Showing papers by "Thomas L. Marzetta published in 2018"
TL;DR: This work devise a new concept of large-scale fading precoding (LSFP) that leads to the effective elimination of inter-cell interference and derives a capacity lower bound for massive MIMO systems with LSFP and a finite number of base station antennas.
Abstract: A wireless massive multiple-input multiple-output (MIMO) system entails a large number of base station antennas serving a much smaller number of users, with large gains in spectral efficiency and energy efficiency compared with the conventional MIMO technology Until recently, it was believed that as the number of base station antennas tends to infinity, the performance of such systems is limited by directed inter-cellular interference caused by unavoidable re-use of training sequences (pilot contamination) by users in different cells We devise a new concept of large-scale fading precoding (LSFP) that leads to the effective elimination of inter-cell interference The main idea of LSFP is that base stations linearly combine messages aimed at users from different cells that re-use the same training sequence Crucially, the combining coefficients depend only on the large-scale fading coefficients between the users and the base stations These coefficients change slowly and their number does not depend on the number of base station antennas Thus, the traffic between base stations stays constant even if the number of antennas tends to infinity Furthermore, we derive a capacity lower bound for massive MIMO systems with LSFP and a finite number of base station antennas In this regime, mitigation of all types of interference, not only the pilot contamination, is required We consider optimal and suboptimal LSFP precodings that take into account all sources of interference Our simulations results show that LSFP provides significant gain even for the case of moderate number of base station antennas
76 citations
03 Jun 2018
TL;DR: It is demonstrated that in suburban and rural scenarios, cell-free Massive MIMO systems can more than double the radiated energy efficiency and at the same time dramatically increase the 95% likely per user throughput, while in an urban scenario, the gain in radiatedEnergy efficiency can be moderate at less than 50% with a comparable 95%.
Abstract: Cell-free Massive MIMO (Multiple Input Multiple Output) employs a large number of AP's (Access Points) that are distributed throughout the intended coverage area to simultaneously serve a much smaller number of user AT's (Access Terminals). Conjugate beamforming is the simplest precoding method for the downlink transmission, and allows decentralized precoding processing. Max-min power control maximizes the minimal effective SINR (Signal-to-Interference-plus-Noise Ratio) among all the active users, therefore provides a uniform throughput to all users. Cell- free Massive MIMO with conjugate beamforming precoding and max-min power control is naturally radiated energy efficient due to two facts: a well-known fact that with high probability at least one AP is nearby every user, and a surprising fact that, with max-min power control, a large portion of the AP's does not transmit with full power. We find that a cell-free Massive MIMO can deliver more than 80 Mb/joule in urban, and more than 40 Mb/joule in suburban and rural scenarios. We compare its energy efficiency and spectral efficiency with single cell Massive MIMO systems and demonstrate that in suburban and rural scenarios, cell-free systems can more than double the radiated energy efficiency and at the same time dramatically increase the 95% likely per user throughput, while in an urban scenario, the gain in radiated energy efficiency can be moderate at less than 50% with a comparable 95% likely per user throughput.
75 citations
TL;DR: The max–min fairness (MMF) problem is studied, where channel state information at the transmitter is used to design precoding vectors that maximize the minimum spectral efficiency (SE) of the system, given fixed power budgets for uplink training and downlink transmission.
Abstract: This paper considers the downlink precoding for physical layer multicasting in massive multiple-input multiple-output (MIMO) systems. We study the max–min fairness (MMF) problem, where channel state information at the transmitter is used to design precoding vectors that maximize the minimum spectral efficiency (SE) of the system, given fixed power budgets for uplink training and downlink transmission. Our system model accounts for channel estimation, pilot contamination, arbitrary path-losses, and multi-group multicasting. We consider six scenarios with different transmission technologies (unicast and multicast), different pilot assignment strategies (dedicated or shared pilot assignments), and different precoding schemes (maximum ratio transmission and zero forcing), and derive achievable spectral efficiencies for all possible combinations. Then, we solve the MMF problem for each of these scenarios, and for any given pilot length, we find the SE maximizing uplink pilot and downlink data transmission policies, all in closed forms. We use these results to draw a general guideline for massive MIMO multicasting design, where for a given number of base station antennas, number of users, and coherence interval length, we determine the multicasting scheme that shall be used.
64 citations
17 Jun 2018
TL;DR: A Fourier planewave representation that provides a computationally efficient way to generate samples of the random field, second, an inverse representation that enables the efficient computation of the joint likelihood of noisy measurements of the field over continuous segments of lines, planes, and volumes.
