The fifth-generation mobile network (5G), as the fundamental enabler of Industry 4.0, has facilitated digital transformation and smart manufacturing through AI and cloud computing (CC). However, B5G is viewed as a turning point that will fundamentally transform existing global trends in wireless communication practices as well as in the lives of masses. B5G foresees a world where physical–digital confluence takes place. This study intends to see the world beyond 5G with the transition to 6G assuming the lead as future wireless communication technology. However, despite several developments, the dream of an era without latency, unprecedented speed internet, and extraterrestrial communication has yet to become a reality. This article explores main impediments and challenges that the 5G–6G transition may face in achieving these greater ideals. This article furnishes the vision for 6G, facilitating technology infrastructures, challenges, and research leads towards the ultimate achievement of “technology for humanity” objective and better service to underprivileged people.

The trials and rollout of the fifth generation (5G) network technologies are gradually intensifying as 5G is positioned as a platform that not only accommodates exploding data traffic but also unlocks a multitude use cases, services and deployment scenarios. However, the need for hyperdense 5G deployments is revealing some of the limitations of planning approaches that hitherto proved adequate for pre-5G systems. The hyperdensification envisioned in 5G networks not only adds complexity to network planning and optimization problems, but underlines need for more realistic data-driven approaches that consider cost, varying demands and other contextual attributes to produce feasible topologies. Furthermore, the quest for network programmability and automation including the 5G radio access network (RAN), as manifested by network slicing technologies and more flexible RAN architectures, are also among other factors that influence planning and optimization frameworks. Collectively, these deployment trends, technological developments and evolving (and diverse) service demands point towards the need for more holistic frameworks. This article proposes a data-driven multiobjective optimization framework for hyperdense 5G network planning with practical case studies used to illustrate added value compared to contemporary network planning and optimization approaches. Comparative results from the case study with real network data reveal potential performance and cost improvements of hyperdense optimized networks produced by the proposed framework due to increased use of contextual data of planning area and focus on objectives that target demand satisfaction.

Machine-to-machine (M2M) communication represents a new paradigm for mobile cellular networks, where a massive number of low-cost devices request the transfer of small amounts of data without human intervention. One option to tackle this problem is obtained by combining random beamforming (RBF) with opportunistic scheduling. RBF can be used to induce larger channel fluctuations, and opportunistic scheduling can be used to select M2M devices when their overall channel quality is good. Traditional RBF does not fulfill M2M requirements, because overall channel quality needs to be tracked continuously. In order to tackle this limitation, a novel codebook-based RBF architecture that identifies in advance the time instants in which overall channel quality should be reported, within a coherence time window, is proposed. This opportunistic feedback mechanism reduces signaling overhead and enables energy saving at M2M devices. A simplified methodology is presented to evaluate the system mean data rate, using for this purpose closed form formulas derived from SNR distribution approximations. Results reveal that the performance loss that is experienced for introducing the proposed modifications to traditional RBF scheme is negligible. The concepts analyzed in this paper provide useful insights, and show that codebook-based RBF with simplified opportunistic scheduling algorithms is an excellent combination to provide wide-area M2M services with low-cost devices and limited signaling overhead.

In cellular networks, users are grouped into different cells and served by different access points (base stations) that provide wireless access to services and applications. In general, the service demand is very heterogeneous, non-uniformly distributed, and dynamic. Consequently, radio access networks create very irregular topologies with more access points, where service demand is concentrated. While this dynamism requires networks with the ability to adapt to time-varying conditions, the non-uniformity of the service demand makes the planning, analysis, and optimization difficult. In order to help with these tasks, a framework based on canonical domains and spatial mappings (e.g., conformal mapping) have recently been proposed. The idea is to carry out part of the planning in a canonical (perfectly symmetric) domain that is connected to the physical one (real-scenario) by means of a spatial transformation designed to map the access points consistently with the service demand. This paper continues the research in that direction by introducing additional tools and possibilities to that framework, namely the use of centroidal Voronoi algorithms and non-conformal composite mappings. Moreover, power optimization is also introduced to the framework. The results show the usability and effectiveness of the proposed method and its promising research perspectives.

A simple quantization scheme is proposed to adjust separately the amplitudes and phases of signals in a codebook-based Transmit Beamforming (TBF) scheme. The proposed quantization scheme is valid for both desired signal power maximization (egoistic TBF) and co-channel interference mitigation (altruistic TBF), and does not require an exhaustive search to find the optimal TBF vector. Performance results are presented when variable numbers of bits for amplitude and phase feedback resolutions are used for both egoistic and altruistic TBF schemes.

This letter investigates the role of massive multiple-input multiple-output (MIMO) in improving the spectral and energy efficiency of monostatic backscatter communication systems, where a multiple-antenna reader aims to decode information backscattered from multiple tags. Specifically, we investigate the performance of the two most prominent precoders and combiners, namely, the matched filter and zero forcing. First, we derive capacity lower bounds for the four different underlying transceiver design configurations. Then, asymptotic analysis is conducted and it is shown that with perfect channel state information, the total transmit power can be scaled down by a factor of the square of the number of transmit antennas without loss in the performance. To further corroborate the practical utility of the considered massive MIMO multi-tag setting, the optimization of the backscatter coefficients for sum rate maximization and the effect of imperfect channel state information are also considered.