AMC and HARQ: How to Increase the Throughput

Jabi, Mohammed; Szczeciński, Leszek; Benjillali, Mustapha; Benyouss, Abdellatif; Pelletier, Benoît

doi:10.1109/tcomm.2018.2808262

Cited by 8 publications

(12 citation statements)

References 35 publications

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“…To maximize the throughput, the receiver should be able to store the unsuccessfully decoded packets and efficiently combine them with the most recently received packets and subsequently, re-attempt the decoding. There are several different signal combining techniques available in the literature [7]. To effectively combine the received signals, applying rateless codes on the PHY layer has shown a great deal of attention [8].…”

Section: Packet Combining Methodsmentioning

confidence: 99%

“…Since the terms in summation of (7) are independent, the minimization of (X p 1 , • • • , X p U ) can be formulated via the minimization of each term. Thus, denoting the specific patterns assigned for Users 1 to U by (p 1 , ..., p U ), the conventionally modulated symbols for Users 1 to U that minimize (7) for a given (p 1 , ..., p U ) can be formulated as…”

Section: B Gstsm Receiver Processing For Multi-user Scenariosmentioning

confidence: 99%

“…Using a dedicated control channel, these ACK messages are broadcasted from AP and it is assumed that they are perfectly received. While the proposed grant-free multiple access scheme is based on rateless codes, the application of GSTSM is transparent to the choice of the channel coding method and it can be used in conjunction with other schemes that detect and combine the received information [7]. Also, the proposed approach is applicable to other wireless networks such as ad-hoc MTCs, as well as to scenarios with random data transmission.…”

Section: Gstsm For Headerless Grant-free Medium Accessmentioning

confidence: 99%

“…This can be achieved either by transmitting long orthogonal sequences, or by transmitting long and strongly (channel) encoded headers [6]. After reliably identifying the transmitting machine, packet combining techniques can be used to store the unsuccessfully decoded packets, and combine them with the subsequently received packets [7]. In this regard, rateless codes, applied at the physical (PHY) layer, are promising candidates to effectively combine and decode the accumulated information with the most recently received packets [8].…”

Section: Introductionmentioning

confidence: 99%

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Generalized Space-Time Super-Modulation and Its Application to Grant-Free Medium Access

Mehran

Nikitopoulos

Jafarkhani

2021

IEEE Trans. Commun.

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In this work, Generalized Space-Time Super-Modulation (GSTSM) is introduced which enables the transmission of an additional flexible-rate and highly-reliable information stream concurrently with the conventionally transmitted symbols, without the need for increasing the corresponding packet length. This is attained by jointly exploiting the spatial and temporal dimensions of multiple-antenna systems, which enables efficient detection for conventional and additional information subchannels even in highly correlated channel conditions or AWGN channels. In the context of machine-type communications, GSTSM enables grant-free medium access without transmitting additional headers to convey each machine's signature information. Hence, it is shown that even at an extreme case where the data packets of two users are always colliding, GSTSM offers throughput gains of up to 33% compared to the best examined header-based scheme. For the same scenario, it is shown that GSTSM based on joint multi-user detection provides throughput gains of up to 2.5× compared with the case where users' signals are detected independently. In addition, it yields over 90% improvement in achievable rates compared with the schemes that require centralized medium-access coordination. For both joint and independent signal detection schemes, it is also shown that adopting an iterative detection/decoding approach allows to further improve the throughput gains.

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Section: Packet Combining Methodsmentioning

confidence: 99%

Section: B Gstsm Receiver Processing For Multi-user Scenariosmentioning

confidence: 99%

Section: Gstsm For Headerless Grant-free Medium Accessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generalized Space-Time Super-Modulation and Its Application to Grant-Free Medium Access

Mehran

Nikitopoulos

Jafarkhani

2021

IEEE Trans. Commun.

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“…To improve the adaptation of transmission parameters with delayed CSI, Q‐learning‐based HARQ (QL‐HARQ) and FQL‐based HARQ (FQL‐HARQ) were proposed 20–23 . Furthermore, based on CSI from adaptive modulation and coding (AMC), the HARQ process can reduce the redundancy of signal retransmission and improve the system throughput 24–26 . In the latter scheme, the receiver memory is classified according to the quality of service (QoS) level of packets, which reduces memory demand and improves the system throughput 27 .…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive code block group hybrid automatic repeat request scheme for low earth orbit satellite networks

Deng

Liu

Wang

et al. 2021

Satell Commun Network

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Summary Low earth orbit (LEO) satellite networks provide important support to expand 5G‐and‐beyond networks to realize global coverage. Hybrid automatic repeat requests (HARQs) can greatly improve the reliability of transmission through dynamic satellite channels. Due to the large propagation delay in LEO satellite channels, the biggest challenges of HARQ in satellite networks are the delayed channel state information (CSI) and overflow of receiver memory. In this paper, we propose a novel adaptive code block group HARQ (ACBG‐HARQ) scheme that achieves lower retransmission rates with reliable data receiving. First, we build a new channel state model (CSM) to describe the relationship between the packet error rate (PER) and global channel degradation. Three states of receiver memory allocation for HARQ are designed with respect to the channel state and memory occupation: idle state, non‐full state, and full state. Then, we propose an optimal HARQ scheme for LEO satellite networks with the Markov decision process (MDP) based on the fuzzy Q‐learning (FQL) algorithm to improve the accuracy of ACBG‐HARQ state transfer, which contributes to adaptive selection of the HARQ code block group size. Simulation results show that the ACBG‐HARQ scheme can achieve a maximum spectrum efficiency gain of 10% and that the probability of receiver memory overflow is reduced by 1%∼6%.

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Age of error information and throughput for truncated and layer‐coded HARQ‐based satellite‐IoT systems

Zhao,

Jia,

Xie

et al. 2023

Int J Communication

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SummaryThe work focuses on the simultaneous improvement on the throughput and information freshness of the promising satellite‐based Internet of Things (S‐IoT). To this end, we first propose a method based on the adaptive modulation coding (AMC) and truncated layer‐coded hybrid automatic repeat request (L‐HARQ) strategies by especially considering the difference in the channel state information (CSI) between the transmitter and receiver. On account of the large signal delay of the S‐IoT systems, this paper establishes the channel model based on the correlation of the signal‐to‐noise ratio (SNR) under the outdated channel and the ideal channel. Secondly, considering the fact that the improved backtrack decoding is used in the L‐HARQ, the classical definition of age of information (AoI) is insufficient to quantify the status update freshness, we propose the age of error information (AoEI), which is defined as the elapsed time since the generation of the lastly successfully backtrack‐decoded status update. Thirdly, we characterize the statistical descriptions of the maximum wait time of the successfully recovered status updates and the interdeparture time between the two consecutive status update packets that are lastly successfully received. Then, the closed‐form expression of average AoEI is derived under the assumption of independent and identically distributed fading channels. Finally, the Markov decision process (MDP) framework is used to optimize the packet parameters in each round of L‐HARQ process under the general fading scenario. Thereby, the optimized transmission scheme that minimizes AoI with throughput constraint is solved. The simulations investigate the effect of system parameters on the average AoEI.

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AMC and HARQ: How to Increase the Throughput

Cited by 8 publications

References 35 publications

Generalized Space-Time Super-Modulation and Its Application to Grant-Free Medium Access

Generalized Space-Time Super-Modulation and Its Application to Grant-Free Medium Access

A novel adaptive code block group hybrid automatic repeat request scheme for low earth orbit satellite networks

Age of error information and throughput for truncated and layer‐coded HARQ‐based satellite‐IoT systems

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