Low-Complexity Multi-User MIMO Algorithms for mmWave WLANs

Very high throughput and high-efficiency wireless local area networks (WLANs) have become essential for today's significant global Internet traffic and the expected significant global increase of public WiFi hotspots. Total Internet traffic is predicted to expand 3.7-fold from 2017 to 2022. In 2017, 53% of overall Internet traffic used by WiFi networks, and that number is expected to increase to 56.8% by 2022. Furthermore, 80% of overall Internet traffic is expected to be video traffic by 2022, up from 70% in 2017. WiFi networks are also expected to move towards denser deployment scenarios, such as stadiums, large office buildings, and airports, with very high data rate applications, such as ultra-high definition video wireless streaming. Thus, in order to meet the predicted growth of wireless traffic and the number of WiFi networks in the world, an efficient Internet access solution is required for the current IEEE 802.11 standards. Millimeter wave (mmWave) communication technology is expected to play a crucial role in future wireless networks with large user populations because of the large spectrum band it can provide. To further improve spectrum efficiency over mmWave bands in WLANs with large numbers of users, the IEEE 802.11ay standard was developed from the traditional IEEE 802.11ad standard, aiming to support multi-user MIMO. Propagation challenges associated with mmWave bands necessitate the use of analog beamforming (BF) technologies that employ directional transmissions to determine the optimal sector beam between a transmitter and a receiver. However, the multi-user MIMO is not exploited, since analog BF is limited to a single-user, single-transmission. The computational complexity of achieving traditional multi-user MIMO BF methods, such as full digital BF, in the mmWave systems becomes significant due to the hardware constraints. Our research focuses on how to effectively and efficiently realize multi-user MIMO transmission to improve spectrum efficiency over the IEEE 802.11ay mmWave band system while also resolving the computational complexity challenges for achieving a multi-user MIMO in mmWave systems. This thesis focuses on MAC protocol algorithms and analysis of the IEEE 802.11ay mmWave WLANs to provide multi-user MIMO support in various scenarios to improve the spectrum efficiency and system throughput. Specifically, from a downlink single-hop scenario perspective, a VG algorithm is proposed to schedule simultaneous downlink transmission links while mitigating the multi-user interference with no additional computational complexity. From a downlink multi-hop scenario perspective, a low-complexity MHVG algorithm is conducted to realize simultaneous transmissions and improve the network performance by taking advantage of the spatial reuse in a dense network. The proposed MHVG algorithm permits simultaneous links scheduling and mitigates both the multi-user interference and co-channel interference based only on analog BF information, without the necessity for feedback overhead, such as channel state information (CSI). From an uplink scenario perspective, a low-complexity user selection algorithm, HBF-VG, incorporates user selection with the HBF algorithm to achieve simultaneous uplink transmissions for IEEE 802.11ay mmWave WLANs. With the HBF-VG algorithm, the users can be selected based on an orthogonality criterion instead of collecting CSI from all potential users. We optimize the digital BF to mitigate the residual interference among selected users. Extensive analytical and simulation evaluations are provided to validate the performance of the proposed algorithms with respect to average throughput per time slot, average network throughput, average sum-rate, energy efficiency, signal-to-interference-plus-noise ratio (SINR), and spatial multiplexing gain

Analysis and Design of Algorithms for the Improvement of Non-coherent Massive MIMO based on DMPSK for beyond 5G systems

