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Register a new userRealizing Neural Decoder at the Edge with Ensembled BNN
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In this work, we propose extreme compression techniques like binarization, ternarization for Neural Decoders such as TurboAE. These methods reduce memory and computation by a factor of 64 with a performance better than the quantized (with 1-bit or 2-bits) Neural Decoders. However, because of the limited representation capability of the Binary and Ternary networks, the performance is not as good as the real-valued decoder. To fill this gap, we further propose to ensemble 4 such weak performers to deploy in the edge to achieve a performance similar to the real-valued network. These ensemble decoders give 16 and 64 times saving in memory and computation respectively and help to achieve performance similar to real-valued TurboAE.
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Multi-point detection of the full-scale environment is an important issue in autonomous driving. The state-of-the-art positioning technologies (such as RADAR and LIDAR) are incapable of real-time detection without line-of-sight. To address this issue, this paper presents a novel multi-point vehicular positioning technology via emph{millimeter-wave} (mmWave) transmission that exploits multi-path reflection from a emph{target vehicle} (TV) to a emph{sensing vehicle} (SV), which enables the SV to fast capture both the shape and location information of the TV in emph{non-line-of-sight} (NLoS) under the assistance of multi-path reflections. A emph{phase-difference-of-arrival} (PDoA) based hyperbolic positioning algorithm is designed to achieve the synchronization between the TV and SV. The emph{stepped-frequency-continuous-wave} (SFCW) is utilized as signals for multi-point detection of the TVs. Transceiver separation enables our approach to work in NLoS conditions and achieve much lower latency compared with conventional positioning techniques.
The so-called improved soft-aided bit-marking algorithm was recently proposed for staircase codes (SCCs) in the context of fiber optical communications. This algorithm is known as iSABM-SCC. With the help of channel soft information, the iSABM-SCC decoder marks bits via thresholds to deal with both miscorrections and failures of hard-decision (HD) decoding. In this paper, we study iSABM-SCC focusing on the parameter optimization of the algorithm and its performance analysis, in terms of the gap to the achievable information rates (AIRs) of HD codes and the fiber reach enhancement. We show in this paper that the marking thresholds and the number of modified component decodings heavily affect the performance of iSABM-SCC, and thus, they need to be carefully optimized. By replacing standard decoding with the optimized iSABM-SCC decoding, the gap to the AIRs of HD codes can be reduced to 0.26-1.02 dB for code rates of 0.74-0.87 in the additive white Gaussian noise channel with 8-ary pulse amplitude modulation. The obtained reach increase is up to 22% for data rates between 401 Gbps and 468 Gbps in an optical fiber channel.
We introduce a novel soft-aided hard-decision decoder for product codes adopting bit marking via updated reliabilities at each decoding iteration. Gains up to 0.8 dB vs. standard iterative bounded distance decoding and up to 0.3 dB vs. our previously proposed bit-marking decoder are demonstrated.
In this work, we develop a pair of rate-diverse encoder and decoder for a two-user Gaussian multiple access channel (GMAC). The proposed scheme enables the users to transmit with the same codeword length but different coding rates under diverse user channel conditions. First, we propose the row-combining (RC) method and row-extending (RE) method to design practical low-density parity-check (LDPC) channel codes for rate-diverse GMAC. Second, we develop an iterative rate-diverse joint user messages decoding (RDJD) algorithm for GMAC, where all user messages are decoded with a single parity-check matrix. In contrast to the conventional network-coded multiple access (NCMA) and compute-forward multiple access (CFMA) schemes that first recover a linear combination of the transmitted codewords and then decode both user messages, this work can decode both the user messages simultaneously. Extrinsic information transfer (EXIT) chart analysis and simulation results indicate that RDJD can achieve gains up to 1.0 dB over NCMA and CFMA in the two-user GMAC. In particular, we show that there exists an optimal rate allocation for the two users to achieve the best decoding performance given the channel conditions and sum rate.
This paper studies a federated edge learning system, in which an edge server coordinates a set of edge devices to train a shared machine learning model based on their locally distributed data samples. During the distributed training, we exploit the joint communication and computation design for improving the system energy efficiency, in which both the communication resource allocation for global ML parameters aggregation and the computation resource allocation for locally updating MLparameters are jointly optimized. In particular, we consider two transmission protocols for edge devices to upload ML parameters to edge server, based on the non orthogonal multiple access and time division multiple access, respectively. Under both protocols, we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy, by jointly optimizing the transmission power and rates at edge devices for uploading MLparameters and their central processing unit frequencies for local update. We propose efficient algorithms to optimally solve the formulated energy minimization problems by using the techniques from convex optimization. Numerical results show that as compared to other benchmark schemes, our proposed joint communication and computation design significantly improves the energy efficiency of the federated edge learning system, by properly balancing the energy tradeoff between communication and computation.
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