Automatic-repeat-request error control schemes

Error detection incorporated with automatic-repeat-request (ARQ) is widely used for error control in data communication systems. This method of error control is simple and provides high system reliability. If a properly chosen code is used for error detection, virtually error-free data transmission can be attained. Various types of ARQ and hybrid ARQ schemes, and error detection using linear block codes are surveyed

Telemetry problems with pacemakers in a noisy environment

This report covers the investigation of problems relating to the telemetry of pacemakers in a noisy environment. An attempt was made to suggest schemes that improve system performance. We define system performance to be improved noise immunity with high reliability and increased message throughput. Three aspects were considered, with implementation schemes that incur minimal protocol changes over systems in existence. 1. The present scheme is writing to the pacemaker and echoing each bit back within a 2ms frame. If any echoed bit is not in agreement with that sent, the message is re-transmitted from the start of the block. Because the return link is poor in terms of signal to noise ratio, echoing bits up this link degrades system performance, particularly as the noise power increases. An ARQ (autamatic repeat request) scheme is suggested as a solution to this problem. 2. When data is to be read from the pacemaker, reply is via two 4ms frames each of 6 bits. A CRC (cyclic redundancy check) is performed on the returned data (8 bits) which checks for three or less errors, and the redundancy bits are appended to the end of the 9 bits. If any errors are detected, a retransmission is required. Using a FEC (forward error correction) scheme to correct errors, together with a CRC to check the integrity of data, throughput can be significantly improved especially in a noisy environment. The scheme we suggest is to encode data bits plus CRC with a 23,12 Golay code, and send the data using four by 4ms frames. The Golay code suggested is a perfect code, triple error correcting. 3. The final aspect we considered is the possible system performance improvement using soft decision quantization at the programmer of received data. This system gives gains of the order of 1.75 dB over current practice

Optimal Control of a Single Queue with Retransmissions: Delay-Dropping Tradeoffs

A single queue incorporating a retransmission protocol is investigated, assuming that the sequence of per effort success probabilities in the Automatic Retransmission reQuest (ARQ) chain is a priori defined and no channel state information at the transmitter is available. A Markov Decision Problem with an average cost criterion is formulated where the possible actions are to either continue the retransmission process of an erroneous packet at the next time slot or to drop the packet and move on to the next packet awaiting for transmission. The cost per slot is a linear combination of the current queue length and a penalty term in case dropping is chosen as action. The investigation seeks policies that provide the best possible average packet delay-dropping trade-off for Quality of Service guarantees. An optimal deterministic stationary policy is shown to exist, several structural properties of which are obtained. Based on that, a class of suboptimal -policies is introduced. These suggest that it is almost optimal to use a K-truncated ARQ protocol as long as the queue length is lower than L, else send all packets in one shot. The work concludes with an evaluation of the optimal delay-dropping tradeoff using dynamic programming and a comparison between the optimal and suboptimal policies.Comment: 29 pages, 8 figures, submitted to IEEE Transactions on Wireless Communication

Multi-level Turbo Decoding Assisted Soft Combining Aided Hybrid ARQ

Hybrid Automatic Repeat reQuest (ARQ) plays an essential role in error control. Combining the incorrectly received packet replicas in hybrid ARQ has been shown to reduce the resultant error probability, while improving the achievable throughput. Hence, in this contribution, multi-level turbo codes have been amalgamated both with hybrid ARQ and efficient soft combining techniques for taking into account the Log- Likelihood Ratios (LLRs) of retransmitted packet replicas. In this paper, we present a soft combining aided hybrid ARQ scheme based on multi-level turbo codes, which avoid the capacity loss of the twin-level turbo codes that are typically employed in hybrid ARQ schemes. More specifically, the proposed receiver dynamically appends an additional parallel concatenated Bahl, Cocke, Jelinek and Raviv (BCJR) algorithm based decoder in order to fully exploit each retransmission, thereby forming a multi-level turbo decoder. Therefore, all the extrinsic information acquired during the previous BCJR operations will be used as a priori information by the additional BCJR decoders, whilst their soft output iteratively enhances the a posteriori information generated by the previous decoding stages. We also present link- level Packet Loss Ratio (PLR) and throughput results, which demonstrate that our scheme outperforms some of the previously proposed benchmarks

Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G

We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and low-latency communication (URLLC). To this end, we propose machine learning methods to predict the outcome of the decoding process ahead of the end of the transmission. We discuss different input features and classification algorithms ranging from traditional methods to newly developed supervised autoencoders. These methods are evaluated based on their prospects of complying with the URLLC requirements of effective block error rates below $10^{-5}$ at small latency overheads. We provide realistic performance estimates in a system model incorporating scheduling effects to demonstrate the feasibility of E-HARQ across different signal-to-noise ratios, subcode lengths, channel conditions and system loads, and show the benefit over regular HARQ and existing E-HARQ schemes without machine learning.Comment: 14 pages, 15 figures; accepted versio