Experimental comparison of antenna clustering strategies in MIMO distributed antenna systems

In this paper the effect of partitioning arrays of transmitting antennas into spatially separated clusters on the condition number and capacity of MIMO wireless systems is examined using experimental channel measurements of an indoor MIMO-enabled distributed antenna system (DAS). It is first shown for a 3 _ 2 MIMO system that distributing the transmit antennas into spatially separated clusters provides an improvement in channel conditioning (_1dB) and hence capacity, in line with previous findings. Next, a configuration with 6 transmit antennas and 2 receive antennas is examined and it is found that when it is operated as a 6 _ 2 MIMO system, it makes negligible difference to the conditioning of the channel whether the transmit antennas are grouped into 3 clusters of 2 antennas or 2 clusters of 3 antennas. The capacity varies by only a small amount (_%1) between the two clustering schemes, which can be accounted for by environment-specific signal-tonoise ratio (SNR) effects. It is then concluded that for the two typical indoor DAS scenarios tested here, if sufficient transmit diversity is provided (i.e. there are a relatively large number of transmit antennas compared to receive antennas), greater spatial distribution through increased clustering provides diminishing performance improvements. Given the typically lower installation cost of less distributed arrangements, they may then be a preferred option in commercial DAS deployments.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/VTCFall.2014.696597

Performance Evaluation of Massive MIMO with Beamforming and Non Orthogonal Multiple Access based on Practical Channel Measurements

International audienceThis paper presents a comprehensive performance analysis of a massive multiple-input multiple-output (MIMO) system using non-orthogonal multiple access (NOMA) in both indoor and outdoor environments, based on practical channel measurements. The latter are performed using frequency-domain channel sounding experiments conducted at 3.5 GHz with 18 MHz bandwidth. Multiuser beamforming and NOMA clustering are used in the massive MIMO system. The system performance is evaluated in terms of sum-rate capacity for two precoding schemes: zero-forcing (ZF) and maximum ratio transmission (MRT). Two inter-beam power allocation (PA) schemes are investigated: equal PA and water filling. Fractional transmit PA (FTPA) is used to perform intra-cluster PA between paired users. The study allows the identification of practical scenarios that are propitious to NOMA with beamforming. Results show that NOMA is particularly interesting with MRT, compared to ZF, especially when combined with water filling. However, ZF generally outperforms MRT for all system configurations

Analysis and Design of Cell-Free Massive MIMO Systems under Spatially Correlated Fading Channels

Mención Internacional en el título de doctorWireless communications have become a key pillar in our modern society. It can be hard to think of a service that somehow does not rely on them. Particularly, mobile networks are one of the most necessary technologies in our daily life. This produces that the demand for data rates is by no means stopping from increasing. The cellular architecture is facing a crucial challenge under limited performance by interference and spectrum saturation. This involves cell-edge users experiencing poor performance due to the close vicinity of base stations (BSs) using the same carrier frequency. Based on a combination of the coordinated multi-point (CoMP) technique and traditional massive multiple-input multiple-output (MIMO) systems, cell-free (CF) massive MIMO networks have irrupted as a solution for avoiding inter-cell interference issues and for providing uniform service in large coverage areas. This thesis focuses on the analysis and design of CF massive MIMO networks assuming a spatially correlated fading model. A general-purpose channel model is provided and the whole network functioning is given in detail. Despite the many characteristics a CF massive MIMO system shares with conventional colocated massive MIMO its distributed nature brings along new issues that need to be carefully accounted for. In particular, the so-called channel hardening effect that postulates that the variance of the compound wireless channel experienced by a given user from a large number of transmit antennas tends to vanish, effectively making the channel deterministic. This critical assumption, which permeates most theoretical results of massive MIMO, has been well investigated and validated in centralized architectures, however, it has received little attention in the context of CF massive MIMO networks. Hardening in CF architectures is potentially compromised by the different large-scale gains each access point (AP) impinges on the transmitted signal to each user, a condition that is further stressed when not all APs transmit to all users as proposed in the user-centric (UC) variations of CF massive MIMO. In this document, the presence of channel hardening in this new architecture scheme is addressed using distributed and cooperative precoders and combiners and different power control strategies. It is shown that the line-of-sight (LOS) component, spatially correlated antennas, and clustering schemes have an impact on how the channel hardens. In addition, we examine the existent gap between the estimated achievable rate and the true network performance when channel hardening is compromised. Exact closed-form expressions for both a hardening metric and achievable downlink (DL) and uplink (UL) rates are given as well. We also look into the pilot contamination problem in the UL and DL with different degrees of cooperation between the APs. The optimum minimum mean-squared error (MMSE) processing can take advantage of large-scale fading coefficients for canceling the interference of pilot-sharing users and thus achieves asymptotically unbounded capacity. However, it is computationally demanding and can only be implemented in a fully centralized network. Here, sub-optimal schemes are derived that provide unbounded capacity with much lower complexity and using only local channel estimates but global channel statistics. This makes them suited for both centralized and distributed networks. In this latter case, the best performance is achieved with a generalized maximum ratio combiner that maximizes a capacity bound based on channel statistics only.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Rui Dinis.- Secretario: María Julia Fernández-Getino García.- Vocal: Carmen Botella Mascarel

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

An indoor variance-based localization technique utilizing the UWB estimation of geometrical propagation parameters

A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays. The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values