The 6th Generation (6G) radio access technology is expected to support extreme communication requirements in terms of throughput, latency and reliability, which can only be achieved by providing capillary wireless coverage. In this paper, we present our vision for short-range low power 6G ‘in-X’ subnetworks, with the ‘X’ standing for the entity in which the cell is deployed, e.g., a production module, a robot, a vehicle, a house or even a human body. Such cells can support services that can be life-critical and that traditionally relied on wired systems. We discuss potential deployment options, as well as candidate air interface components and spectrum bands. Interference management is identified as a major challenge in dense deployments, which needs to handle also non-cellular types of interference like jamming attacks and impulsive noise. A qualitative example of interference-robust system design is also presented.

Factory automation is one of the use cases for 5G-and-beyond mobile networks where strict requirements in terms of latency, availability and reliability are required. In this paper, we investigate the potentials of massive MIMO in delivering those promises for industrial automation. Namely, communications between actuators (ACs) and Access Points (APs) inside an industrial scenario is considered and different transmission modes are compared: joint transmission (JT) where the distributed antennas are used to communicate with each AC, cell-free transmission (CFT) where all the ACs are served by all APs, single AP transmission (SAT) where each AC is served by only one AP, and user-centric transmission (UCT) where each AC is served by a subset of APs. A power control strategy, aimed at maximizing the minimum signal-to-interference plus noise ratio (SINR), is also introduced. Numerical results, shown in terms of downlink SINR and achievable rate, evaluated using the final block length capacity formula (FBLC), demonstrate that the use of distributed antenna setting and of power control bring substantial performance improvements in terms of reliability and latency.

In this paper, we focus on one of the key technologies for the fifth-generation wireless communication networks, massive multiple-input-multiple-output (mMIMO), by investigating two of its most relevant architectures: 1) to provide in-band backhaul for the ultra-dense network (UDN) of self-backhauled small cells (SCs), and 2) to provide direct access (DA) to user equipments (UEs). Through comprehensive 3GPP-based system-level simulations and analytical formulations, we show the end-to-end UE rates achievable with these two architectures. Differently from the existing works, we provide results for two strategies of self-backhauled SC deployments, namely random and ad-hoc, where in the latter SCs are purposely positioned close to UEs to achieve line-of-sight (LoS) access links. We also evaluate the optimal backhaul and access time resource partition due to the in-band self-backhauling (s-BH) operations. Our results show that the ad-hoc deployment of self-backhauled SCs closer to the UEs with optimal resource partition and with directive antenna patterns, provides rate improvements for cell-edge UEs that amount to 30% and tenfold gain, as compared to mMIMO DA architecture with pilot reuse 3 and reuse 1, respectively. On the other hand, mMIMO s-BH underperforms mMIMO DA above the median value of the UE rates when the effect of pilot contamination is less severe, and the LoS probability of the DA links improves.

Directional measurements of over 2600 links in four distinct factories at 28 GHz are used to formulate path gain and azimuth gain models to allow reliable 90% coverage estimates. A simple theoretical model of path gain, dependent on ceiling and clutter heights, is found to represent path gain across the 4 factories with 4.4 dB RMSE, contrasted with 6.9 dB slope-intercept fit and 8.5 to 14.9 dB RMSE for 3GPP factory models. The model also did well against 3.5 GHz path loss data collected over 18 MHz bandwidth in one of the factories, with RMSE of 3.3 dB. In non-line-of-sight conditions, scattering reduces available antenna azimuth gain from nominal value by up to 7.3 dB in 90% of links. Line of sight blockage by a 1.7 m × 1 m obstacle in factory aisle leads to 7 dB signal reduction, attributed to availability of other paths. It is found that an Access Point (AP) using 25 dBm transmit power per polarization, with 23 dBi nominal gain and omnidirectional terminals, supporting 2×2 MIMO in 400 MHz bandwidth, can provide 130 Mbps for 90% of factory locations within 50 m.

We propose a novel interference prediction scheme to improve link adaptation (LA) in densely deployed industrial sub-networks (SNs) with high-reliability and low-latency communication (HRLLC) requirements. The proposed method aims to improve the LA framework by predicting and leveraging the heavy-tailed interference probability density function (pdf). Interference is modeled as a latent vector of available channel quality indicator (CQI), using a vector discrete-time state-space model (vDSSM) at the SN controller, where the CQI is subjected to compression, quantization, and delay-induced errors. To robustly estimate interference power values under these impairments, we employ a low-complexity, outlier-robust, sparse Student-t process regression (SPTPR) method. This is integrated into a modified unscented Kalman filter, which recursively refines predicted interference using CQI, enabling accurate estimation and compensating protocol feedback delays–crucial for accurate LA. Numerical results show that the proposed method achieves over 10× lower complexity compared to a similar non-parametric baseline. It also maintains a BLER below the 90th percentile target of 10-6 while delivering performance comparable to a state-of-the-art supervised technique using only CQI reports.

In this paper, we focus on one of the key technologies for the fifth-generation wireless communication networks, massive multiple-input-multiple-output (mMIMO), by investigating two of its most relevant architectures: 1) to provide in-band backhaul for the ultra-dense network (UDN) of self-backhauled small cells (SCs), and 2) to provide direct access (DA) to user equipments (UEs). Through comprehensive 3GPP-based system-level simulations and analytical formulations, we show the end-to-end UE rates achievable with these two architectures. Differently from the existing works, we provide results for two strategies of self-backhauled SC deployments, namely random and ad-hoc, where in the latter SCs are purposely positioned close to UEs to achieve line-of-sight (LoS) access links. We also evaluate the optimal backhaul and access time resource partition due to the in-band self-backhauling (s-BH) operations. Our results show that the ad-hoc deployment of self-backhauled SCs closer to the UEs with optimal resource partition and with directive antenna patterns, provides rate improvements for cell-edge UEs that amount to 30% and tenfold gain, as compared to mMIMO DA architecture with pilot reuse 3 and reuse 1, respectively. On the other hand, mMIMO s-BH underperforms mMIMO DA above the median value of the UE rates when the effect of pilot contamination is less severe, and the LoS probability of the DA links improves.