How Well Sensing Integrates with Communications in MmWave Wi-Fi?

The development of integrated sensing and communication (ISAC) systems has recently gained interest for its ability to offer a variety of services including resources sharing and new applications, for example, localization, tracking, and health care related. While the sensing capabilities are offered through many technologies, rending to their wide deployments and the high frequency spectrum they provide and high range resolution, its accessibility through the Wi-Fi networks IEEE 802.11ad and 802.11ay has been getting the interest of research and industry. Even though there is a dedicated standardization body, namely the 802.11bf task group, working on enhancing the Wi-Fi sensing performance, investigations are needed to evaluate the effectiveness of various sensing techniques. In this project, we, in addition to surveying related literature, we evaluate the sensing performance of the millimeter wave (mmWave) Wi-Fi systems by simulating a scenario of a human target using Matlab simulation tools. In this analysis, we processed channel estimation data using the short time Fourier transform (STFT). Furthermore, using a channel variation threshold method, we evaluated the performance while reducing feedback. Our findings indicate that using STFT window overlap can provide good tracking results, and that the reduction in feedback measurements using 0.05 and 0.1 threshold levels reduces feedback measurements by 48% and 77%, respectively, without significantly degrading performance.Comment: arXiv admin note: substantial text overlap with arXiv:2207.04859 by other author

Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part I: template-based generic programming

An approach for incorporating embedded simulation and analysis capabilities in complex simulation codes through template-based generic programming is presented. This approach relies on templating and operator overloading within the C++ language to transform a given calculation into one that can compute a variety of additional quantities that are necessary for many state-of-the-art simulation and analysis algorithms. An approach for incorporating these ideas into complex simulation codes through general graph-based assembly is also presented. These ideas have been implemented within a set of packages in the Trilinos framework and are demonstrated on a simple problem from chemical engineering

Traffic measurement and analysis

Measurement and analysis of real traffic is important to gain knowledge about the characteristics of the traffic. Without measurement, it is impossible to build realistic traffic models. It is recent that data traffic was found to have self-similar properties. In this thesis work traffic captured on the network at SICS and on the Supernet, is shown to have this fractal-like behaviour. The traffic is also examined with respect to which protocols and packet sizes are present and in what proportions. In the SICS trace most packets are small, TCP is shown to be the predominant transport protocol and NNTP the most common application. In contrast to this, large UDP packets sent between not well-known ports dominates the Supernet traffic. Finally, characteristics of the client side of the WWW traffic are examined more closely. In order to extract useful information from the packet trace, web browsers use of TCP and HTTP is investigated including new features in HTTP/1.1 such as persistent connections and pipelining. Empirical probability distributions are derived describing session lengths, time between user clicks and the amount of data transferred due to a single user click. These probability distributions make up a simple model of WWW-sessions

The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Rate-adaptive H.264 for TCP/IP networks

While there has always been a tremendous demand for streaming video over TCP/IP networks, the nature of the application still presents some challenging issues. These applications that transmit multimedia data over best-effort networks like the Internet must cope with the changing network behavior; specifically, the source encoder rate should be controlled based on feedback from a channel estimator that probes the network periodically. First, one such Multimedia Streaming TCP-Friendly Protocol (MSTFP) is considered, which iteratively integrates forward estimation of network status with feedback control to closely track the varying network characteristics. Second, a network-adaptive embedded bit stream is generated using a r-domain rate controller. The conceptual elegance of this r-domain framework stems from the fact that the coding bit rate ) (R is approximately linear in the percentage of zeros among the quantized spatial transform coefficients ) ( r , as opposed to the more traditional, complex and highly nonlinear ) ( Q R characterization. Though the r-model has been successfully implemented on a few other video codecs, its application to the emerging video coding standard H.264 is considered. The extensive experimental results show thatrobust rate control, similar or improved Peak Signal to Noise Ratio (PSNR), and a faster implementation

Cognitive Interference Management in Retransmission-Based Wireless Networks

Cognitive radio methodologies have the potential to dramatically increase the throughput of wireless systems. Herein, control strategies which enable the superposition in time and frequency of primary and secondary user transmissions are explored in contrast to more traditional sensing approaches which only allow the secondary user to transmit when the primary user is idle. In this work, the optimal transmission policy for the secondary user when the primary user adopts a retransmission based error control scheme is investigated. The policy aims to maximize the secondary users' throughput, with a constraint on the throughput loss and failure probability of the primary user. Due to the constraint, the optimal policy is randomized, and determines how often the secondary user transmits according to the retransmission state of the packet being served by the primary user. The resulting optimal strategy of the secondary user is proven to have a unique structure. In particular, the optimal throughput is achieved by the secondary user by concentrating its transmission, and thus its interference to the primary user, in the first transmissions of a primary user packet. The rather simple framework considered in this paper highlights two fundamental aspects of cognitive networks that have not been covered so far: (i) the networking mechanisms implemented by the primary users (error control by means of retransmissions in the considered model) react to secondary users' activity; (ii) if networking mechanisms are considered, then their state must be taken into account when optimizing secondary users' strategy, i.e., a strategy based on a binary active/idle perception of the primary users' state is suboptimal.Comment: accepted for publication on Transactions on Information Theor

A passive available bandwidth estimation methodology

The Available Bandwidth (AB) of an end-to-end path is its remaining capacity and it is an important metric for several applications such as overlay routing and P2P networking. That is why many AB estimation tools have been published recently. Most of these tools use the Probe Rate Model, which requires sending packet trains at a rate matching the AB. Its main issue is that it congests the path under measurement. We present a different approach: a novel passive methodology to estimate the AB that does not introduce probe traffic. Our methodology, intended to be applied between two separate nodes, estimates the path’s AB by analyzing specific parameters of the traffic exchanged. The main challenge is that we cannot rely on any given rate of this traffic. Therefore we rely on a different model, the Utilization Model. In this paper we present our passive methodology and a tool (PKBest) based on it. We evaluate its applicability and accuracy using public NLANR data traces. Our results -more than 300Gb- show that our tool is more accurate than pathChirp, a state-of-the-art active PRM-based tool. At the best of the authors’ knowledge this is the first passive AB estimation methodology.Preprin