In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks is studied. Therein, the network-wide power ...consumption of vehicular users (VUEs) is minimized subject to high reliability in terms of probabilistic queuing delays. Using extreme value theory (EVT), a new reliability measure is defined to characterize extreme events pertaining to vehicles' queue lengths exceeding a predefined threshold. To learn these extreme events, assuming they are independently and identically distributed over VUEs, a novel distributed approach based on federated learning (FL) is proposed to estimate the tail distribution of the queue lengths. Considering the communication delays incurred by FL over wireless links, Lyapunov optimization is used to derive the JPRA policies enabling URLLC for each VUE in a distributed manner. The proposed solution is then validated via extensive simulations using a Manhattan mobility model. Simulation results show that FL enables the proposed method to estimate the tail distribution of queues with an accuracy that is close to a centralized solution with up to 79% reductions in the amount of exchanged data. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, while reducing the average power consumption by two folds, compared to an average queue-based baseline.
In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, are ...investigated. In particular, to enable reliable uplink communications for the IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the 3D placement and the mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs' locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total-transmit power of the IoT devices is reduced by 45% compared with a case, in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28% enhanced system reliability compared with the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs.
In this paper, the problem of resource management is studied for a network of wireless virtual reality (VR) users communicating over small cell networks (SCNs). In order to capture the VR users' ...quality-of-service (QoS) in SCNs, a novel VR model, based on multi-attribute utility theory, is proposed. This model jointly accounts for VR metrics, such as tracking accuracy, processing delay, and transmission delay. In this model, the small base stations (SBSs) act as the VR control centers that collect the tracking information from VR users over the cellular uplink. Once this information is collected, the SBSs will then send the 3-D images and accompanying audio to the VR users over the downlink. Therefore, the resource allocation problem in VR wireless networks must jointly consider both the uplink and downlink. This problem is then formulated as a noncooperative game and a distributed algorithm based on the machine learning framework of echo state networks (ESNs) is proposed to find the solution of this game. The proposed ESN algorithm enables the SBSs to predict the VR QoS of each SBS and is guaranteed to converge to mixed-strategy Nash equilibrium. The analytical result shows that each user's VR QoS jointly depends on both VR tracking accuracy and wireless resource allocation. Simulation results show that the proposed algorithm yields significant gains, in terms of VR QoS utility, that reach up to 22.2% and 37.5%, respectively, compared with Q-learning and a baseline proportional fair algorithm. The results also show that the proposed algorithm has a faster convergence time than Q-learning and can guarantee low delays for VR services.
Providing connectivity to unmanned aerial vehicle-user equipment (UAV-UE), such as drones or flying taxis, is a major challenge for tomorrow's cellular systems. In this paper, the use of coordinated ...multi-point (CoMP) transmission for providing seamless connectivity to UAV-UEs is investigated. In particular, a network of clustered ground base stations (BSs) that cooperatively serve a number of UAV-UEs is considered. Two scenarios are studied: scenarios with static, hovering UAV-UEs and scenarios with mobile UAV-UEs. Under a maximum ratio transmission, a novel framework is developed and leveraged to derive upper and lower bounds on the UAV-UE coverage probability for both scenarios. Using the derived results, the effects of various system parameters such as collaboration distance, UAV-UE altitude, and UAV-UE speed on the achievable performance are studied. Results reveal that, for both static and mobile UAV-UEs, when the BS antennas are tilted downwards, the coverage probability of a high-altitude UAV-UE is upper bounded by that of ground user equipments (UEs) regardless of the transmission scheme. Moreover, for low signal-to-interference-ratio thresholds, it is shown that CoMP transmission can improve the coverage probability of UAV-UEs, e.g., from 28% under the nearest association scheme to 60% for an average of 2.5 collaborating BSs. Meanwhile, key results on mobile UAV-UEs unveil that not only the spatial displacements of UAV-UEs but also their vertical motions affect their handover rate and coverage probability. In particular, UAV-UEs that have frequent vertical movements and high direction switch rates are expected to have low handover probability and handover rate. Finally, the effect of the UAV-UE vertical movements on its coverage probability is marginal if the UAV-UE retains the same mean altitude.
In this paper, the problem of joint caching and resource allocation is investigated for a network of cache-enabled unmanned aerial vehicles (UAVs) that service wireless ground users over the LTE ...licensed and unlicensed bands. The considered model focuses on users that can access both licensed and unlicensed bands while receiving contents from either the cache units at the UAVs directly or via content server-UAV-user links. This problem is formulated as an optimization problem, which jointly incorporates user association, spectrum allocation, and content caching. To solve this problem, a distributed algorithm based on the machine learning framework of liquid state machine (LSM) is proposed. Using the proposed LSM algorithm, the cloud can predict the users' content request distribution while having only limited information on the network's and users' states. The proposed algorithm also enables the UAVs to autonomously choose the optimal resource allocation strategies that maximize the number of users with stable queues depending on the network states. Based on the users' association and content request distributions, the optimal contents that need to be cached at UAVs and the optimal resource allocation are derived. Simulation results using real datasets show that the proposed approach yields up to 17.8% and 57.1% gains, respectively, in terms of the number of users that have stable queues compared with two baseline algorithms: Q-learning with cache and Q-learning without cache. The results also show that the LSM significantly improves the convergence time of up to 20% compared with conventional learning algorithms such as Q-learning.
