Physical Layer Channel Coding Research Articles

Optimal Allocation of Redundancy Between Packet-Level Erasure Coding and Physical-Layer Channel Coding in Fading Channels

For a block-fading channel, this paper optimizes the allocation of redundancy between packet-level erasure coding (which provides additional packets to compensate for packet loss) and physical layer channel coding (which lowers the probability of packet loss). After some manipulation, standard optimization techniques determine the trade-off between the amount of packet-level erasure coding and physical-layer channel coding that minimizes the transmit power required to provide reliable communication. Our results indicate that the optimal combination of packet-level erasure coding and physical-layer coding provides a significant benefit over pure physical-layer coding when no form of channel diversity is present within a packet transmission. However, the benefit of including packet-level erasure coding diminishes as more diversity becomes available within a packet transmission. Even with no diversity within a packet transmission, this paper shows that as the total redundancy becomes large the optimal redundancy for packet-level erasure coding reaches a limit while the optimal redundancy for physical-layer coding continues to increase. Hence providing limitless redundancy at the packet-level with rateless codes such as fountain codes is not the best use of limitless redundancy for block-fading channels.

Price-based distributed algorithms for rate-reliability tradeoff in network utility maximization

The current framework of network utility maximization for rate allocation and its price-based algorithms assumes that each link provides a fixed-size transmission "pipe" and each user's utility is a function of transmission rate only. These assumptions break down in many practical systems, where, by adapting the physical layer channel coding or transmission diversity, different tradeoffs between rate and reliability can be achieved. In network utility maximization problems formulated in this paper, the utility for each user depends on both transmission rate and signal quality, with an intrinsic tradeoff between the two. Each link may also provide a higher (or lower) rate on the transmission "pipes" by allowing a higher (or lower) decoding error probability. Despite nonseparability and nonconvexity of these optimization problems, we propose new price-based distributed algorithms and prove their convergence to the globally optimal rate-reliability tradeoff under readily-verifiable sufficient conditions. We first consider networks in which the rate-reliability tradeoff is controlled by adapting channel code rates in each link's physical-layer error correction codes, and propose two distributed algorithms based on pricing, which respectively implement the "integrated" and "differentiated" policies of dynamic rate-reliability adjustment. In contrast to the classical price-based rate control algorithms, in our algorithms, each user provides an offered price for its own reliability to the network, while the network provides congestion prices to users. The proposed algorithms converge to a tradeoff point between rate and reliability, which we prove to be a globally optimal one for channel codes with sufficiently large coding length and utilities whose curvatures are sufficiently negative. Under these conditions, the proposed algorithms can thus generate the Pareto optimal tradeoff curves between rate and reliability for all the users. In addition, the distributed algorithms and convergence proofs are extended for wireless multiple-inpit-multiple-output multihop networks, in which diversity and multiplexing gains of each link are controlled to achieve the optimal rate-reliability tradeoff. Numerical examples confirm that there can be significant enhancement of the network utility by distributively trading-off rate and reliability, even when only some of the links can implement dynamic reliability.