Secrecy Spectrum and Energy Efficiency Analysis in Massive MIMO-enabled Multi-Tier Hybrid HetNets

Massive multiple antenna systems in conjunction with millimeter (mmWave) communication have gained tremendous attention in the recent years owing to their high speed data delivery. However, security in these networks has been overlooked; thereby necessitating a comprehensive study. This paper analyzes the physical layer security performance of the downlink of a massive multiple-input multiple-output (MIMO)-based hybrid heterogeneous network (HetNet) where both mmWave and sub-6 GHz small cells coexist. Specifically, a tractable approach using stochastic geometry is proposed to analyze the secrecy outage probability, secrecy energy efficiency (SEE) and secrecy spectrum efficiency (SSE) of the hybrid HetNets. Our study further characterizes the impact of large antenna arrays, directional beamforming gains, transmit power, and cell density on the above mentioned secrecy performance measures. The results show that at low transmit power operation, the secrecy performance enhances for higher small cell density. It has also been observed that the higher directivity gains at mmWave cells lead to a drop in secrecy performance of the network; thus a tradeoff exists between better coverage or secrecy

A Data-Aided Channel Estimation Scheme for Decoupled Systems in Heterogeneous Networks

Uplink/downlink (UL/DL) decoupling promises more flexible cell association and higher throughput in heterogeneous networks (HetNets), however, it hampers the acquisition of DL channel state information (CSI) in time-division-duplex (TDD) systems due to different base stations (BSs) connected in UL/DL. In this paper, we propose a novel data-aided (DA) channel estimation scheme to address this problem by utilizing decoded UL data to exploit CSI from received UL data signal in decoupled HetNets where a massive multiple-input multiple-output BS and dense small cell BSs are deployed. We analytically estimate BER performance of UL decoded data, which are used to derive an approximated normalized mean square error (NMSE) expression of the DA minimum mean square error (MMSE) estimator. Compared with the conventional least square (LS) and MMSE, it is shown that NMSE performances of all estimators are determined by their signal-to-noise ratio (SNR)-like terms and there is an increment consisting of UL data power, UL data length and BER values in the SNR-like term of DA method, which suggests DA method outperforms the conventional ones in any scenarios. Higher UL data power, longer UL data length and better BER performance lead to more accurate estimated channels with DA method. Numerical results verify that the analytical BER and NMSE results are close to the simulated ones and a remarkable gain in both NMSE and DL rate can be achieved by DA method in multiple scenarios with different modulations

Backhaul-Aware Dimensioning and Planning of Millimeter-Wave Small Cell Networks

The massive deployment of Small Cells (SCs) is increasingly being adopted by mobile operators to face the exponentially growing traffic demand. Using the millimeter-wave (mmWave) band in the access and backhaul networks will be key to provide the capacity that meets such demand. However, dimensioning and planning have become complex tasks, because the capacity requirements for mmWave links can significantly vary with the SC location. In this work, we address the problem of SC planning considering the backhaul constraints, assuming that a line-of-sight (LOS) between the nodes is required to reliably support the traffic demand. Such a LOS condition reduces the set of potential site locations. Simulation results show that, under certain conditions, the proposed algorithm is effective in finding solutions and strongly efficient in computational cost when compared to exhaustive search approaches.H2020 research and innovation project 5G-CLARITY 871428Spanish Ministry of Science, Innovation and Universities PID2019-108713RB-C5

Multiobjective Optimization in 5G Hybrid Networks

The increasing adoption of the Internet of Things has led to the need for systems with higher spectral and energy efficiency (EE) in order to enable communication. Larger data rate demands had led researchers to look at millimeter wave (mmWave) bands to boost network rates. This paper investigates the downlink performance of a three-tier heterogeneous network that consists of sub-6 GHz macrocells overlaid with small cells operating on both the mmWave and sub-6 GHz bands. A model is developed using tools from stochastic geometry to analyze the coverage, rate, area spectral efficiency, and EE of such a network. Various deployment strategies and their impacts on the considered metrics are studied. Simulation results are used to verify the validity of the proposed model

MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model

This paper presents High density heterogeneous networks (HetNet) which are the most promising technology for the fifth generation (5G) cellular network. Since 5G will be available for a long time, previous generation networking systems will need customization and updates. We examine the merits and drawbacks of legacy and Q-Learning (QL)-based adaptive resource allocation systems. Furthermore, various comparisons between methods and schemes are made for the purpose of evaluating the solutions for future generation. Microwave macro cells are used to enable extra high capacity such as Long-Term Evolution (LTE), eNodeB (eNB), and Multimedia Communications Wireless technology (MC), in which they are most likely to be deployed. This paper also presents four scenarios for 5G mm-Wave implementation, including proposed system architectures. The WL algorithm allocates optimal power to the small cell base station (SBS) to satisfy the minimum necessary capacity of macro cell user equipment (MUEs) and small cell user equipment (SCUEs) in order to provide quality of service (QoS) (SUEs). The challenges with dense HetNet and the massive backhaul traffic they generate are discussed in this study. Finally, a core HetNet design based on clusters is aimed at reducing backhaul traffic. According to our findings, MM-wave HetNet and MEC can be useful in a wide range of applications, including ultra-high data rate and low latency communications in 5G and beyond. We also used the channel model simulator to examine the directional power delay profile with received signal power, path loss, and path loss exponent (PLE) for both LOS and NLOS using uniform linear array (ULA) 2X2 and 64x16 antenna configurations at 38 GHz and 73 GHz mmWave bands for both LOS and NLOS (NYUSIM). The simulation results show the performance of several path loss models in the mmWave and sub-6 GHz bands. The path loss in the close-in (CI) model at mmWave bands is higher than that of open space and two ray path loss models because it considers all shadowing and reflection effects between transmitter and receiver. We also compared the suggested method to existing models like Amiri, Su, Alsobhi, Iqbal, and greedy (non adaptive), and found that it not only enhanced MUE and SUE minimum capacities and reduced BT complexity, but it also established a new minimum QoS threshold. We also talked about 6G researches in the future. When compared to utilizing the dual slope route loss model alone in a hybrid heterogeneous network, our simulation findings show that decoupling is more visible when employing the dual slope path loss model, which enhances system performance in terms of coverage and data rate

Heterogeneous Cellular Networks Mixed with LoS and NLoS Transmissions

In the last decades, the rapid increase of user traffc demand for better user experience has pushed the traditional macrocell-only networks being evolving to modern heterogeneous networks(HetNets) with a multi-tier structure. The dense deployment of small-cell base stations (BSs) implies short distances between BSs and users. It is therefore likely that users will see line-of-sight (LoS) links from its serving BS and even nearby interfering BSs, which has not been considered in performance analysis for multi-tier HetNets yet. In this thesis, the dense multi-tier HetNet with LoS and non-line-of-sight (NLoS) transmissions based on a multi-slope path loss model is analyzed. The spatial locations of BSs of any given network tier and those of mobile users are modeled as independent spatial Poisson point processes (PPPs). The expressions of downlink coverage probability are divided for a multi-tier HetNet, based on that the calculations of the area spectral effciency (ASE) and energy effciency (EE) are further proposed. The results demonstrate that in an extremely dense HetNet, both the ASE and EE of the HetNet will drop quickly with further increase of the small-cell density due to the dominance of LoS interfering small-cell links. Following that, the investigation is moved to the probabilistic events of LoS and NLoS transmissions. Four transmission scenarios are simulated with different path loss models, including a linear LoS probability function, a suburban area, a millimetre wave transmission and a 3D path loss model. Accordingly, a user-centric BS clustering strategy is proposed for a non-coherent joint transmissions (JTs) in dense small-cell networks, based on the idea of grouping the BSs with their LoS probabilities to such user above a predefined threshold. The proposed BS clustering strategy is evaluated in the above four transmission environments. Our simulation results show that the coverage probability and spectrum effciency (SE) achieved by the proposed user-centric BS clustering strategy achieve a rapid growth rate with the increasing BS density, and even at extremely high BS densities in all four considered environments. Lastly, following the proposed BS clustering strategy above, a further developed clustering strategy called multi-BS multi-user-equipment (UE) clustering is proposed to allow multiple BSs to serve multiple UEs simultaneously. The main idea of this clustering strategy is to boost network performance in terms of coverage probability and SE at high BS density without sacrificing the ASE. Utilizing stochastic geometry, the closed form expressions of the network performance in terms of coverage probability, SE, ASE and EE are derived in a downlink small-cell network. The results show that the proposed clustering strategy achieves high coverage probability and linear increasing SE and ASE in ultra dense networks at same time