive distortion degrades further than the receive distortion the system performance. Moreover, serving fewer users proves to be preferable, and the more directive the mmWaves are, the higher the ASE becomes.Peer reviewedFinal Accepted Versio

Enhancing massive MIMO: A new approach for Uplink training based on heterogeneous coherence time

Massive multiple-input multiple-output (MIMO) is one of the key technologies in future generation networks. Owing to their considerable spectral and energy efficiency gains, massive MIMO systems provide the needed performance to cope with the ever increasing wireless capacity demand. Nevertheless, the number of scheduled users stays limited in massive MIMO both in time division duplexing (TDD) and frequency division duplexing (FDD) systems. This is due to the limited coherence time, in TDD systems, and to limited feedback capacity, in FDD mode. In current systems, the time slot duration in TDD mode is the same for all users. This is a suboptimal approach since users are subject to heterogeneous Doppler spreads and, consequently, different coherence times. In this paper, we investigate a massive MIMO system operating in TDD mode in which, the frequency of uplink training differs among users based on their actual channel coherence times. We argue that optimizing uplink training by exploiting this diversity can lead to considerable spectral efficiency gain. We then provide a user scheduling algorithm that exploits a coherence interval based grouping in order to maximize the achievable weighted sum rate

Numerical Simulation and Design Assessment of Limited Feedback Channel Estimation in Massive MIMO Communication System

The Internet of Things (IoT) has attracted a great deal of interest in various fields including governments, business, academia as an evolving technology that aims to make anything connected, communicate, and exchange of data. The massive connectivity, stringent energy restrictions, and ultra-reliable transmission requirements are also defined as the most distinctive features of IoT. This feature is a natural IoT supporting technology, as massive multiple input (MIMO) inputs will result in enormous spectral/energy efficiency gains and boost IoT transmission reliability dramatically through a coherent processing of the large-scale antenna array signals. However, the processing is coherent and relies on accurate estimation of channel state information (CSI) between BS and users. Massive multiple input (MIMO) is a powerous support technology that fulfils the Internet of Things' (IoT) energy/spectral performance and reliability needs. However, the benefit of MIMOs is dependent on the availability of CSIs. This research proposes an adaptive sparse channel calculation with limited feedback to estimate accurate and prompt CSIs for large multi-intimate-output systems based on Duplex Frequency Division (DFD) systems. The minimal retro-feedback scheme must retrofit the burden of the base station antennas in a linear proportion. This work offers a narrow feedback algorithm to elevate the burden by means of a MIMO double-way representation (DD) channel using uniform dictionaries linked to the arrival angle and start angle (AoA) (AoD). Although the number of transmission antennas in the BS is high, the algorithms offer an acceptable channel estimation accuracy using a limited number of feedback bits, making it suitable for 5G massively MIMO. The results of the simulation indicate the output limit can be achieved with the proposed algorithm