Joint Network/Channel Decoding for Heterogeneous Multi-Source/Multi-Relay Cooperative Networks

International audienceIn this paper, we study joint network/channel decoding for multi{source multi{relay heterogeneous wireless networks. When convolutional and network codes are used at the phys- ical and network layers, respectively, we show that error correction and diversity properties of the whole network can be characterized by an equivalent and distributed convo- lutional network/channel code. In particular, it is shown that, by properly choosing the network code, the equivalent code can show Unequal Error Protection (UEP) properties, which might be useful for heterogeneous wireless networks in which each source might ask for a di®erent quality{of{ service requirement or error probability. Using this repre- sentation, we show that Maximum{Likelihood (ML) joint network/channel decoding can be performed by using the trellis representation of the distributed convolutional net- work/channel code. Furthermore, to deal with decoding er- rors at the relays, a ML{optimum receiver which exploits side information on the source{to{relay links is proposed

Interference Networks with Point-to-Point Codes

The paper establishes the capacity region of the Gaussian interference channel with many transmitter-receiver pairs constrained to use point-to-point codes. The capacity region is shown to be strictly larger in general than the achievable rate regions when treating interference as noise, using successive interference cancellation decoding, and using joint decoding. The gains in coverage and achievable rate using the optimal decoder are analyzed in terms of ensemble averages using stochastic geometry. In a spatial network where the nodes are distributed according to a Poisson point process and the channel path loss exponent is $\beta > 2$, it is shown that the density of users that can be supported by treating interference as noise can scale no faster than $B^{2/\beta}$ as the bandwidth $B$ grows, while the density of users can scale linearly with $B$ under optimal decoding

On the optimization of distributed compression in multirelay cooperative networks

In this paper, we consider multirelay cooperative networks for the Rayleigh fading channel, where each relay, upon receiving its own channel observation, independently compresses it and forwards the compressed information to the destination. Although the compression at each relay is distributed using Wyner-Ziv coding, there exists an opportunity for jointly optimizing compression at multiple relays to maximize the achievable rate. Considering Gaussian signaling, a primal optimization problem is formulated accordingly. We prove that the primal problem can be solved by resorting to its Lagrangian dual problem, and an iterative optimization algorithm is proposed. The analysis is further extended to a hybrid scheme, where the employed forwarding scheme depends on the decoding status of each relay. The relays that are capable of successful decoding perform a decode-and-forward (DF) scheme, and the rest conduct distributed compression. The hybrid scheme allows the cooperative network to adapt to the changes of the channel conditions and benefit from an enhanced level of flexibility. Numerical results from both spectrum and energy efficiency perspectives show that the joint optimization improves efficiency of compression and identify the scenarios where the proposed schemes outperform the conventional forwarding schemes. The findings provide important insights into the optimal deployment of relays in a realistic cellular network

Distributed Remote Vector Gaussian Source Coding for Wireless Acoustic Sensor Networks

In this paper, we consider the problem of remote vector Gaussian source coding for a wireless acoustic sensor network. Each node receives messages from multiple nodes in the network and decodes these messages using its own measurement of the sound field as side information. The node's measurement and the estimates of the source resulting from decoding the received messages are then jointly encoded and transmitted to a neighboring node in the network. We show that for this distributed source coding scenario, one can encode a so-called conditional sufficient statistic of the sources instead of jointly encoding multiple sources. We focus on the case where node measurements are in form of noisy linearly mixed combinations of the sources and the acoustic channel mixing matrices are invertible. For this problem, we derive the rate-distortion function for vector Gaussian sources and under covariance distortion constraints.Comment: 10 pages, to be presented at the IEEE DCC'1

Multiframe coded computation for distributed uplink channel decoding

The latest 5G technology in wireless communication has led to an increasing demand for higher data rates and low latencies. The overall latency of the system in a cloud radio access network is greatly affected by the decoding latency in the uplink channel. Various proposed solutions suggest using network function virtualization (NFV). NFV is the process of decoupling the network functions from hardware appliances. This provides the exibility to implement distributed computing and network coding to effectively reduce the decoding latency and improve the reliability of the system. To ensure the system is cost effective, commercial off the shelf (COTS) devices are used, which are susceptible to random runtimes and server failures. NFV coded computation has shown to provide a significant improvement in straggler mitigation in previous work. This work focuses on reducing the overall decoding time while improving the fault tolerance of the system. The overall latency of the system can be reduced by improving the computation efficiency and processing speed in a distributed communication network. To achieve this, multiframe NFV coded computation is implemented, which exploits the advantage of servers with different runtimes. In multiframe coded computation, each server continues to decode coded frames of the original message until the message is decoded. Individual servers can make up for straggling servers or server failures, increasing the fault tolerance and network recovery time of the system. As a consequence, the overall decoding latency of a message is significantly reduced. This is supported by simulation results, which show the improvement in system performance in comparison to a standard NFV coded system

Distributed Space Time Coding for Wireless Two-way Relaying

We consider the wireless two-way relay channel, in which two-way data transfer takes place between the end nodes with the help of a relay. For the Denoise-And-Forward (DNF) protocol, it was shown by Koike-Akino et. al. that adaptively changing the network coding map used at the relay greatly reduces the impact of Multiple Access interference at the relay. The harmful effect of the deep channel fade conditions can be effectively mitigated by proper choice of these network coding maps at the relay. Alternatively, in this paper we propose a Distributed Space Time Coding (DSTC) scheme, which effectively removes most of the deep fade channel conditions at the transmitting nodes itself without any CSIT and without any need to adaptively change the network coding map used at the relay. It is shown that the deep fades occur when the channel fade coefficient vector falls in a finite number of vector subspaces of $\mathbb{C}^2$, which are referred to as the singular fade subspaces. DSTC design criterion referred to as the \textit{singularity minimization criterion} under which the number of such vector subspaces are minimized is obtained. Also, a criterion to maximize the coding gain of the DSTC is obtained. Explicit low decoding complexity DSTC designs which satisfy the singularity minimization criterion and maximize the coding gain for QAM and PSK signal sets are provided. Simulation results show that at high Signal to Noise Ratio, the DSTC scheme provides large gains when compared to the conventional Exclusive OR network code and performs slightly better than the adaptive network coding scheme proposed by Koike-Akino et. al.Comment: 27 pages, 4 figures, A mistake in the proof of Proposition 3 given in Appendix B correcte

Implicit Coordination in Two-Agent Team Problems; Application to Distributed Power Allocation

The central result of this paper is the analysis of an optimization problem which allows one to assess the limiting performance of a team of two agents who coordinate their actions. One agent is fully informed about the past and future realizations of a random state which affects the common payoff of the agents whereas the other agent has no knowledge about the state. The informed agent can exchange his knowledge with the other agent only through his actions. This result is applied to the problem of distributed power allocation in a two-transmitter $M-$band interference channel, $M\geq 1$, in which the transmitters (who are the agents) want to maximize the sum-rate under the single-user decoding assumption at the two receivers; in such a new setting, the random state is given by the global channel state and the sequence of power vectors used by the informed transmitter is a code which conveys information about the channel to the other transmitter.Comment: 6 pages, appears as WNC3 2014: International Workshop on Wireless Networks: Communication, Cooperation and Competition - International Workshop on Resource Allocation, Cooperation and Competition in Wireless Network