The ongoing evolution of mobile wireless communications has resulted in the vision of a multiradio heterogeneous network (HetNet) that comprises cells of different scales controlled by various radio access technologies (RATs). These emerging architectures call for more advanced methods of cross-RAT radio resource allocation, which are the primary focus of this article. In this paper, based on network flow optimization techniques, we adapt the concept of weighted α-fairness for efficient resource management in future HetNets. The corresponding scheme relies on a certain degree of centralized control of the HetNet architecture and allows to achieve the desired balance between the overall system throughput and the fairness of the resulting resource allocations based on a single parameter. Our analytical findings, validated with detailed system-level simulations, are expected to further advance the understanding of feasible resource control strategies in intelligent multi-radio networks, as well as help optimize the performance of next-generation HetNets.

Millimeter-wave (mmWave) systems suffer from a significant loss in performance when the line-of-sight (LoS) path between the transmitter and the receiver is obstructed due to blockage caused by humans or other obstacles. However, due to blocker or user motion, the mmWave receiver may transition between the LoS and non-LoS (nLoS) states. Following the recent 3GPP requirements on spatial consistency for channel modeling, this paper aims to analyze the spatial correlation of blockage statistics and characterize their evolution due to user mobility in a static field of blockers in urban mmWave systems. In particular, we derive conditional probabilities of residing in LoS or nLoS state at a given time $t_1$ , provided that a user was in LoS/nLoS state at a prior time $t_0$ . We demonstrate that for realistic values of user speed, the angle of motion, height of transmitter and receiver, as well as the density of blockers, there always is a significant correlation between user channel states at time $t_0$ and $t_1$ , across the time scales relevant for mmWave resource scheduling. Hence, our model can serve as an important tool for optimizing system performance in the presence of blockage.

Despite the prevalence of wireless connectivity in urban areas around the globe, there remain numerous and diverse situations where connectivity is insufficient or unavailable. To address this, we introduce mobile wireless infrastructure on demand , a system of UAVs that can be rapidly deployed to establish an ad-hoc wireless network. This network has the capability of reconfiguring itself dynamically to satisfy and maintain the required quality of communication. The system optimizes the positions of the UAVs and the routing of data flows throughout the network to achieve this quality of service (QoS). By these means, task agents using the network simply request a desired QoS, and the system adapts accordingly while allowing them to move freely. We have validated this system both in simulation and in real-world experiments. The results demonstrate that our system effectively offers mobile wireless infrastructure on demand, extending the operational range of task agents and supporting complex mobility patterns, all while ensuring connectivity and being resilient to agent failures.

The integration of millimeter wave (mmWave) and low frequency interfaces brings an unique opportunity to unify the communications and positioning technologies in the future wireless heterogeneous networks (HetNets), which offer great potential for efficient handover using location awareness, hence a location-aware handover (LHO). Targeting a self-organized communication system with autonomous vehicles, we conduct and describe an experimental and analytical study on the LHO using a mmWave-enabled robotic platform in a multi-radio environment. Compared to the conventional received signal strength indicator (RSSI)-based handover, the studied LHO not only improves the achievable throughput, but also enhances the wireless link robustness for the industrial Internet-of-things (IIoT)-oriented applications. In terms of acquiring location awareness, a geometry-based positioning (GBP) algorithm is proposed and implemented in both simulation and experiments, where its achievable accuracy is assessed and tested. Based on the performed experiments, the location-related measurements acquired by the robot are not accurate enough for the standalone-GBP algorithm to provide an accurate location awareness to perform a reliable handover. Nevertheless, we demonstrate that by combining the GBP with the dead reckoning, more accurate location awareness becomes achievable, the LHO can therefore be performed in a more optimized manner compared to the conventional RSSI-based handover scheme, and is therefore able to achieve approximately twice as high average throughput in certain scenarios. Our study confirms that the achieved location awareness, if accurate enough, could enable an efficient handover scheme, further enhancing the autonomous features in the HetNets.