Mención Internacional en el título de doctorNowadays, it is nearly impossible to think of a service that does not rely on wireless communications. By the end of 2022, mobile internet represented a 60% of the total global online traffic. There is an increasing trend both in the number of subscribers and in the traffic handled by each subscriber. Larger data rates, smaller extreme-to-extreme (E2E) delays and greater number of devices are current interests for the development of mobile communications. Furthermore, it is foreseen that these demands should also be fulfilled in scenarios with stringent conditions, such as very fast varying wireless communications channels (either in time or frequency) or scenarios with power constraints, mainly found when the equipment is battery powered. Since most of the wireless communications techniques and standards rely on the fact that the wireless channel is somehow characterized or estimated to be pre or post-compensated in transmission (TX) or reception (RX), there is a clear problem when the channels vary rapidly or the available power is constrained. To estimate the wireless channel and obtain the so-called channel state information (CSI), some of the available resources (either in time, frequency or any other dimension), are utilized by including known signals in the TX and RX typically known as pilots, thus avoiding their use for data transmission. If the channels vary rapidly, they must be estimated many times, which results in a very low data efficiency of the communications link. Also, in case the power is limited or the wireless link distance is large, the resulting signal-tointerference- plus-noise ratio (SINR) will be low, which is a parameter that is directly related to the quality of the channel estimation and the performance of the data reception. This problem is aggravated in massive multiple-input multiple-output (massive MIMO), which is a promising technique for future wireless communications since it can increase the data rates, increase the reliability and cope with a larger number of simultaneous devices. In massive MIMO, the base station (BS) is typically equipped with a large number of antennas that are coordinated. In these scenarios, the channels must be estimated for each antenna (or at least for each user), and thus, the aforementioned problem of channel estimation aggravates. In this context, algorithms and techniques for massive MIMO without CSI are of interest. This thesis main topic is non-coherent massive multiple-input multiple-output (NC-mMIMO) which relies on the use of differential M-ary phase shift keying (DMPSK) and the spatial diversity of the antenna arrays to be able to detect the useful transmitted data without CSI knowledge. On the one hand, hybrid schemes that combine the coherent and non-coherent schemes allowing to get the best of both worlds are proposed. These schemes are based on distributing the resources between non-coherent (NC) and coherent data, utilizing the NC data to estimate the channel without using pilots and use the estimated channel for the coherent data. On the other hand, new constellations and user allocation strategies for the multi-user scenario of NC-mMIMO are proposed. The new constellations are better than the ones in the literature and obtained using artificial intelligence techniques, more concretely evolutionary computation.This work has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No. 813391. The PhD student was the Early Stage Researcher (ESR) number 2 of the project. This work has also received funding from the Spanish National Project IRENE-EARTH (PID2020-115323RB-C33) (MINECO/AEI/FEDER, UE), which funded the work of some coauthors.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretario: Matilde Pilar Sánchez Fernández.- Vocal: Eva Lagunas Targaron

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

Spectrum Sharing in mmWave Cellular Networks via Cell Association, Coordination, and Beamforming

This paper investigates the extent to which spectrum sharing in mmWave networks with multiple cellular operators is a viable alternative to traditional dedicated spectrum allocation. Specifically, we develop a general mathematical framework by which to characterize the performance gain that can be obtained when spectrum sharing is used, as a function of the underlying beamforming, operator coordination, bandwidth, and infrastructure sharing scenarios. The framework is based on joint beamforming and cell association optimization, with the objective of maximizing the long-term throughput of the users. Our asymptotic and non-asymptotic performance analyses reveal five key points: (1) spectrum sharing with light on-demand intra- and inter-operator coordination is feasible, especially at higher mmWave frequencies (for example, 73 GHz), (2) directional communications at the user equipment substantially alleviate the potential disadvantages of spectrum sharing (such as higher multiuser interference), (3) large numbers of antenna elements can reduce the need for coordination and simplify the implementation of spectrum sharing, (4) while inter-operator coordination can be neglected in the large-antenna regime, intra-operator coordination can still bring gains by balancing the network load, and (5) critical control signals among base stations, operators, and user equipment should be protected from the adverse effects of spectrum sharing, for example by means of exclusive resource allocation. The results of this paper, and their extensions obtained by relaxing some ideal assumptions, can provide important insights for future standardization and spectrum policy.Comment: 15 pages. To appear in IEEE JSAC Special Issue on Spectrum Sharing and Aggregation for Future Wireless Network