The next generation vehicular applications substantially shifting the paradigm of human activity have been projected to empower intelligent transportation systems. Targeting at supporting vehicle-to-everything connections, conventional mobile network architectures mandatorily requiring data routing through the core network, however, induce unacceptable costs both in end-to-end latency and backhaul resource consumption. The technical merit of moving computation and storage resources along with mobile vehicles consequently renders the mobile edge computing (MEC) a promising remedy to relieve the burden at the core network. To practice MEC, 3GPP has launched the normative works of a new paradigm known as the local area data network (LADN). Through performing in-network cache to store popular information at LADNs, a vehicle locating within the service area of an LADN is able to access particular location-based wireless application and information. Avoiding data routing through the core network, LADNs, however, encounter two critical challenges in downlink radio access to induce additional latency issues: 1) resource starvation at fronthaul links and 2) discrimination of quality-of-service requirements of vehicles with distinct capabilities. To tackle these challenges, through formulating the Lyapunov function, a stochastic optimization maximizing the utilization of fronthaul resources while stabilizing the queue (and thus latency) of each vehicle is proposed to address the resource starvation. Subsequently, a reinforcement learning-based multiarmed bandit algorithm is further proposed to achieve optimum harmonization of feedback-based and feedbackless transmissions, so as to strikes the tradeoff among energy efficiency, latency, and reliability. The performance evaluation results full demonstrate the effectiveness of the proposed design, to serve urgent needs in the deployment of LADNs.

Unmanned technologies facilitating human activities have been regarded as the most promising innovation to empower fully automatic and intelligent ecosystems. Targeting at extending the processing capabilities of humans, unmanned mobile machines (UMMs) are designated to optimally process the action under varying operating conditions, which relies on prompt information provisioning through existing cellular infrastructures, and renders latency control to information acquisition an inevitable challenge. For this purpose, caching information at network edges has been a remedy for substantial latency reduction, which however ignores practical cell deployment inducing imbalanced wireless services to each UMM in hotspot and rural areas. In this paper, through formulating the Lyapunov function, an algorithm optimizing the utilization of fronthaul resources while stabilizing each UMM's queue is proposed for edge information cache and dissemination in hotspot areas. Furthermore, through formulating the cost measurement as the Cobb-Douglas production function, the optimal beginning time of cache is also derived for UMMs in rural areas. With the provided analytical foundations and simulation studies, the effectiveness of our latency control scheme is fully demonstrated.

In the heterogeneous cloud radio access network (H-CRAN), which consists of multiple access points (APs) providing smaller coverage and a high power node (HPN) providing ubiquitous coverage, the mobile machines can connect to multiple APs and HPN by coordinated multi-point transmission (CoMP) concurrently to achieve ultra-reliable and low-latency communication. However, the current network association (or priorly known as handovers), which only focuses on switching between two base stations, may not be an efficient scheme in H-CRAN. In this paper, we innovate a proactive network association mechanism by taking CoMP into consideration under the H-CRAN architecture. We consider two scenarios under the H-CRAN architecture: with and without the assistance of HPN in the network. By regarding APs/HPN in H-CRAN as resources that allocated to mobile machines, a novel proactive network association concept is proposed, and then generalized from one-to-one to multiple-to-multiple case. With the assistance of Lyapunov optimization theory, effective bandwidth, and capacity theory, we can prove that this proactive network association scheme can guarantee that the queueing delay performance and the delay violation probability can be both smaller than a corresponding upper bound. That is, both low-latency and ultra-reliable communication can be guaranteed. We also conduct experiments by using real trace from taxis movement data to verify the analytical results. Our results suggest the guidelines to design the proactive network association scheme in H-CRAN.

This paper presents a framework for pursuing lowlatency communication among V2V networks underlaying V2I networks. To achieve low-latency communication, solely relying on the improvement of the air-interface may not be enough. To cope with the highly dynamic environment of vehicular networks, a time dynamic optimization approach is proposed that improves the latency performance through not only optimization of spectrum resources but also by constraining the network switching rate. To further decrease the complexity of the time dynamic optimization problem, we convert the original problem to a deterministic optimization problem through the Lyapunov Optimization Theory. The proposed algorithm becomes a more suitable scheme for the vehicular network. Analytical results show that the proposed scheme can approach the best tradeoff between the latency performance and the network switching rate. Simulation results are provided to verify the proposed algorithm.

