Performance analysis of convolutionally-coded telegram splitting telemetry systems under different ISM/SRD collision behaviors - Elshabrawy, Tallal; Nafie, Sally; Robert, Jörg (2016 IEEE Wireless Communications and Networking Conference, WCNC 2016, 2016)
Telemetry systems suffer from inter-system interference due to other technologies competing for the shared unlicensed ISM/SRD band. The resultant collisions could have a profound impact on the underlying BLER performance of telemetry systems. A telegram splitting approach has been recently proposed in the literature that suggests splitting transmitted telemetry blocks into multiple telegrams and transmitting them in a frequency hopping manner across multiple ISM/SRD channels. As a result, collisions probabilistically hit a subset of sent telegrams instead of destroying the whole block. In this paper, the BLER performance of convolutionally-coded telegram splitting is theoretically analyzed under different ISM/SRD collision behaviors. Particularly, ISM/SRD interference may exhibit deterministic, random and quasi-deterministic collision behaviors over transmitted telegrams. For each of the three collision modes, a binary erasure channel model accompanied by hard-decision decoding is presumed for collided bits. A Gaussian channel associated with soft-decision decoding is simulated and included for comparison. Numerical results suggest that by synchronizing the transmitted block and telegram sizes with the number of ISM/SRD channels to create either deterministic or quasi-deterministic collision scenarios, measurable BLER gains may be achieved. Furthermore, BLER performance could be significantly improved by deriving LLR information from collided bits in association with soft decision decoding.
Keywords: Collision Channel; Convolutional Codes; Frequency Hopping; ISM/SRD Interference; Telegrams Splitting; Telemetry Systems

SCRAM: A Novel Approach for Reliable Ultra-Low Latency M2M Applications - Nafie, Sally; Robert, Jörg; Heuberger, Albert (IEEE Vehicular Technology Conference, 2019)
This paper presents a novel hybrid decoder dubbed SCRAM (Slotted Coded Random Access Multiplexing), for Coded Slotted ALOHA (CSA). The proposed decoder is provisioned to meet the challenging requirements of the future Machine to Machine (M2M) Internet of Things (IoT) in the 5G standard. These challenges are mainly portrayed in a reliable robust communication with ultra-low latency. At the User Equipment (UE) terminal, a Low Density Parity Check (LDPC) encoder is deployed, and the encoded codewords are transmitted on the uplink channel using Slotted ALOHA (SA). While LDPC codes are chosen because of their near Shannon capacity performance, SA serves as a Random Access (RA) mechanism that guarantees the low-latency requirements. However, the incorporation of uncoordinated SA introduces collisions between the transmitted packets. The proposed decoder aims at resolving these collisions by means of a joint LDPC-SA hybrid decoder. Unlike the sequential decoders presented in the literature, the proposed joint decoder concurrently decodes both the SA and LDPC blocks in a similar fashion to the Belief Propagation (BP) algorithm applied for LDPC codes. The results presented herein show that the proposed SCRAM decoder significantly outperforms the state-of-the-art decoding schemes of CSA by more than 5.5 dB.
CRIS-Team Scopus Importer:2019-05-09
Keywords: 5G; Belief Propagation; Coded Slotted ALOHA; Internet of Things; IoT; LDPC; Low Density Parity Check Codes; M2M; Machine to Machine; Random Access; SIC; Slotted ALOHA; Successive Interference Cancellation; V2V; V2X