Compressed sensing at the ICC 2010

After a short vacation today I'd like to finish the review of this year's ICC. I was curious if the trend on compressed sensing I had seen at the ICASSP continued at the ICC... it seems that the ratio of papers related to compressed sensing in communications is smaller than in other fields yet. Here some of these papers:

Husheng Li presents in Reconstructing Spectrum Occupancies for Wideband Cognitive Radio Networks: A Matrix Completion via Belief Propagation a distributed spectrum monitoring scheme based on belief propagation. While this paper is not directly related to compressed sensing, other matrix completion techniques use compressed sensing theory by minimizing the nuclear norm of the matrix of interest.

In Distributed Compressive Spectrum Sensing in Cooperative Multi-hop Cognitive Networks, Z. Fanzi et al. present an elaborated cognitive radio multihop network model where the adquisition at the individual nodes is performed by means of compressed sampling. They divide the exchanged information into a common support and a series of innovations seen at individual nodes.

Also related with compressed sensing is the work Space-Time Turbo Bayesian Compressed Sensing for UWB Systems by D. Yang et al. They propose an algorithm for the joint reconstruction of ultra-wideband (UWB) signals based on the sparsity derived from both spatial and temporal redundancies.

More theoretical is the work RIP-fulfilling Complex-Valued Matrices by A. Amini et al. The abstract reads:

Although the theoretical results in the field of compressed sensing show that large classes of random matrices fulfill the so called Restricted Isometry Property (RIP) with high probability, only a few deterministic matrix designs are known. In this paper, we generalize one of the recent schemes based on binary BCH codes to p-ary codes which are useful for construction of complex sampling matrices. Though the design approach is similar, due to the use of p-ary codes (with p a prime power) and then complex matrices, the results are not similar. The new matrices are of the size (p^a - 1) × p^b using a prime power p; the previous BCH structures are the special cases for p = 2^1 which means that the new matrices provide more options in the number of samples.

In Does Compressed Sensing Improve the Throughput of Wireless Sensor Networks? Jun Luo et al. disscuss how much (in terms of throughput) can be gained by applying compressed sensing schemes at the network layer. However the results seem to be model dependent.

Linda M. Davis et al. present in Multi-antenna Downlink Broadcast using Compressed-Sensed Medium Access a communications scheme where the channel state information adquisition and user selection are performed by means of compressed sensing. Somehow related is the work Compressive Sensing for Reducing Feedback in MIMO Broadcast Channels by Syed T. Qaseem et al. The scheme prtesented is based in that only a limited number of mobile users present a channel quality above a threshold in a given time instant and spatial direction, thus the response vector is sparse in the "user domain".

A different application for compressed sensing algorithms can be found in Cooperative Sensing and Compression in Vehicular Sensor Networks for Urban Monitoring by Xiaoxiao Yu et al. In this work the authors propose an urban environment surveillance scheme utilizing vehicle-based sensors. The possible information loss due to the dynamic and unpredictable network topology is attenuated through a cooperative data sensing based on sparse random projections and a compressed sensing based recostruction.

New sensing schemes (ICC 2010)

Yesterday Spain could not have started worse the World Cup Soccer championship in South Africa. At least I had a chance to look at the ICC proceedings (from South Africa too), with better results. In a previous post I had made a list of the papers related to spectral sensing. I will leave out for now the papers related to cooperative sensing and comment only on the non-cooperative sensing schemes:

The paper Wavelet-Thresholded Multitaper Spectrum Sensing for Cognitive Radios in Unknown Noise by Jitendra Tugnait deals with spectrum sensing techniques belonging to the class of wavelet-thresholded multitaper spectrum estimators using sine tapers and relates it to the classical Welch windowed spectral estimator. The resulting detector does not need knowledge of the background noise level.

In Spectrum Sensing of OFDM Waveforms Using Embedded Pilot Subcarriers, Arash Zahedi-Ghasabeh et al. propose a new detection method for OFDM signals exploiting the available embedded pilot tones that translates into spectral correlation between the frequencies associated to the different pilots. Just looking at the signal model I realized that a certain synchronization is assumed at the cognitive receiver.

I could not find in the proceedings the papers related to "Stochastic Resonance" based spectrum sensing I had seen in the ICC program. Where did they go?

The paper Spectrum Sensing based on the Detection of Fourth-Order Cyclic Features by Julien Renard et al. proposes a fourth-order detector that performs similarly to the more complex second order detectors at SNR around 0 dB. The proposed detector is derived using the theory of higher-order cyclostationarity (HOCS). The performance is shown in the simulations section by means of a 4-QAM signal.