Different from the International Mobile Telecommunications Advanced (IMT-Advanced) system solely enhancing the transmission data rates regardless the variety of emerging wireless traffic, the IMT-2020 system supports enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low latency communications (URLCC) to fully capture diverse wireless services in 2020. To satisfactorily gratify the scope of IMT-2020, 3GPP has launched the standardization activity of the fifth generation (5G) New Radio (NR) to deploy the first phase (Release 15) system in 2018 and the ready (Release 16) system in 2020. As eMBB is a legacy system from IMT- Advanced, URLLC jointly demanding low latency and high reliability, and mMTC emphasizes on high reliability may consequently induce significant impacts on the designs of NR air interface. On the advert of the conventional feedback based transmission in LTE/LTE-A designed for eMBB imposing potential inefficiency in the support of URLLC and mMTC, in this paper, we revisit the feedbackless transmission framework, and reveal a tradeoff between these two transmission frameworks. A multi-armed bandit (MAB) based reinforcement learning approach is therefore proposed to achieve the optimum harmonization of feedback and feedbackless transmissions. Our simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC and mMTC, to justify the potential of our approach in the design of 5G NR.

To achieve low delay for vehicular communication in cellular networks, the use of direct device-to-device (D2D) communication among vehicles is regarded as a key functional requirement. When D2D users share the spectrum with regular cellular users (D2D underlay), resource allocation schemes assure the coexistence between D2D and cellular users. Due to the high mobility of the vehicles, the resources need to be reallocated, but frequent updates cause high signaling overhead and degrade the delay performance. In this paper, we present a radio resource re-allocation scheme for vehicular D2D users in a platoon scenario. The scheme reduces the re-allocation rate and gives delay guarantees for each vehicle.With the help of Lyapunov optimization, a closed form of the upper delay bound and resource re-allocation rate is also derived. The simulation results show that the proposed scheme can provide an delay upper bound and simultaneously minimizes the resource re-allocation rate.

In the following decades, traffic volumes and the number of devices are projected to increase a ten to thousand-fold. Unfortunately, existing cellular networks adopting closed-loop communications impose too large spectrum overheads, which lead to an unacceptably low spectrum efficiency and unaffordable traffic burdens. To support the extremely challenging and unavoidable massive data exchanges in 2020 and beyond, we fundamentally re-consider an efficient scheme of the fully packet-switching radio access, that is, open-loop communications. To eliminate the concerns on the capabilities of enhancing the spectrum efficiency and reliability to support multimedia transmissions via open-loop communications, in this paper, we develop the optimum transmission repetition scheme to maximize the resource utilization while providing quality-of-service (QoS) guarantees. Our results confirm the efficiency, effectiveness, and practicability of the fully packet-switching radio access as compared with existing closed-loop communications, which suggest a revolutionary system design as the foundations for the fifth generation (5G) networks.

Emerging multi-hop machine-to-machine (M2M) communications that likely support a large number of wireless devices create new challenges for spectrum scarcity and energy efficiency. In parallel to pursuing physical layer transmission efficiency, traffic compression to reduce required wireless transmissions suggests a new paradigm of wireless networks. Utilizing the natures of broadcasting and information collection in wireless sensor or machine networks, cognitive traffic compression can be facilitated by our proposed optimal fusion rules and topology compression algorithm. Therefore, only the necessary and connected sensors/machines in M2M networks are required to transmit, to achieve the desirable distortion of information collection (i.e. detection/estimation error). In other words, given the desirable distortion, the number of sensors to transmit, or equivalently the total energy consumption, serves our purpose of energy efficiency for end-to-end networking. Numerical results show successful compression of total network traffic to significantly enhance networking energy efficiency.

Cooperative spectrum sensing is widely known to collect sensing information from cooperative sensors to assist cognitive radio transmitter to better judge the transmission opportunities, with appearance of hidden primary system terminals. However, it is usually ignored that a successful transmission can go through by requiring not only the spectrum availability at cognitive radio transmitter, but also spectrum availability at cognitive radio receiver. Using this special observation, we use Poisson distributed nodes and Boolean random network model to analyze the geometric region allowing CR transmission with the help of cooperative sensors. Contrary to intuition, we find that cooperative sensing is not always helpful and the region allowing CR transmission is no longer circular anymore. Using this geometric property, we further find that transmission link can remain bidirectional only under certain geometric conditions. Due to these observations, we develop a complete methodology so that a secondary transmitter can select helpful cooperative sensors corresponding to different receivers.