Performing cellular long term evolution (LTE) communications in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome wireless spectrum scarcity. ...However, to reap the benefits of LTE-LAA, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-LAA small base stations (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and f ractional spectrum access while guaranteeing fairness with existing WiFi networks and other LTE-LAA operators. Adopting a proactive coexistence mechanism enables future delay-tolerant LTE-LAA data demands to be served within a given prediction window ahead of their actual arrival time thus avoiding the underutilization of the unlicensed spectrum during off-peak hours while maximizing the total served LTE-LAA traffic load. To this end, a noncooperative game model is formulated in which SBSs are modeled as homo egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with wireless local area network and other LTE-LAA operators over a given time horizon. The proposed deep learning algorithm is then shown to reach a mixed-strategy Nash equilibrium, when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-LAA network.
In this paper, the effective use of flight-time constrained unmanned aerial vehicles (UAVs) as flying base stations that provide wireless service to ground users is investigated. In particular, a ...novel framework for optimizing the performance of such UAV-based wireless systems in terms of the average number of bits (data service) transmitted to users as well as the UAVs' hover duration (i.e. flight time) is proposed. In the considered model, UAVs hover over a given geographical area to serve ground users that are distributed within the area based on an arbitrary spatial distribution function. In this case, two practical scenarios are considered. In the first scenario, based on the maximum possible hover times of UAVs, the average data service delivered to the users under a fair resource allocation scheme is maximized by finding the optimal cell partitions associated to the UAVs. Using the powerful mathematical framework of optimal transport theory, this cell partitioning problem is proved to be equivalent to a convex optimization problem. Subsequently, a gradient-based algorithm is proposed for optimally partitioning the geographical area based on the users' distribution, hover times, and locations of the UAVs. In the second scenario, given the load requirements of ground users, the minimum average hover time that the UAVs need for completely servicing their ground users is derived. To this end, first, an optimal bandwidth allocation scheme for serving the users is proposed. Then, given this optimal bandwidth allocation, optimal cell partitions associated with the UAVs are derived by exploiting the optimal transport theory. Simulation results show that our proposed cell partitioning approach leads to a significantly higher fairness among the users compared with the classical weighted Voronoi diagram. Furthermore, the results demonstrate that the average hover time of the UAVs can be reduced by 64% by adopting the proposed optimal bandwidth allocation scheme as well as the optimal cell partitioning approach. In addition, our results reveal an inherent tradeoff between the hover time of UAVs and bandwidth efficiency while serving the ground users.
Next-generation wireless networks must enable emerging technologies such as augmented reality and connected autonomous vehicles via a wide range of wireless services that span enhanced mobile ...broadband (eMBB) and ultra-reliable low-latency communication (URLLC). Existing wireless systems that solely rely on the scarce sub-6 GHz, mW frequency bands will be unable to meet such stringent and mixed service requirements for future wireless services due to spectrum scarcity. Meanwhile, operating at high-frequency mmWave bands is seen as an attractive solution, primarily due to the bandwidth availability and possibility of large-scale multi-antenna communication. However, even though leveraging the large bandwidth at mmWave frequencies can potentially boost the wireless capacity for eMBB services and reduce the transmission delay for low-latency applications, mmWave communication is inherently unreliable due to its susceptibility to blockage, high path loss, and channel uncertainty. Hence, to provide URLLC and high-speed wireless access, it is desirable to seamlessly integrate the reliability of mW networks with the high capacity of mmWave networks. To this end, in this article, the first comprehensive tutorial for integrated mmWave-mW communications is introduced. This envisioned integrated design will enable wireless networks to achieve URLLC along with eMBB by leveraging the best of two worlds: reliable, long-range communications at the mW bands and directional high-speed communications at the mmWave frequencies. To achieve this goal, key solution concepts are discussed that include new architectures for the radio interface, URLLC-aware frame structure and resource allocation methods along with mobility management, to realize the potential of integrated mmWave-mW communications. The opportunities and challenges of each proposed scheme are discussed and key results are presented to show the merits of t
Cellular-connected UAVs will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging ...from online video streaming to medical delivery. However, to enable reliable wireless connectivity for the UAVs as well as secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this article, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, ANN-based solution schemes are introduced. The introduced approaches enable UAVs to adaptively exploit wireless system resources while guaranteeing secure operation in real time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use cases.
In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage intelligent, ...data-driven functions enabled by the integration of machine learning (ML) notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems. For this purpose, we first present a detailed overview of a number of key types of ANNs that include recurrent, spiking, and deep neural networks, that are pertinent to wireless networking applications. For each type of ANN, we present the basic architecture as well as specific examples that are particularly important and relevant wireless network design. Such ANN examples include echo state networks, liquid state machine, and long short term memory. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality applications over wireless networks as well as edge computing and caching. For each individual application, we present the main motivation for using ANNs along with the associated challenges while we also provide a detailed example for a use case scenario and outline future works that can be addressed using ANNs. In a nutshell, this paper constitutes the first holistic tutorial on the development of ANN-based ML techniques tailored to the needs of future wireless networks.
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