Xingjian Zhang

Adaptively Regularized Compressive Spectrum Sensing from Real-Time Signals to Real-Time Processing

Wideband spectrum sensing is regarded as one of the key features in cognitive radio systems. Compressive sensing (CS) has recently become one of the promising techniques to deal with the Nyquist sampling rate bottleneck of wideband spectrum sensing. Theoretical analyses and simulation have shown that CS could achieve high detection probability and low false alarm for wideband spectrum sensing. However, implementation of CS on the real-time signals and real-time processing poses significant challenges due to the iterative nature of the CS algorithms. In this paper, we propose a novel adaptively regularized iterative reweighted least squares (AR-IRLS) algorithm to implement the real-time signal recovery on the CS based wideband spectrum sensing. The proposed algorithm moves estimated solutions along an exponential-linear path by regularizing weights with a series of non- increasing penalty terms, which significantly speeds up the convergence of reconstruction and provides high fidelity guarantee to cope with the varying bandwidths and power levels of occupied channels. The proposed algorithm presents robustness against different sparsity levels at low compressive ratio without degradation on the reconstruction performance, and is tested on the real-time signals over TV white space spectrum after having been validated on the simulated signals. Both the simulation and real-time experiments show that the proposed algorithm outperforms the conventional iterative reweighted least squares (IRLS) algorithms in terms of convergence speed, reconstruction accuracy, and compressive ratio requirement.

Dynamic adjustment of sparsity upper bound in wideband compressive spectrum sensing

Compressive sensing (CS) techniques play a key role for fast spectrum sensing in cognitive radio (CR) as it allows perfect signal reconstruction at sub-Nyquist sampling rates. However, for traditional compressing sampling approaches, the sparsity level of a signal is normally assumed as static and known, is impossible in practice. Traditionally, a statistical value of sparsity level upper bound is used as the sparsity level for signal reconstruction. In this paper, we proposed a dynamic adjustment scheme to estimate signal sparsity accurately and recover signals efficiently. In the proposed scheme, a Shrink Algorithm and Enlargement Algorithm are designed to adaptively adjust the value of sparsity level upper bound. Simulation results show that if sparsity level is too large or too small, our proposed scheme can adjust it to an proper value.

Dynamic Adaptive Video Streaming on Heterogeneous TVWS and Wi-Fi Networks

Nowadays, people usually connect to the Internet through a multitude of different devices. Video streaming takes the lion’s share of the bandwidth, and represents the real challenge for the service providers and for the research community. At the same time, most of the connections come from indoor, where Wi-Fi already experiences congestion and coverage holes, directly translating into a poor experience for the user. A possible relief comes from the TV white space (TVWS) networks, which can enhance the communication range thanks to sub-GHz frequencies and favorable propagation characteristics, but offer slower datarates compared with other 802.11 protocols. In this paper, we show the benefits that TVWS networks can bring to the end user, and we present CABA, a connection aware balancing algorithm able to exploit multiple radio connections in the favor of a better user experience. Our experimental results indicate that the TVWS network can effectively provide a wider communication range, but a load balancing middleware between the available connections on the device must be used to achieve better performance. We conclude this paper by presenting real data coming from field trials in which we streamed an MPEG dynamic adaptive streaming over HTTP video over TVWS and Wi-Fi. Practical quantitative results on the achievable quality of experience for the end user are then reported. Our results show that balancing the load between Wi-Fi and TVWS can provide a higher playback quality (up to 15% of average quality index) in scenarios in which the Wi-Fi is received at a low strength.

TV White Space Network Provisioning with Directional and Omni-Directional Terminal Antennas

Operating at ultra-high frequency (UHF), TV white space (TVWS) can achieve long-distance communication and good in-building penetration, and has attracted increasing attention of regulators, researchers and stakeholders. This paper explores the potential of TVWS for network provisioning within a cluster of buildings, through a succession of tests. Different transmission distances, from 10m to over 120m, and through multiple layers of walls as well as complex transmission environment imposed by other factors like office and construction facilities, are considered. Further, a compact ultra-wide band (UWB) printed monopole antenna is designed for the client white space terminal, and compared with a commercial directional UHF antenna on the same client. Measurement results show that the in-house compact antenna achieves fast network speed and a high signal-to-interference-plus-noise ratio (SINR), and it is orientation independent.

Autonomous compressive spectrum sensing approach for 3.5 GHz shared spectrum

The underutilized 3.5 GHz shared spectrum poses an excellent opportunity and potential for more intensive secondary usage by innovative applications/services. To find more spectral holes, a wide portion of spectrum must be sensed, which requires high sampling rates and a lot of measurements to be processed. Compressive sensing (CS) has recently become one of the promising techniques to deal with the sampling rate bottleneck of the wideband spectrum sensing. However, there are two significant challenges in the implementation of CS based wideband spectrum sensing: 1) no apriori knowledge of users activity statistics and 2) the varying bandwidth of channels and power levels. To address these issues, we proposed an autonomous compressive spectrum sensing approach that enables a secondary user to choose the number of measurements automatically, while the exact wideband signal reconstruction is guaranteed without assumption on spectral sparsity or channel characteristics. Specifically, the compressive measurements are collected block-by-block while the spectral is gradually reconstructed and the measurements collection process can be terminated once the variation of the Euclidean distance among the sequence of recovery solutions falls below a desired tolerance.

