A New MIMO Scheme for Saving Power Consumption

In this letter, we introduce a novel transmission scheme to a multi-input multi-output (MIMO) system employing an M × M antenna structure. The conventional MIMO loads parallel signals onto all transmit antennas to realize essentially M separate channels to achieve high channel capacity. In contrast, the proposed method can dynamically activate a subset of the transmit antennas at each transmission while maintaining the same channel capacity as conventional MIMO. The dynamic activation of the transmit antennas offers a statistically similar advantage to spatial modulation, leading to a reduced number of active RF chains and power saving as well. The theoretical analysis is conducted through the proposed transformation matrix that operates from the signal source to the transmit signal. The effectiveness of this approach is investigated regarding capacity issues and antenna utilization, which are confirmed by simulation results

6G for Bridging the Digital Divide: Wireless Connectivity to Remote Areas

In telecommunications, network service accessibility as a requirement is closely related to equitably serving the population residing at locations that can most appropriately be described as remote. Remote connectivity, however, would have benefited from a more inclusive consideration in the existing generations of mobile communications. To remedy this, sustainability and its social impact are being positioned as key drivers of the sixth generation's (6G) research and standardization activities. In particular, there has been a conscious attempt to understand the demands of remote wireless connectivity, which has led to a better understanding of the challenges that lie ahead. In this perspective, this article overviews the key challenges associated with constraints on network design and deployment to be addressed for providing broadband connectivity to rural areas, and proposes novel approaches and solutions for bridging the digital divide in those regions

High-Throughput Air-to-Ground Connectivity for Aircraft

Permanent connectivity to the Internet has become the defacto standard in the second decade of the 21st century. However, on-board aircraft connectivity is still limited. While the number of airlines offering in-flight connectivity increases, the current performance is insufficient to satisfy several hundreds of passengers simultaneously. There are several options to connect aircraft to the ground, i.e. direct air-to-ground, satellites and relaying via air-to-air links. However, each single solution is insufficient. The direct air-to-ground coverage is limited to the continent and coastal regions, while the satellite links are limited in the minimum size of the spot beams and air-to-air links need to be combined with a link to the ground. Moreover, even if a direct air-to-ground or satellite link is available, the peak throughput offered on each link is rarely achieved, as the capacity needs to be shared with other aircraft flying in the same coverage area. The main challenge in achieving a high throughput per aircraft lies in the throughput allocation. All aircraft should receive a fair share of the available throughput. More specifically, as an aircraft contains a network itself, a weighted share according to the aircraft size should be provided. To address this problem, an integrated air-to-ground network, which is able to provide a high throughput to aircraft, is proposed here. Therefore, this work introduces a weighted-fair throughput allocation scheme to provide such a desired allocation. While various aspects of aircraft connectivity are studied in literature, this work is the first to address an integrated air-to-ground network to provide high-throughput connectivity to aircraft. This work models the problem of throughput allocation as a mixed integer linear program. Two throughput allocation schemes are proposed, a centralized optimal solution and a distributed heuristic solution. For the optimal solution, two different objectives are introduced, a max-min-based and a threshold-based objective. The optimal solution is utilized as a benchmark for the achievable throughput for small scenarios, while the heuristic solution offers a distributed approach and can process scenarios with a higher number of aircraft. Additionally, an option for weighted-fair throughput allocation is included. Hence, large aircraft obtain a larger share of the throughput than smaller ones. This leads to fair throughput allocation with respect to the size of the aircraft. To analyze the performance of throughput allocation in the air-to-ground network, this work introduces an air-to-ground network model. It models the network realistically, but independent from specific network implementations, such as 5G or WiFi. It is also adaptable to different scenarios. The aircraft network is studied based on captured flight traces. Extensive and representative parameter studies are conducted, including, among others, different link setups, geographic scenarios, aircraft capabilities, link distances and link capacities. The results show that the throughput can be distributed optimally during high-aircraft-density times using the optimal solution and close to optimal using the heuristic solution. The mean throughput during these times in the optimal reference scenario with low Earth orbit satellites is 20 Mbps via direct air-to-ground links and 4 Mbps via satellite links, which corresponds to 10.7% and 1.9% of the maximum link throughput, respectively. Nevertheless, during low-aircraft-density times, which are less challenging, the throughput can reach more than 200 Mbps. Therefore, the challenge is on providing a high throughput during high-aircraft-density times. In the larger central European scenario, using the heuristic scheme, a minimum of 22.9 Mbps, i.e. 3.2% of the maximum capacity, can be provided to all aircraft during high-aircraft-density times. Moreover, the critical parameters to obtain a high throughput are presented. For instance, this work shows that multi-hop air-to-air links are dispensable for aircraft within direct air-to-ground coverage. While the computation time of the optimal solution limits the number of aircraft in the scenario, larger scenarios can be studied using the heuristic scheme. The results using the weighted-fair throughput allocation show that the introduction of weights enables a user-fair throughput allocation instead of an aircraft-fair throughput allocation. As a conclusion, using the air-to-ground model and the two introduced throughput allocation schemes, the achievable weighted-fair throughput per aircraft and the respective link choices can be quantified

