Demystifying the Power Scaling Law of Intelligent Reflecting Surfaces and Metasurfaces

Intelligent reflecting surfaces (IRSs) have recently attracted the attention of communication theorists as a means to control the wireless propagation channel. It has been shown that the signal-to-noise ratio (SNR) of a single-user IRS-aided transmission increases as N2, with N being the number of passive reflecting elements in the IRS. This has been interpreted as a major potential advantage of using IRSs, instead of conventional Massive MIMO (mMIMO) whose SNR scales only linearly in N. This paper shows that this interpretation is incorrect. We first prove analytically that mMIMO always provides higher SNRs, and then show numerically that the gap is substantial; a very large number of reflecting elements is needed for an IRS to obtain SNRs comparable to mMIMO

Utility-Based Precoding Optimization Framework for Large Intelligent Surfaces

The spectral efficiency of wireless networks can be made nearly infinitely large by deploying many antennas, but the deployment of very many antennas requires new topologies beyond the compact and discrete antenna arrays used by conventional base stations. In this paper, we consider the large intelligent surface scenario where small antennas are deployed on a large and dense two-dimensional grid. Building on the heritage of MIMO, we first analyze the beamwidth and sidelobes when transmitting from large intelligent surfaces. We compare different precoding schemes and determine how to optimize the transmit power with respect to different utility functions

Making Cell-Free Massive MIMO Competitive with MMSE Processing and Centralized Implementation

Cell-free Massive MIMO is considered as a promising technology for satisfying the increasing number of users and high rate expectations in beyond-5G networks. The key idea is to let many distributed access points (APs) communicate with all users in the network, possibly by using joint coherent signal processing. The aim of this paper is to provide the first comprehensive analysis of this technology under different degrees of cooperation among the APs. Particularly, the uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary linear processing. It turns out that it is possible to outperform conventional Cellular Massive MIMO and small cell networks by a wide margin, but only using global or local minimum mean-square error (MMSE) combining. This is in sharp contrast to the existing literature, which advocates for maximum-ratio combining. Also, we show that a centralized implementation with optimal MMSE processing not only maximizes the SE but largely reduces the fronthaul signaling compared to the standard distributed approach. This makes it the preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding is also investigated and shown to bring negligible improvements

Centralized and Distributed Power Allocation for Max-Min Fairness in Cell-Free Massive MIMO

Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve users by coherent joint transmission. Downlink power allocation is important in these systems, to determine which APs should transmit to which users and with what power. If the system is implemented correctly, it can deliver a more uniform user performance than conventional cellular networks. To this end, previous works have shown how to perform system-wide max-min fairness power allocation when using maximum ratio precoding. In this paper, we first generalize this method to arbitrary precoding, and then train a neural network to perform approximately the same power allocation but with reduced computational complexity. Finally, we train one neural network per AP to mimic system-wide max-min fairness power allocation, but using only local information. By learning the structure of the local propagation environment, this method outperforms the state-of-the-art distributed power allocation method from the Cell-free Massive MIMO literature

Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination

Since the seminal paper by Marzetta from 2010, Massive MIMO has changed from being a theoretical concept with an infinite number of antennas to a practical technology. The key concepts are adopted into the 5G New Radio Standard and base stations (BSs) with M = 64 fully digital transceivers have been commercially deployed in sub-6GHz bands. The fast progress was enabled by many solid research contributions of which the vast majority assume spatially uncorrelated channels and signal processing schemes developed for single-cell operation. These assumptions make the performance analysis and optimization of Massive MIMO tractable but have three major caveats: 1) practical channels are spatially correlated; 2) large performance gains can be obtained by multicell processing, without BS cooperation; 3) the interference caused by pilot contamination creates a finite capacity limit, as M → ∞. There is a thin line of papers that avoided these caveats, but the results are easily missed. Hence, this tutorial article explains the importance of considering spatial channel correlation and using signal processing schemes designed for multicell networks. We present recent results on the fundamental limits of Massive MIMO, which are not determined by pilot contamination but the ability to acquire channel statistics. These results will guide the journey towards the next level of Massive MIMO, which we call "Massive MIMO 2.0"