Joint Sub-Nyquist Spectrum Sensing Scheme With Geolocation Database Over TV White Space

To maximize spectrum access opportunities for white space devices, incorporating real-time spectrum sensing with geolocation database is a promising approach to enhance detection resolution with reduced computation complexity. Advanced spectrum sensing techniques are needed to quickly and accurately identify spectrum occupancy over a wide frequency range. However, the stringent requirements from wideband signal acquisition and processing pose a major implementation challenge in compact devices with limited energy storage and computation capabilities. In this paper, a hybrid scheme of sub-Nyquist wideband spectrum sensing with geolocation database is proposed to achieve accurate detection of the surrounding spectrum with reduced number of required measurements and computation complexity. Two iterative algorithms are modified to incorporate a priori information from geolocation database, therefore enabling spectrum sensing to be performed only on a limited number of potentially vacant channels over TV white space. Theoretical analyses and simulation results show that the proposed joint scheme speeds up the sensing process with enhanced detection performance and smaller required sampling rate, whereas the updated channel information from wideband spectrum sensing reduces the risk of interferences to the dynamic incumbent users.

RealSense: Real-time compressive spectrum sensing testbed over TV white space

Nowadays, wideband spectrum sensing, as one of the vital technologies of cognitive radio (CR), has the potential to find more temporarily available frequency bands to meet the growing demands of wireless services. As the vast number of samples are required to be collected and processed, traditional wideband spectrum sensing methods become inefficient and cause large energy consumption. Therefore, many theoretical work focus on applying compressive sensing (CS) into wideband spectrum sensing to alleviate this issue. In this paper, to verify the CS-based spectrum sensing scheme in real-world scenarios, a real-time compressive spectrum sensing testbed is proposed to process the real-time data collected from the TV white space (TVWS) spectrum. The proposed testbed consists of two parts: a senor node, and a real-time signal processing platform based on National Instruments (NI) LabVIEW software to process the spectral data and control the sensor.

2016 IEEE 84th Vehicular Technology Conference, VTC Fall 2016 - Proceedings

Operating at ultra-high frequency (UHF), TV white space (TVWS) can achieve long-distance communication and good in-building penetration, and has attracted increasing attention of regulators, researchers and stakeholders. This paper explores the potential of TVWS for network provisioning within a cluster of buildings, through a succession of tests. Different transmission distances, from 10m to over 120m, and through multiple layers of walls as well as complex transmission environment imposed by other factors like office and construction facilities, are considered. Further, a compact ultra-wide band (UWB) printed monopole antenna is designed for the client white space terminal, and compared with a commercial directional UHF antenna on the same client. Measurement results show that the in-house compact antenna achieves fast network speed and a high signal-to-interference-plus-noise ratio (SINR), and it is orientation independent.

Real-time Adaptively-Regularized Compressive Sensing in Cognitive Radio Networks

Wideband spectrum sensing is regarded as one of the key functional blocks in cognitive radio systems, where compressive sensing (CS) has become one of the promising techniques to deal with the Nyquist sampling rate bottleneck. Theoretical analyses and simulations have shown that CS could achieve both high detection and low false alarm probabilities in wideband spectrum sensing. However, the implementation of CS over real-world signals and real-time processing poses significant challenges due to the high computational burden and reconstruction errors against noise. In this paper, we propose an efficient adaptively regularized iterative reweighted least squares (AR-IRLS) algorithm to implement the real-time signal recovery in CS-based wideband spectrum sensing. The proposed AR-IRLS algorithm moves the estimated solutions along an exponential–linear path by regularizing weights with a series of nonincreasing penalty terms, which significantly speeds up the convergence of reconstruction and provides a high fidelity guarantee to cope with spectral signals with varying bandwidths and power levels. Furthermore, a descent-based decision threshold setting algorithm is proposed to distinguish the primary signals from the mixture of the reconstruction errors and unknown noises. The proposed scheme demonstrates robustness against different sparsity levels at low compressive ratios without degradation of the reconstruction performance. It is tested with the real-world signals over the TV white space after being validated with the simulated signals. Both the simulation and real-time experiments show that the proposed scheme outperforms the conventional iterative reweighted least squares algorithms in terms of convergence speed, reconstruction accuracy, and compressive ratio.

IEEE Vehicular Technology Conference 2016-Fall (VTC 2016-Fall), 2016 IEEE 84th

Operating at ultra-high frequency (UHF), TV white space (TVWS) can achieve long-distance communication and good in-building penetration, and has attracted increasing attention of regulators, researchers and stakeholders. This paper explores the potential of TVWS for network provisioning within a cluster of buildings, through a succession of tests. Different transmission distances, from 10m to over 120m, and through multiple layers of walls as well as complex transmission environment imposed by other factors like office and construction facilities, are considered. Further, a compact ultra-wide band (UWB) printed monopole antenna is designed for the client white space terminal, and compared with a commercial directional UHF antenna on the same client. Measurement results show that the in-house compact antenna achieves fast network speed and a high signal-to-interference-plus-noise ratio (SINR), and it is orientation independent.