Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

The Effect of Macrodiversity on the Performance of Maximal Ratio Combining in Flat Rayleigh Fading

The performance of maximal ratio combining (MRC) in Rayleigh channels with co-channel interference (CCI) is well-known for receive arrays which are co-located. Recent work in network MIMO, edge-excited cells and base station collaboration is increasing interest in macrodiversity systems. Hence, in this paper we consider the effect of macrodiversity on MRC performance in Rayleigh fading channels with CCI. We consider the uncoded symbol error rate (SER) as our performance measure of interest and investigate how different macrodiversity power profiles affect SER performance. This is the first analytical work in this area. We derive approximate and exact symbol error rate results for M-QAM/BPSK modulations and use the analysis to provide a simple power metric. Numerical results, verified by simulations, are used in conjunction with the analysis to gain insight into the effects of the link powers on performance.Comment: 10 pages, 5 figures; IEEE Transaction of Communication, 2012 Corrected typo

An Iteratively Decodable Tensor Product Code with Application to Data Storage

The error pattern correcting code (EPCC) can be constructed to provide a syndrome decoding table targeting the dominant error events of an inter-symbol interference channel at the output of the Viterbi detector. For the size of the syndrome table to be manageable and the list of possible error events to be reasonable in size, the codeword length of EPCC needs to be short enough. However, the rate of such a short length code will be too low for hard drive applications. To accommodate the required large redundancy, it is possible to record only a highly compressed function of the parity bits of EPCC's tensor product with a symbol correcting code. In this paper, we show that the proposed tensor error-pattern correcting code (T-EPCC) is linear time encodable and also devise a low-complexity soft iterative decoding algorithm for EPCC's tensor product with q-ary LDPC (T-EPCC-qLDPC). Simulation results show that T-EPCC-qLDPC achieves almost similar performance to single-level qLDPC with a 1/2 KB sector at 50% reduction in decoding complexity. Moreover, 1 KB T-EPCC-qLDPC surpasses the performance of 1/2 KB single-level qLDPC at the same decoder complexity.Comment: Hakim Alhussien, Jaekyun Moon, "An Iteratively Decodable Tensor Product Code with Application to Data Storage