286 research outputs found
HARQ Buffer Management: An Information-Theoretic View
A key practical constraint on the design of Hybrid automatic repeat request
(HARQ) schemes is the size of the on-chip buffer that is available at the
receiver to store previously received packets. In fact, in modern wireless
standards such as LTE and LTE-A, the HARQ buffer size is one of the main
drivers of the modem area and power consumption. This has recently highlighted
the importance of HARQ buffer management, that is, of the use of buffer-aware
transmission schemes and of advanced compression policies for the storage of
received data. This work investigates HARQ buffer management by leveraging
information-theoretic achievability arguments based on random coding.
Specifically, standard HARQ schemes, namely Type-I, Chase Combining and
Incremental Redundancy, are first studied under the assumption of a
finite-capacity HARQ buffer by considering both coded modulation, via Gaussian
signaling, and Bit Interleaved Coded Modulation (BICM). The analysis sheds
light on the impact of different compression strategies, namely the
conventional compression log-likelihood ratios and the direct digitization of
baseband signals, on the throughput. Then, coding strategies based on layered
modulation and optimized coding blocklength are investigated, highlighting the
benefits of HARQ buffer-aware transmission schemes. The optimization of
baseband compression for multiple-antenna links is also studied, demonstrating
the optimality of a transform coding approach.Comment: submitted to IEEE International Symposium on Information Theory
(ISIT) 2015. 29 pages, 12 figures, submitted to journal publicatio
Outage-based ergodic link adaptation for fading channels with delayed CSIT
Link adaptation in which the transmission data rate is dynamically adjusted
according to channel variation is often used to deal with time-varying nature
of wireless channel. When channel state information at the transmitter (CSIT)
is delayed by more than channel coherence time due to feedback delay, however,
the effect of link adaptation can possibly be taken away if this delay is not
taken into account. One way to deal with such delay is to predict current
channel quality given available observation, but this would inevitably result
in prediction error. In this paper, an algorithm with different view point is
proposed. By using conditional cdf of current channel given observation, outage
probability can be computed for each value of transmission rate . By
assuming that the transmission block error rate (BLER) is dominated by outage
probability, the expected throughput can also be computed, and can be
determined to maximize it. The proposed scheme is designed to be optimal if
channel has ergodicity, and it is shown to considerably outperform conventional
schemes in certain Rayleigh fading channel model
Statistical Analysis of a Posteriori Channel and Noise Distribution Based on HARQ Feedback
In response to a comment on one of our manuscript, this work studies the
posterior channel and noise distributions conditioned on the NACKs and ACKs of
all previous transmissions in HARQ system with statistical approaches. Our main
result is that, unless the coherence interval (time or frequency) is large as
in block-fading assumption, the posterior distribution of the channel and noise
either remains almost identical to the prior distribution, or it mostly follows
the same class of distribution as the prior one. In the latter case, the
difference between the posterior and prior distribution can be modeled as some
parameter mismatch, which has little impact on certain type of applications.Comment: 15 pages, 2 figures, 4 table
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
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
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