Cognitive radio (CR) technology allows devices to share the wireless spectrum with other users that have a license for operation in these spectrum bands. This area of research promises to solve the ...problem of spectrum scarcity in the unlicensed bands, and improve the inefficient spectrum utilization in the bands reserved for the licensed users. However, the opportunistic use of the available spectrum by the CR users must not affect the licensed users. This raises several concerns regarding spectrum sensing, sharing and reliable end-to-end communication in CR networks. This thesis is concerned with the design and implementation of communication protocols for the multi-hop infrastructure-less CR ad-hoc networks (CRAHNs). In addition, it also addresses the critical issue of interference-free spectrum usage in specific ad-hoc architectures, such as, resource-constrained wireless sensor networks and wireless mesh networks that have high traffic volumes. The problems of spectrum management that are unique to CR networks are first identified in this thesis. These issues are then addressed at each layer of the network protocol stack while considering the distributed operation in CRAHNs. At the physical layer an algorithmic suite is proposed that allows the CR devices to detect and adapt to the presence of wireless LANs and commercial microwave ovens. A common control channel is designed that allows sharing of the spectrum information between the CR users, even when the available spectrum varies dynamically. A spectrum sharing scheme for mesh networks is proposed at the link layer that allows cooperative detection of the licensed users and fair utilization of the available spectrum among the mesh devices. The spectrum availability and route formation are then considered jointly at the network layer, so that the licensed users are protected as well as the CRAHN performance is maximized. Finally, we extend the classical TCP at the transport layer to ensure end-to-end reliability in a multi-hop CR environment.
Full duplex communication promises a paradigm shift in wireless networks by allowing simultaneous packet transmission and reception within the same channel. While recent prototypes indicate the ...feasibility of this concept, there is a lack of rigorous theoretical development on how full duplex impacts medium access control (MAC) protocols in practical wireless networks. In this paper, we formulate the first analytical model of a CSMA/CA based full duplex MAC protocol for a wireless LAN network composed of an access point serving mobile clients. There are two major contributions of our work: First, our Markov chain-based approach results in closed form expressions of throughput for both the access point and the clients for this new class of networks. Second, our study provides quantitative insights on how much of the classical hidden terminal problem can be mitigated through full duplex. We specifically demonstrate that the improvement in the network throughput is up to 35-40 percent over the half duplex case. Our analytical models are verified through packet level simulations in ns-2. Our results also reveal the benefit of full duplex under varying network configuration parameters, such as number of hidden terminals, client density, and contention window size.
: This paper describes how the frequency domain analysis provides an alternative approach to time domain analysis of a given time series. Spectral and periodogram analyses of a given time series are ...performed to detect trends and seasonalities in the data. A cross-spectral analysis is done to find causality and comovements in two different time series. Univariate frequency domain analysis is done using time series of varying nature including simulated white noise process, random walk process, AR(1) process, Wolfer’s Sunspot data and Box-Jenkins Airlines data; while bivariate (cross-spectral) analysis is done for macroeconomic variables such as money in circulation and inflation.
This article presents a network architecture for the next generation of MHNs, where mmW, terahertz, and conventional mW bands coexist, with cost-benefit trade-offs of each type of link. We envision a ...radically different communication paradigm and outline a MAC protocol design that switches among the aforementioned bands for data transmissions, falling back on the slower link each time for the reverse channel ACKs. The use of the higher-capacity link in the forward direction for data communication and the slower reverse channel for the returning ACKs allows for uninterrupted unidirectional communication and efficient use of the channel. The article discusses the challenges in analyzing and parameter setting for the various features of the protocol, and identifies candidate solutions. A performance evaluation of the approach is undertaken using a realistic scenario of vehicle-to-infrastructure communication enabling data center traffic backhauling. This performance shows that by adopting the proposed MAC protocol data transfer of around 100 Terabits, it is possible for typical vehicle-to-infrastructure contact times.
Massive multiple-input multiple-output (mMIMO) is a critical component in upcoming 5G wireless deployment as an enabler for high data rate communications. mMIMO is effective when each corresponding ...antenna pair of the respective transmitter-receiver arrays experiences an independent channel. While increasing the number of antenna elements increases the achievable data rate, at the same time computing the channel state information (CSI) becomes prohibitively expensive. In this article, we propose to use deep learning via a multi-layer perceptron architecture that exceeds the performance of traditional CSI processing methods like least square (LS) and linear minimum mean square error (LMMSE) estimation, thus leading to a beyond fifth generation (B5G) networking paradigm wherein machine learning fully drives networking optimization. By computing the CSI of all pairwise channels simultaneously via our deep learning approach, our method scales with large antenna arrays as opposed to traditional estimation methods. The key insight here is to design the learning architecture such that it is implementable on massively parallel architectures, such as GPU or FPGA. We validate our approach by simulating a 32-element array base station and a user equipment with a 4-element array operating on millimeter-wave frequency band. Results reveal an improvement up to five and two orders of magnitude in BER with respect to fastest LS estimation and optimal LMMSE, respectively, substantially improving the end-to-end system performance and providing higher spatial diversity for lower SNR regions, achieving up to 4 dB gain in received power signal compared to performance obtained through LMMSE estimation.
