Wireless Interference Identification with Convolutional Neural Networks

The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference mitigation, proper wireless interference identification (WII) is essential. In this work we propose the first WII approach based upon deep convolutional neural networks (CNNs). The CNN naively learns its features through self-optimization during an extensive data-driven GPU-based training process. We propose a CNN example which is based upon sensing snapshots with a limited duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs between 15 classes. They represent packet transmissions of IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the 2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII approaches and has a classification accuracy greater than 95% for signal-to-noise ratio of at least -5 dB

Sparse Reconstruction-based Detection of Spatial Dimension Holes in Cognitive Radio Networks

In this paper, we investigate a spectrum sensing algorithm for detecting spatial dimension holes in Multiple Inputs Multiple Outputs (MIMO) transmissions for OFDM systems using Compressive Sensing (CS) tools. This extends the energy detector to allow for detecting transmission opportunities even if the band is already energy filled. We show that the task described above is not performed efficiently by regular MIMO decoders (such as MMSE decoder) due to possible sparsity in the transmit signal. Since CS reconstruction tools take into account the sparsity order of the signal, they are more efficient in detecting the activity of the users. Building on successful activity detection by the CS detector, we show that the use of a CS-aided MMSE decoders yields better performance rather than using either CS-based or MMSE decoders separately. Simulations are conducted to verify the gains from using CS detector for Primary user activity detection and the performance gain in using CS-aided MMSE decoders for decoding the PU information for future relaying.Comment: accepted for PIMRC 201

Xampling: Compressed Sensing of Analog Signals

Xampling generalizes compressed sensing (CS) to reduced-rate sampling of analog signals. A unified framework is introduced for low rate sampling and processing of signals lying in a union of subspaces. Xampling consists of two main blocks: Analog compression that narrows down the input bandwidth prior to sampling with commercial devices followed by a nonlinear algorithm that detects the input subspace prior to conventional signal processing. A variety of analog CS applications are reviewed within the unified Xampling framework including a general filter-bank scheme for sparse shift-invariant spaces, periodic nonuniform sampling and modulated wideband conversion for multiband communications with unknown carrier frequencies, acquisition techniques for finite rate of innovation signals with applications to medical and radar imaging, and random demodulation of sparse harmonic tones. A hardware-oriented viewpoint is advocated throughout, addressing practical constraints and exemplifying hardware realizations where relevant. It will appear as a chapter in a book on "Compressed Sensing: Theory and Applications" edited by Yonina Eldar and Gitta Kutyniok.Comment: 58 pages, 26 figure

Sub-Nyquist Radar: Principles and Prototypes

In the past few years, new approaches to radar signal processing have been introduced which allow the radar to perform signal detection and parameter estimation from much fewer measurements than that required by Nyquist sampling. These systems - referred to as sub-Nyquist radars - model the received signal as having finite rate of innovation and employ the Xampling framework to obtain low-rate samples of the signal. Sub-Nyquist radars exploit the fact that the target scene is sparse facilitating the use of compressed sensing (CS) methods in signal recovery. In this chapter, we review several pulse-Doppler radar systems based on these principles. Contrary to other CS-based designs, our formulations directly address the reduced-rate analog sampling in space and time, avoid a prohibitive dictionary size, and are robust to noise and clutter. We begin by introducing temporal sub-Nyquist processing for estimating the target locations using less bandwidth than conventional systems. This paves the way to cognitive radars which share their transmit spectrum with other communication services, thereby providing a robust solution for coexistence in spectrally crowded environments. Next, without impairing Doppler resolution, we reduce the dwell time by transmitting interleaved radar pulses in a scarce manner within a coherent processing interval or "slow time". Then, we consider multiple-input-multiple-output array radars and demonstrate spatial sub-Nyquist processing which allows the use of few antenna elements without degradation in angular resolution. Finally, we demonstrate application of sub-Nyquist and cognitive radars to imaging systems such as synthetic aperture radar. For each setting, we present a state-of-the-art hardware prototype designed to demonstrate the real-time feasibility of sub-Nyquist radars.Comment: 51 pages, 26 figures, 2 tables, Book chapter. arXiv admin note: text overlap with arXiv:1611.0644

