Novel Radio Access Network Technologies: The Physical Layer point of view

In today’s mobile networks, we are used to having high speed connectivity most of the times. Hence, a natural question is why do we need to invest so much effort and resources in developing new and more advanced techniques? The answer is that the value is shifting from providing high speed connectivity for mainly man-controlled devise, such as smartphones, to enabling the coexistence of a network of things such as wearable devices, cars or smart cities objects under the same network ecosystem. Moreover, new applications are being envisioned such as the real-time tactile internet which promises to allow instant feedback from the network thus enabling virtual reality and mission-critical industrial automation, bringing new challenges to meet extremely low-latency requirements. The role of research in telecommunications in this respect is to look ahead and explore how enhanced connectivity can change our daily life. In this context, my specific research activities focus on the study of novel Radio Access Network (RAN) technologies, which represent the first link from the end user (or thing) to the rest of the network, to support the upcoming demands of new wireless standards, such as the 5G and beyond.

Developing a new RAN is a crucial step towards successful deployment of future mobile networks, as it represents the first fundamental layer onto which all other building blocks of a mobile network are laid. This implies understanding the physical reality of the wireless medium onto which the signals travel and, by suitably modelling it, design new communication strategies to be operated both at the network-side and at the user-side to maximize some given utility.

The disruptive ability to allow uncoordinated uplink transmissions, i.e., from the end users to the network, is, in conjunction with downlink traffic delivery, i.e., from the network to the end users, by massive multiple-input multiple-output (MIMO) systems key to support the unprecedented demands of data rate and low latency communications.

• Massive MIMO is a multi-user technology that can provide uniformly good service to many wireless terminals even in high-mobility environments. The key concept is to equip base stations with arrays of many antennas, thus adding communication resources. Hence, the word “massive” refers to the number of antennas and not the physical size. It is believed to be the key enabler of all advancements of mobile networks, towards successful deployment of 5G-and-beyond technologies. Thanks to the high number of antennas which are typically assumed to me in number greater than the number of users within one cell, a massive MIMO system has many excess degrees of freedom. The latter can be exploited to drastically improve the network’s performance in many ways. Indeed, a massive MIMO base station is able to serve many tens of users in the same time-frequency resources and hence increasing dramatically the data rate. This is in contrast with the old idea of increasing spectral efficiencies, i.e., the number of bits transmitted per second and per unit of bandwidth, by densification of the number of (costly) base station installations. Since the bandwidth is a scarce resource, particularly at the frequencies below 5 GHz that are suitable for network coverage, it is highly desirable to improve the network’s performance by increasing the spectral efficiency rather than increasing the bandwidth. In other words, we must deliver higher data rates with the same amount of bandwidth and within the same time frames that we have been using so far. A great way to improve the spectral efficiency is indeed to simultaneously serve many user terminals in the cell over the same bandwidth and time slots by means of space division multiple access. This is where massive MIMO will play a big role in the future.

Fig 1: Massive MIMO

• Communication strategies refer to finding the best way to transfer data from the network to the users and vice versa. In modern mobile networks, interference coming from transmissions intended to different users is unavoidable. This is because the network serves many users simultaneously in the same time-frequency resources, in order to meet the requirements for high data rate and low latency. From the physical layer point of view designing a communication strategy means finding a way to deal with interference, distortion and attenuation of the signals travelling through air and noise originated by practical hardware equipment. All such unwanted effects can be managed efficiently by using antenna arrays to either transmit one or multiple spatially directive signals simultaneously at the transmitter side, or to separate the useful signal from interference and noise at the receiver side. Every antenna of the transmit array emits a different signal, designed in the digital domain according to some optimization criteria. The design of such signals is referred to as precoding. Thus, precoding is particularly desirable for spatial multiplexing, where we want to transmit a superposition of signals, each with a separate directivity (i.e., each directed to a different receiver, located in different geographical positions). The transmit signal is matched to the physical propagation medium (i.e., the channel) so that the signal that reaches the intended user is received with minimum distortion. Precoding thus exploits transmit diversity by properly weighting the information stream. This is done essentially by choosing the directivity (beamforming), i.e., aligning the transmit beam towards the intended user, and choosing the transmit power (power allocation). In essence, precoding is a particular strategy chosen at the transmitter to convey information to one or multiple receivers. Whereas, at the receiver side the antenna array can be used to filter the received signal in order to extract only the useful part and reject interference coming from transmissions intended to other users and noise. This is achieved by designing suitable combinations of the signal received at each antenna. Such operations are referred to as decoding.

Figure 2: Example of separation of user transmissions in space

• Uncoordinated transmissions refer to the capacity to offload some of the current functions performed by the network to manage its communication links to the users. For instance, consider the allocation of separate sets of time-frequency resources to each user served or the process of acquiring channel state information, i.e., information about how will the physical propagation environment impact the transmitted signal. By allowing the users to cooperate among them, thus reducing the amount of orchestration performed by the network, we can improve the network’s performance by offloading it and reducing the overall system overhead. Such paradigm comes at a cost of increased signalling between the users and more energy consumption at the latter. Indeed, a key enabler is the increased resources available in new generation mobile terminals which should mitigate such cost. Decentralized algorithms for precoding and decoding