In Trace Based Semi-blind and Blind Spectrum Sensing Schemes for Cognitive Radio by Xi Yang et al. propose an ad-hoc detector based on the fact that the statistical covariance matrices of received signal samples and noise samples are different with high probability. The resulting detector is something like the trace of the prewhitened empirical covariance matrix.

The abstract of Cognitive Radio Wideband Spectrum Sensing Using Multitap Windowing and Power Detection with Threshold Adaptation Tsung-Han Yu et al. reads:

A common technique for cognitive radio wideband spectrum sensing is energy/power detection of primary users (PU) in frequency domain. Specifically, power spectrum estimation methods are combined with power detection statistics to test the PU presence. However, when detecting in a particular band of interest these techniques suffer from energy leakage and adjacent channel interference. In this paper, we derive a common matrix framework for the analytical performance of power detectors when FFT, windowed FFT, or multitap windowed FFT are used. Our matrix model is verified by simulations of modulated PU signals. We further propose a low-complexity compensation method to adapt the thresholds in the presence of large power difference between channels. By using both the multitap windowing and the constant false-alarm-rate method in the presence of strong signals, we demonstrate a 2-times increase in the detection rate performance as compared to existing methods. The proposed algorithm achieves similar P_FA and P_D as FFT at lower sample complexity, leading to reduced sensing times.

The paper Cyclostationarity Approach for the Recognition of Cyclically Prefixed Single Carrier Signals in Cognitive Radio by Qiyun Zhang et al. does not address the detection of primary users, but the recognition of which modulation they are employed. To this end it uses a cyclostationarity approach that does not require the recovery of carrier, waveform, and symbol timing information.

In Spectrum Sensing for DTMB System Based on PN Cross-Correlation Aolin Xu et al. disscuss spectrum sensing methods based on PN cross-correlation (PNCC) are proposed for the digital terrestrial television broadcasting standard in China (DTMB). This standard features a PN sequence both as guard interval between data blocks which gives cyclic property to DTMB signals.

Finally, the abstract of Spectrum Sensing Technique for Cognitive Radio Systems with Selection Diversity by Chang Kyung Sung et al. reads:

In this paper, we consider complementary sensing nodes to increase the spectrum sensing efficiency of cognitive radio (CR) systems. As the CR system has no prior knowledge about the operation of the licensed network, it is difficult to employ efficient diversity techniques such as the selection diversity. In this paper, by jointly designed with a medium access layer protocol, we propose a sensor node selection technique on the channel where the primary user is active. Collaborated with the mode of operation defined for CR nodes, the proposed scheme selects the dedicated sensing node for the channel with the best sensing performance. Numerical results show that the performance of the proposed scheme is almost the same as the cooperative spectrum sensing while our proposed scheme requires only one sensing node for the spectrum sensing.

Spectrum Sensing at the ICC 2010

The International Communications Conference (ICC) was recently hold in Cape Town, South Africa... just before the World Cup Soccer championship kicks off. In the ICC's final program we can find quite a lot of papers related to spectrum sensing. While I do not have access to the proceedings of the conference yet I wrote down a list of the spectrum sensing related papers organized in the sections sensing schemes, coopearitve spectrum sensing, sensing policy, and other papers related to spectrum sensing. Let's go.

Edit: I had access to a copy of the proceedings and I revised some of these papers. If you are interested take a look at the posts related to the ICC 2010. Proceedings available online: IEEE Xplore.

Spectrum sensing schemes

Wavelet-Thresholded Multitaper Spectrum Sensing for Cognitive Radios in Unknown Noise
Jitendra Tugnait (Auburn University, USA)

Spectrum Sensing of OFDM Waveforms Using Embedded Pilot Subcarriers
Arash Zahedi-Ghasabeh (University of California, Los Angeles, USA), Alireza Tarighat (Wilinx Corp., USA) and Babak Daneshrad (University of California, Los Angeles, USA)

A Cyclostationary-Based Spectrum Sensing Method Using Stochastic Resonance in Cognitive Radio
Yingpei Lin, Chen He, Lingge Jiang, Di He (Shanghai Jiao Tong University, China)
Spectrum Sensing Approach Based on Optimal Stochastic Resonance Technique under Color Noise Background in Cognitive Radio Networks
Di He, Chen He, Lingge Jiang, Yingpei Lin (Shanghai Jiao Tong University, China)
This was the first time I see the term Stochastic Resonance. Googling it I found that Stochastic Resonance refers to a peak that appears in the power spectrum of a dynamical system subject to both periodic forcing and random perturbation. This peak dissapears when either the forcing or the perturbation is absent.

