Physical Layer Security in Full-Duplex Cellular Networks

In this work, we investigate the physical layer security (PHYLS) performance of full-duplex (FD) cellular networks, where the downlink (DL) and uplink (UL) occur over the same radio-frequency (RF) resources. Here, the locations of the base stations (BSs) and mobile terminals (MTs) are drawn from stationary Poisson point processes (PPPs). Moreover, the eavesdroppers (EDs) locations are unknown to the network, and are thus modeled from an independent PPP. We characterize the signal-to-interference-plus-noise ratio (SINR) distributions at the reference BS, MT, and most malicious EDs. Accordingly, we develop explicit expressions for the secrecy rates in both UL and DL of the FD cellular network under consideration. Our finding show that the choice of FD versus HD operation, in addition to improving the spectral efficiency, can enhance the secrecy rate, particularly for ultra-dense deployments

Deep Learning-Based Decision Region for MIMO Detection

In this work, a deep learning-based symbol detection method is developed for multi-user multiple-input multiple-output (MIMO) systems. We demonstrate that the linear threshold-based detection methods, which were designed for AWGN channels, are suboptimal in the context of MIMO fading channels. Furthermore, we propose a MIMO detection framework which replaces the linear thresholds with decision boundaries trained with neural network (NN) classifiers. The symbol error rate (SER) performance of the proposed detection model is compared against conventional methods under state-of-the-art system parameters. Here, we report to up to a 2 dB gain in SER performance using the proposed NN classifiers, allowing for exploiting higher-order modulation schemes, or transmitting with reduced power. The underlying gain in performance may be further enhanced from improvements to the NN architecture and hyper-parameter optimization

Recurrent neural network channel estimation using measured massive MIMO data

In this work, we develop a novel channel estimation method using recurrent neural networks (RNNs) for massive multiple-input multiple-output (MIMO) systems. The proposed framework alleviates the need for channel-state-information (CSI) feedback and pilot assignment through exploiting the inherent time and frequency correlations in practical propagation environments. We carry out the analysis using empirical MIMO channel measurements between a 64T64R active antenna system and a state-of-the-art multi-antenna scanner for both mobile and stationary use-cases. We also capture and analyze similar MIMO channel data from a legacy 2T2R base station (BS) for comparison purposes. Our findings confirm the applicability of utilising the proposed RNN-based massive MIMO channel acquisition scheme particularly for channels with long time coherence and hardening effects. In our practical setup, the proposed method reduced the number of pilots used by 25%

Joint Antenna Selection and Spatial Switching for Energy Efficient MIMO SWIPT System

In this paper, we investigate joint antenna selection and spatial switching for quality-of-service-constrained energy efficiency (EE) optimization in a multiple-input multiple-output simultaneous wireless information and power transfer system. A practical linear power model taking into account the entire transmit-receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigenchannel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. In order to tackle this, we separate the eigenchannel assignment and power allocation procedure with the antenna selection functionality. In particular, we first tackle the EE maximization problem under fixed receive antenna set using Dinkelbach-based convex programming, iterative joint eigenchannel assignment and power allocation, and low-complexity multi-objective optimization-based approach. On the other hand, the number of active receive antennas induces a tradeoff in the achievable sum-rate and power transfer versus the transmit-independent power consumption. We provide a fundamental study of the achievable EE with antenna selection and accordingly develop dynamic optimal exhaustive search and Frobenius-norm-based schemes. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithms can efficiently approach the optimal EE

Energy Efficiency Optimization with SWIPT in MIMO Broadcast Channels for Internet of Things

IEEE Simultaneous wireless information and power transfer (SWIPT) is anticipated to have great applications in fifth-generation (5G) communication systems and the Internet-of-Things (IoT). In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (MIMO-BC) with time-switching (TS) receiver design. Our aim is to maximize the EE of the system whilst satisfying certain constraints in terms of maximum transmit power and minimum harvested energy per user. The coupling of the optimization variables, namely, transmit covariance matrices and TS ratios, leads to an EE problem which is non-convex, and hence very difficult to solve directly. Hence, we transform the original maximization problem with multiple constraints into a suboptimal min-max problem with a single constraint and multiple auxiliary variables. We propose a dual inner/outer layer resource allocation framework to tackle the problem. For the inner-layer, we invoke an extended SWIPT-based BC-multiple access channel (MAC) duality approach and provide two iterative resource allocation schemes under fixed auxiliary variables for solving the dual MAC problem. A sub-gradient searching scheme is then proposed for the outer-layer in order to obtain the optimal auxiliary variables. Numerical results confirm the effectiveness of the proposed algorithms and illustrate that significant performance gain in terms of EE can be achieved by adopting the proposed extended BC-MAC duality-based algorithm

