Arman Shojaeifard

Resource Allocation for Energy Efficiency Optimization in Heterogeneous Networks

Heterogeneous network (HetNet) deployment is considered a de facto solution for meeting the ever increasing mobile traffic demand. However, excessive power usage in such networks is a critical issue, particularly for mobile operators. Characterizing the fundamental energy efficiency (EE) performance of HetNets is therefore important for the design of green wireless systems. In this paper, we address the EE optimization problem for downlink two-tier HetNets comprised of a single macro-cell and multiple pico-cells. Considering a heterogeneous real-time and non-real-time traffic, transmit beamforming design and power allocation policies are jointly considered in order to optimize the system energy efficiency. The EE resource allocation problem under consideration is a mixed combinatorial and non-convex optimization problem, which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original problem with multiple inequality constraints into multiple optimization problems with single inequality constraint. For the latter problem, a two-layer resource allocation algorithm is proposed based on the quasiconcavity property of EE. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE.

Exact SINR Statistics in the Presence of Heterogeneous Interferers

We derive new results for the higher order moments of signal-to-interference-plus-noise ratio (SINR) in the presence of an arbitrary Poisson point process (PPP)-based heterogeneous interference field. The analysis leverages on a moment-generating-function (MGF) methodology, which only requires the statistics of intended signal and aggregate interference, thus eliminating the need for the exact distribution of SINR. We extend the existing results on interference statistics by deriving a generalized closed-form expression of the interference MGF considering Nakagami-m fading channels with exclusion region. In certain special cases, explicit expressions for the averages of different functions of SINR are found, which also lead to closed-form solutions for the probability distributions of aggregate interference reciprocal and signal-to-interference ratio. We prove that in such cases the effect of total PPP-based interference power on useful transmission is mathematically equivalent to the severe fluctuations from a one-sided Gaussian fading channel. As an application example, the proposed methodology is used together with stochastic geometry theory to characterize the average SINR and rate in heterogeneous cellular networks. The validity of our analytical derivations is confirmed via Monte Carlo simulations for various system settings. We show that with cellular network densification there exists a tradeoff between the average SINR and rate performance.

A Unified Model for the Design and Analysis of Spatially-Correlated Load-Aware HetNets

We develop a unified framework for the performance analysis of arbitrary-loaded downlink heterogeneous networks (HetNets) in which interfering sources are inherently spatially-correlated. Considering a randomly-deployed multi-tier cellular network comprised of a diverse set of large-and small-cells, we incorporate the notion of load-awareness and spatial-correlations in characterizing the activities of base stations (BSs) using binary decision variables. A stochastic geometry-based approach is accordingly employed to systematically develop a bounded expression of ergodic rate with different cellular association and load-balancing strategies. Employing the proposed unified framework hence allows for relaxation of several major limitations in the existing state-of-the-art models, in particular the always-transmitting-BSs, uncorrelated interferers, and Rayleigh fading assumptions. We elaborate on the usefulness of adopting this methodology by providing detailed analysis of the aggregate network interference generated by interdependent load-proportional sources over Nakagami-m fading interfering channels. The analytical formulations are validated through Monte-Carlo (MC) simulations for various scenarios and system settings of interest. We observe that the heavily-adopted fully-loaded model as well as the more recent interference-thinning-based approximations are significantly limited in capturing the actual performance curve. The proposed bounded load-aware model and MC trials reveal several important trends and design guidelines for the practical deployment of HetNets.

Design, Modeling, and Performance Analysis of Multi-Antenna Heterogeneous Cellular Networks

This paper presents a stochastic geometry-based framework for the design and analysis of downlink multi-user multiple-input multiple-output (MIMO) heterogeneous cellular networks with linear zero-forcing transmit precoding and receive combining, assuming Rayleigh fading channels and perfect channel state information. The generalized tiers of base stations may differ in terms of their Poisson point process spatial density, number of transmit antennas, transmit power, artificial-biasing weight, and number of user equipments served per resource block. The spectral efficiency of a typical user equipped with multiple receive antennas is characterized using a non-direct moment-generating-function-based methodology with closed-form expressions of the useful received signal and aggregate network interference statistics systematically derived. In addition, the area spectral efficiency is formulated under different space-division multiple-access and single-user beamforming transmission schemes. We examine the impact of different cellular network deployments, propagation conditions, antenna configurations, and MIMO setups on the achievable performance through theoretical and simulation studies. Based on the state-of-the-art system parameters, the results highlight the inherent limitations of baseline single-input single-output transmission and conventional sparse macro-cell deployment, as well as the promising potential of multi-antenna communications and small-cell solution in interference-limited cellular environments.

