### Cooperative spectrum sensing and resource allocation at ICASSP 2011

After spending the weekend in Vienna, I continue today the series of posts devoted to ICASSP 2011. Here I will present some works related to

This paper proposes a sparsity-aware spline-based method for field (RF power in space and frequency) estimation from a set of measurements provided by a set of sensors distributed on the region under investigation. The authors propose the adoption of an overcomplete set of basis functions, together with a sparsity-promoting regularization term, which endows the estimator with the ability to select a few of these bases that “better” explain the data. The algorithm results into a group-Lasso estimator of the spline basis expansion coefficients. Results from empirical measurements are provided.

This works investigates the performance limitation of collaborative spectrum sensing in cognitive radios with imperfect reporting channels. The authors study the problem of hard decision based cooperative sensing, in which each secondary node sends a one-bit binary decision over a binary channel with errors and the fusion center applies a K-out-of-N fusion rule, trying to find a similar result to the SNR walls under noise uncertainty. If the bit error rate of the reporting channel is above a wall value, the authors show that constraints on the cooperative detection performance cannot be met at the fusion center irrespective of the received signal quality and sensing time. My point here is that if sensing time is allowed to grow without bounds one could also allow better error correction codes, couldn't one?

This paper considers the problem of finding sparse solutions from multiple measurement vectors with joint sparsity. To this end the authors propose a decentralized row-based Lasso (DR-Lasso) algorithm in which a penalty term is introduced to enforce joint sparsity of the solution. In order to exchange information between neighbors the algorithm relies in a consensus based iterative procedure.

This paper develops a new cooperative spectrum sensing technique based on matrix rank minimization. A nuclear norm minimization problem is formulated to jointly identify the nonzero support of the monitored wide spectrum (featuring possibly multiple primary signals).

The session SPCOM-L3 (

In this work two algorithms are proposed to sum-rate (and other utilities) maximization for multiantenna broadcast and interference channels (non-convex problem). The first finds the global optimum by performing a suitably designed branch-and-bound method (as you can imagine fairly complex). The second approach is a suboptimal iterative algorithm that converges to a stationary point fulfilling the KKT conditions, however, its final performance depends on the initial parameters and hence does not guarantee global optimality.

This work proposes a two-tier network modeling a macrocell/femtocells scenario. The scheme reserves a certain amount of resources for exclusive use of macrocell, a different set of resources for exclusive use of femtocells, and the remaining are left open for both tiers, hence generating cochannel interference.

This work presents a high level approach to the problem of scheduling the transmission/sensing instants among different secondary users under a global primary-user collision constraint. If secondary users are assumed to sense/transmit in a single channel each time slot the optimum policy under certain conditions is shown to be a round-robin policy in which each secondary user cycles through all the channels in the band.

**cognitive radio**from a couple of sessions. In the session SPCOM-L4 (**Cooperative Spectrum Sensing**) we could find different collaborative schemes (several of them relying in compressed sensing) for primary user monitoring/detection:**"BASIS PURSUIT FOR SPECTRUM CARTOGRAPHY"**; Juan Andrés Bazerque, Gonzalo Mateos, Georgios B. Giannakis, University of Minnesota, USThis paper proposes a sparsity-aware spline-based method for field (RF power in space and frequency) estimation from a set of measurements provided by a set of sensors distributed on the region under investigation. The authors propose the adoption of an overcomplete set of basis functions, together with a sparsity-promoting regularization term, which endows the estimator with the ability to select a few of these bases that “better” explain the data. The algorithm results into a group-Lasso estimator of the spline basis expansion coefficients. Results from empirical measurements are provided.

**"BEP WALLS FOR COLLABORATIVE SPECTRUM SENSING"**; Sachin Chaudhari, Jarmo Lunden, Visa Koivunen, Aalto University, FinlandThis works investigates the performance limitation of collaborative spectrum sensing in cognitive radios with imperfect reporting channels. The authors study the problem of hard decision based cooperative sensing, in which each secondary node sends a one-bit binary decision over a binary channel with errors and the fusion center applies a K-out-of-N fusion rule, trying to find a similar result to the SNR walls under noise uncertainty. If the bit error rate of the reporting channel is above a wall value, the authors show that constraints on the cooperative detection performance cannot be met at the fusion center irrespective of the received signal quality and sensing time. My point here is that if sensing time is allowed to grow without bounds one could also allow better error correction codes, couldn't one?

