No Arabic abstract
The use of unmanned aerial vehicle (UAV)-based communication in millimeter-wave (mmWave) frequencies to provide on-demand radio access is a promising approach to improve capacity and coverage in beyond-5G (B5G) systems. There are several design aspects to be addressed when optimizing for the deployment of such UAV base stations. As traffic demand of mobile users varies across time and space, dynamic algorithms that correspondingly adjust the UAV locations are essential to maximize performance. In addition to careful tracking of spatio-temporal user/traffic activity, such optimization needs to account for realistic backhaul constraints. In this work, we first review the latest 3GPP activities behind integrated access and backhaul system design, support for UAV base stations, and mmWave radio relaying functionality. We then compare static and mobile UAV-based communication options under practical assumptions on the mmWave system layout, mobility and clusterization of users, antenna array geometry, and dynamic backhauling. We demonstrate that leveraging the UAV mobility to serve moving users may improve the overall system performance even in the presence of backhaul capacity limitations.
Cellular has always relied on static deployments for providing wireless access. However, even the emerging fifth-generation (5G) networks may face difficulty in supporting the increased traffic demand with rigid, fixed infrastructure without substantial over-provisioning. This is particularly true for spontaneous large-scale events that require service providers to augment capacity of their networks quickly. Today, the use of aerial devices equipped with high-rate radio access capabilities has the potential to offer the much needed on-demand capacity boost. Conversely, it also threatens to rattle the long-standing business strategies of wireless operators, especially as the gold rush for cheaper millimeter wave (mmWave) spectrum lowers the market entry barriers. However, the intricate structure of this new market presently remains a mystery. This paper sheds light on competition and cooperation behavior of dissimilar aerial mmWave access suppliers, concurrently employing licensed and license-exempt frequency bands, by modeling it as a vertically differentiated market where customers have varying preferences in price and quality. To understand viable service provider strategies, we begin with constructing the Nash equilibrium for the initial market competition by employing the Bertrand and Cournot games. We then conduct a unique assessment of short-term market dynamics, where two licensed-band service providers may cooperate to improve their competition positions against the unlicensed-band counterpart intruding the market. Our unprecedented analysis studies the effects of various market interactions, price-driven demand evolution, and dynamic profit balance in this novel type of ecosystem.
Aerial relays have been regarded as an alternative and promising solution to extend and improve satellite-terrestrial communications, as the probability of line-of-sight transmissions increases compared with adopting terrestrial relays. In this paper, a cooperative satellite-aerial-terrestrial system including a satellite transmitter (S), a group of terrestrial receivers (D), and an aerial relay (R) is considered. Specifically, considering the randomness of S and D and employing stochastic geometry, the coverage probability of R-D links in non-interference and interference scenarios is studied, and the outage performance of S-R link is investigated by deriving an approximated expression for the outage probability. Moreover, an optimization problem in terms of the transmit power and the transmission time over S-R and R-D links is formulated and solved to obtain the optimal end-to-end energy efficiency for the considered system. Finally, some numerical results are provided to validate our proposed analysis models, as well as to study the optimal energy efficiency performance of the considered system.
The next generations of mobile networks will be deployed as ultra-dense networks, to match the demand for increased capacity and the challenges that communications in the higher portion of the spectrum (i.e., the mmWave band) introduce. Ultra-dense networks, however, require pervasive, high-capacity backhaul solutions, and deploying fiber optic to all base stations is generally considered to be too expensive for network operators. The 3rd Generation Partnership Project (3GPP) has thus introduced Integrated Access and Backhaul (IAB), a wireless backhaul solution in which the access and backhaul links share the same hardware, protocol stack, and also spectrum. The multiplexing of different links in the same frequency bands, however, introduces interference and capacity sharing issues, thus calling for the introduction of advanced scheduling and coordination schemes. This paper proposes a semi-centralized resource allocation scheme for IAB networks, designed to be flexible, with low complexity, and compliant with the 3GPP IAB specifications. We develop a version of the Maximum Weighted Matching (MWM) problem that can be applied on a spanning tree that represents the IAB network and whose complexity is linear in the number of IAB-nodes. The proposed solution is compared with state-of-the-art distributed approaches through end-to-end, full-stack system-level simulations with a 3GPP-compliant channel model, protocol stack, and a diverse set of user applications. The results show how that our scheme can increase the throughput of cell-edge users up to 5 times, while decreasing the overall network congestion with an end-to-end delay reduction of up to 25 times.
The wireless backhaul network provides an attractive solution for the urban deployment of fifth generation (5G) wireless networks that enables future ultra dense small cell networks to meet the ever-increasing user demands. Optimal deployment and management of 5G wireless backhaul networks is an interesting and challenging issue. In this paper we propose the optimal gateways deployment and wireless backhaul route schemes to maximize the cost efficiency of 5G wireless backhaul networks. In generally, the changes of gateways deployment and wireless backhaul route are presented in different time scales. Specifically, the number and locations of gateways are optimized in the long time scale of 5G wireless backhaul networks. The wireless backhaul routings are optimized in the short time scale of 5G wireless backhaul networks considering the time-variant over wireless channels. Numerical results show the gateways and wireless backhaul route optimization significantly increases the cost efficiency of 5G wireless backhaul networks. Moreover, the cost efficiency of proposed optimization algorithm is better than that of conventional and most widely used shortest path (SP) and Bellman-Ford (BF) algorithms in 5G wireless backhaul networks.
We present DeepIA, a deep neural network (DNN) framework for enabling fast and reliable initial access for AI-driven beyond 5G and 6G millimeter (mmWave) networks. DeepIA reduces the beam sweep time compared to a conventional exhaustive search-based IA process by utilizing only a subset of the available beams. DeepIA maps received signal strengths (RSSs) obtained from a subset of beams to the beam that is best oriented to the receiver. In both line of sight (LoS) and non-line of sight (NLoS) conditions, DeepIA reduces the IA time and outperforms the conventional IAs beam prediction accuracy. We show that the beam prediction accuracy of DeepIA saturates with the number of beams used for IA and depends on the particular selection of the beams. In LoS conditions, the selection of the beams is consequential and improves the accuracy by up to 70%. In NLoS situations, it improves accuracy by up to 35%. We find that, averaging multiple RSS snapshots further reduces the number of beams needed and achieves more than 95% accuracy in both LoS and NLoS conditions. Finally, we evaluate the beam prediction time of DeepIA through embedded hardware implementation and show the improvement over the conventional beam sweeping.