To alleviate the problem of scarce spectrum resources and meet the ever-increasing of mobile broadband data traffic demands, Licensed Assisted Access (LAA)-Long Term Evolution (LTE), operating in the unlicensed spectrum, is a promising solution. Considering that the unlicensed spectrum is shared by a few incumbent systems, such as IEEE 802.11 (i.e., WiFi), one main target is to guarantee the friendly and harmonious coexistence of LTE with other wireless systems in the unlicensed spectrum. Both listen-before-talk (LBT) and duty cycle methods are regarded as effective ways to solve the coexistence problem in academia and industry so far. Although there are a large number of theoretical researches on LTE in unlicensed spectrum (LTE-U), field trail results are still lacking. In this paper, an experimental testing platform is deployed to model the realistic environment. This paper focuses on three aspects. First, a typical indoor field trial scenario in 5.8 GHz unlicensed bands is deployed, and the performance of LTE-U and WiFi, including coverage and capacity, is evaluated. Specifically, a methodology to determine the proper clear channel assessment energy detection (CCA-ED) threshold for LTE-U is proposed to implement the friendly coexistence between LTE-U and WiFi systems. Second, supplementary downlink (SDL) and Cell ON/OFF mechanisms are investigated to verify the fair coexistence between LAA and WiFi in the unlicensed spectrum. Third, the Enhanced Cell ON/OFF scheme, which introduces Clear to Send (CTS)-to-Self (CTS2S) message, is discussed and evaluated. Based on the built testbed, we obtain threefold conclusions. First of all, introducing LTE into unlicensed spectrums can greatly improve the spectrum efficiency and optimize wireless resources. Furthermore, test results and analyses show that a proper CCA-ED threshold is necessary for coexisting friendly and fairly among different systems, and experiments are also provided to validate the feasibility of the suggested method in various scenarios. Second, experimental results show that SDL mechanism guarantees relatively friendly and harmonious coexistence between LAA and WiFi only in the sparse scenario, while basic Cell ON/OFF mechanism is more effective to ensure coexistence between LAA and WiFi than the SDL. Finally, with the introduction of CTS2S message, the Enhanced Cell ON/OFF scheme is able to achieve more peaceful coexistence between LTE and WiFi users employed in the same bands compared with the Basic Cell ON/OFF scheme.

With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, the dynamic environment of vehicles, and the long distances between the cloud servers and VUE induce some potential issues, such as extra communication delay and energy consumption. Fortunately, mobile edge computing (MEC), a promising computing paradigm, can ameliorate the above problems by enhancing the computing abilities of VUE through allocating the computational resources to VUE. In this paper, we propose a joint optimization algorithm based on a deep reinforcement learning algorithm named the double deep Q network (double DQN) to minimize the cost constituted of energy consumption, the latency of computation, and communication with the proper policy. The proposed algorithm is more suitable for dynamic scenarios and requires low-latency vehicular scenarios in the real world. Compared with other reinforcement learning algorithms, the algorithm we proposed algorithm improve the performance in terms of convergence, defined cost, and speed by around 30%, 15%, and 17%.

The cell-free network is regarded as an enabling technology for alleviating inter-cell interference due to its user-centric network paradigm. Reconfigurable intelligent surfaces (RISs) are used to assist cell-free networks in improving network capacity at a low cost. However, only half-space coverage can be achieved by RISs. With the unique capability of extending half-space coverage to full-space coverage, simultaneously transmitting and reflecting RIS (STAR-RIS) is emerged as an attractive technology. Motivated by this distinctive characteristic, multiple STAR-RISs deployed in millimeter wave (mmWave) cell-free systems to assist users in downlink transmission are investigated in this paper. Our objective is to maximize the weighted sum-rate (WSR) via joint optimization of association indicators, active beamforming vectors at mmWave base stations (MBSs) as well as transmission and reflection beamforming vectors of STAR-RISs, while the limits of STAR-RIS-user association, each MBS maximum transmit power as well as passive coefficients of STAR-RISs ought to be satisfied. To handle the non-convex problem, we develop the Lagrangian dual transform (LDT)- quadratic transform (QT)-based algorithm. To be specific, the LDT and QT are utilized for reformulating the non-convex problem as a more solvable equivalent one, afterwards, the transformed problem is decomposed into five subproblems via the alternating optimization (AO) method. Particularly, the solutions of association indicators, active beamforming vectors, and passive beamforming vectors are obtained through the linear conic relaxation (LCR)-based association scheme, the rank-one relaxation approach as well as the penalty-based norm approximation algorithm, respectively. Simulation results indicate that the proposed method can obtain better WSR performance over benchmarks. Some useful guidances are also provided for mmWave cell-free systems.

