Robust Pilot Decontamination Based on Joint Angle and Power Domain Discrimination

We address the problem of noise and interference corrupted channel estimation in massive MIMO systems. Interference, which originates from pilot reuse (or contamination), can in principle be discriminated on the basis of the distributions of path angles and amplitudes. In this paper we propose novel robust channel estimation algorithms exploiting path diversity in both angle and power domains, relying on a suitable combination of the spatial filtering and amplitude based projection. The proposed approaches are able to cope with a wide range of system and topology scenarios, including those where, unlike in previous works, interference channel may overlap with desired channels in terms of multipath angles of arrival or exceed them in terms of received power. In particular we establish analytically the conditions under which the proposed channel estimator is fully decontaminated. Simulation results confirm the overall system gains when using the new methods.Comment: 14 pages, 5 figures, accepted for publication in IEEE Transactions on Signal Processin

Dealing with Interference in Distributed Large-scale MIMO Systems: A Statistical Approach

This paper considers the problem of interference control through the use of second-order statistics in massive MIMO multi-cell networks. We consider both the cases of co-located massive arrays and large-scale distributed antenna settings. We are interested in characterizing the low-rankness of users' channel covariance matrices, as such a property can be exploited towards improved channel estimation (so-called pilot decontamination) as well as interference rejection via spatial filtering. In previous work, it was shown that massive MIMO channel covariance matrices exhibit a useful finite rank property that can be modeled via the angular spread of multipath at a MIMO uniform linear array. This paper extends this result to more general settings including certain non-uniform arrays, and more surprisingly, to two dimensional distributed large scale arrays. In particular our model exhibits the dependence of the signal subspace's richness on the scattering radius around the user terminal, through a closed form expression. The applications of the low-rankness covariance property to channel estimation's denoising and low-complexity interference filtering are highlighted.Comment: 12 pages, 11 figures, to appear in IEEE Journal of Selected Topics in Signal Processin

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.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Channel Estimation for Massive MIMO-OFDM Systems by Tracking the Joint Angle-Delay Subspace

In this paper, we propose joint angle-delay subspace based channel estimation in single cell for broadband massive multiple-input and multiple-output (MIMO) systems employing orthogonal frequency division multiplexing (OFDM) modulation. Based on a parametric channel model, we present a new concept of the joint angle-delay subspace which can be tracked by the low-complexity low-rank adaptive filtering (LORAF) algorithm. Then, we investigate an interference-free transmission condition that the joint angle-delay subspaces of the users reusing the same pilots are non-overlapping. Since the channel statistics are usually unknown, we develop a robust minimum mean square error (MMSE) estimator under the worst precondition of pilot decontamination, considering that the joint angle-delay subspaces of the interfering users fully overlap. Furthermore, motivated by the interference-free transmission criteria, we present a novel low-complexity greedy pilot scheduling algorithm to avoid the problem of initial value sensitivity. Simulation results show that the joint angle-delay subspace can be estimated effectively, and the proposed pilot reuse scheme combined with robust MMSE channel estimation offers significant performance gains

Soft Pilot Reuse and Multi-Cell Block Diagonalization Precoding for Massive MIMO Systems

The users at cell edge of a massive multiple-input multiple-output (MIMO) system suffer from severe pilot contamination, which leads to poor quality of service (QoS). In order to enhance the QoS for these edge users, soft pilot reuse (SPR) combined with multi-cell block diagonalization (MBD) precoding are proposed. Specifically, the users are divided into two groups according to their large-scale fading coefficients, referred to as the center users, who only suffer from modest pilot contamination and the edge users, who suffer from severe pilot contamination. Based on this distinction, the SPR scheme is proposed for improving the QoS for the edge users, whereby a cell-center pilot group is reused for all cell-center users in all cells, while a cell-edge pilot group is applied for the edge users in the adjacent cells. By extending the classical block diagonalization precoding to a multi-cell scenario, the MBD precoding scheme projects the downlink transmit signal onto the null space of the subspace spanned by the inter-cell channels of the edge users in adjacent cells. Thus, the inter-cell interference contaminating the edge users' signals in the adjacent cells can be efficiently mitigated and hence the QoS of these edge users can be further enhanced. Our theoretical analysis and simulation results demonstrate that both the uplink and downlink rates of the edge users are significantly improved, albeit at the cost of the slightly decreased rate of center users.Comment: 13 pages, 12 figures, accepted for publication in IEEE Transactions on Vehicular Technology, 201