Massive MIMO is a reality - What is next? Five promising research directions for antenna arrays

Massive MIMO (multiple-input multiple-output) is no longer a “wild” or “promising” concept for future cellular networks—in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies—once viewed prohibitively complicated and costly—is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO

Robust leakage-based distributed precoder for cooperative multicell systems

Coordinated multipoint (CoMP) from long term evolution (LTE)-advanced is a promising technique to enhance the system spectral efficiency. Among the CoMP techniques, joint transmission has high communication requirements, because of the data sharing phase through the backhaul network, and coordinated scheduling and beamforming reduces the backhaul requirements, since no data sharing is necessary. Most of the available CoMP techniques consider perfect channel knowledge at the transmitters. Nevertheless for practical systems this is unrealistic. Therefore in this study the authors address this limitation by proposing a robust precoder for a multicell-based systems, where each base station (BS) has only access to an imperfect local channel estimate. They consider both the case with and without data sharing. The proposed precoder is designed in a distributed manner at each BS by maximising the signal-to-leakage-and-noise ratio of all jointly processed users. By considering the channel estimation error in the design of the precoder, they are able to reduce considerably the impact of these errors in the system's performance. The results show that the proposed scheme has improved performance especially for the high signal-to-noise ratio regime, where the impact of the channel estimation error may be more pronounced

Habitual physical activity and cardiometabolic risk factors in adults with cerebral palsy

2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/3.0/).This article has been made available through the Brunel Open Access Publishing Fund.Adults with cerebral palsy (CP) are known to participate in reduced levels of total physical activity. There is no information available however, regarding levels of moderate-to-vigorous physical activity (MVPA) in this population. Reduced participation in MVPA is associated with several cardiometabolic risk factors. The purpose of this study was firstly to compare levels of sedentary, light, MVPA and total activity in adults with CP to adults without CP. Secondly, the objective was to investigate the association between physical activity components, sedentary behavior and cardiometabolic risk factors in adults with CP. Adults with CP (n = 41) age 18–62 yr (mean ± SD = 36.5 ± 12.5 yr), classified in Gross Motor Function Classification System level I (n = 13), II (n = 18) and III (n = 10) participated in this study. Physical activity was measured by accelerometry in adults with CP and in age- and sex-matched adults without CP over 7 days. Anthropometric indicators of obesity, blood pressure and several biomarkers of cardiometabolic disease were also measured in adults with CP. Adults with CP spent less time in light, moderate, vigorous and total activity, and more time in sedentary activity than adults without CP (p < 0.01 for all). Moderate physical activity was associated with waist-height ratio when adjusted for age and sex (β = −0.314, p < 0.05). When further adjustment was made for total activity, moderate activity was associated with waist-height ratio (β = −0.538, p < 0.05), waist circumference (β = −0.518, p < 0.05), systolic blood pressure (β = −0.592, p < 0.05) and diastolic blood pressure (β = −0.636, p < 0.05). Sedentary activity was not associated with any risk factor. The findings provide evidence that relatively young adults with CP participate in reduced levels of MVPA and spend increased time in sedentary behavior, potentially increasing their risk of developing cardiometabolic disease

Cosmological test of the Yilmaz theory of gravity

We test the Yilmaz theory of gravitation by working out the corresponding Friedmann-type equations generated by assuming the Friedmann-Robertson-Walker cosmological metrics. In the case that space is flat the theory is consistent only with either a completely empty universe or a negative energy vacuum that decays to produce a constant density of matter. In both cases the total energy remains zero at all times, and in the latter case the acceleration of the expansion is always negative. To obtain a more flexible and potentially more realistic cosmology, the equation of state relating the pressure and energy density of the matter creation process must be different from the vacuum, as for example is the case in the steady-state models of Gold, Bondi, Hoyle and others. The theory does not support the cosmological principle for curved space K =/= 0 cosmological metrics