Uplink Performance of Multi-Antenna Cellular Networks With Co-Operative Base Stations and User-Centric Clustering

Govindasamy, Siddhartan; Bergel, Itsik

doi:10.1109/twc.2018.2801290

Cited by 19 publications

(24 citation statements)

References 47 publications

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“…We consider the WSN-edge offloading scenario in Figure 2 and assume that the urban area is divided into hexagonal cells, as typical in mobile cellular networks [16]. The sensors are connected through ad-hoc wireless networks rather than wired installations to simplify the wide-area deployment as shown in Figure 1.…”

Section: Ares Overviewmentioning

confidence: 99%

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ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

Aral

Demaio

Brandić

2022

IEEE Trans. Sustain. Comput.

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Section: Ares Overviewmentioning

confidence: 99%

“…The area is divided into hexagonal cells, which identify clusters of nodes managed by a single node. This is typical in mobile cellular networks [16]. The map M m,n is defined as a m × n, m, n ∈ N hexagonal grid.…”

Section: Map Modelmentioning

confidence: 99%

ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

Aral

Demaio

Brandić

2022

IEEE Trans. Sustain. Comput.

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“…where the term τ u /τ c accounts for the fact that only τ u out of τ c channel uses are employed for UL transmission. The unquantized signal vector at the CPU corresponding to the signal received by the mth AP during the training phase is given by (11). As this vector contains τ p samples, the required average rate (in bit/s) to transfer the quantized vector y pm = Q pm y pm on the mth UL fronthaul link is given bŷ…”

Section: Volume 4 2016mentioning

confidence: 99%

“…To the best of authors' knowledge, the first distributed massive MIMO architecture was proposed in [10] where the uplink segment was investigated when relying on maximum-ratio combining (MRC) in combination with a BS selection procedure that effectively limited the number of remote radio heads (RRHs) involved in the detection of the signal transmitted by a particular mobile station (MS). A similar setup was addressed in [11] but using minimum mean square error (MMSE) detection, VOLUME 4, 2016 which provides an upper bound on the performance linear detectors can offer in practical setups (i.e., finite number of RRHs). Conceptually similar to the C-RAN and distributed massive MIMO architectures, but explicitly renouncing to the cellular network philosophy, an alternative distributed massive MIMO-based infrastructure has been recently termed as cell-free massive MIMO [4], [12].…”

Section: Introductionmentioning

confidence: 99%

Fronthaul-Constrained Cell-Free Massive MIMO With Low Resolution ADCs

Femenias

Riera-Palou

2020

IEEE Access

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In cell-free massive MIMO networks, a large number of distributed access points (APs) provide service to a much smaller number of mobile stations (MSs) over the same time/frequency resources. The key idea is to use a central processing unit (CPU) to manage such a densely populated network of APs. This centralization helps reducing operational costs and eases implementation of joint power control and coherent signal processing through a proper orchestration of the functional split between the CPU and the APs. Cell-free massive MIMO networks, however, are often subject to stringent capacity requirements on the fronthaul links connecting the APs to the CPU and thus, low-resolution ADCs must be used to quantize the signals shared among CPU and APs. In this paper, analytical closed-form expressions for the achievable user rates on both the uplink (UL) and downlink (DL) of a fronthaul-capacity constrained cell-free massive MIMO network using low-resolution ADCs are obtained. These expressions, jointly with the use of theoretical models characterizing the fronthaul capacity consumption of different CPU-AP functional splits, allow posing max-min fairness power control optimization problems that can be solved using standard convex optimization algorithms. Numerical results show that, under fronthaul capacity constraints, CPU-AP functional splits where the precoding/decoding schemes are implemented at the APs are clearly outperformed by those functional splits in which, thanks to sharing CSI among APs and CPU, the precoding/decoding functions are implemented at the CPU. In contrast, if the limiting factor is the resolution of the ADCs used to quantize the samples to be transmitted on the fronthaul links, the preferred CPU-AP functional splits are those in which the baseband processing is performed at the APs. Moreover, they also reveal that in such functional splits there is always an optimal range of values of the UL fronthaul capacity fraction allocated to share the CSI. INDEX TERMS Cell-free massive MIMO, capacity-constrained fronthaul, normalized conjugate beamforming, matched filtering, CPU-AP functional split, low-resolution ADCs.

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“…MIMO communication is a mature technology with a wellestablished theory and practical algorithms (e.g., [5], [6]) which are incorporated into 5G [7] and other protocols [8].…”

Section: Introductionmentioning

confidence: 99%

Downlink Cooperative MIMO in LEO Satellites

et al. 2020

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We consider a communication scheme in which two low earth orbit (LEO) satellites jointly transmit (at the same time and frequency) to a multi antenna land terminal (LT). This scheme can increase the achievable terminal throughput by up to a factor of 2, depending on the channel matrices. The implementation aspects of this scheme are well-known but the usefulness of this scheme for LEO satellites has yet to be studied. Because the satellite channel is dominated by its line-of-sight (LOS) component, the terminal's ability to separate the two streams depends critically on the system's instantaneous-configuration; i.e., the relative location of the satellites and the terminal antennas. Since the relative locations of LEO satellites vary rapidly, the throughput characterization is not straightforward. To characterize the network performance we consider two satellites having one antenna, transmitting to a single LT having multiple antennas. We introduce a novel stochastic framework that assumes the terminal orientation as random. Using the proposed framework, we show that if the terminal antennas are close, the network throughput is nearly independent of the terminal orientation. When the terminal antennas are sufficiently separated, the result is completely different. For this case, we define an outage event and calculate the outage probability. The definition of outage in our novel stochastic framework allows us to prove that dual satellite transmission can indeed increase the downlink throughput with high probability. INDEX TERMS multiple input multiple output (MIMO), Satellite communication, LEO satellites, Cooperative transmission.

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Uplink Performance of Multi-Antenna Cellular Networks With Co-Operative Base Stations and User-Centric Clustering

Cited by 19 publications

References 47 publications

ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor Networks

Fronthaul-Constrained Cell-Free Massive MIMO With Low Resolution ADCs

Downlink Cooperative MIMO in LEO Satellites

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