Multi-Drone-Cell 3D Trajectory Planning and Resource Allocation for Drone-Assisted Radio Access Networks

Equipped with communication modules, drones can perform as drone-cells (DCs) that provide on-demand communication services to users in various scenarios, such as traffic monitoring, Internet of things (IoT) data collections, and temporal communication provisioning. As the aerial relay nodes between terrestrial users and base stations (BSs), DCs are leveraged to extend wireless connections for uncovered users of radio access networks (RAN), which forms the drone-assisted RAN (DA-RAN). In DA-RAN, the communication coverage, quality-of-service (QoS) performance and deployment flexibility can be improved due to the line-of-sight DC-to-ground (D2G) wireless links and the dynamic deployment capabilities of DCs. Considering the special mobility pattern, channel model, energy consumption, and other features of DCs, it is essential yet challenging to design the flying trajectories and resource allocation schemes for DA-RAN. In specific, given the emerging D2G communication models and dynamic deployment capability of DCs, new DC deployment strategies are required by DA-RAN. Moreover, to exploit the fully controlled mobility of DCs and promote the user fairness, the flying trajectories of DCs and the D2G communications must be jointly optimized. Further, to serve the high-mobility users (e.g. vehicular users) whose mobility patterns are hard to be modeled, both the trajectory planning and resource allocation schemes for DA-RAN should be re-designed to adapt to the variations of terrestrial traffic. To address the above challenges, in this thesis, we propose a DA-RAN architecture in which multiple DCs are leveraged to relay data between BSs and terrestrial users. Based on the theoretical analyses of the D2G communication, DC energy consumption, and DC mobility features, the deployment, trajectory planning and communication resource allocation of multiple DCs are jointly investigated for both quasi-static and high-mobility users. We first analyze the communication coverage, drone-to-BS (D2B) backhaul link quality, and optimal flying height of the DC according to the state-of-the-art drone-to-user (D2U) and D2B channel models. We then formulate the multi-DC three-dimensional (3D) deployment problem with the objective of maximizing the ratio of effectively covered users while guaranteeing D2B link qualities. To solve the problem, a per-drone iterated particle swarm optimization (DI-PSO) algorithm is proposed, which prevents the large particle searching space and the high violating probability of constraints existing in the pure PSO based algorithm. Simulations show that the DI-PSO algorithm can achieve higher coverage ratio with less complexity comparing to the pure PSO based algorithm. Secondly, to improve overall network performance and the fairness among edge and central users, we design 3D trajectories for multiple DCs in DA-RAN. The multi-DC 3D trajectory planning and scheduling is formulated as a mixed integer non-linear programming (MINLP) problem with the objective of maximizing the average D2U throughput. To address the non-convexity and NP-hardness of the MINLP problem due to the 3D trajectory, we first decouple the MINLP problem into multiple integer linear programming and quasi-convex sub-problems in which user association, D2U communication scheduling, horizontal trajectories and flying heights of DBSs are respectively optimized. Then, we design a multi-DC 3D trajectory planning and scheduling algorithm to solve the sub-problems iteratively based on the block coordinate descent (BCD) method. A k-means-based initial trajectory generation scheme and a search-based start slot scheduling scheme are also designed to improve network performance and control mutual interference between DCs, respectively. Compared with the static DBS deployment, the proposed trajectory planning scheme can achieve much lower average value and standard deviation of D2U pathloss, which indicate the improvements of network throughput and user fairness. Thirdly, considering the highly dynamic and uncertain environment composed by high-mobility users, we propose a hierarchical deep reinforcement learning (DRL) based multi-DC trajectory planning and resource allocation (HDRLTPRA) scheme for high-mobility users. The objective is to maximize the accumulative network throughput while satisfying user fairness, DC power consumption, and DC-to-ground link quality constraints. To address the high uncertainties of environment, we decouple the multi-DC TPRA problem into two hierarchical sub-problems, i.e., the higher-level global trajectory planning sub-problem and the lower-level local TPRA sub-problem. First, the global trajectory planning sub-problem is to address trajectory planning for multiple DCs in the RAN over a long time period. To solve the sub-problem, we propose a multi-agent DRL based global trajectory planning (MARL-GTP) algorithm in which the non-stationary state space caused by multi-DC environment is addressed by the multi-agent fingerprint technique. Second, based on the global trajectory planning results, the local TPRA (LTPRA) sub-problem is investigated independently for each DC to control the movement and transmit power allocation based on the real-time user traffic variations. A deep deterministic policy gradient based LTPRA (DDPG-LTPRA) algorithm is then proposed to solve the LTPRA sub-problem. With the two algorithms addressing both sub-problems at different decision granularities, the multi-DC TPRA problem can be resolved by the HDRLTPRA scheme. Simulation results show that 40% network throughput improvement can be achieved by the proposed HDRLTPRA scheme over the non-learning-based TPRA scheme. In summary, we have investigated the multi-DC 3D deployment, trajectory planning and communication resource allocation in DA-RAN considering different user mobility patterns in this thesis. The proposed schemes and theoretical results should provide useful guidelines for future research in DC trajectory planning, resource allocation, as well as the real deployment of DCs in complex environments with diversified users

