Search

In this paper, amplify-and-forward beamforming (AFB) is considered to establish a communication, through wireless sensor networks (WSNs) of $K$ sensor nodes, from a source to a receiver in the presence of both scattering and interference. All sources send their data to the WSN during the first time slot, while the nodes forward a properly weighted version of their received signals during the second slot. These weights are properly selected to maximize the desired power while completely canceling the interference signals. We show, however, that they depend on information locally unavailable at each node, making the zero-forcing beamformer (ZFB) unsuitable for WSNs, due to the prohibitive data exchange overhead and the power depletion it would require. To address this issue, we exploit the asymptotic expression at large $K$ of the ZFB weights that is locally computable at every node and, further, well-approximates their original counterparts. The performance of the resulting new distributed ZFB (DZFB) version is analyzed and compared with the conventional ZFB and two other distributed AFB benchmarks: the monochromatic (i.e., single-ray) AFB whose design neglects the presence of scattering and the bichromatic AFB which relies on an efficient two-ray channel approximation valid only for low angular spread (AS). We show that the proposed DZFB outperforms its monochromatic and bichromate counterparts while incurring much less overhead and power depletion than ZFB. We show also that it is able to provide optimal performance even in highly scattered environments as in the latter. [ABSTRACT FROM AUTHOR]

Owing to the multiple antennas equipped at modern Wi-Fi infrastructures, the angle-of-arrival (AoA) based indoor localization systems have successfully achieved the accuracy of tens of centimeters. However, the high accuracy is acquired at the cost of employing the additional resources in the domains of frequency, space or time, which requires complex processing and hinders the practical application. In this paper, we present the design and implementation of RcLoc, which takes full advantages of the flexible array orientations and receiver positions, based on limited resources. Particularly, RcLoc devises a receiver configuration scheme for guiding the system deployment. Optimized array orientation could effectively improve the AoA estimation accuracy and well-designed receiver positions contribute to the Cramer-Rao lower bound of localization error. In the stage of system realization, we further devise an array calibration method to calibrate the actual array and develop an improved AoA estimation algorithm, which make RcLoc robust to the array arrangement. We prototype RcLoc on commodity Wi-Fi devices without manual intervention. Comprehensive experiments in a multipath-rich indoor environment show that RcLoc achieves a median localization accuracy of 0.4 m, which provides useful insights for receiver deployment. [ABSTRACT FROM AUTHOR]

This paper aims at the problem of time-of-flight (ToF) estimation using channel state information (CSI) obtainable from commercialized multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless local area network (WLAN) receivers. It was often claimed that the CSI phase is contaminated with errors of known and unknown natures rendering the ToF-based positioning difficulty. To search for an answer, we take a bottom-up approach by first understanding CSI, its constituent building blocks, and the sources of error that contaminate it. We then model these effects mathematically. The correctness of these models is corroborated based on the CSI collected in extensive measurement campaign, including radiated, conducted, and chamber tests. Knowing the nature of contaminations in the CSI phase and amplitude, we proceed with introducing pre-processing methods to clean CSI from those errors and make it usable for range estimation. To check the validity of the proposed algorithms, the MUSIC super-resolution algorithm is applied to post-processed CSI to perform range estimates. The results substantiate that a median accuracy of 0.7, 0.8, and 0.9 m is achievable in a highly multipath line-of-sight environment where the transmitter and the receiver are 5, 10, and 15 m apart. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of velocity estimation in vehicular networks by exploiting the sparsity of vehicle velocity trajectory. First, we exploit the sparsity of vehicular velocity trajectories to reduce the beaconing load on the channel. To this end, we propose a sublayer that reduces the number of transmitted packets through a superframe. At each superframe, each vehicle transmits a few samples of its velocity as well as an encoded measurement of its past velocities. Sparse recovery of each velocity vector is performed at the receiver. We propose the use of repetitions at the medium access control (MAC) layer. Moreover, we extend our scheme into a streaming estimation system without the superframe concept. The streaming system uses a sliding window concept. We thoroughly study the estimation error of the streaming system, and we propose an algorithm to find the best solution over a streaming sliding window. The proposed superframe and streaming schemes are tested with real velocity traces collected in the city of Toronto to capture the performance in the city and highway conditions. Experiment results show that the proposed schemes significantly reduce the number of exchanged packets while preserving the velocity information with an excellent accuracy at the receiver. We also demonstrate how past velocity can be used to enhance vehicle localization accuracy. [ABSTRACT FROM PUBLISHER]