Energy Efficiency Optimization With Interference Alignment in Multi-Cell MIMO Interfering Broadcast Channels

Characterizing the fundamental energy efficiency (EE) performance of multiple-input-multiple-output interfering broadcast channels (MIMO-IFBC) is important for the design of green wireless system. In this paper, we propose a new network architecture proposition based on EE maximization for Multi-Cell MIMO-IFBC within the context of interference alignment (IA). Particularly, EE is maximized subject to maximum power and minimum throughput constraints. We propose two schemes to optimize EE for different signal-to-noise ratio (SNR) regions. For high-SNR operating regions, we employ a grouping-based IA scheme to jointly cancel intra- and inter-cell interferences and thus transform the MIMO-IFBC to a single-cell MIMO scenario. A gradient-based power adaptation scheme is proposed based on water-filling power adaptation and singular value decomposition to maximize EE for each cell. For moderate SNR cases, we propose an approach using dirty paper coding (DPC) with the principle of multiple access channel and broadcast channel duality to perform IA while maximizing EE in each cell. The algorithm in its dual form is solved using a subgradient method and a bisection searching scheme. Simulation results demonstrate the superior performance of the proposed schemes over several existing approaches. It also shows that interference-nulling-based IA approaches outperform hybrid DPC-IA approach in high-SNR region, and the opposite occurs in low-SNR region.

On the Energy Efficiency–Spectral Efficiency Tradeoff in MIMO-OFDMA Broadcast Channels

This paper investigates the fundamental energy efficiency-spectral efficiency (EE-SE) relationship in a multiple-input-multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA) broadcast channel with a practical power model considering the power consumption due to the number of admitted users, as well as the number of active transmit antennas. However, with this power model, the EE-SE tradeoff optimization problem, which jointly optimizes the transmit covariance matrices while determining the optimal admitted user set and the active transmit antenna set, is nonconvex, and hence, it is extremely difficult to solve directly. As a result, we propose an algorithm that decouples the multicarrier EE optimization problem to a set of single-carrier EE optimization problems. For the single-carrier EE optimization problem, we first investigate the EE-SE tradeoff problem with a fixed admitted user set and transmit antenna set. Under this setup, we prove that the EE-SE relationship is a quasiconcave function. Furthermore, EE is proved to be either strictly decreasing with SE or first strictly increasing and then strictly decreasing with SE. Based on these findings, we propose a two-layer resource allocation algorithm to tackle the comprehensive EE-SE tradeoff problem. Meanwhile, since admitting more users and activating more transmit antennas can achieve a higher sum rate at the cost of larger transmit-independent power consumption, there exists a tradeoff between sum-rate gain and power consumption. We therefore study the user and antenna selection approach to further explore the optimal tradeoff. Both the optimal exhaustive search and the Frobenius-norm-based dynamic selection schemes are developed to further improve the achievable EE. To further reduce the computational complexity, a strategy that chooses a fixed admitted user set for all the subcarriers is developed. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE-SE tradeoff.

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.

Self-Interference in Full-Duplex Multi-User MIMO Channels

We consider a multi-user multiple-input multiple-output setup, where full-duplex multi-antenna nodes apply linear beamformers to simultaneously transmit and receive multiple streams over Rician fading channels. The exact first and second positive moments of the residual self-interference (SI), involving the squared norm of a sum of non-identically distributed random variables, are derived in closed-form. The method of moments is hence invoked to provide a Gamma approximation for the residual SI distribution. The proposed theorem holds under arbitrary linear precoder/decoder design, antenna array size, number of information streams, and SI cancellation capability.

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

Simultaneous wireless information and power transfer (SWIPT) is anticipated to have great applications in 5G communication systems and the Internet of Things. In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (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 nonconvex, 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 subgradient 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.

Massive MIMO-Enabled Full-Duplex Cellular Networks

We provide a theoretical framework for the study of massive multiple-input multiple-output (MIMO)-enabled full-duplex (FD) cellular networks in which the residual self-interference (SI) channels follow the Rician distribution and other channels are Rayleigh distributed. In order to facilitate bi-directional wireless functionality, we adopt: 1) in the downlink (DL), a linear zero-forcing (ZF) with SI-nulling precoding scheme at the FD base stations and 2) in the uplink (UL), an SI-aware fractional power control mechanism at the FD mobile terminals. Linear ZF receivers are further utilized for signal detection in the UL. The results indicate that the UL rate bottleneck in the FD baseline single-input single-output system can be overcome via exploiting massive MIMO. On the other hand, the findings may be viewed as a reality-check, since we show that, under state-of-the-art system parameters, the spectral efficiency gain of FD massive MIMO over its half-duplex counterpart is largely limited by the cross-mode interference between the DL and the UL. In point of fact, the anticipated twofold increase in SE is shown to be only achievable when the number of antennas tends to be infinitely large.