Constant envelope precoding in multi-cell massive MIMO systems with channel uncertainty

Shahabi, Seyyed MohammadMahdi; Ardebilipour, Mehrdad; Hosseini, Seyed Ali; Omid, Yasaman

doi:10.1016/j.phycom.2019.02.008

Cited by 11 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to solve the main optimization problem in (6), at first, assuming a fixed Ψ I (by selecting an initial value for Ψ I ) and a variant x, the optimization problem in ( 14) is solved according to the proposed algorithm. In the following, based on the optimum value of x obtained in the previous stage, the optimization problem in (20) is solved by assuming a fixed x and a variant Ψ I .…”

Section: Trellis-based Solution For the Single-cell Scenariomentioning

confidence: 99%

IRS-aided Large-Scale MIMO Systems with Passive Constant Envelope Precoding

Omid,

Shahabi,

Pan

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, an intelligent reflecting surface (IRS)aided large-scale MIMO system is investigated in which constant envelope precoding (CEP) is utilized at each base station (BS). It is both cost-effective and energy-efficient to implement CEP in a large-scale antenna array. We aim to optimize the discrete phase shifts at both the BS and the IRS to minimize the sum power of multi-user interference (MUI) in the system via our proposed three algorithms. For the sake of simplicity, a simple single-cell scenario is considered, where the optimization of the BS and IRS phase shifts is solved by a low-complexity trellisbased algorithm. Then, this algorithm is extended to a multi-cell scenario, where the precoding operation in each BS is performed individually. With the aid of stochastic optimization method, a low-overhead trellis-based solution is proposed which has better performance than the first one. Finally, we solve the optimization problem via the semi-definite relaxation (SDR) scheme, to serve as a performance benchmark for the proposed algorithms. Meanwhile, interference and complexity analysis is provided for the proposed algorithms. Numerical results demonstrate that while the performance of the trellis-based algorithms is negligibly lower than that of the continuous-phase SDR-based solution, the computational complexity and the implementation cost of the former is much lower than the latter, which is appealing for practical applications.

show abstract

Section: Trellis-based Solution For the Single-cell Scenariomentioning

confidence: 99%

IRS-aided Large-Scale MIMO Systems with Passive Constant Envelope Precoding

Omid,

Shahabi,

Pan

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Despite the historical context and related works (refer to the introduction section of [1] and [19]), our primary approach is relying on the deep unfolding of existing iterative algorithms by mainly interpreting every iteration as a set of layers. The deep unfolding of existing iterative algorithms is discussed in [27].…”

Section: Introductionmentioning

confidence: 99%

“…Recent contributions seem to advocate for the potentials of using DL for communication system design [1,[19][20][21]. Even if most signal processing algorithms have solid well-established roots in statistics and information theory for tractable mathematical models, it remains that a practical system has many impairments and non-linearities, which can be roughly captured by such models [22].…”

Section: Introductionmentioning

confidence: 99%

“…The deep unfolding of existing iterative algorithms is discussed in [27]. It has been recently applied in the context of MIMO detection and channel decoding in [19] and [28], respectively.…”

Section: Introductionmentioning

confidence: 99%

“…They have introduced a new way of addressing a communication system as an end-to-end reconstruction optimization task using autoencoders. On the other hand, two different deep architectures for point-to-point MIMO detection are introduced in [19] wherein the promising architecture relies on unfolding the iterations of a projected gradient descent algorithm into a network.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Early results on deep unfolded conjugate gradient‐based large‐scale MIMO detection

Ouameur

Massicotte

2021

IET Communications

View full text Add to dashboard Cite

Deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large‐scale multiple‐input multiple‐output detection. The proposed technique combines the advantages of a model‐driven approach in readily incorporating domain knowledge and deep learning in effective parameters learning. The parameters are trained via backpropagation over a data flow graph inspired from the iterative conjugate gradient method. We derive the closed‐form expressions for the gradients for parameters training and discuss early results on the performance in a statistically identical and independent distributed channel where the training overhead is considerably low. It is worth noting that the loss function is based on the residual error that is not an explicit function of the desired signal, which makes the proposed algorithm blind. As an initial framework, we will point to the inherent issues and future directions.

show abstract

Low-complexity and fast-convergence linear precoding based on modified SOR for massive MIMO systems

Yang

Cheng

et al. 2020

Digital Signal Processing

View full text Add to dashboard Cite

Constant envelope precoding in multi-cell massive MIMO systems with channel uncertainty

Cited by 11 publications

References 10 publications

IRS-aided Large-Scale MIMO Systems with Passive Constant Envelope Precoding

IRS-aided Large-Scale MIMO Systems with Passive Constant Envelope Precoding

Early results on deep unfolded conjugate gradient‐based large‐scale MIMO detection

Low-complexity and fast-convergence linear precoding based on modified SOR for massive MIMO systems

Contact Info

Product

Resources

About