Spectrum Sensing based on the Detection of Fourth-Order Cyclic Features
Julien Renard, Jonathan Verlant-Chenet, Jean-Michel Dricot, Philipe De Doncker and François Horlin (Université Libre de Bruxelles, Belgium)

Trace Based Semi-blind and Blind Spectrum Sensing Schemes for Cognitive Radio
Xi Yang (Southeast University, China), Kejun Lei (Jishou University, China) and Shengliang Peng, Xiuying Cao (Southeast University, China)

Cognitive Radio Wideband Spectrum Sensing Using Multitap Windowing and Power Detection with Threshold Adaptation
Tsung-Han Yu (University of California, Los Angeles, USA), Santiago Rodriguez-Parera (University of California, Los Angeles, Belgium) and Dejan Markovic, Danijela Cabric (University of California, Los Angeles, USA)
This paper is related to the one I cited in the last post.
A Low-Complexity Wideband Spectrum-Sensing Processor with Adaptive Detection Threshold and Sensing Time by Tsung-Han Yu, Oussama Sekkat, Santiago Rodriguez-Parera, Dejan Marković, and Danijela Čabrić.

Cyclostationarity Approach for the Recognition of Cyclically Prefixed Single Carrier Signals in Cognitive Radio
Qiyun Zhang, Octavia A. Dobre (Memorial University of Newfoundland, Canada), Sreeraman Rajan, Robert J. Inkol (DRDC-Ottawa, Canada), Erchin Serpedin (Texas A&M University, USA)

Spectrum Sensing for DTMB System Based on PN Cross-Correlation
Aolin Xu, Qicun Shi, Zhixing Yang Kewu Peng (Tsinghua University, China), Jian Song (Research Institute of Information Technology, China)

Spectrum Sensing Technique for Cognitive Radio Systems with Selection Diversity
Chang Kyung Sung, Iain B. Collings (CSIRO, Australia)

Cooperative spectrum sensing

Cyclic Prefix Based Cooperative Sequential Spectrum Sensing Algorithms for OFDM
Arunkumar Jayaprakasam, Vinod Sharma, Chandra R. Murthy and Prashant Narayanan (Indian Institute of Science, India)

Cooperative Spectrum Sensing for Multiband under Noise Uncertainty in Cognitive Radio Networks
Zhaoxia Song, Xuan Sun, Zhichao Qin, Zheng Zhou (Beijing University of Posts and Telecommunications, China)

A Robust and Efficient Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks
Feng Gao, Wei Yuan, Wei Liu, Wenqing Cheng, Shu Wang (Huazhong University of Science and Technology, China)

Doubly Sequential Energy Detection for Distributed Dynamic Spectrum Access
Nikhil Kundargi, Ahmed Tewfik (University of Minnesota, USA)

We study the distributed sequential energy detection problem in the context of spectrum sensing for cognitive radio networks. We formulate a novel Doubly Sequential Energy Detector (DSED) and provide a comprehensive study of its performance. Specifically, we present the first method that sequentially combines the decisions of the Cognitive Radio nodes wherein each node is running an independent Sequential Energy Detector (SED). Through extensive simulations it is demonstrated that (i) our novel sequential version of the energy detector delivers a significant throughput improvement of 2 to 6 times over the fixed sample size test while maintaining equivalent operating characteristics as measured by the Probabilities of Detection (P_D) and False Alarm (P_FA), and (ii) the Doubly Sequential Procedure at the Base Station further boosts the SED performance while improving the robustness for shadowed Cognitive Radio nodes. For example, for a P_D > 0.95, our simulations demonstrate that the DSED has a P_FA < 0.20 while utilizing upto 8 times fewer samples than the equivalent energy detector upto a Signal to Noise Ratio of -10 dB, below which its performance gracefully degrades.