In this article, we present a survey of recent advances in passive human behavior recognition in indoor areas using the channel state information (CSI) of commercial WiFi systems. The movement of the human body parts cause changes in the wireless signal reflections, which result in variations in the CSI. By analyzing the data streams of CSIs for different activities and comparing them against stored models, human behavior can be recognized. This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers. The techniques from the literature that are presented herein have great performance; however, instead of the machine learning techniques employed in these works, we propose to use deep learning techniques such as long-short term memory (LSTM) recurrent neural networking (RNN) and show the improved performance. We also discuss different challenges such as environment change, frame rate selection, and the multi-user scenario; and finally suggest possible directions for future work. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of minimizing the completion delay for instantly decodable network coding (IDNC) in wireless multicast and broadcast scenarios. We are interested in this class of network coding due to its numerous benefits, such as low decoding delay, low coding and decoding complexities, and simple receiver requirements. We first extend the IDNC graph, which represents all feasible IDNC coding opportunities, to efficiently operate in both multicast and broadcast scenarios. We then formulate the minimum completion delay problem for IDNC as a stochastic shortest path (SSP) problem. Although finding the optimal policy using SSP is intractable, we use this formulation to draw the theoretical guidelines for the policies that can minimize the completion delay in IDNC. Based on these guidelines, we design a maximum weight clique selection algorithm, which can efficiently reduce the IDNC completion delay in polynomial time. We also design a quadratic-time heuristic clique selection algorithm, which can operate in real-time applications. Simulation results show that our proposed algorithms significantly reduce the IDNC completion delay compared to the random and maximum-rate algorithms, and almost achieve the global optimal completion delay performance over all network codes in broadcast scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we study the multicast completion and decoding delay minimization problems for instantly decodable network coding (IDNC) in the case of lossy feedback. When feedback loss events occur, the sender falls into uncertainties about packet reception at the different receivers, which forces it to perform partially blind selections of packet combinations in subsequent transmissions. To determine efficient selection policies that reduce the completion and decoding delays of IDNC in such an environment, we first extend the perfect feedback formulation in our previous works to the lossy feedback environment, by incorporating the uncertainties resulting from unheard feedback events in these formulations. For the completion delay problem, we use this formulation to identify the maximum likelihood state of the network in events of unheard feedback and employ it to design a partially blind graph update extension to the multicast IDNC algorithm in our earlier work. For the decoding delay problem, we derive an expression for the expected decoding delay increment for any arbitrary transmission. This expression is then used to find the optimal policy that reduces the decoding delay in such lossy feedback environment. Results show that our proposed solutions both outperform previously proposed approaches and achieve tolerable degradation even at relatively high feedback loss rates. [ABSTRACT FROM PUBLISHER]

In this paper, we aim to identify the strategies that maximize and monotonically increase the density of coding opportunities in instantly decodable network coding (IDNC). Using the graph representation of IDNC, we first derive an expression for the exact evolution of the edge set size after the transmission of any arbitrary coded packet. From the derived expression, we show that sending commonly wanted packets for all the receivers can maximize the number of coding opportunities. Since guaranteeing such property in IDNC is usually impossible, this strategy does not guarantee the achievement of our target. Consequently, we further investigate the problem by deriving an expression for the expected edge set size evolution after ignoring the identities of the packets requested by the different receivers and considering only their numbers. This expression was then employed to show that serving the maximum number of receivers with largest numbers of missing packets and erasure probabilities tends to maximize and monotonically increase the expected density of coding opportunities. Simulation results justify our theoretical findings. Finally, we validate the importance of our work through two case studies showing that our\ignore identified strategy outperforms several well-known IDNC solutions in optimizing the IDNC completion delay and receiver goodput. [ABSTRACT FROM PUBLISHER]

Network coding has recently attracted attention as a substantial improvement to packet retransmission schemes in wireless multicast broadcast services (MBS). Since the problem of finding the optimal network code maximizing the bandwidth efficiency is hard to solve and hard to approximate, two main network coding heuristic schemes, namely opportunistic and full network coding, were suggested in the literature to improve the MBS bandwidth efficiency. However, each of these two schemes usually outperforms the other in different receiver, demand, and feedback settings. The continuous and rapid change of these settings in wireless networks limits the bandwidth efficiency gains if only one scheme is always employed. In this paper, we propose an adaptive scheme that maintains the highest bandwidth efficiency obtainable by both opportunistic and full network coding schemes in wireless MBS. The proposed scheme adaptively selects, between these two schemes, the one that is expected to achieve the better bandwidth efficiency performance. The core contribution in this adaptive selection scheme lies in our derivation of performance metrics for opportunistic network coding, using random graph theory, which achieves efficient selection when compared to appropriate full network coding parameters. To compare between different complexity levels, we present three approaches to compute the performance metric for opportunistic coding using different levels of knowledge about the opportunistic coding graph. For the three considered approaches, simulation results show that our proposed scheme almost achieves the bandwidth efficiency performance that could be obtained by the optimal selection between the opportunistic and full coding schemes. [ABSTRACT FROM PUBLISHER]