In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and frequency synchronization using the first field of the preamble known as the legacy short training field (L-STF). The L-STF ...occupies upto 40% of the preamble length and takes upto 32 μ s of airtime. With the goal of reducing communication overhead, we propose a modified waveform, where the preamble length is reduced by eliminating the L-STF. To decode this modified waveform, we propose a neural network (NN)-based scheme called PRONTO that performs coarse time and frequency estimations using other preamble fields, specifically the legacy long training field (L-LTF). Our contributions are threefold: (i) We present PRONTO featuring customized convolutional neural networks (CNNs) for packet detection and coarse carrier frequency offset (CFO) estimation, along with data augmentation steps for robust training. (ii) We propose a generalized decision flow that makes PRONTO compatible with legacy waveforms that include the standard L-STF. (iii) We validate the outcomes on an over-the-air WiFi dataset from a testbed of software defined radios (SDRs). Our evaluations show that PRONTO can perform packet detection with 100% accuracy, and coarse CFO estimation with errors as small as 3%. We demonstrate that PRONTO provides upto 40% preamble length reduction with no bit error rate (BER) degradation. We further show that PRONTO is able to achieve the same performance in new environments without the need to re-train the CNNs. Finally, we experimentally show the speedup achieved by PRONTO through GPU parallelization over the corresponding CPU-only implementations.
Radio Frequency Fingerprinting on the Edge Jian, Tong; Gong, Yifan; Zhan, Zheng ...
IEEE transactions on mobile computing,
2022-Nov.-1, 2022-11-1, Letnik:
21, Številka:
11
Magazine Article
Recenzirano
Deep learning methods have been very successful at radio frequency fingerprinting tasks, predicting the identity of transmitting devices with high accuracy. We study radio frequency fingerprinting ...deployments at resource-constrained edge devices. We use structured pruning to jointly train and sparsify neural networks tailored to edge hardware implementations. We compress convolutional layers by a <inline-formula><tex-math notation="LaTeX">27.2\times</tex-math> <mml:math><mml:mrow><mml:mn>27</mml:mn><mml:mo>.</mml:mo><mml:mn>2</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="jian-ieq1-3064466.gif"/> </inline-formula> factor while incurring a negligible prediction accuracy decrease (less than 1 percent). We demonstrate the efficacy of our approach over multiple edge hardware platforms, including a Samsung Gallaxy S10 phone and a Xilinx-ZCU104 FPGA. Our method yields significant inference speedups, <inline-formula><tex-math notation="LaTeX">11.5\times</tex-math> <mml:math><mml:mrow><mml:mn>11</mml:mn><mml:mo>.</mml:mo><mml:mn>5</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="jian-ieq2-3064466.gif"/> </inline-formula> on the FPGA and <inline-formula><tex-math notation="LaTeX">3\times</tex-math> <mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="jian-ieq3-3064466.gif"/> </inline-formula> on the smartphone, as well as high efficiency: the FPGA processing time is <inline-formula><tex-math notation="LaTeX">17\times</tex-math> <mml:math><mml:mrow><mml:mn>17</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="jian-ieq4-3064466.gif"/> </inline-formula> smaller than in a V100 GPU. To the best of our knowledge, we are the first to explore the possibility of compressing networks for radio frequency fingerprinting; as such, our experiments can be seen as a means of characterizing the informational capacity associated with this specific learning task.
With the recent surge in autonomous driving vehicles, the need for accurate vehicle detection and tracking is critical now more than ever. Detecting vehicles from visual sensors fails in ...non-line-of-sight (NLOS) settings. This can be compensated by the inclusion of other modalities in a multi-domain sensing environment. We propose several deep learning based frameworks for fusing different modalities (image, radar, acoustic, seismic) through the exploitation of complementary latent embeddings, incorporating multiple state-of-the-art fusion strategies. Our proposed fusion frameworks considerably outperform unimodal detection. Moreover, fusion between image and non-image modalities improves vehicle tracking and detection under NLOS conditions. We validate our models on the real-world multimodal ESCAPE dataset, showing 33.16% improvement in vehicle detection by fusion (over visual inference alone) over test scenarios with 30-42% NLOS conditions. To demonstrate how well our framework generalizes, we also validate our models on the multimodal NuScene dataset, showing <inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula>22% improvement over competing methods.
Motor neuron diseases are a rare group of neurodegenerative disorders with considerable phenotypic heterogeneity and a multitude of etiologies in the pediatric population. In this study, we report 2 ...unrelated adolescents (a boy and a girl) who presented with 4-6 years of progressive difficulty in walking, thinning of limbs, and gradually progressive darkening of the skin. Examination revealed generalized hyperpigmentation of skin and features suggestive of motor neuron involvement such as tongue atrophy, wasting of distal extremities, and brisk deep tendon reflexes. On detailed exploration for systemic involvement, history of dysphagia, inability to produce tears, and Addisonian crises were evident. An etiologic diagnosis of Allgrove syndrome, which is characterized by a triad of achalasia, alacrimia, and adrenal insufficiency was considered. Next-generation sequencing revealed pathogenic variants in the
gene, confirming the diagnosis. Steroid replacement therapy was initiated along with relevant multidisciplinary referrals. The disease stabilized in the boy and a significant improvement was noted in the girl. These cases highlight the value of non-neurologic cues in navigating the etiologic complexities of motor neuron diseases in children and adolescents. It is imperative for neurologists to develop awareness of the diverse neurologic manifestations associated with Allgrove syndrome because they are often the first to be approached. A multidisciplinary team of experts including neurologists, endocrinologists, gastroenterologists, ophthalmologists, and dermatologists is essential for planning comprehensive care for these patients.
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