1,721,016 research outputs found
Compressive Sampling based Multiple Symbol Differential Detection for UWB Communications
Full text linkCompressive sampling (CS) based multiple sym- bol differential detectors are proposed for impulse-radio ultra- wideband signaling, using the principles of generalized likelihood ratio tests. The CS based detectors correspond to two communica- tion scenarios. One, where the signaling is fully synchronized at the receiver and the other, where there exists a symbol level synchro- nization only. With the help of CS, the sampling rates are reduced much below the Nyquist rate to save on the high power consumed by the analog-to-digital converters. In stark contrast to the usual compressive sampling practices, the proposed detectors work on the compressed samples directly, thereby avoiding a complicated reconstruction step and resulting in a reduction of the implemen- tation complexity. To resolve the detection of multiple symbols, compressed sphere decoders are proposed as well, for both com- munication scenarios, which can further help to reduce the sys- tem complexity. Differential detection directly on the compressed symbols is generally marred by the requirement of an identical measurement process for every received symbol. Our proposed detectors are valid for scenarios where the measurement process is the same as well as where it is different for each received symbol
Compressed Sensing Techniques for Dynamic Resource Allocation in Wideband Cognitive Networks
No full textFor multi-user cognitive networks, joint dynamic resource allocation (DRA) and waveform adaptation techniques have been developed that effectively represent, manipulate and utilize the physical-layer radio resources by synthesizing both transmitter and receiver waveforms from generalized signal expansion functions. To effect distributed DRA games, this paper discusses the intertwined sensing task and develops compressed sensing techniques that simultaneously estimate all the channel and interference links using only a small number of samples collected from a sparse set of expansion functions. By properly identifying and utilizing the sparsity properties of a wideband environment, the proposed schemes considerably reduce both sensing time and implementation costs
Detection of Sparse Signals under Finite-Alphabet Constraints
No full textIn this paper, we solve the problem of detecting the entries of a sparse finite-alphabet signal from a limited amount of data, for instance obtained by compressive sampling. While existing methods either rely on the sparsity property, the finite-alphabet property, or none of those properties to solve the under-determined system of linear equations, we capitalize on both the sparsity and the finite-alphabet features of the signal. The problem is first formulated in a Bayesian framework to incorporate the prior knowledge of sparsity, which is then shown to be solvable using sphere decoding (SD) or semi-definite relaxation (SDR) for efficient Boolean programming. A few toy simulations show how our method can outperform existing works
Sparsity-Aware Wireless Networks: Localization and Sensor Selection
No full textWireless networks have revolutionized nowadays world by providing real time cost-efficient service and connectivity. Even such an unprecedented level of service could not fulfill the insatiable desire of the modern world for more advanced technologies. As a result, a great deal of attention has been directed towards (mobile) wireless sensor networks (WSNs) which are comprised of considerably cheap nodes that can cooperate to perform complex tasks in a distributed fashion in extremely harsh environments. Unique features of wireless environments, added complexity owing to mobility, distributed nature of the network setup, and tight performance and energy constraints, pose a challenge for researchers to devise systems which strike a proper balance between performance and resource utilization. We study some of the fundamental challenges of wireless (sensor) networks associated with resource efficiency, scalability, and location awareness. The pivotal point which distinguishes our studies from existing literature is employing the concept of sparse reconstruction and compressive sensing (CS) in our problem formulation and system design. We explore sparse structures embedded within the models we deal with and try to benefit from the undersampling offered by incorporating sparsity and thereby developing sparsity-aware system-level solutions. We prove that looking at these challenges from our perspective not only guarantees an expected cost efficiency due to taking less measurements, but also if properly designed, can promise an acceptable accuracy.Microelectronics & Computer EngineeringElectrical Engineering, Mathematics and Computer Scienc
Observing bandlimited graph processes from subsampled measurements
No full textThis work merges tools from graph signal processing and linear systems theory to propose sampling strategies for observing the initial state of a process evolving over a graph. The proposed method is ratified by a mathematical analysis that provides insights on the role played by the different actors, such as the graph topology, the process bandwidth, and the sampling strategy. Moreover, conditions when the graph process is observable from a few samples and (sub)optimal sampling strategies that jointly exploit the nature of the graph structure and graph process are proposed. Finally, numerical tests are conducted to illustrate the benefits of the proposed approach