Abstract: The spatially uncorrelated Rayleigh small-scale fading model is a useful stochastic tool for analyzing multiple-antenna wireless communication systems, and, as experiments have shown, it often is a good approximation to physical propagation. However, the assumption that the propagating field is uncorrelated from one point in space to another breaks down when, for example, antenna spacings are smaller than one-half wavelength - a model defect typically addressed by assuming some spatial correlation. Spatial correlation can have huge effects even in the absence of close spacing between antennas. While an ad-hoc correlation versus distance, such as exponential, may add an element of realism to the model, in general it does not capture the peculiar “action at a distance” phenomena associated with the wave equation. The very desirable property of spatial stationarity can be retained, provided the spatial autocorrelation is chosen such that the complex Gaussian small-scale fading random field satisfies the homogeneous wave equation. The fading model that is closest to iid Rayleigh fading, and that is still consistent with the wave equation, has an autocorrelation equal to sinc(2πR/λ, corresponding to planewaves arriving uniformly from all directions, and having independent, equal variance complex Gaussian amplitudes. The contribution of this paper is twofold: first, a Fourier planewave representation that provides a computationally efficient way to generate samples of the random field, second, an inverse representation that enables the efficient computation of the joint likelihood of noisy measurements of the field over continuous segments of lines, planes, and volumes.
33 citations
TL;DR: This work quantifies the number of mmWave service antennas that are needed to duplicate the performance of a specified number of PCS (1.9 GHz) service antennas and considers a modest PCS deployment of 128 antennas serving 18 terminals.
Abstract: If we assume line-of-sight propagation and perfect channel state information at the base station -- consistent with slow moving terminals -- then a direct performance comparison between Massive MIMO at PCS and mmWave frequency bands is straightforward and highly illuminating. Line-of-sight propagation is considered favorable for mmWave because of minimal attenuation and its facilitation of hybrid beamforming to reduce the required number of active transceivers. We quantify the number of mmWave (60 GHz) service antennas that are needed to duplicate the performance of a specified number of PCS (1.9 GHz) service antennas. As a baseline we consider a modest PCS deployment of 128 antennas serving 18 terminals. At one extreme, we find that, to achieve the same per-terminal maxmin 95 percent-likely downlink throughput in a single-cell system, 20,000 mmWave antennas are needed. To match the total antenna area of the PCS array would require 128,000 half-wavelength mmWave antennas, but a much reduced number is adequate because the large number of antennas also confers greater channel orthogonality. At the other extreme, in a highly interference-limited multi-cell environment, only 215 mmWave antennas are needed; in this case, increasing the transmitted power yields little improvement in service quality.
33 citations
TL;DR: It is proved that the Pareto region is convex, and hence, the system should serve the unicast and multicast UTs at the same time–frequency resource, and the results are validated using numerical simulations.
Abstract: We study the joint unicast and multi-group multicast transmission in massive multiple-input multiple-output systems. We consider a system model that accounts for channel estimation and pilot contamination and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs) under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto optimal point describes a particular efficient tradeoff between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, and so on) to achieve any desired Pareto optimal point. Moreover, we prove that the Pareto region is convex, and hence, the system should serve the unicast and multicast UTs at the same time–frequency resource. Finally, we validate our results using numerical simulations.
20 citations
01 Oct 2018
TL;DR: The downlink performance of the system for zero-forcing precoding and propose centralized and decentralized power control schemes are analyzed and results reveal that sectorization, and multi-point coordinated sectorization lead to 2.36× and 8.56× improvements in the 95%-likely per-user throughput, respectively.
Abstract: Non-cooperative cellular Massive MIMO, combined with max-min power control, is known to give substantial improvements in per-user throughput compared with conventional 4G technology. We investigate further refinements to Massive MIMO, first, in the form of three-fold sectorization, which can be viewed as an effective reduction in cell size, and second, three-fold sectorization combined with coordinated multi-point operation, in which the three sectors cooperate in the joint service of their users. We analyze the downlink performance of the system for zero-forcing precoding and propose centralized and decentralized power control schemes. The simulation results reveal that sectorization, and multi-point coordinated sectorization lead to 2.36× and 8.56× improvements in the 95%-likely per-user throughput, respectively.
14 citations
15 Apr 2018
TL;DR: It is proved that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource, and derive thePareto boundary of the MOOP analytically and determine the values of the system parameters to achieve any desired Pare to optimal point.
Abstract: We study joint unicast and multigroup multicast transmission in single-cell massive multiple-input-multiple-output (MIMO) systems, under maximum ratio transmission. For the unicast transmission, the objective is to maximize the weighted sum spectral efficiency (SE) of the unicast user terminals (UTs) and for the multicast transmission the objective is to maximize the minimum SE of the multicast UTs. These two problems are coupled to each other in a conflicting manner, due to their shared power resource and interference. To address this, we formulate a multiobjective optimization problem (MOOP). We derive the Pareto boundary of the MOOP analytically and determine the values of the system parameters to achieve any desired Pareto optimal point. Moreover, we prove that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource.
2 citations