5 research outputs found
Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks
The key to successive cancellation (SC) flip decoding of polar codes is to
accurately identify the first error bit. The optimal flipping strategy is
considered difficult due to lack of an analytical solution. Alternatively, we
propose a deep learning aided SC flip algorithm. Specifically, before each SC
decoding attempt, a long short-term memory (LSTM) network is exploited to
either (i) locate the first error bit, or (ii) undo a previous `wrong' flip. In
each SC attempt, the sequence of log likelihood ratios (LLRs) derived in the
previous SC attempt is exploited to decide which action to take. Accordingly, a
two-stage training method of the LSTM network is proposed, i.e., learn to
locate first error bits in the first stage, and then to undo `wrong' flips in
the second stage. Simulation results show that the proposed approach identifies
error bits more accurately and achieves better performance than the
state-of-the-art SC flip algorithms.Comment: 5 pages, 7 figure
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models
Accurate prediction of fading channel in the upcoming transmission frame is
essential to realize adaptive transmission for transmitters, and receivers with
the ability of channel prediction can also save some computations of channel
estimation. However, due to the rapid channel variation and channel estimation
error, reliable prediction is hard to realize. In this situation, an
appropriate channel model should be selected, which can cover both the
statistical model and small scale fading of channel, this reminds us the
natural languages, which also have statistical word frequency and specific
sentences. Accordingly, in this paper, we take wireless channel model as a
language model, and the time-varying channel as talking in this language, while
the realistic noisy estimated channel can be compared with mumbling.
Furthermore, in order to utilize as much as possible the information a channel
coefficient takes, we discard the conventional two features of absolute value
and phase, replacing with hundreds of features which will be learned by our
channel model, to do this, we use a vocabulary to map a complex channel
coefficient into an ID, which is represented by a vector of real numbers.
Recurrent neural networks technique is used as its good balance between
memorization and generalization, moreover, we creatively introduce
sequence-to-sequence (seq2seq) models in time series channel prediction, which
can translates past channel into future channel. The results show that
realistic channel prediction with superior performance relative to channel
estimation is attainable.Comment: 7 pages, 7 figures, updated figure 6&7, added reference
Realistic Channel Models Pre-training
In this paper, we propose a neural-network-based realistic channel model with
both the similar accuracy as deterministic channel models and uniformity as
stochastic channel models. To facilitate this realistic channel modeling, a
multi-domain channel embedding method combined with self-attention mechanism is
proposed to extract channel features from multiple domains simultaneously. This
'one model to fit them all' solution employs available wireless channel data as
the only data set for self-supervised pre-training. With the permission of
users, network operators or other organizations can make use of some available
user specific data to fine-tune this pre-trained realistic channel model for
applications on channel-related downstream tasks. Moreover, even without
fine-tuning, we show that the pre-trained realistic channel model itself is a
great tool with its understanding of wireless channel.Comment: 6 pages, 5 figure
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
In this paper, the downlink packet scheduling problem for cellular networks
is modeled, which jointly optimizes throughput, fairness and packet drop rate.
Two genie-aided heuristic search methods are employed to explore the solution
space. A deep reinforcement learning (DRL) framework with A2C algorithm is
proposed for the optimization problem. Several methods have been utilized in
the framework to improve the sampling and training efficiency and to adapt the
algorithm to a specific scheduling problem. Numerical results show that DRL
outperforms the baseline algorithm and achieves similar performance as
genie-aided methods without using the future information.Comment: submitted to WCNC202
A Flip-Syndrome-List Polar Decoder Architecture for Ultra-Low-Latency Communications
We consider practical hardware implementation of Polar decoders. To reduce
latency due to the serial nature of successive cancellation (SC), existing
optimizations improve parallelism with two approaches, i.e., multi-bit decision
or reduced path splitting. In this paper, we combine the two procedures into
one with an error-pattern-based architecture. It simultaneously generates a set
of candidate paths for multiple bits with pre-stored patterns. For rate-1 (R1)
or single parity-check (SPC) nodes, we prove that a small number of
deterministic patterns are required to guarantee performance preservation. For
general nodes, low-weight error patterns are indexed by syndrome in a look-up
table and retrieved in O(1) time. The proposed flip-syndrome-list (FSL) decoder
fully parallelizes all constituent code blocks without sacrificing performance,
thus is suitable for ultra-low-latency applications. Meanwhile, two code
construction optimizations are presented to further reduce complexity and
improve performance, respectively.Comment: 10 pages, submitted to IEEE Access (Special Issue on Advances in
Channel Coding for 5G and Beyond