Harnessing the abundant availability of spectral resources at millimeter wave (mmWave) frequencies is an attractive solution to meet the escalating data rate demands. Additionally, it has been shown that full-duplex (FD) communication has the potential of doubling the bandwidth efficiency. However, the presence of significant residual self-interference (SI), which is especially more pronounced at mmWave frequencies because of the non-linearities in the hardware components, erodes the full potential of FD in practice. Conventionally, the residual SI is canceled in the baseband using digital processing with the aid of a transmit precoder. In this work, we propose a hybrid beamforming design for FD mmWave communications, where the SI is canceled by the joint design of beamformer weights at the radio-frequency (RF) and the precoder as well as combiner in the baseband. Our proposed design preserves the dimensions of the transmit signal, while suppressing the SI. We demonstrate that our joint design is capable of reducing the SI by upto 30 dB, hence performing similarly to the interference-free FD system while being computationally efficient. Our simulation results show that the proposed design significantly outperforms eigen-beamforming.

Reconfigurable intelligent surfaces (RIS) have been actively researched as a potential technique for future wireless communications, which intelligently ameliorate the signal propagation environment. In the conventional design, each RIS element configures and reflects its received signal independently of all other RIS elements, which results in a diagonal phase shift matrix. By contrast, we propose a novel RIS architecture, where the incident signal impinging on one element can be reflected from another element after an appropriate phase shift adjustment, which increases the flexibility in the design of RIS phase shifts, hence, potentially improving the system performance. The resultant RIS phase shift matrix also has off-diagonal elements, as opposed to the pure diagonal structure of the conventional design. Compared to the state-of-art fully-connected/group-connected RIS structures, our proposed RIS architecture has lower complexity, while attaining a higher channel gain than the group-connected RIS structure, and approaching that of the fully-connected RIS structure. We formulate and solve the problem of maximizing the achievable rate of our proposed RIS architecture by jointly optimizing the transmit beamforming and the non-diagonal phase shift matrix based on alternating optimization and semi-define relaxation (SDR) methods. Moreover, the closed-form expressions of the channel gain, the outage probability and bit error ratio (BER) are derived. Simulation results demonstrate that our proposed RIS architecture results in an improved performance in terms of the achievable rate compared to the conventional architecture, both in single-user as well as in multi-user scenarios.

With massive deployment, multiple-input–multiple-output (MIMO) systems continue to take mobile communications to new heights, but the ever-increasing demands mean that there is a need to look beyond MIMO and pursue the next disruptive wireless technologies. Reconfigurable intelligent surface (RIS) is widely considered a key candidate technology block to provide the next generational leap. The first part of this article provides an updated overview of the conventional reflection-based RIS technology, which complements the existing literature to include active and semiactive RIS, and the synergies with cell-free massive MIMO (CF mMIMO). Then, we widen the scope to discuss the surface-wave-assisted RIS that represents a different design dimension in utilizing metasurface technologies. This goes beyond being a passive reflector and can use the surface as an intelligent propagation medium for superb radio propagation efficiency. The third part of this article turns the attention to the fluid antenna, a novel antenna technology that enables a diverse form of reconfigurability that can combine with RIS for ultrahigh capacity, power efficiency, and scalability. This article concludes with a discussion of the potential synergies that can be exploited between MIMO, RIS, and fluid antennas.

Harnessing the substantial bandwidth available at millimeter wave (mmWave) carrier frequencies has proved to be beneficial to accommodate a large number of users with increased data rates. However, owing to the high propagation losses observed at mmWave frequencies, directional transmission has to be employed. This necessitates efficient beam-alignment for a successful transmission. Achieving perfect beam-alignment is however challenging, especially in the scenarios when there is a rapid movement of vehicles associated with ever-changing traffic density, which is governed by the topology of roads as well as the time of the day. Therefore, in this paper, we take the approach of fingerprint based beam-alignment, where a set of beam pairs constitute the fingerprint of a given location. Furthermore, given the time-varying traffic density, we propose a multi-fingerprint based database for a given location, where the base station (BS) intelligently adapts the fingerprints with the aid of learning. Additionally, we propose multi-functional beam transmission as an application of our proposed design, where the beam-pairs that satisfy the required received signal strength (RSS) participate in increasing the spectral efficiency or improving the end-to-end performance in some other way. Explicitly, the BS leverages the plurality of beam-pairs to attain both multiplexing and diversity gains. Furthermore, if the plurality of beam-pairs is higher than the number of RF chains, the BS may also employ beam-index modulation to further improve the spectral efficiency. We demonstrate that having multiple fingerprint-based beam-alignment provides superior performance than that of the single fingerprint based beam-alignment. Furthermore, we show that our learning-aided multiple fingerprint design provides a better fidelity compared to that of the benchmark scheme also employing multiple fingerprint but dispensing with learning. Additionally, our reduced-search based learning-aided beam-alignment design performs similarly to beam-sweeping based beam-alignment, even though an exhaustive beam-search is carried out by the latter. More explicitly, our design is capable of maintaining the target performance in dense vehicular environments, while both single fingerprint and line-of-sight (LOS) based beam-alignment suffer from blockages.

This paper presents a baseline roadmap for the evolution of 5G new radio over the next decade. Three timescales are considered, namely short-term (2022-ish), medium-term (2025-ish), and long-term (2030-ish). The evolution of the target key performance indicators (KPIs) is first analyzed by accounting for forecasts on the emerging use cases and their requirements, together with assumptions on the pace of technology advancements. The baseline roadmap is derived next by capturing the top-10 and next the top-5 technology trends envisioned to bring significant added value at each timescale. Being intrinsically predictive, our proposed baseline roadmap cannot assert with certainty the values of the target KPIs and the shortlisting of the technology trends. It is, however, aimed at driving discussions and collecting feedback from the wireless research community for future tuning and refinement as the 5G evolution journey progresses.

Private 5G networks has become a popular choice of various vertical industries to build dedicated and secure wireless networks in industry environments to deploy their services with enhanced service flexibility and device connectivity to foster industry digitalization. This article proposes multiple multi-domain solutions to deploy private 5G networks for vertical industries across their local premises and interconnecting them with the public networks. Such scenarios open up a new market segment for various stakeholders, and break the current operators’ business and service provisioning models. This, in turn, demands new interactions among the different stakeholders across their administrative domains. To this aim, three distinct levels of multi-domain solutions for deploying vertical’s 5G private networks are proposed in this work, which can support interactions at different layers among various stakeholders, allowing for different levels of service exposure and control. Building on a set of industry verticals (comprising Industry 4.0, Transportation and Energy), different deployment models are analyzed and the proposed multi-domain solutions are applied. These solutions are implemented and validated through two proof-of-concept prototypes integrating a 5G private network platform (5Growth platform) with public ones. These solutions are being implemented in three vertical pilots conducted with real industry verticals. The obtained results demonstrated the feasibility of the proposed multi-domain solutions applied at the three layers of the system enabling various levels of interactions among the different stakeholders. The achieved end-to-end service instantiation time across multiple domains is in the range of minutes, where the delay impact caused by the resultant multi-domain interactions is considerably low. The proposed multi-domain approaches offer generic solutions and standard interfaces to support the different private network deployment models.