Deep Learning for Uplink Spectral Efficiency in Cell-Free Massive MIMO Systems

@article{Khanh2021DeepLF,
  title={Deep Learning for Uplink Spectral Efficiency in Cell-Free Massive MIMO Systems},
  author={Le Ty Khanh and Viet Quoc Pham and Ha Hoang Kha and Nguyen Minh Hoang},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.04968}
}
In this paper, we introduce a Deep Neural Network (DNN) to maximize the Proportional Fairness (PF) of the Spectral Efficiency (SE) of uplinks in Cell-Free (CF) massive Multiple-Input Multiple-Output (MIMO) systems. The problem of maximizing the PF of the SE is a nonconvex optimization problem in the design variables. We will develop a DNN which takes pilot sequences and largescale fading coefficients of the users as inputs and produces the outputs of optimal transmit powers. By consisting of… 
View PDF on arXiv
1 Citations
Methods Citations
1

Figures and Tables from this paper

One Citation

Citation Type

Citation Type

Effective Insect Recognition Based on Deep Neural Network Models in Complex Background
In biology, insects have the most species, number, distribution, and adaptability. Insect recognition is the basis of insect research and pest control. However, current insect recognition work mainly…

References

SHOWING 1-10 OF 34 REFERENCES
On proportional fairness of uplink spectral efficiency in cell-free massive MIMO systems
TLDR
Simulation results show that the SE PF maximization not only offers approximately the achievable sum rates as compared to the sum-rate maximization but also provides an improved trade-off between the user rate fairness and the achievablesum rate.
Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning
TLDR
This paper proposes a new optimization algorithm, inspired by the weighted minimum mean square error approach, to obtain a stationary point in polynomial time, and uses this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime.
On the Total Energy Efficiency of Cell-Free Massive MIMO
TLDR
This work considers the cell-free massive multiple-input multiple-output (MIMO) downlink, where a very large number of distributed multiple-antenna access points (APs) serve many single-ant antenna users in the same time-frequency resource, and derives a closed-form expression for the spectral efficiency taking into account the effects of channel estimation errors and power control.
Full-Duplex Cell-Free Massive MIMO
TLDR
Numerical results show that when the RI is sufficiently low, the FD mode can achieve a spectral efficiency gain of 140% over the HD mode in the cell-free massive MIMO system and outperform the FD collocated massive M IMO systems in terms of spectral efficiency.
Linear precoding via conic optimization for fixed MIMO receivers
TLDR
The proposed precoder design is general, and as a special case, it solves the transmit rank-one beamforming problem and can significantly outperform existing linear precoders.
Deep MIMO detection
  • N. Samuel, Tzvi Diskin, A. Wiesel
  • Computer Science, Mathematics
    2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
  • 2017
TLDR
The results show that deep networks can achieve state of the art accuracy with significantly lower complexity while providing robustness against ill conditioned channels and mis-specified noise variance.
On the Uplink Max–Min SINR of Cell-Free Massive MIMO Systems
TLDR
A novel sub-optimal scheme is presented which provides a GP formulation to efficiently and globally maximize the minimum uplink user rate and substantially outperforms the existing schemes in the literature.
Cell-Free Massive MIMO Versus Small Cells
TLDR
Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly fivefold improvement in 95%-likely per-user throughput over the small-cell scheme, and tenfold improvement when shadow fading is correlated.
Solution of the multiuser downlink beamforming problem with individual SINR constraints
  • M. Schubert, H. Boche
  • Mathematics, Computer Science
    IEEE Transactions on Vehicular Technology
  • 2004
TLDR
The optimality and global convergence of the algorithm is proven and stopping criteria are given, and the global optimum of the downlink beamforming problem is equivalently obtained from solving a dual uplink problem, which has an easier-to-handle analytical structure.
Fairness Considerations for Full Duplex Multi-User MIMO Systems
  • A. Cirik
  • Mathematics, Computer Science
    IEEE Wireless Communications Letters
  • 2015
TLDR
A gradient projection method is developed to solve the non-convex optimization problem of a cellular system with a full-duplex base station serving multiple uplink and downlink users simultaneously, and it is demonstrated that the proposed algorithm provides a good balance between maximizing the sum-rate and maintaining fairness among users.
...
1
2
3
4
...