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In this paper, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). Every worker in Q-GADMM communicates only with two neighbors, and updates its model via the group alternating direct method of multiplier (GADMM), thereby ensuring fast convergence while reducing the number of communication rounds. Furthermore, each worker quantizes its model updates before transmissions, thereby decreasing the communication payload sizes.


Use of low resolution analog to digital converters (ADCs) is an effective way to reduce the high power consumption of millimeter wave (mmWave) receivers. In this paper, a receiver with low resolution ADCs based on adaptive thresholds is considered in downlink mmWave communications in which the channel state information is not known a-priori and acquired through channel estimation. A performance comparison of low-complexity algorithms for power and ADC allocation among transmit and receive terminals, respectively, is provided.


SC-Flip (SCF) decoding is a low-complexity polar code decoding algorithm alternative to SC-List (SCL) algorithm with small list sizes. To achieve the performance of the SCL algorithm with large list sizes, the Dynamic SC-Flip (DSCF) algorithm was proposed. However, DSCF involves logarithmic and exponential computations that are not suitable for practical hardware implementations. In this work, we propose a simple approximation that replaces the transcendental computations of DSCF decoding. Moreover, we show how to incorporate fast decoding techniques with the DSCF algorithm.


The cost of uncompressing (decoding) data can be prohibitive in certain real-time applications,
for example when predicting using compressed deep learning models. In many scenarios, it is
acceptable to sacrifice to some extent on compression in the interest of fast decoding. In this
work, we are interested in finding the prefix tree having the best decode time under the constraint
that the code length does not exceed a certain threshold for a natural class of algorithms under


We consider the problem of coding for computing with maximal distortion, where the sender communicates with a receiver, which has its own private data and wants to compute a function of their combined data with some fidelity constraint known to both agents. We show that the minimum rate for this problem is equal to the conditional entropy of a hypergraph and design practical codes for the problem. Further, the minimum rate of this problem may be a discontinuous function of the fidelity constraint.


This paper designs a Distributed Arithmetic Coding (DAC) decoder using the depth- first search method. In addition, a method is proposed to control the decoder complexity. Simulation results compare the DFD with the traditional Breadth-First Decoder (BFD)
showing that under the same complexity constraints, the DFD outperforms the BFD when the code length is not too long and the quality of side information is not too poor.


Hybrid beamforming has attracted considerable attention in recent years as an efficient and promising technique for the practical implementation of millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) wireless systems. In this paper, we investigate hybrid analog/digital beamforming designs based on a single RF chain architecture (SRCA) for mmWave massive-MIMO. We first revisit the SRCA and then explore its shortcomings.