Applications of Compressed Sensing in Communications Networks

This paper presents a tutorial for CS applications in communications networks. The Shannon's sampling theorem states that to recover a signal, the sampling rate must be as least the Nyquist rate. Compressed sensing (CS) is based on the surprising fact that to recover a signal that is sparse in certain representations, one can sample at the rate far below the Nyquist rate. Since its inception in 2006, CS attracted much interest in the research community and found wide-ranging applications from astronomy, biology, communications, image and video processing, medicine, to radar. CS also found successful applications in communications networks. CS was applied in the detection and estimation of wireless signals, source coding, multi-access channels, data collection in sensor networks, and network monitoring, etc. In many cases, CS was shown to bring performance gains on the order of 10X. We believe this is just the beginning of CS applications in communications networks, and the future will see even more fruitful applications of CS in our field.Comment: 18 page

Sub-Nyquist Sampling: Bridging Theory and Practice

Sampling theory encompasses all aspects related to the conversion of continuous-time signals to discrete streams of numbers. The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal processing. In modern applications, an increasingly number of functions is being pushed forward to sophisticated software algorithms, leaving only those delicate finely-tuned tasks for the circuit level. In this paper, we review sampling strategies which target reduction of the ADC rate below Nyquist. Our survey covers classic works from the early 50's of the previous century through recent publications from the past several years. The prime focus is bridging theory and practice, that is to pinpoint the potential of sub-Nyquist strategies to emerge from the math to the hardware. In that spirit, we integrate contemporary theoretical viewpoints, which study signal modeling in a union of subspaces, together with a taste of practical aspects, namely how the avant-garde modalities boil down to concrete signal processing systems. Our hope is that this presentation style will attract the interest of both researchers and engineers in the hope of promoting the sub-Nyquist premise into practical applications, and encouraging further research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin

Joint Data Compression and MAC Protocol Design for Smartgrids with Renewable Energy

In this paper, we consider the joint design of data compression and 802.15.4-based medium access control (MAC) protocol for smartgrids with renewable energy. We study the setting where a number of nodes, each of which comprises electricity load and/or renewable sources, report periodically their injected powers to a data concentrator. Our design exploits the correlation of the reported data in both time and space to efficiently design the data compression using the compressed sensing (CS) technique and theMAC protocol so that the reported data can be recovered reliably within minimum reporting time. Specifically, we perform the following design tasks: i) we employ the two-dimensional (2D) CS technique to compress the reported data in the distributed manner; ii) we propose to adapt the 802.15.4 MAC protocol frame structure to enable efficient data transmission and reliable data reconstruction; and iii) we develop an analytical model based on which we can obtain efficient MAC parameter configuration to minimize the reporting delay. Finally, numerical results are presented to demonstrate the effectiveness of our proposed framework compared to existing solutions.Comment: https://arxiv.org/admin/q/1589135, Wireless Communications and Mobile Computing, 2016. arXiv admin note: substantial text overlap with arXiv:1506.0831

Analog to Digital Cognitive Radio: Sampling, Detection and Hardware

The proliferation of wireless communications has recently created a bottleneck in terms of spectrum availability. Motivated by the observation that the root of the spectrum scarcity is not a lack of resources but an inefficient managing that can be solved, dynamic opportunistic exploitation of spectral bands has been considered, under the name of Cognitive Radio (CR). This technology allows secondary users to access currently idle spectral bands by detecting and tracking the spectrum occupancy. The CR application revisits this traditional task with specific and severe requirements in terms of spectrum sensing and detection performance, real-time processing, robustness to noise and more. Unfortunately, conventional methods do not satisfy these demands for typical signals, that often have very high Nyquist rates. Recently, several sampling methods have been proposed that exploit signals' a priori known structure to sample them below the Nyquist rate. Here, we review some of these techniques and tie them to the task of spectrum sensing in the context of CR. We then show how issues related to spectrum sensing can be tackled in the sub-Nyquist regime. First, to cope with low signal to noise ratios, we propose to recover second-order statistics from the low rate samples, rather than the signal itself. In particular, we consider cyclostationary based detection, and investigate CR networks that perform collaborative spectrum sensing to overcome channel effects. To enhance the efficiency of the available spectral bands detection, we present joint spectrum sensing and direction of arrival estimation methods. Throughout this work, we highlight the relation between theoretical algorithms and their practical implementation. We show hardware simulations performed on a prototype we built, demonstrating the feasibility of sub-Nyquist spectrum sensing in the context of CR.Comment: Submitted to IEEE Signal Processing Magazin