Mobile node-aided localization and tracking in terrestrial and underwater networks

In large-scale wireless sensor networks (WSNs), the position information of individual sensors is very important for many applications. Generally, there are a small number of position-aware nodes, referred to as the anchors. Every other node can estimate its distances to the surrounding anchors, and then employ trilateration or triangulation for self-localization. Such a system is easy to implement, and thus popular for both terrestrial and underwater applications, but it suffers from some major drawbacks. First, the density of the anchors is generally very low due to economical considerations, leading to poor localization accuracy. Secondly, the energy and bandwidth consumptions of such systems are quite significant. Last but not the least, the scalability of a network based on fixed anchors is not good. Therefore, whenever the network expands, more anchors should be deployed to guarantee the required performance. Apart from these general challenges, both terrestrial and underwater networks have their own specific ones. For example, realtime channel parameters are generally required for localization in terrestrial WSNs. For underwater networks, the clock skew between the target sensor and the anchors must be considered. That is to say, time synchronization should be performed together with localization, which makes the problem complicated. An alternative approach is to employ mobile anchors to replace the fixed ones. For terrestrial networks, commercial drones and unmanned aerial vehicles (UAVs) are very good choices, while autonomous underwater vehicles (AUVs) can be used for underwater applications. Mobile anchors can move along a predefined trajectory and broadcast beacon signals. By listening to the messages, the other nodes in the network can localize themselves passively. This architecture has three major advantages: first, energy and bandwidth consumptions can be significantly reduced; secondly, the localization accuracy can be much improved with the increased number of virtual anchors, which can be boosted at negligible cost; thirdly, the coverage can be easily extended, which makes the solution and the network highly scalable. Motivated by this idea, this thesis investigates the mobile node-aided localization and tracking in large-scale WSNs. For both terrestrial and underwater WSNs, the system design, modeling, and performance analyses will be presented for various applications, including: (1) the drone-assisted localization in terrestrial networks; (2) the ToA-based underwater localization and time synchronization; (3) the Doppler-based underwater localization; (4) the underwater target detection and tracking based on the convolutional neural network and the fractional Fourier transform. In these applications, different challenges will present, and we will see how these challenges can be addressed by replacing the fixed anchors with mobile ones. Detailed mathematical models will be presented, and extensive simulation and experimental results will be provided to verify the theoretical results. Also, we will investigate the channel estimation for the fifth generation (5G) wireless communications. A pilot decontamination method will be presented for the massive multiple-input-multiple-output communications, and the data-aided channel tracking will be discussed for millimeter wave communications. We will see that the localization problem is highly coupled with the channel estimation in wireless communications

Network efficiency enhancement by reactive channel state based allocation scheme

Now a day the large MIMO has considered as the efficient approach to improve the spectral and energy efficiency at WMN. However, the PC is a big issue that caused by reusing similar pilot sequence at cells, which also restrict the performance of massive MIMO network. Here, we give the alternative answer, where each of UEs required allotting a channel sequences before passing the payload data, so as to avoid the channel collision of inter-cell. Our proposed protocol will ready to determine the channel collisions in distributed and scalable process, however giving unique properties of the large MIMO channels. Here we have proposed a RCSA (Reactive channel state based allocation) scheme, which will very productively work with the RAP blockers at large network of MIMO. The position of time-frequency of RAP blocks is modified in the middle of the adjacent cells, because of this design decision the RAP defend from the hardest types of interference at inter-cell. Further, to validate the performance of our proposed scheme it will be compared with other existing technique