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pilot-design-and-allocation-for-multi-cell-massive-mimo-systems-with-two-tier-receiving

pilot-design-for-large-scale-multi-cell-multiuser-mimo-systems

This one is about the paper and the code of "Pilot design for large-scale multi-cell multiuser MIMO systems".

pimrc-2021-dldunn

pimrc2021-dldunn

pix2pix-eager

Tensorflow eager implementation of Pix2Pix (Image-to-image translation with conditional adversarial networks)

polarair-itw2023

polarized-channel-modeling

The tri-/dual-polarized channel modeling for HMIMO surfaces, the details refer to the paper "Tri-Polarized Holographic MIMO Surfaces for Near-Field Communications: Channel Modeling and Precoding Design"

poolformer

PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)

power-allocation-algorithms-for-massive-mimo-systems-with-multi-antenna-users

Modern 5G wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This concept entails using an antenna array at a base station to concurrently service many mobile devices that have several antennas on their side. In this field, a significant role is played by the precoding (beamforming) problem. During downlink, an

power-allocation-for-multi-user-ofdm-dcsk-system-in-frequency-selective-fading-channel

This project is related to the paper: "Power allocation for multi-user OFDM-DCSK system in frequency selective fading channel", Majid Mobini, M.Reza Zahabi, Physical Communication, 24, (2017), pp. 146–153. In this paper, we introduce a grouping approach for power allocation in the multi-user OFDM-DCSK (MU-OFDM-DCSK) system under the frequency selective fading channels. The suggested procedure is convenient also for the other comb-type non-coherent schemes with similar structure. Furthermore, we derive analytical bit error rate (BER) expression for the grouped scheme and offer an optimal power distribution policy for both the single- and multi-user scenarios. This power assignment strategy is formulated by a min–max problem with the target of the worst group BER minimization incorporating total power and interference constraints. Simulation results confirm the advantages of the proposed power allocation scheme.

power-resource-allocation-for-isac

power_allocation_ddpg

Simulation code for "Downlink Power Control for Cell-Free Massive MIMO with Deep Reinforcement Learning" by Lirui Luo, Jiayi Zhang, Shuaifei Chen, Bo Ai, and Derrick Wing Kwan Ng, IEEE Transactions on Vehicular Technology.

ppapr-aware-beam-division-multiple-access-for-mmwave-massive-mimo-systems

precoderdesignincompletecsi4jt

SSOCP and Branch and Bound source code for the paper: T.R. Lakshmana, A. Tölli, R. Devassy, and T. Svensson, “Precoder Design with Incomplete Feedback for Joint Transmission,” accepted in IEEE Trans- actions on Wireless Communications, Oct. 2015. This code provided is as, and if you use any part of this code, please cite the above work. Thanks.

precodernet

PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning

precoding-mimo-crn

precoding-polynomial-expansion

Minimum work examples for "Linear Precoding based on Polynomial Expansion"

precoding_code

Precoding

precoding_rl

A reinforcement learning approach to learn precoder and combiner directions

predictive-and-adaptive-deep-coding-for-wireless-image-transmission-in-semantic-communication

Pytorch code for IEEE TWC paper "Predictive and Adaptive Deep Coding for Wireless Image Transmission in Semantic Communication"

project_dcnnmld

Offical repo for the paper titled "A MIMO Detector with Deep Learning in the Presence of Correlated Interference"

prune-then-distill

ps_momab

Multi-Objective Multi-Armed Bandit

psl-mimo-codes

Minimum PSL Sequence Set Design for MIMO Radars via Manifold-Based Optimization

pyphysim

Simulation of Digital Communication (physical layer) in Python.

qcs-sgm

qcs-sgm-plus

quantizedfeedback

Learning of a communication system over a binary feedback channel

quantum-cp

Code for the paper "Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning"

quantum_machine_learning