Practical Methods for Wireless Network Coding With Multiple Unicast Transmissions

Abstract

We propose a simple yet effective wireless network coding and decoding technique for a multiple unicast network. It utilizes spatial diversity through cooperation between nodes which carry out distributed encoding operations dictated by generator matrices of linear block codes. In order to exemplify the technique, we make use of greedy codes over the binary field and show that the arbitrary diversity orders can be flexibly assigned to nodes. Furthermore, we present the optimal detection rule for the given model that accounts for intermediate node errors and suggest a low-complexity network decoder using the sum-product (SP) algorithm. The proposed SP detector exhibits near optimal performance. We also show asymptotic superiority of network coding over a method that utilizes the wireless channel in a repetitive manner without network coding (NC) and give related rate-diversity trade-off curves. Finally, we extend the given encoding method through selective encoding in order to obtain extra coding gains. Index TermsWireless network coding, cooperative communication, linear block code, sum-product decoding, unequal error protection

Generalizing the sampling property of the Q-function for Error Rate analysis of cooperative communication in fading channels

“…following application of the L'Hôpital rule twice. Hence, in the asymptotic sense, the third integral in (8) does not contribute to the result as well. Therefore, we obtain…”

“…If we remember (8), two regions that asymptotically yield no contribution to the integral should be examined for convergence rate comparison. For the 0 < t < 1 region, both q(t) and f (t) converge to nonzero constants in the limit.…”

“…In this work, we have four goals: (i) A more rigorous method to show the applicability of the sampling property, (ii) Characterization of the SNR regions for sampling property which yields improved approximate expressions, (iii) A more accurate identification of the impulse function parameters, (iv) Generalization of the idea of sampling to integrals involving more than one variables (here for the canonical cooperative scenario, 2 variables) to obtain closed form BER expressions approximating these integrals. Finally, as an application of sampling property, we sketch the method to obtain closed form expressions that well-approximate the simulated performance of a sample network coded scenario from [8].…”

Scheduling Management for Multiple Real-Time Data over On-Demand Mobile Environments

“…However, when system loads are high, the LS and EDF strategies lose their advantage, even over FCFS, as most transactions are likely to miss their deadlines. LWF algorithm has been shown to outperform all other strategies at minimizing wait time [9]. In LWF, However, LWF has been recognized as expensive to implement [10].…”

Network Coding-based Maximum Lifetime Algorithm for Sliding Window in WSNs

“…Wireless sensor network (WSN) is composed of many distributed, randomly deployed, small sensors, disaster relief and sensor nodes with communication capability. Typically, WSN consists of many wireless sensor nodes that perform special calculations and specific tasks [1][2][3][4][5][6]. Since the sensor nodes do not need to be charged or replaced with batteries, the WSN nodes are usually energy constrained.…”