Observing and tracking bandlimited graph processes from sampled measurements
Full text linkA critical challenge in graph signal processing is the sampling of bandlimited graph signals; signals that are sparse in a well-defined graph Fourier domain. Current works focused on sampling time-invariant graph signals and ignored their temporal evolution. However, time can bring new insights on sampling since sensor, biological, and financial network signals are correlated in both domains. Hence, in this work, we develop a sampling theory for time varying graph signals, named graph processes, to observe and track a process described by a linear state-space model. We provide a mathematical analysis to highlight the role of the graph, process bandwidth, and sample locations. We also propose sampling strategies that exploit the coupling between the topology and the corresponding process. Numerical experiments corroborate our theory and show the proposed methods trade well the number of samples with accuracy
Multi-Carrier Wakeup Radio Receiver
No full textIn the development of wireless sensor networks, power consumption is one of the bottlenecks for wide applications. In order to improve the energy efficiency of wireless sensor networks, a wakeup radio scheme is presented. The main transceiver that is responsible for data communication is in the sleep mode most of the time. An additional device called the wakeup radio receiver is a simplified receiver with much lower power consumption and data rate than the main transceiver. It is always on to monitor the channels continuously. It detects the wakeup packet, and sends the main transceiver a wakeup trigger upon successful detection of a wakeup packet. However, the detection of a wakeup packet is a challenging task. Since the wakeup radio receiver operates in the 2.4 GHz industrial, scientific, and medical band, a wakeup packet can be greatly interfered in such a noisy channel, which may lead to detection performance degradation. Therefore, a multi-carrier wakeup radio receiver is proposed as a solution to interference mitigation by making use of frequency diversity. But in practical implementations, the impairments from the multi-carrier wakeup receiver itself cannot be neglected. The receiver detection performance may degrade due to the non-idealities at the receiver. In this thesis, the detection performance of the multi-carrier wakeup radio receiver in the presence of channel noise, fading, co-channel interference, as well as non-idealities is explored.TelecommunicationsTelecommunicationsElectrical Engineering, Mathematics and Computer Scienc
Indoor Granular Presence Sensing and Control Messaging with an Ultrasonic Circular Array Sensor
No full textProviding automated granular control of lighting, along with user-driven control, results in an energy-efficient smart lighting system design while catering to personal occupant preferences. Two functional ingredients in such a system are: (i) sensing that provides granular information on occupant location, and (ii) a communication system to transmit control messages from a user. We consider an ultrasonic circular array sensor that provides the dual functionality of granular occupant sensing and a communication receiver for user control transmissions. A ceiling-mounted sensor configuration with a co-located ultrasonic transmitter and array receiver is considered. Algorithms for localization and tracking of an occupant in an indoor environment is presented.The resulting occupant location and tracks may be used for energy-efficient lighting. In addition, a user may control lighting by sending messages at a near-ultrasonic frequency through a mobile device, which are processed by the receiver array, and used to adapt a requested parameter of the lighting system. The proposed sensing and messaging solution is tested in an indoor office space with an 8-element receiver array sensor prototype. The efficacy of both systems is evaluated empirically and through simulations.TelecommunicationsCircuits and systemsElectrical Engineering, Mathematics and Computer Scienc
Compressive Sampling Based Differential Detection of Ultra Wideband Signals
No full textIn this paper we focus on compressive sampling (CS) based ultra wideband (UWB) differential detection. We formulate an optimization problem to jointly recover the sparse received UWB signals as well as the differentially encoded data symbol. We utilize an alternating direction method of multipliers (ADMoM) to solve this joint optimization problem. Our proposed joint recovery method outperforms the straightforward separate recovery method, which recovers the sparse received UWB signals in a first step and then detects the differentially encoded symbol based on the recovered signals
MAP based Differential Detectors for Compressed UWB Impulse Radio Signals
No full textWe propose maximum a posteriori (MAP) based noncoherent differential detector for ultra-wideband (UWB) impulse radio (IR) signals, received at a sub-Nyquist sampling rate. We build our detector for a Laplacian distributed multipath channel, which models sparsity. Our MAP based detector outperforms differential detectors based on other state-of-the-art approaches from a practical point of view. Our work highlights the critical role of different measurement matrices for the compressed differential detectors in general and the MAP based compressed differential detectors in particular
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