TY - JOUR
T1 - Joint Compression, Channel Coding and Retransmission for Data Fidelity with Energy Harvesting
AU - Pielli, Chiara
AU - Stefanovic, Cedomir
AU - Popovski, Petar
AU - Zorzi, Michele
PY - 2018/4/1
Y1 - 2018/4/1
N2 - We consider a monitoring application where sensors periodically report data to a common receiver using time division multiplexing. The sensors are constrained by the limited and unpredictable energy availability provided by energy harvesting (EH), and by the channel impairments. To maximize the quality of the reported data, the packets transmitted contain newly generated data blocks together with up to $r - 1$ previously unsuccessfully delivered ones, where $r$ is a design parameter. These data blocks are compressed, concatenated, and encoded with a channel code. The scheme applies lossy compression, such that the fidelity of the individual blocks is traded off with the reliability provided by the channel code. We show that the proposed strategy outperforms the one in which retransmissions are not allowed. We also investigate the tradeoff between the value of $r$ , the compression and the coding rates, under the constraints of the energy availability, and, once $r$ has been decided, use a Markov decision process (MDP) to optimize the compression/coding rates. Finally, we implement a reinforcement learning algorithm, through which devices can learn the optimal transmission policy without knowing a priori the statistics of the EH process, and show that it indeed reaches the performance obtained via MDP.
AB - We consider a monitoring application where sensors periodically report data to a common receiver using time division multiplexing. The sensors are constrained by the limited and unpredictable energy availability provided by energy harvesting (EH), and by the channel impairments. To maximize the quality of the reported data, the packets transmitted contain newly generated data blocks together with up to $r - 1$ previously unsuccessfully delivered ones, where $r$ is a design parameter. These data blocks are compressed, concatenated, and encoded with a channel code. The scheme applies lossy compression, such that the fidelity of the individual blocks is traded off with the reliability provided by the channel code. We show that the proposed strategy outperforms the one in which retransmissions are not allowed. We also investigate the tradeoff between the value of $r$ , the compression and the coding rates, under the constraints of the energy availability, and, once $r$ has been decided, use a Markov decision process (MDP) to optimize the compression/coding rates. Finally, we implement a reinforcement learning algorithm, through which devices can learn the optimal transmission policy without knowing a priori the statistics of the EH process, and show that it indeed reaches the performance obtained via MDP.
KW - Channel coding
KW - Distortion
KW - Energy harvesting
KW - energy management
KW - Heuristic algorithms
KW - Markov processes
KW - Rate-distortion theory
KW - Receivers
KW - scheduling
KW - Sensors
UR - http://www.scopus.com/inward/record.url?scp=85039792434&partnerID=8YFLogxK
U2 - 10.1109/TCOMM.2017.2785323
DO - 10.1109/TCOMM.2017.2785323
M3 - Journal article
AN - SCOPUS:85039792434
SN - 0090-6778
VL - 66
SP - 1425
EP - 1439
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 4
ER -