Performance Analysis of Cache-Enabled Millimeter Wave Small Cell Networks

Millimeter wave (mmWave) small-cell networks can provide high regional throughput, but the backhaul requirement has become a performance bottleneck. This paper proposes a hybrid system that combines traditional backhaul-connected small base stations (SBSs) and cache-enabled SBSs to achieve the maximum area spectral efficiency (ASE) while saving backhaul consumption in mmWave small cell networks. We derive and compare the ASE results for both the traditional and hybrid networks, and also show that the optimal content placement to maximize ASE is to cache the most popular contents. Numerical results demonstrate the performance improvement of deploying cache-enabled SBSs. Furthermore, given a total caching capacity, it is revealed that there is a tradeoff between the cache-enabled SBSs density and individual cache size to maximize the ASE

MMSE-based beamforming techniques for relay broadcast channels

We propose minimum mean square error (MMSE) based beamforming techniques for a multiantenna relay network, where a base station (BS) equipped with multiple antennas communicates with a number of single antenna users through a multiantenna relay.We specifically solve three optimization problems: a) sum-power minimization problem b) mean square error (MSE) balancing problem and c)mixed quality of services (QoS) problem. Unfortunately, these problems are not jointly convex in terms of beamforming vectors at the BS and the relay amplification matrix. To circumvent this non-convexity issue, the original problems are divided into two subproblems where the beamforming vectors and the relay amplification matrix are alternately optimized while other one is fixed. Three iterative algorithms have been developed based on convex optimization techniques and general MSE duality. Simulation results have been provided to validate the convergence of the proposed algorithms

A Mean Field Game Theoretic Approach to Electric Vehicles Charging

Electric vehicles (EVs) provide environmentally friendly transport and they are considered to be an important component of distributed and mobile electric energy storage and supply system. It is possible that EVs can be used to store and transport energy from one geographical area to another as a supportive energy supply. Electricity consumption management should consider carefully the inclusion of EVs. One critical challenge in the consumption management for EVs is the optimization of battery charging. This paper provides a dynamic game theoretic optimization framework to formulate the optimal charging problem. The optimization considers a charging scenario where a large number of EVs charge simultaneously during a flexible period of time. Based on stochastic mean field game theory, the optimization will provide an optimal charging strategy for the EVs to proactively control their charging speed in order to minimize the cost of charging. Numerical results are presented to demonstrate the performance of the proposed framework

A Game Theoretic Optimization Framework for Home Demand Management Incorporating Local Energy Resources

Facilitated by advanced ICT infrastructure and optimization techniques, smart grid has the potential to bring significant benefits to the energy consumption management. This paper presents a game theoretic consumption scheduling framework based on the use of mixed integer programming to schedule consumption plan for residential consumers. In particular, the optimization framework incorporates integration of locally generated renewable energy in order to minimise dependency on conventional energy and the consumption cost. The game theoretic model is designed to coordinatively manage the scheduling of appliances of consumers. The Nash equilibrium of the game exists and the scheduling optimization converges to an equilibrium where all consumers can benefit from participating in. Simulation results are presented to demonstrate the proposed approach and the benefits of home demand management

Sensitivity and Asymptotic Analysis of Inter-Cell Interference Against Pricing for Multi-Antenna Base Stations

We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism

Secrecy and Energy Efficiency in Massive MIMO Aided Heterogeneous C-RAN: A New Look at Interference