Cooperative Spectrum Sensing with Multi-channel Coordination in Cognitive Radio Networks
Chengqi Song, Qian Zhang (Hong Kong University of Science and Technology, Hong Kong)

Cooperative Cyclostationary Spectrum Sensing in Cognitive Radios at Low SNR Regimes
Mahsa Derakhshani (McGill University, Canada), Masoumeh Nasiri-Kenari (Sharif University of Technology, Iran) and Tho Le-Ngoc (McGill University, Canada)

Distributed Compressive Spectrum Sensing in Cooperative Multi-hop Cognitive Networks
Zeng Fanzi (School of Computer and Communication Hunan University, China) and Zhi Tian, Chen Li (Michigan Technological University, USA)

Centralized Cooperative Spectrum Sensing for Ad-hoc Disaster Relief Network Clusters
Nuno Pratas (Center for TeleInFrastructure / Aalborg University, Denmark), Nicola Marchetti (Aalborg University, Denmark), Neeli Rashmi Prasad (Center for TeleInFrastructure, Denmark), António J. Rodrigues (IT / Instituto Superior Técnico, Portugal) and Ramjee Prasad (Center for TeleInFrastruktur / Aalborg University, Denmark)

Time-Divisional Cooperative Periodic Spectrum Sensing for Cognitive Radio Networks
Sithamparanathan Kandeepan (Create-Net International Research Centre, Italy) and Andrea Giorgetti, Marco Chiani (University of Bologna, Italy)

No-Regret Learning in Collaborative Spectrum Sensing with Malicious Nodes
Quanyan Zhu (University of Illinois, Urbana-Champaign, USA), Zhu Han (University of Houston, USA) and Tamer Basar (University of Illinois, Urbana-Champaign, USA)

Spectrum Sensing policy

An Optimal Algorithm for Wideband Spectrum Sensing in Cognitive Radio Systems.
Pedram Paysarvi Hoseini, Norman C. Beaulieu (University of Alberta, Canada)

An optimal wideband spectrum sensing algorithm which jointly detects the primary activities over multiple narrowband channels is presented. The algorithm enhances the overall secondary user performance while protecting the primary network at a desired level. The problem is formulated as an optimization problem to maximize the available secondary throughput capacity given a bound on the imposed aggregate interference. It is demonstrated that the problem can be solved as a convex optimization if certain practical constraints are applied. Simulation results attest that the proposed algorithm achieves a superior performance compared to contemporary algorithms.

Opportunistic Wideband Spectrum Sensing for Cognitive Radios with Genetic Optimization.
Michele Sanna, Maurizio Murroni (University of Cagliari, Italy)

Energy-Efficient Spectrum Sensing for Cognitive Radio Networks
Hang Su, Xi Zhang (Texas A&M University, USA)

This paper focuses on the spectrum sensing issues in the unslotted cognitive radio networks with wireless fading channels. To overcome the energy-inefficiency problem of the existing continuous/fixed-schedule spectrum sensing schemes in the cognitive radio networks, we propose an efficient spectrum sensing scheme for secondary users (SUs). The design goal of our proposed scheme is to save the sensing energy consumption while guaranteeing the priority of the primary users (PUs) and the spectrum opportunity for SUs in terms of available spectrum usage time. In particular, our proposed energy-efficient spectrum sensing scheme adaptively adjusts the spectrum sensing periods and determines between the presence and vacancy of the PU by taking advantage of PU’s activity patterns. We also develop a novel two-threshold based sequential sensing policy to reduce the false alarm probability while limiting the missed detection probability. We conduct simulations to validate and evaluate our proposed scheme.

Queue-Aware Spectrum Sensing for Interference-Constrained Transmissions in Cognitive Radio Networks
Qinghe Du, Xi Zhang (Texas A&M University, USA)

On Spectrum Probing in Cognitive Radio Networks: Does Randomization Matter?
Chao Chen, Zesheng Chen, Todor Cooklev (Indiana University / Purdue University, Fort Wayne, USA) and Carlos A. Pomalaza-Ráez (University of Oulu, Finland)

Overcoming the Sensing-Throughput Tradeoff in Cognitive Radio Networks
Stergios Stotas, Nallanathan Arumugam (King's College London, UK)

Agile Spectrum Evacuation in Cognitive Radio Networks
Mohammad Iqbal Bin Shahid, Joarder Kamruzzaman (Monash University, Australia)

Related to spectrum sensing

Interference-Aware Power Allocation in Cognitive Radio Networks with Imperfect Spectrum Sensing
Sami M. Almalfouh, Gordon Stuber (Georgia Institute of Technology, USA)

Fair and Efficient Channel Allocation and Spectrum Sensing for Cognitive OFDMA Networks
Chunhua Sun (Hong Kong University of Science and Technology, China),Wei Chen (Tsinghua University, China) and Khaled Ben Letaief (Hong Kong University of Science & Technology, Hong Kong)

Sampling Clock Frequency Offset Compensation for Feature Detection in Spectrum Sensing
Arash Zahedi-Ghasabeh (University of California, Los Angeles, USA), Alireza Tarighat (Wilinx Corp., USA) and Babak Daneshrad (University of California, Los Angeles, USA)