Precoding and Power Optimization in Cell-Free Massive MIMO Systems

Ashikhmin²,

IEEE Internet Things J.

2020

Self Cite

In this paper, we propose a wirelessly powered Internet of Things (IoT) system based on the cell-free massive MIMO technology. In such a system, during the downlink phase, the sensors harvest radio-frequency (RF) energy emitted by the distributed access points (APs). During the uplink phase, sensors transmit data to the APs using the harvested energy. Collocated massive MIMO and small-cell IoT can be treated as special cases of cell-free IoT. We derive the tight closed-form lower bound on the amount of harvested energy, and the closedform expression of SINR as the metrics of power transfer and data transmission, respectively. To improve the energy efficiency, we jointly optimize the uplink and downlink power control coefficients to minimize the total transmit energy consumption while meeting the target SINRs. Extended simulation results show that cell-free IoT outperforms collocated massive MIMO and small-cell IoT both in terms of the per user throughput for uplink, and the amount of energy harvested for downlink. Moreover, significant gains can be achieved by the proposed joint power control in terms of both per user throughput and energy consumption.Index Terms-Cell-free massive MIMO, Internet-of-things, power control, wireless power transfer.

Section: Numerical Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Wirelessly Powered Cell-Free IoT: Analysis and Optimization

Ashikhmin²,

IEEE Internet Things J.

2020

Self Cite

2019 53rd Asilomar Conference on Signals, Systems, and Computers

“…The performance variations within the coverage area can consequently be greatly reduced as compared to cellular networks. The same effect cannot be achieved by densifying a cellular network [17,18], following the fact that the transmitted and received signals are coherently processed among the distributed antennas to achieve array gains and spatial interference suppression.…”

Section: Cell-free Network: Concept and Value For Radioweavesmentioning

confidence: 99%

“…Recent works have developed new tools to analyze cellfree massive MIMO performance under practical channel state information [17,18] and developed distributed signal processing algorithms and resource allocation protocols that are scalable for large-scale deployment [19]. While a central network entity is needed to control a cell-free network, it is desirable to distribute the computations as much as possible over the network infrastructure, to limit the required signaling between the antennas and from the antennas to the central entity.…”

Section: Cell-free Network: Concept and Value For Radioweavesmentioning

confidence: 99%

RadioWeaves for efficient connectivity: analysis and impact of constraints in actual deployments

Perre

Larsson

Tufvesson

et al. 2019

We present a new type of wireless access infrastructure consisting of a fabric of dispersed electronic circuits and antennas that collectively function as a massive, distributed antenna array. We have chosen to name this new wireless infrastructure "RadioWeaves" and anticipate they can be integrated into indoor and outdoor walls, furniture, and other objects, rendering them a natural part of the environment. Technologically, RadioWeaves will deploy distributed arrays to create both favorable propagation and antenna array interaction. The technology leverages on the ideas of large-scale intelligent surfaces and cell-free wireless access. Offering close to the service connectivity and computing, new grades in energy efficiency, reliability, and low latency can be reached. The new concept moreover can be scaled up easily to offer a very high capacity in specific areas demanding so. In this paper we anticipate how two different demanding use cases can be served well by a dedicated RadioWeaves deployment: a crowd scenario and a highly reflective factory environment. A practical approach towards a RadioWeaves prototype, integrating dispersed electronics invisibly in a room environment, is introduced. We outline the many and diverse R&D challenges that need to be addressed to realize the great potential of the RadioWeaves technology. Index Terms-RadioWaves, energy efficient and ultra reliable connectivity, large-scale antenna arrays, cell-free wireless access. L. Van der Perre and L. De Strycker are with the Department of Electrical Engineering, KU Leuven, Belgium; L. Van der Perre, F. Tufvesson, and O. Edfors are with the

“…However, inter-cell interference is inherent in all cell-centric systems and becomes a major performance limiting factor [7]. To overcome this, while preserving the main benefits of massive MIMO (m-MIMO), cell-free (CF) mMIMO has been proposed [8], [9]. The basic premise of it is that a large number of spatially-distributed APs serve many single-antenna users in the same time-frequency resources [8].…”

Section: Introductionmentioning

confidence: 99%

Rate Analysis of Cell-Free Massive MIMO-NOMA With Three Linear Precoders

Rezaei

Tellambura

Tadaion

et al. 2020

IEEE Trans. Commun.

Although the hybrid of cell-free (CF) massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) promises massive spectral efficiency gains, the type of precoders employed at the access points (APs) impacts the gains. In this paper, we thus comprehensively evaluate the system performance with maximum ratio transmission (MRT), fullpilot zero-forcing (fpZF) and modified regularized ZF (mRZF) precoders. We derive their closed-form sum rate expressions by considering Rayleigh fading channels, the effects of intra-cluster pilot contamination, inter-cluster interference, and imperfect successive interference cancellation (SIC). Our results reveal that this system supports significantly more users simultaneously at the same coherence interval compared to its OMA equivalent. However, intra-cluster pilot contamination and imperfect SIC degrade the system performance when the number of users is low. Moreover, with perfect SIC, mRZF and fpZF significantly outperform MRT. Also, we show that this system with either mRZF or fpZF precoding outperforms OMA systems with MRT. The analytical findings are verified by numerical results.Index Terms-NOMA, cell-free massive MIMO, MRT, fpZF, modified RZF, achievable sum rate.