In this paper, we investigate the potential benefits of the massive multiple-input multiple-output (MIMO) enabled heterogeneous cloud radio access network (C-RAN) in terms of the secrecy and energy efficiency (EE). In this network, both remote radio heads (RRHs) and massive MIMO macrocell base stations (BSs) are deployed and soft fractional frequency reuse (S-FFR) is adopted to mitigate the inter-tier interference. We first examine the physical layer security by deriving the area ergodic secrecy rate and secrecy outage probability. Our results reveal that the use of massive MIMO and C-RAN can greatly improve the secrecy performance. For C-RAN, a large number of RRHs achieves high area ergodic secrecy rate and low secrecy outage probability, due to its powerful interference management. We find that for massive MIMO aided macrocells, having more antennas and serving more users improves secrecy performance. Then we derive the EE of the heterogeneous C-RAN, illustrating that increasing the number of RRHs significantly enhances the network EE. Furthermore, it is indicated that allocating more radio resources to the RRHs can linearly increase the EE of RRH tier and improve the network EE without affecting the EE of the macrocells.Comment: 26 pages, 11 figures, to appear in IEEE Journal of Selected Topics in Signal Processin

Parameter estimation and equalization techniques for communication channels with multipath and multiple frequency offsets

We consider estimation of frequency offset (FO) and equalization of a wireless communication channel, within a general framework which allows for different frequency offsets for various multipaths. Such a scenario may arise due to different Doppler shifts associated with various multipaths, or in situations where multiple basestations are used to transmit identical information. For this general framework, we propose an approximative maximum-likelihood estimator exploiting the correlation property of the transmitted pilot signal. We further show that the conventional minimum mean-square error equalizer is computationally cumbersome, as the effective channel-convolution matrix changes deterministically between symbols, due to the multiple FOs. Exploiting the structural property of these variations, we propose a computationally efficient recursive algorithm for the equalizer design. Simulation results show that the proposed estimator is statistically efficient, as the mean-square estimation error attains the Crame´r-Rao lower bound. Further, we show via extensive simulations that our proposed scheme significantly outperforms equalizers not employing FO estimation

Low-complexity iterative method of equalization for single carrier with cyclic prefix in doubly selective channels

Orthogonal frequency division multiplexing (OFDM)requires an expensive linear amplifier at the transmitter due to its high peak-to-average power ratio (PAPR). Single carrier with cyclic prefix (SC-CP) is a closely related transmission scheme that possesses most of the benefits ofOFDMbut does not have the PAPR problem. Although in a multipath environment, SC-CP is very robust to frequency-selective fading, it is sensitive to the time-selective fading characteristics of the wireless channel that disturbs the orthogonality of the channel matrix (CM) and increases the computational complexity of the receiver. In this paper, we propose a time-domain low-complexity iterative algorithm to compensate for the effects of time selectivity of the channel that exploits the sparsity present in the channel convolution matrix. Simulation results show the superior performance of the proposed algorithm over the standard linear minimum mean-square error (L-MMSE) equalizer for SC-CP

A Hybrid Training-Time and Run-Time Defense Against Adversarial Attacks in Modulation Classification

Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless networks. However, several recent works have pointed out that imperceptible and carefully designed adversarial examples (attacks) can significantly deteriorate the classification accuracy. In this letter, we investigate a defense mechanism based on both training-time and run-time defense techniques for protecting machine learning-based radio signal (modulation) classification against adversarial attacks. The training-time defense consists of adversarial training and label smoothing, while the run-time defense employs a support vector machine-based neural rejection (NR). Considering a white-box scenario and real datasets, we demonstrate that our proposed techniques outperform existing state-of-the-art technologies

A Phase Feedback Based Extended Space-Time Block Code for Enhancement of Diversity

This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.In this paper we propose a generalization of extended orthogonal space-time block codes (EO-STBCs) for MIMO (multi-input/multi-output) channels using four transmit antennas for quasi-static flat fading channels. Since full rate and complex orthogonal space-time block codes (STBCs) do not exist for more than two transmit antennas, we propose a feedback based STBC scheme. In this scheme, phases of certain symbols are rotated according to the feedback from the receiver which is equivalent to rotating the phases of the corresponding channel coefficients. Simulation results show that this rotation phase feedback method achieves a satisfactory performance and outperforms the previous closed-loop space-time block codes, even when the feedback is quantized