Transmission channel noise aware energy effective LDPC decoding

In communication systems channel quality variation, mostly induced by interferences, mobility, and environmental factors, is an unhindered physical phenomenon, which is usually perceived as a threat in pursuing reliable communication. There is a direct relation between the channel condition and the amount of computational resources and energy that have to be spend in order to reconstruct the correct messages at the reception side.When the quality is good, the decoding requires less resources and energy to identify and correct channel condition induced message errors, while when the channel noise level is high more resources and energy are needed to correct the errors. To be able to properly handle high noise levels while keeping the QoS requirements satisfied, telecom platforms are built upon largely over-designed hardware, i.e., they rely on worse case designs, which results in a substantial energy waste during most of their operation. In this chapter we introduce a methodology to dynamically adapt the platform operation mode to the channel noise level. The main objective is to keep QoS requirements satisfied regardless of the actual channel conditions while minimizing the energy consumption footprint. In particular, we propose a technique to exploit channel noise variability towards energy effective LDPC decoding amenable to adaptable low-energy operation. Endowed with the instantaneous channel noise level knowledge, our technique dynamically adjusts the operating voltage on-the-fly, aiming to achieve the optimal tradeoff between decoder performance and energy consumption without ignoring the fulfillment of the QoS requirements expressed in terms of frame/bit error rate. To demonstrate the capabilities of our proposal we implemented it and other state of the art energy reduction methods in the framework of a fully parallel LDPC decoder mapped on a Virtex-6 FPGA. Our experiments indicate that the proposed technique outperforms state of the art counterparts, in terms of energy reduction, with 71% to 76% and 15% to 28%, w.r.t. early termination without and with DVS, respectively, while maintaining the targeted decoding robustness. Moreover, the measurements suggest that in certain conditions Degradation Stochastic Resonance occurs, i.e., timing faults caused by unpredictable underpowered components in the circuit unexpectedly become supporters rather than enemies of the decoding process.