On Quality of Monitoring for Multi-channel Wireless Infrastructure Networks

Passive monitoring utilizing distributed wireless sniffers is an effective technique to monitor activities in wireless infrastructure networks for fault diagnosis, resource management and critical path analysis. In this paper, we introduce a quality of monitoring (QoM) metric defined by the expected number of active users monitored, and investigate the problem of maximizing QoM by judiciously assigning sniffers to channels based on the knowledge of user activities in a multi-channel wireless network. Two types of capture models are considered. The user-centric model assumes frame-level capturing capability of sniffers such that the activities of different users can be distinguished while the sniffer-centric model only utilizes the binary channel information (active or not) at a sniffer. For the user-centric model, we show that the implied optimization problem is NP-hard, but a constant approximation ratio can be attained via polynomial complexity algorithms. For the sniffer-centric model, we devise stochastic inference schemes to transform the problem into the user-centric domain, where we are able to apply our polynomial approximation algorithms. The effectiveness of our proposed schemes and algorithms is further evaluated using both synthetic data as well as real-world traces from an operational WLAN.Comment: Accepted for publication in IEEE TMC 201

Wireless Scheduling Algorithms in Complex Environments

Efficient spectrum use in wireless sensor networks through spatial reuse requires effective models of packet reception at the physical layer in the presence of interference. Despite recent progress in analytic and simulations research into worst-case behavior from interference effects, these efforts generally assume geometric path loss and isotropic transmission, assumptions which have not been borne out in experiments. Our paper aims to provide a methodology for grounding theoretical results into wireless interference in experimental reality. We develop a new framework for wireless algorithms in which distance-based path loss is replaced by an arbitrary gain matrix, typically obtained by measurements of received signal strength (RSS). Gain matrices allow for the modeling of complex environments, e.g., with obstacles and walls. We experimentally evaluate the framework in two indoors testbeds with 20 and 60 motes, and confirm superior predictive performance in packet reception rate for a gain matrix model over a geometric distance-based model. At the heart of our approach is a new parameter $\zeta$ called metricity which indicates how close the gain matrix is to a distance metric, effectively measuring the complexity of the environment. A powerful theoretical feature of this parameter is that all known SINR scheduling algorithms that work in general metric spaces carry over to arbitrary gain matrices and achieve equivalent performance guarantees in terms of $\zeta$ as previously obtained in terms of the path loss constant. Our experiments confirm the sensitivity of $\zeta$ to the nature of the environment. Finally, we show analytically and empirically how multiple channels can be leveraged to improve metricity and thereby performance. We believe our contributions will facilitate experimental validation for recent advances in algorithms for physical wireless interference models

Resource Allocation and Interference Mitigation Techniques for Cooperative Multi-Antenna and Spread Spectrum Wireless Networks

This chapter presents joint interference suppression and power allocation algorithms for DS-CDMA and MIMO networks with multiple hops and amplify-and-forward and decode-and-forward (DF) protocols. A scheme for joint allocation of power levels across the relays and linear interference suppression is proposed. We also consider another strategy for joint interference suppression and relay selection that maximizes the diversity available in the system. Simulations show that the proposed cross-layer optimization algorithms obtain significant gains in capacity and performance over existing schemes.Comment: 10 figures. arXiv admin note: substantial text overlap with arXiv:1301.009

Detection and Estimation Algorithms in Massive MIMO Systems

This book chapter reviews signal detection and parameter estimation techniques for multiuser multiple-antenna wireless systems with a very large number of antennas, known as massive multi-input multi-output (MIMO) systems. We consider both centralized antenna systems (CAS) and distributed antenna systems (DAS) architectures in which a large number of antenna elements are employed and focus on the uplink of a mobile cellular system. In particular, we focus on receive processing techniques that include signal detection and parameter estimation problems and discuss the specific needs of massive MIMO systems. Simulation results illustrate the performance of detection and estimation algorithms under several scenarios of interest. Key problems are discussed and future trends in massive MIMO systems are pointed out.Comment: 7 figures, 14 pages. arXiv admin note: substantial text overlap with arXiv:1310.728

Channel Tracking for Relay Networks via Adaptive Particle MCMC

This paper presents a new approach for channel tracking and parameter estimation in cooperative wireless relay networks. We consider a system with multiple relay nodes operating under an amplify and forward relay function. We develop a novel algorithm to efficiently solve the challenging problem of joint channel tracking and parameters estimation of the Jakes' system model within a mobile wireless relay network. This is based on \textit{particle Markov chain Monte Carlo} (PMCMC) method. In particular, it first involves developing a Bayesian state space model, then estimating the associated high dimensional posterior using an adaptive Markov chain Monte Carlo (MCMC) sampler relying on a proposal built using a Rao-Blackwellised Sequential Monte Carlo (SMC) filter.Comment: 30 pages, 11 figure

Framework of Channel Estimation for Hybrid Analog-and-Digital Processing Enabled Massive MIMO Communications

We investigate a general channel estimation problem in the massive multiple-input multiple-output (MIMO) system which employs the hybrid analog/digital precoding structure with limited radio-frequency (RF) chains. By properly designing RF combiners and performing multiple trainings, the proposed channel estimation can approach the performance of fully-digital estimations depending on the degree of channel spatial correlation and the number of RF chains. Dealing with the hybrid channel estimation, the optimal combiner is theoretically derived by relaxing the constant-magnitude constraint in a specific single-training scenario, which is then extended to the design of combiners for multiple trainings by Sequential and Alternating methods. Further, we develop a technique to generate the phase-only RF combiners based on the corresponding unconstrained ones to satisfy the constant-magnitude constraints. The performance of the proposed hybrid channel estimation scheme is examined by simulations under both nonparametric and spatial channel models. The simulation results demonstrate that the estimated CSI can approach the performance of fully-digital estimations in terms of both mean square error and spectral efficiency. Moreover, a practical spatial channel covariance estimation method is proposed and its effectiveness in hybrid channel estimation is verified by simulations

Low-Complexity Adaptive Channel Estimation over Multipath Rayleigh Fading Non-Stationary Channels Under CFO

In this paper, we propose novel low-complexity adaptive channel estimation techniques for mob ile wireless chan- n els in presence of Rayleigh fading, carrier frequency offsets (CFO) and random channel variations. We show that the selective p artial update of the estimated channel tap-weight vector offers a better trade-off between the performance and computational complexity, compared to the full update of the estimated channel tap-weight vector. We evaluate the mean-square weight error of th e proposed methods and demonstrate the usefulness of its via simulation studies.Comment: 18th IEEE International Conference on Telecommunications (ICT2011) Ayia Napa, Cypru

Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access

We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive in general due to the large state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. Experimental results demonstrate strong performance of the algorithm.Comment: This work has been accepted for publication in the IEEE Transactions on Wireless Communication

Effective Capacity in Wireless Networks: A Comprehensive Survey

Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physical-layer channel models, however, do not explicitly consider quality-of-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization

Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information

Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose to use spatial information extracted at sub-6 GHz to help establish the mmWave link. First, we review the prior work on frequency dependent channel behavior and outline a simulation strategy to generate multi-band frequency dependent channels. Second, assuming: (i) narrowband channels and a fully digital architecture at sub-6 GHz; and (ii) wideband frequency selective channels, OFDM signaling, and an analog architecture at mmWave, we outline strategies to incorporate sub-6 GHz spatial information in mmWave compressed beam selection. We formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active OFDM subcarriers. The simulation results for achievable rate show that out-of-band aided beam-selection can reduce the training overhead of in-band only beam-selection by 4x.Comment: 30 pages, 11 figure