How to make key 5G wireless technologies environmental friendly: A review

Massive multiple‐input multiple‐output is an attractive technology that aims to ameliorate both the energy and the spectrum efficiencies of fifth‐generation cellular networks. The main problem that limits the performance of a massive multiple‐input multiple‐output system is pilot contamination, which happens due to the limited number of orthogonal pilot sequences. In this paper, we assume that each cell is assigned a set of orthogonal pilot sequences and that these same pilot sequences are reused in other cells. Consequently, the contamination comes only from users in other cells, so that it is less powerful than an active user in the cell of interest. We propose a novel iterative algorithm to ensure the mitigation of the pilot contamination effect. This algorithm, dubbed as the “channel zooming algorithm”, is able to zoom through iterations on the signal of the strongest user by taking advantage of the difference in space signatures and transmitted data sequences. Moreover, we derive an analytical expression of the bit error probability, resulting after a large number of iterations of the proposed algorithm. The analytical results developed in our work are confirmed by simulation results.

Section: Iterative Steps Of the Czamentioning

confidence: 99%

Section: Iterative Steps Of the Czamentioning

confidence: 99%

Section: Iterative Steps Of the Czamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Channel zooming for the mitigation of pilot contamination in massive MIMO

Boudaya

Kammoun

Siala

2018

A Seysen's algorithm–based incremental lattice reduction

Schmeink

2019

Lattice reduction (LR)–aided detections have attracted great attention in the symbol detection of multiple‐input–multiple‐output communication, especially for systems with larger scale antennas. Recently, the Lenstra‐Lenstra‐Lovász (LLL)–based incremental LR (ILR) algorithms have been proposed to employ an early termination to jointly conduct the LR and retrieve the symbol based on a partial successive interference cancellation (SIC) detection, which induces a significant performance improvement. In this paper, we propose to apply the Seysen's algorithm (SA) to the ILR. After providing the feasibility analysis, two novel SA involved algorithms are introduced and studied. In particular, a new sorted QR decomposition‐based SA algorithm is proposed and tested as it plays a key role in the SIC detection. Subsequently, a novel SA‐based ILR scheme is further developed and investigated. By simulations, we show that the proposed approach outperforms conventional LLL‐based ILR in various aspects, while only a marginal increment in the complexity, restricted to medium signal‐to‐noise‐ratio regions, is observed.

Structured analysis/synthesis compressive sensing‐based channel estimation in wideband mmWave large‐scale multiple input multiple output systems

Mejri

Hajjaj

Hasnaoui

2020

The hybrid architecture still be the most attractive solution for the analog‐digital beamforming in millimeter wave (mmWave) large‐scale multiple input multiple output (LS‐MIMO) systems, since it provides the best trade‐off between the achievable rate and the power consumption. When maximizing performance metrics, channel knowledge is required to optimally design the hybrid beamforming. The channel estimation (CE) is essential for wideband mmWave LS‐MIMO to substantially enhance data rates, however, is a challenging task. Practically, the antenna number and the bandwidth for such communications are very large. Therefore, the CE scheme should account for the frequency‐dependence of the array response vectors and thereby the beam squint effect. In the first part, this article addresses a structured analysis compressive sensing (CS) for CE in a frequency selective mmWave LS‐MIMO system without neglecting the beam squint effect. Compared to sparse mmWave CE approach that mostly uses on‐grid dictionaries, our proposed approach considers continually distributed angles of arrival and departure. In the second part, we show that the frequency‐selective mmWave MIMO channels share a common sparsity structure within the same subcarrier‐group. Then, we propose a block‐sparsity‐based CS approach that uses the simultaneous orthogonal matching pursuit method to estimate the mmWave channel. Moreover, we prove the partial sparsity structure shared between all the subcarrier‐groups. Hence, we develop our proposed joint subcarrier‐block‐based CE scheme exploiting these two block‐sparsity structures. Numerical results evaluate the performances in terms of normalized mean‐squared error and spectral efficiency to select the most accurate channel estimator with lower overhead.