Resource and Interference Management in UAV-Cellular Network

The future Sixth-Generation (6G) network is anticipated to extend connectivity for millions of Unmanned Aerial Vehicles (UAVs) worldwide and support various innovative use cases, such as cargo transport, inspection, and intelligent agriculture. The terrestrial cellular networks provide real-time information exchange between UAVs and Ground Control Stations (GCS), which facilitates the evolution of UAV communication systems while bringing promising economic benefits to cellular network operators. However, the tremendous growth in the UAV data traffic, with diverse and stringent service requirements, would add another pressure on the already congested terrestrial cellular network that is facing a rigorous challenge to increase network capacity with the limited spectrum resources. Moreover, since Macro Base Station (MBS) antennas are typically downtilt, UAVs, which are served by the MBS antenna’s side lobes, suffer from sharp signal fluctuations causing throughput reduction and coverage drop. Besides, due to the Line-of-Sight (LoS) between UAVs and MBSs, UAVs experience higher uplink/downlink interference compared to ground Cellular Users (CUs). In this thesis, we propose two novel aerial network architectures in which we design efficient interference and resource management strategies to support the UAV Quality-of-Service (QoS) guarantee while considering different types of interference. Firstly, we propose a novel standalone aerial multi-cell network where multiple UAV Base Stations (UAV-BSs) provide cellular services to UAV Users by reusing the licensed and unlicensed spectrum. Our objective is to jointly optimize the subchannels and power allocations of UAV-Users in the licensed and unlicensed spectrum to maximize the network uplink sum rate, considering inter-cell interference, co-existence with terrestrial cellular and WiFi systems, and the QoS of UAV-Users. We prove mathematically that the formulated optimization problem is an NP-hard problem. Therefore, the original problem is decomposed into three subproblems to solve it efficiently. We first use convex optimization and the Hungarian algorithm to obtain the global optimal of power and subchannel allocations in the licensed spectrum, respectively. Then, we design a matching game with externalities and coalition game algorithms to obtain the Nash stable of the subchannel allocation in the unlicensed band. Local optimal power assignment in the unlicensed spectrum is obtained using the successive convex approximation method. Lastly, we develop an iterative algorithm to solve the three subproblems sequentially until convergence is reached. Simulation results demonstrate that the proposed algorithm achieves a significantly higher uplink sum rate compared with other resource allocation schemes. Moreover, the proposed algorithm improves the network throughput and capacity by nearly two times comparing to the Long Term Evolution-Advanced (LTE-A). Secondly, we propose a novel integrated aerial-terrestrial multi-operator network. In the network, each operator deploys a number of UAV-BSs besides the terrestrial MBS, where each BS reuses the operator’s licensed spectrum to provide downlink connectivity for UAV-Users. Moreover, the operators allow the UAV-Users, whose demand cannot be satisfied by the licensed band, to compete with others to obtain bandwidth from the unlicensed spectrum. Given the QoS requirements of UAV-Users, we aim to maximize the total sum rate by jointly optimizing user association, BSs transmit power, and dynamic spectrum allocation considering inter-cell interference in the licensed band and inter-operator interference in the unlicensed spectrum. In particular, we divide the resulting non-convex Mixed-Integer Non-Linear Programming (MINLP) optimization problem into two sequential subproblems: user association and power control in the licensed spectrum; and dynamic spectrum allocation and user association in the unlicensed spectrum. Furthermore, the former subproblem is decomposed into multiple subproblems for distributed and parallel problem-solving. Since the resulting former subproblem is still a non-convex MINLP problem, we propose a distributed iterative algorithm consisting of a matching game, coalition game, and successive convex approximation technique to solve it. Afterwards, in the latter subproblem, we first use a matching game to associate UAV-Users with the UAV-BSs for each operator in the unlicensed spectrum. Then, we propose a three-layers auction algorithm to allocate the unlicensed spectrum among operators dynamically. Extensive simulation results demonstrate that the proposed algorithm in the licensed spectrum significantly improves network throughput per operator than the conventional terrestrial network alone. Moreover, the achieved system throughput of the proposed algorithms in both licensed and unlicensed spectrum is 86.8% higher compared with that of using the licensed spectrum only. In summary, we have proposed integrated aerial-terrestrial network architectures that leverage the aerial network to complete the terrestrial network to serve cellular-connected UAVs by reusing licensed and unlicensed spectrum considering multi-cell and multi-operator scenarios. Under the proposed network architectures, we have investigated the subchannel allocation, UAV-Users’ transmit power, user association, BSs’ transmit power, and dynamic spectrum management to maximize the network throughput considering the QoS of UAV-User. The proposed architectures and algorithms should provide valuable guidelines for future research in designing resource and interference management schemes, improving network capacity, and enhancing spectrum utilization for complex interference environments in integrated UAV-cellular networks