The unmanned aerial vehicle (UAV) technique combined with the cellular network is a promising and reliable technique to provide a low-cost, flexible, and easy-to-expandable network. In this article, we propose a UAV-assisted cooperative transmission network (UCTN), where UAVs and the base station (BS) transmit data jointly through the cooodination of the software-defined network (SDN). As the UAV has limited on-board energy, we aim to maximize the network energy efficiency (EE) by jointly optimizing the UAV–user association, UAV location, BS resource allocation, and the load allocation between the two systems. The formulated problem is a mixed-integer nonconvex optimization problem, which is intractable. To solve it, alternative optimization (AO) and Dinkelbach’s method are adopted to recast the original problem into a tractable form. After that, the corresponding problem is divided into three subproblems and can be solved iteratively. An energy-constrained coalition game is proposed to solve the UAV–user association problem, and the UAV hovering point is carefully designed to balance the coalition serving time. Furthermore, the successive convex approximation (SCA) technique is adopted to optimize the BS transmit power and bandwidth allocation. Numerical experiments demonstrate that the proposed algorithm achieves significant performance gains over other benchmark schemes.

Narrowband Internet of Things (NB-IoT) is a novel narrowband radio technology particularly designed for low power, low data rate, enhanced coverage and long battery life. To save power, the discontinuous reception mechanism (DRX) is standardized by 3rd Generation Partnership Project (3GPP) and also is adopted in the NB-IoT specifications as important methods to obtain long battery life. However, current DRX schemes used in the Long Term Evolution (LTE) are inadequate to the large-scale NB-IoT system. In this paper, aim to obtain better power saving and balance weak-up latency performance as well as avoid the possible signaling storm in an NB-IoT system with massive devices, a grouping based energy efficient DRX strategy is proposed. Moreover, different DRX schemes are devised for the group leader and group members to further reduce power consumption. A discrete time semi-Markov process is applied to numerically analyze the system performance and to obtain the power saving factor and weak-up latency. Simulations show the fact that how the various DRX parameters impact the system performance. Specifically, simulation results demonstrate our proposed DRX mechanism outperforms the LTE DRX scheme in terms of power-saving performance although at the slight and sustainable cost of latency.

Mobile-edge computing (MEC) has been deployed to enhance the data analysis performance of Internet of Things (IoT) and alleviate the shortage of computing resources for IoT devices. At the same time, energy harvesting (EH) is considered as a potential technology to prolong the network lifetime of the energy-limited IoT devices. In this article, we investigate the data analysis scenario in IoT architecture, where data are generated by wireless devices (WDs) and uploaded to the MEC server for centralized data processing. WDs, powered by the EH technology, upload the collected data in a frequency-division multiple access (FDMA) manner. Our goal is to maximize the long-term average system utility under the constraint of the Age of Information (AoI) and the limited system resources, by jointly optimizing the communication resource allocation, the data generating and discarding strategies, and the computing resource allocation. In order to solve the time-related AoI constraint, a series of virtual queues are applied to rewrite the optimization problem. To deal with the time-varying channel and the randomly data arrival, we propose a Lyapunov-based algorithm, decoupling the optimizing problem into several subproblems which can be fixed by traditional optimization algorithms and successive convex approximation (SCA) algorithm. With rigorous analysis, the proposed algorithm is proved to be an asymptotically optimal. Numerical results verify the theoretical analysis and reveal the effectiveness of the Lyapunov-based algorithm.