Algorithm Development and VLSI Implementation of Energy Efficient Decoders of Polar Codes

With its low error-floor performance, polar codes attract significant attention as the potential standard error correction code (ECC) for future communication and data storage. However, the VLSI implementation complexity of polar codes decoders is largely influenced by its nature of in-series decoding. This dissertation is dedicated to presenting optimal decoder architectures for polar codes. This dissertation addresses several structural properties of polar codes and key properties of decoding algorithms that are not dealt with in the prior researches. The underlying concept of the proposed architectures is a paradigm that simplifies and schedules the computations such that hardware is simplified, latency is minimized and bandwidth is maximized. In pursuit of the above, throughput centric successive cancellation (TCSC) and overlapping path list successive cancellation (OPLSC) VLSI architectures and express journey BP (XJBP) decoders for the polar codes are presented. An arbitrary polar code can be decomposed by a set of shorter polar codes with special characteristics, those shorter polar codes are referred to as constituent polar codes. By exploiting the homogeneousness between decoding processes of different constituent polar codes, TCSC reduces the decoding latency of the SC decoder by 60% for codes with length n = 1024. The error correction performance of SC decoding is inferior to that of list successive cancellation decoding. The LSC decoding algorithm delivers the most reliable decoding results; however, it consumes most hardware resources and decoding cycles. Instead of using multiple instances of decoding cores in the LSC decoders, a single SC decoder is used in the OPLSC architecture. The computations of each path in the LSC are arranged to occupy the decoder hardware stages serially in a streamlined fashion. This yields a significant reduction of hardware complexity. The OPLSC decoder has achieved about 1.4 times hardware efficiency improvement compared with traditional LSC decoders. The hardware efficient VLSI architectures for TCSC and OPLSC polar codes decoders are also introduced. Decoders based on SC or LSC algorithms suffer from high latency and limited throughput due to their serial decoding natures. An alternative approach to decode the polar codes is belief propagation (BP) based algorithm. In BP algorithm, a graph is set up to guide the beliefs propagated and refined, which is usually referred to as factor graph. BP decoding algorithm allows decoding in parallel to achieve much higher throughput. XJBP decoder facilitates belief propagation by utilizing the specific constituent codes that exist in the conventional factor graph, which results in an express journey (XJ) decoder. Compared with the conventional BP decoding algorithm for polar codes, the proposed decoder reduces the computational complexity by about 40.6%. This enables an energy-efficient hardware implementation. To further explore the hardware consumption of the proposed XJBP decoder, the computations scheduling is modeled and analyzed in this dissertation. With discussions on different hardware scenarios, the optimal scheduling plans are developed. A novel memory-distributed micro-architecture of the XJBP decoder is proposed and analyzed to solve the potential memory access problems of the proposed scheduling strategy. The register-transfer level (RTL) models of the XJBP decoder are set up for comparisons with other state-of-the-art BP decoders. The results show that the power efficiency of BP decoders is improved by about 3 times

Compressed sensing approach to ultra-wideband receiver design

One of the scarcest resources in the wireless communication system is the limited frequency spectrum. Many wireless communication systems are hindered by the bandwidth limitation and are not able to provide high speed communication. However, Ultra-wideband (UWB) communication promises a high speed communication because of its very wide bandwidth of 7.5GHz (3.1GHz-10.6GHz). The unprecedented bandwidth promises many advantages for the 21st century wireless communication system. However, UWB has many hardware challenges, such as a very high speed sampling rate requirement for analog to digital conversion, channel estimation, and implementation challenges. In this thesis, a new method is proposed using compressed sensing (CS), a mathematical concept of sub-Nyquist rate sampling, to reduce the hardware complexity of the system. The method takes advantage of the unique signal structure of the UWB symbol. Also, a new digital implementation method for CS based UWB is proposed. Lastly, a comparative study is done of the CS-UWB hardware implementation methods. Simulation results show that the application of compressed sensing using the proposed method significantly reduces the number of hardware complexity compared to the conventional method of using compressed sensing based UWB receiver