Transceiver Design for Energy-Efficiency Maximization in mmWave MIMO IoT Networks

Kolawole, Oluwatayo Y.; Biswas, Sudip; Singh, Keshav; Ratnarajah, Tharmalingam

doi:10.1109/tgcn.2019.2943956

Cited by 27 publications

(29 citation statements)

References 45 publications

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“…The BS operates in time-division duplexing mode, such that the uplink and downlink channels are reciprocal. The downlink physical channel can thus be estimated through the uplink training from the UEs via a separate control channel 3 .…”

Section: A System Modelmentioning

confidence: 99%

“…The main challenge in this case is that mmWave transceivers usually employ digital or hybrid beamforming, with multiple RF chains and a large number of antenna arrays that allow focusing electromagnetic energy into certain angles (i.e., irradiate beams), in order to combat mmWave's aquaphobia and high attenuation. This strategy is however doomed for energy-constrained IoT devices, as integrating multiple active components draining energy becomes infeasible [3]. Faced with such a challenge, RISs may hold the key to properly exploiting the use of mmWave with its vast bandwidth resources while enabling advanced massive IoT scenarios with a significantly-low service disruption probability [4].…”

Section: Introductionmentioning

confidence: 99%

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RISMA: Reconfigurable Intelligent Surfaces Enabling Beamforming for IoT Massive Access

Mursia

Sciancalepore²,

García‐Saavedra³

et al. 2021

IEEE J. Select. Areas Commun.

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Massive access for Internet-of-Things (IoT) in beyond 5G networks represents a daunting challenge for conventional bandwidth-limited technologies. Millimeter-wave technologies (mmWave)-which provide large chunks of bandwidth at the cost of more complex wireless processors in harsher radio environments-is a promising alternative to accommodate massive IoT but its cost and power requirements are an obstacle for wide adoption in practice. In this context, meta-materials arise as a key innovation enabler to address this challenge by Re-configurable Intelligent Surfaces (RISs).In this paper we take on the challenge and study a beyond 5G scenario consisting of a multi-antenna base station (BS) serving a large set of single-antenna user equipments (UEs) with the aid of RISs to cope with non-line-of-sight paths. Specifically, we build a mathematical framework to jointly optimize the precoding strategy of the BS and the RIS parameters in order to minimize the system sum mean squared error (SMSE). This novel approach reveals convenient properties used to design two algorithms, RISMA and Lo-RISMA, which are able to either find simple and efficient solutions to our problem (the former) or accommodate practical constraints with low-resolution RISs (the latter). Numerical results show that our algorithms outperform conventional benchmarks that do not employ RIS (even with low-resolution meta-surfaces) with gains that span from 20% to 120% in sum rate performance.

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Section: A System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RISMA: Reconfigurable Intelligent Surfaces Enabling Beamforming for IoT Massive Access

Mursia

Sciancalepore²,

García‐Saavedra³

et al. 2021

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

show abstract

“…In recent years, some works have been proposed for narrowband hybrid analog-digital systems [15][16][17][18][19][20][21][22]. In [15], the spatially scattering structure of mmWave channels was explored to formulate the hybrid RF/baseband beamforming scheme as a sparse reconstruction signal recovery problem.…”

Section: Previous Work On Hybrid Architecturesmentioning

confidence: 99%

“…The authors of [21] proposed a low-complexity hybrid beamforming scheme, considering an alternating MMSE algorithm on the basis of the traditional MMSE algorithm to minimize the MSE of the transmitted and received signals, using the orthogonality of the digital matrix and the idea of an iterative update. In order to maximize the energy efficiency of narrowband mmWave MIMO interference channels involving internet of things (IoT) devices, the authors in [22] designed a low-complexity two-stage hybrid transceiver, considering both perfect and imperfect channel state information (CSI).…”

Section: Previous Work On Hybrid Architecturesmentioning

confidence: 99%

Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems

et al. 2020

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Although millimeter-wave (mmWave) and massive multiple input multiple output (mMIMO) can be considered as promising technologies for future mobile communications (beyond 5G or 6G), some hardware limitations limit their applicability. The hybrid analog-digital architecture has been introduced as a possible solution to avoid such issues. In this paper, we propose a two-step hybrid multi-user (MU) equalizer combined with low complexity hybrid precoder for wideband mmWave mMIMO systems, as well as a semi-analytical approach to evaluate its performance. The new digital non-orthogonal multi carrier modulation scheme generalized frequency division multiplexing (GFDM) is considered owing to its efficient performance in terms of achieving higher spectral efficiency, better control of out-of-band (OOB) emissions, and lower peak to average power ratio (PAPR) when compared with the orthogonal frequency division multiplexing (OFDM) access technique. First, a low complexity analog precoder is applied on the transmitter side. Then, at the base station (BS), the analog coefficients of the hybrid equalizer are obtained by minimizing the mean square error (MSE) between the hybrid approach and the full digital counterpart. For the digital part, zero-forcing (ZF) is used to cancel the MU interference not mitigated by the analog part. The performance results show that the performance gap of the proposed hybrid scheme to the full digital counterpart reduces as the number of radio frequency (RF) chains increases. Moreover, the theoretical curves almost overlap with the simulated ones, which show that the semi-analytical approach is quite accurate.

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“…Recently, the transceiver design aiming at energy efficiency maximization is also investigated. Authors in [ 36 ] have proposed a two stage hybrid transceiver design to maximize the energy efficiency involving multiple IoT devices. Pre-coding for fully connected sub arrays is considered in [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithm Based Capacity Maximization of 5G/B5G Hybrid Pre-Coding Systems

Khalid

Abbas

Kim

et al. 2020

Sensors

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Hybrid pre-coding strategies are considered as a potential solution for combating path loss experienced by Massive MIMO systems operating at millimeter wave frequencies. The partially connected structure is preferred over the fully connected structure due to smaller computational complexity. In order to improve the spectral efficiency of a partially connected hybrid pre-coding architecture, which is one of the requirements of future 5G/B5G systems, this work proposes the application of evolutionary algorithms for joint computation of RF and the digital pre-coder. The evolutionary algorithm based scheme jointly evaluates the RF and digital pre-coder for a partially connected hybrid structure by taking into account the current RF chain for computations and therefore it is not based on interference cancellation from all other RF chains as in the case of successive interference cancellation (SIC). The evolutionary algorithm, i.e., Artificial Bee Colony (BEE) based pre-coding scheme outperforms other popular evolutionary algorithms as well as the SIC based pre-coding scheme in terms of spectral efficiency. In addition, the proposed algorithm is not overly sensitive to variations in channel conditions.

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Transceiver Design for Energy-Efficiency Maximization in mmWave MIMO IoT Networks

Cited by 27 publications

References 45 publications

RISMA: Reconfigurable Intelligent Surfaces Enabling Beamforming for IoT Massive Access

RISMA: Reconfigurable Intelligent Surfaces Enabling Beamforming for IoT Massive Access

Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems

Evolutionary Algorithm Based Capacity Maximization of 5G/B5G Hybrid Pre-Coding Systems

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