Extremely Large Aperture Massive MIMO: Low Complexity Receiver Architectures

Amiri, Abolfazl; Angjelichinoski, Marko; Carvalho, Elisabeth de; Heath, Robert W.

doi:10.1109/glocomw.2018.8644126

Cited by 110 publications

(105 citation statements)

References 14 publications

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“…Moreover, for extremely large MIMO system [39]- [41], the wideband effect or beam squint must be considered, which was unfortunately still ignored in their discussion. 4…”

Section: E Summary Of Wideband-narrowband Channel Modelingmentioning

confidence: 99%

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Wang

Gao

Jin

et al. 2019

IEEE J. Sel. Top. Signal Process.

132

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In the past ten years, there have been tremendous research progresses on massive MIMO systems, most of which stand from the communications viewpoint. A new trend of investigating massive MIMO, especially for the sparse scenario like millimeter wave (mmWave) transmission, is to re-build the transceiver design from array signal processing viewpoint that could deeply exploit the half-wavelength array and provide enhanced performances in many aspects. For example, the high dimensional channel could be decomposed into small amount of physical parameters, e.g., angle of arrival (AoA), angle of departure (AoD), multi-path delay, Doppler shift, etc. As a consequence, transceiver techniques like synchronization, channel estimation, beamforming, precoding, multi-user access, etc., can be re-shaped with these physical parameters, as opposed to those designed directly with channel state information (CSI). Interestingly, parameters like AoA/AoD and multi-path delay are frequency insensitive and thus can be used to guide the downlink transmission from uplink training even for FDD systems. Moreover, some phenomena of massive MIMO that were vaguely revealed previously can be better explained now with array signal processing, e.g., the beam squint effect. In all, the target of this paper is to present an overview of recent progress on merging array signal processing into massive MIMO communications as well as its promising future directions.Index Terms-massive MIMO, array signal processing, mmWave transmission, angle-delay-Doppler reciprocity, beam squint.

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“…Moreover, for extremely large MIMO system [39]- [41], the wideband effect or beam squint must be considered, which was unfortunately still ignored in their discussion. 4…”

Section: E Summary Of Wideband-narrowband Channel Modelingmentioning

confidence: 99%

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Wang

Gao

Jin

et al. 2019

IEEE J. Sel. Top. Signal Process.

132

View full text Add to dashboard Cite

show abstract

“…(16) clearly shows the interplay between the wireless transmission part and the computational part via the transmission and computational latency. Furthermore, a coupling between the transmission and fronthaul latency through the number of quantization bits can also be observed from (16). For example, an increase in the quantization bits decreases the quantization error which reduces the transmission latency, while on the other hand, it increases the required number of bits transmitted to the BBU, thereby increasing the fronthaul latency.…”

Section: Computation Offloading and Latency Modelmentioning

confidence: 99%

“…4, we illustrate the obtained communication (transmit power p k , ∀k, first sub-figure) and computational (normalized number of CPU cycles f k F T , ∀k, second sub-figure) resources assigned to each IoTD with respect to the corresponding effective channel gains at the BBU, i.e., h e k = 1 T K (V D H I) , ∀k (third sub- figure), the number of computation bits (b k , ∀k, fourth sub-figure) and the latency thresholds (T th k , ∀k, fifth sub- figure). In the fifth sub-figure, we also plot the overall latency ξ k , ∀k computed using (16).…”

Section: A Performance Per Iotds' Distributionmentioning

confidence: 99%

Computation Offloading for IoT in C-RAN: Optimization and Deep Learning

Pradhan

She

et al. 2020

IEEE Trans. Commun.

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We consider computation offloading for Internet-of-things (IoT) applications in multiple-inputmultiple-output (MIMO) cloud-radio-access-network (C-RAN). Due to the limited battery life and computational capability in the IoT devices (IoTDs), the computational tasks of the IoTDs are offloaded to a MIMO C-RAN, where a MIMO radio resource head (RRH) is connected to a baseband unit (BBU) through a capacity-limited fronthaul link, facilitated by the spatial filtering and uniform scalar quantization. We formulate a computation offloading optimization problem to minimize the total transmit power of the IoTDs while satisfying the latency requirement of the computational tasks, and find that the problem is non-convex. To obtain a feasible solution, firstly the spatial filtering matrix is locally optimized at the MIMO RRH. Subsequently, we leverage the alternating optimization framework for joint optimization on the residual variables at the BBU, where the baseband combiner is obtained in a closed-form, the resource allocation sub-problem is solved through successive inner convexification, and the number of quantization bits is obtained by a line-search method. As a low-complexity approach, we deploy a supervised deep learning method, which is trained with the solutions to our optimization algorithm. Numerical results validate the effectiveness of the proposed algorithm and the deep learning method.

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“…The term 'nonstationary' means that the different parts of the array may observe the same channel paths with varying power or distinct channel paths [11], [12]. Specifically, the received energy from each user to different portions of the array varies, and large received energy from a specific user can be observed on a small part of the array only, which introduces an inherent sparsity in the channel matrix [13].…”

Section: Introductionmentioning

confidence: 99%

Expectation Propagation Detector for Extra-Large Scale Massive MIMO

Wang¹,

Kosasih²,

Wen³

et al. 2020

IEEE Trans. Wireless Commun.

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The deployment of extremely large-scale antenna array at the base station, which is referred to as extra-large-scale massive multiple-input-multiple-output (MIMO) system, is a brand new communication paradigm allowing for significant performance gain at the cost of excessively large amount of fronthaul data and ultra-high computational complexity. These practical challenges inspire the subarray-based processing architecture. Moreover, the spatial no-stationarity occurs and different portions of array observe different channel properties. Therefore, the aforementioned aspects should be considered into detector design for extra-large-scale massive MIMO systems. In this paper, the iterative detector is developed leveraging the expectation propagation (EP) principle based on the factor graph describing the subarray-based architecture. Its convergence is intuitively verified from the evolution analysis. We also demonstrate that different sizes and numbers of subarrays yield similar performance. In addition, we consider various implementation related aspects. We show that the non-stationary property can be exploited to reduce complexity. To avoid excessively high-complexity matrix inversion, we propose a hierarchical implementation architecture to enable parallel computation of simple-form matrix inversion.Moreover, we modify the proposed EP-based detector which requires only one feedforward from each subarray to the central processing unit to minimize the transfer latency. Simulation results demonstrate that the proposed detector outperforms its counterparts and verify the validity of our analyses.

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Extremely Large Aperture Massive MIMO: Low Complexity Receiver Architectures

Cited by 110 publications

References 14 publications

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Computation Offloading for IoT in C-RAN: Optimization and Deep Learning

Expectation Propagation Detector for Extra-Large Scale Massive MIMO

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