On the Design of Irregular HetNets with Flow-Level Traffic Dynamics

The application of stochastic geometry theory for the study of cellular networks has gained huge popularity recently. Most existing works however rely on unrealistic assumptions concerning the underlying user traffic model. This paper aims to make a step in this direction by devising a new model for the performance analysis and optimization of heterogeneous cellular networks (HetNets) with irregular BS deployment and flow- level traffic dynamics. We provide a unified methodology for the evaluation of the flow rate with closed-form expressions of the useful signal power and aggregate network interference over Nakagami-m fading channels. The problem of computing the optimal loading factors which result in the greatest sustainable traffic whilst the system remains stable is formulated and tackled

Design and Analysis of Full-Duplex Massive MIMO Cellular Networks

This paper provides a theoretical framework for the study of full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular networks over Rician self-interference (SI) and Rayleigh intended and other-interference fading channels. To facilitate bi-directional wireless functionality, we incorporate (i) a downlink (DL) linear zero-forcing with self-interference-nulling (ZF-SIN) precoding scheme at the FD base stations (BSs), and (ii) an uplink (UL) self-interference-aware (SIA) fractional power control mechanism at the FD user equipments (UEs). Linear ZF receivers are further utilized for signal detection in the UL. The results indicate that the UL rate bottleneck in the baseline FD single-antenna system can be elevated by several hundred times via exploiting massive MIMO. On the other hand, the findings may be viewed as a reality-check as the largest spectral efficiency gain from the FD massive MIMO cellular network over its half-duplex (HD) counterpart under state-of-the-art system parameters is shown to be in the region of ~40%

Energy-Efficient Heterogeneous Cellular Networks with Spectrum Underlay and Overlay Access

IEEE In this paper, we provide joint subcarrier assignment and power allocation schemes for quality-of-service (QoS)-constrained energy-efficiency (EE) optimization in the downlink of an orthogonal frequency division multiple access (OFDMA)-based two-tier heterogeneous cellular network (HCN). Considering underlay transmission, where spectrum-efficiency (SE) is fully exploited, the EE solution involves tackling a complex mixed-combinatorial and non-convex optimization problem. With appropriate decomposition of the original problem and leveraging on the quasi-concavity of the EE function, we propose a dual-layer resource allocation approach and provide a complete solution using difference-of-two-concave-functions approximation, successive convex approximation and gradient-search method. On the other hand, the inherent inter-tier interference from spectrum underlay access may degrade EE particularly under dense small-cell deployment and large bandwidth utilization. We therefore develop a novel resource allocation approach based on the concepts of spectrum overlay access and resource efficiency (RE) (normalized EE-SE trade-off). Specifically, the optimization procedure is separated where the macro-cell optimal RE and the corresponding bandwidth is first determined, then the EE of small-cells utilizing the remaining spectrum is maximized. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation schemes can approach the optimal EE with each strategy being superior under certain system settings

Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks

Dense cellular networks (DenseNets) are fast becoming a reality with the large scale deployment of base stations aimed at meeting the explosive data traffic demand. In legacy systems, however, this comes at the cost of higher network interference and energy consumption. In order to support network densification in a sustainable manner, the system behavior should be made “load-proportional” thus allowing certain portions of the network to activate on-demand. In this paper, we develop an analytical framework using tools from stochastic geometry theory for the performance analysis of DenseNets where load-awareness is explicitly embedded in the design. The proposed model leverages on a flexible cellular network architecture where there is a complete separation of the data and signaling communications functionalities. Using this stochastic geometric framework, we identify the most energy-efficient deployment solution for meeting certain minimum service criteria and analyze the corresponding power savings through dynamic sleep modes. According to state-of-the-art system parameters, a homogeneous pico deployment for the data plane with a separate layer of signaling macro-cells is revealed to be the most energy-efficient solution in future dense urban environments