**Update:**Sachin Chaudhari commented that indeed their work emphasizes the need of using correction codes in the transmissions to the fusion center. Here his comment:"Please note that the constraints on the cooperative detection performance cannot be met at the fusion center irrespective of the received signal quality 'or' the sensing time. So even for the case when the sensing time is short and the SNRs on the listening channel are very good, the performance constraints cannot be met. The whole point of the paper was to show that you may need the error correction codes while using the counting rules."

**"DECENTRALIZED SUPPORT DETECTION OF MULTIPLE MEASUREMENT VECTORS WITH JOINT SPARSITY"**; Qing Ling, University of Science and Technology of China, China; Zhi Tian, Michigan Technological University, USThis paper considers the problem of finding sparse solutions from multiple measurement vectors with joint sparsity. To this end the authors propose a decentralized row-based Lasso (DR-Lasso) algorithm in which a penalty term is introduced to enforce joint sparsity of the solution. In order to exchange information between neighbors the algorithm relies in a consensus based iterative procedure.

**"COOPERATIVE SPECTRUM SENSING BASED ON MATRIX RANK MINIMIZATION"**; Yue Wang, Beijing University of Posts and Telecommunications, China; Zhi Tian, Michigan Technological University, US; Chunyan Feng, Beijing University of Posts and Telecommunications, ChinaThis paper develops a new cooperative spectrum sensing technique based on matrix rank minimization. A nuclear norm minimization problem is formulated to jointly identify the nonzero support of the monitored wide spectrum (featuring possibly multiple primary signals).

The session SPCOM-L3 (

**Resource Allocation and Game Theory**) featured some works with different approaches to resource allocation and to scheduling of the transmission/sensing instants. In general they study the problem from a network level perspective, for example by assuming slotted transmissions:**"NON-CONVEX UTILITY MAXIMIZATION IN GAUSSIAN MISO BROADCAST AND INTERFERENCE CHANNELS"**; Marco Rossi, New Jersey Institute of Technology, US; Antonia Maria Tulino, Bell Laboratories (Alcatel-Lucent), US; Osvaldo Simeone, Alexander M. Haimovich, New Jersey Institute of Technology, USIn this work two algorithms are proposed to sum-rate (and other utilities) maximization for multiantenna broadcast and interference channels (non-convex problem). The first finds the global optimum by performing a suitably designed branch-and-bound method (as you can imagine fairly complex). The second approach is a suboptimal iterative algorithm that converges to a stationary point fulfilling the KKT conditions, however, its final performance depends on the initial parameters and hence does not guarantee global optimality.

**"STOCHASTIC ANALYSIS OF TWO-TIER NETWORKS: EFFECT OF SPECTRUM ALLOCATION"**; Wang Chi Cheung, Tony Quee Seng Quek, Agency of Science, Technology And Research, Singapore; Marios Kountouris, Supélec, FranceThis work proposes a two-tier network modeling a macrocell/femtocells scenario. The scheme reserves a certain amount of resources for exclusive use of macrocell, a different set of resources for exclusive use of femtocells, and the remaining are left open for both tiers, hence generating cochannel interference.

**"DISTRIBUTED MULTIACCESS IN HIERARCHICAL COGNITIVE RADIO NETWORKS"**; Shiyao Chen, Lang Tong, Cornell University, USThis work presents a high level approach to the problem of scheduling the transmission/sensing instants among different secondary users under a global primary-user collision constraint. If secondary users are assumed to sense/transmit in a single channel each time slot the optimum policy under certain conditions is shown to be a round-robin policy in which each secondary user cycles through all the channels in the band.

Labels: cognitive radio, icassp 2011, sensing

## 2 Comments:

Hello,

My name is Sachin Chaudhari and I am one of the authors of the paper 'BEP WALLS FOR COLLABORATIVE SPECTRUM SENSING'. This is a clarification regarding the last two sentences of your review of our paper. Please note that the constraints on the cooperative detection performance cannot be met at the fusion center irrespective of the received signal quality 'or' the sensing time. So even for the case when the sensing time is short and the SNRs on the listening channel are very good, the performance constraints cannot be met. The whole point of the paper was to show that you may need the error correction codes while using the counting rules.

BTW, your blog on cognitive radios is very nicely written and well maintained.

Regards,

Sachin

Hello Sachin,

thank you for your clarification. I agree with you that, if the sensing time is required to be short, the analysis of the paper makes a lot of sense. In fact, even when using error correction codes, some errors cannot be corrected.

I update the entry with your comment.

Best,

Gonzalo

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