Phisan Kaewprapha

Cooperative Spectrum Sensing for Cognitive Radios: Bounds and Algorithms

This paper considers wireless spectrum sensing in harsh environments dominated by shadowing and fading. By modeling the network of the secondary users as Markov random fields and pulling a group of secondary users to cooperate through distributed probabilistic inference, effective sensing and fusion can be achieved. The proposed framework subsumes belief propagation, as well as conventional weighted hard/soft combining (such as maximal ratio combining and equal gain combing). It can also account for the distance-dependent correlation among individual sensing results by setting appropriate compatibility function. Theoretic upper and lower bounds are derived, demonstrating the significant gains made possible by effective cooperation. Extensive simulations confirm the analytical results.

Nested erasure codes to achieve the singleton bounds

This paper presents innovative ideas to construct maximum distance separable (MDS) codes, optimal error correction codes that achieve the singleton bounds. The proposed codes are based on a circularly symmetric construction applied to a novel class of nested graphs, referred to as complete-graph-of-rings (CGR). We demonstrate the general idea of transforming graphs to array codes, provide the sufficient conditions for achieving MDS, and present the specific algorithms to construct CGR graphs and MDS-CGR codes. The new codes require minimal encoding and decoding complexity that is theoretically possible, and are particularly useful for disk array applications.

Receiver-Cooperation: Network Coding and Distributed Scheduling

We study network-coded receiver cooperation for a wireless system comprising a remote sender and a set of local receivers. Network codes based on GF(2q) random-mixing are complex and prone to errors. Sparse binary random-mixing is considerably simpler, but for it to be space-preserving requires the involvement of a huge number of source packets (vectors). We propose a novel strategy of offset sparse binary random-mixing (OSBram), in which the source vectors are firstly circularly shifted, each by a different random offset, before being XORed. This simple strategy cleverly compensates the low degree of the binary field by the large dimension of the vector space, ensure (near) linear-independence of random binary superpositions, and finds solid structural support from the well-known class of quasi-cyclic low-density parity-check codes. A second innovation is the introduction of scheduling in user cooperation. We show that this previously ignored factor can be critical to cooperative gains. An elegant distributed scheduler is proposed that allows distributed nodes to quickly reach a rational consensus without the need to exchange any side information.

Latency-reduced retransmission in multicast network using systematic network code

The reliability of multicast network where source delivers many frames of data to multiple receivers over a large scale network is degraded by delay of retransmissions after some data frame has been lost. This paper proposes a coding technique applying on network layer to combat this problem by using systematic network code with overheard retransmission technique. Latency of retransmission and throughput will be measured to represent the performance of this algorithm. The results show that this technique can improve reliability and performance of multicast network by decreasing a delay of retransmission for 40-70 % and yet increasing throughput for more than 8-19% compared with a conventional method.

Cognitive radio sensing through belief propagation and distributed consensus

This paper studies distributed algorithms for cooperative spectrum sensing. Formulating the problem as one of computing-on-graph, we evaluate two important classes of algorithms, belief propagation (BP) and distribute consensus (DC). We detail the exact operations of these algorithms for the spectrum sensing application, and provide rigorous algorithmic study on their properties and especially the convergence. Unlike the DC algorithm where convergence to the global optimum is guaranteed irrespective of the network topology, the BP algorithm does not usually converge. We provide a graphtransformation mechanism to evaluate the conditions for its convergence and the speed. Our analysis provides useful insight into how the two algorithms arising from drastically different theoretical grounds can serve a common purpose. Specifically, it is shown that belief propagation is an algorithm of “less is more” and hence favors sparse graphs, whereas distributed consensus is an algorithm of “the more the merrier” and hence favors dense graphs. Simulations confirm the analytical results.