Abstract
The fifth generation (5G) networks and internet of things (IoT) promise to transform our lives by enabling various new applications from driver-less cars to smart cities. These applications will introduce enormous amount of data traffic and number of connected devices in addition to the current wireless networks. Thus 5G networks require many researches to develop novel telecommunication technologies to accommodate these increase in data traffic and connected devices. In this paper, novel power constraint optimization and optimal beam tracking schemes are proposed for mobile mmWave massive MIMO communications. A recently published novel channel model that is different from other widely used ones is considered. The channel model considers the number of clusters and number of rays within each cluster as varying due to user mobility. The proposed power constraint optimization scheme harmonizes conventional total power constraint (TPC) and uniform power constraint (UPC) schemes into a new one called allied power constraint (APC) that can significantly improve the system performance in 5G networks while achieving fairness among users. TPC and UPC have major drawbacks with respect to fairness and achieving quality-of-service (QoS) for users in dense networks. Thus APC aims to harmonize TPC and UPC by adjusting each antenna element’s constraint to adapt for some power resilience to a specific antenna element, hence proposing an intermediate solution between the two extreme case power constraint optimization schemes. Three optimal beam tracking schemes: (i) conventional exhaustive search (CES), (ii) multiobjective joint optimization codebook (MJOC), and (iii) linear hybrid combiner (LHS) scheme, have been provided for the mobile mmWave massive MIMO system with the proposed APC scheme. For the proposed APC scheme a comprehensive performance analysis is provided and compared with TPC and UPC. Spectral efficiency (SE), bit-error-rate (BER), Jain’s fairness index, channel occupancy ratio (COR) and instantaneous interfering power metrics are investigated. It has been shown that the proposed scheme can significantly outperform conventional schemes.
Highlights
Internet of Things (IoT) and Cloud-based services have lead to a tremendous increase in wireless data traffic and it is expected that the total number of mobile subscribers will reach 5.7 billion by 2023 [1,2]
As Channel State Information (CSI) is a crucial aspect of reliable high data-rate communications in MIMO channel, perfect CSI at Tx and Rx is assumed at the simulations denoted as PCSI, which serves as the benchmark performance
This paper proposes a novel power constraint optimization scheme called as allied power constraint (APC) where the optimal covariance matrix is obtained and the power allocation is done instead of performing the allocation solely with respect to conventional total power constraint (TPC) or uniform power constraint (UPC)
Summary
Internet of Things (IoT) and Cloud-based services have lead to a tremendous increase in wireless data traffic and it is expected that the total number of mobile subscribers will reach 5.7 billion by 2023 (which is 71 percent of current global population) [1,2]. Conventional power constraint based optimization, total power constraint (TPC) and uniform power constraint (UPC) schemes, have major drawbacks such as UPC not being able to satisfy users’ QoS when the network become densely populated and TPC not being able to achieve fairness among users while maximizing the system throughput [8,9]. To address these open challenges this paper proposes a novel power constraint optimization and optimal beam tracking schemes for mobile mmWave massive MIMO communications with a recently proposed channel model
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