In recent years, Unmanned Aerial Vehicle (UAV) assisted wireless communication for Internet of Things (IoT) applications has garnered growing attention. Device to Device (D2D)-underlaid Unmanned Aerial Vehicles (UAV) communications (D2UAV) system has also become a promising method to provide high-quality services for the terrestrial users with low transmission delay and high communication demands in IoT. Due to the scarcity of licensed spectrum, the unlicensed spectrum is also expanded to the D2UAV system, which necessarily leads to the interference to incumbent users that mainly refers to WiFi systems. Considering the maneuverability of UAVs and the characteristics of the short distance and low transmit power for D2D communications in the D2UAV system, we propose a new Unlicensed Spectrum Sharing (USS) method called Direct Access (DA) to improve the unlicensed spectrum utilization efficiency and fairness in the paper, where the unlicensed spectrum can be directly used without the need for channel detection and without the limit of channel occupancy time but the interference to WiFi systems is strictly controlled, and then investigate the joint optimization problem of 3D trajectory and resource allocation integrating the DA USS method for the D2UAV system over licensed and unlicensed spectrum, aiming to maximize the total throughput of Cellular Users (CUs) via jointly optimizing 3D flight trajectory and resource allocation under various constraints. To solve this problem efficiently, the original problem is decomposed into three optimization subproblems of 3D trajectory, power control, and bandwidth allocation, and then they are solved alternatively in an iterative manner. Theoretical analysis and results show that the proposed DA USS method with the proposed interference protection method can implement the complete spatial reuse of unlicensed spectrum. A large number of simulation results demonstrated the effectiveness and feasibility of the proposed algorithm integrating the DA USS method.

As the demand for mobile data traffic continues to grow, offloading data traffic to unlicensed spectrum is a promising approach that can relieve the pressure on cellular systems. Therefore, it is an urgent need to propose an unlicensed spectrum access method to guarantee the harmonious and efficient coexistence between cellular network technologies such as LTE and incumbent users such as WiFi in the unlicensed spectrum. However, existing coexistence schemes such as licensed assisted access (LAA) and LTE-unlicensed (LTE-U) still suffer from inefficient spectrum utilization and unsatisfactory fairness. In the paper, we formulate the optimization problem of the unlicensed spectrum access among multiple small bases (SBSs) as a game, and then solve the Nash Equilibrium (NE) with cooperative and distributed multi-agent deep reinforcement learning (MADRL). Specifically, a two level access framework for the coexistence scenario, which consists of feedback cycle and executive cycle, is first proposed, and then the key elements of MADRL including state, action, reward and Q-network are designed in detail based on the proposed access framework. To overcome the problems of learning divergence and prohibitive computation overhead in the coexistence scenario with multiple SBSs due to the non-stability phenomena, we adopt the mean field technology to solve the NE, which can simplify the process of solving NE by converting the interaction of an agent with the remaining multiple agents into an action with the average effect of them. Simulation results show that 1) the proposed algorithm can overcome the learning divergence problem and converge to the NE quickly, and 2) the proposed algorithm can achieve the bi-objective optimization of total throughput and fairness of the coexistence network, and can achieve better performance in terms of throughput and fairness compared with the baseline methods such as Cat-4 LBT, Cooperative LBT and Random schemes.

Narrow band internet of thing (NB-IoT) is an emerging internet of things technology that is based on cellular networks. Since there is only 180KHz uplink spectrum resource in NB-IoT, it is necessary to optimize the uplink resource allocation in order to obtain as many successful communication devices as possible. However, there is a lack of an uplink resource allocation algorithm that can cope with various situations in existing allocation methods. Therefore, an adaptive uplink resource control algorithm is proposed in the paper. In the algorithm, the base station first estimates the maximum number of accessible devices through exhaustively calculation of the retransmission times, the number of preambles and the size of the transmission data, and then the base station adjusts uplink resource and controls the number of access devices according to the current network load. The simulation results show that compared with the traditional algorithms, the proposed algorithm can maximize the number of successful communication devices in various situations.

The coexistence performance of LTE-licensed assisted access (LAA) and WiFi networks has been extensively investigated. However, these works ignore capture effect, which is the phenomenon that the strongest signal may still be successfully received when more than two signals are transmitted simultaneously on the same channel, and which may occur more frequently in the coexistence scenario than in the pure WiFi network. This may lead to very large deviation in the coexistence performance evaluation. In the paper, we deeply investigate the coexistence performance of LAA and WiFi networks with the capture effect. More specifically, a capture model for more than two signals is first proposed in the coexistence scenario, and the capture probability is derived. Then the LAA access schemes are modeled as a new two-dimensional discrete Markov model integrating with the capture effect. A large number of simulation and numerical results verify the validity of the proposed Markov chain and capture model. The results also show that the capture effect can not only significantly decrease the collision probability but also increase LAA and WiFi throughput as well as total throughput. All these results prove the necessity of considering the capture effect in coexistence performance evaluation.

The spectrum resources licensed to the mobile operators become increasingly scarce because of the explosive growth of the mobile traffic. Device-to-Device (D2D) communication is thus proposed to be deployed in unlicensed frequency bands, i.e. D2D-Unlicensed (D2D-U). The fixed duty cycle method is generally adopted in the coexistence scenario of D2D and WiFi, which may lead to unfair unlicensed spectrum usage since it cannot adapt the data traffic change. Therefore, a Q-learning (QL) based resource allocation algorithm for D2D-U is proposed in this paper. In the algorithm, the considered cellular base station acts as the agent. The actions of agent are defined as the different combinations of the transmission power and the duty cycle of D2D-U users, and the states of agent are defined as the different combinations of the total throughput, fairness and signal-to-noise ratio (SNR) of cellular users. Based on the proposed QL framework, the agent can always learn the optimal power allocation and duty cycle by interacting with the environment, which can maximize the total throughput and fairness while ensuring the satisfactory SNR of cellular users. The simulation results show that the proposed algorithm can obtain the largest throughput and the best fairness while ensuring the satisfactory SNR of LTE-U users among all traditional algorithms.

3GPP's standardization of the Narrowband Internet of Things (NB-IoT) paved the way to support the use of low-power wide area (LPWA) in cellular networks. The design goal of NB-IoT is to expand coverage, low-power and low-cost devices, and large-scale connections. As a new wireless access technology, this paper establishes a Markov model with the working state of the terminal device as the state variable for the extended discontinuous acceptance (eDRX) mechanism adopted by NB-IoT, and calculates the corresponding power consumption and Time delay model. Given that the previous calculations of power consumption and delay did not consider the impact of random access. in this article, We propose a Markov chain with random access process. The numerical results show that the backoff time of each access failure of the terminal device during the process of accessing the network has a greater impact on the power consumption and delay.

The energy detection technology is adopted as the detection method of the unlicensed channel in LTE release 13. However, the detection may be imperfect in actual scenario due to the simplicity of the detection method. In order to eliminate the controversy over LTE licensed assisted access (LAA) scheme, the paper deeply investigates the performance of the scheme under imperfect spectrum sensing (ISS). Considering possible ISS during the enhanced clear channel assessment (eCCA), the LAA scheme is modeled as a new two dimensional discrete time markov chain. In order to capture ISS, different from the definition in Bianchi's model, the discrete time scale is defined as the end of backoff slot time in the proposed model. The definition enables LAA small base stations to enter the defer state from backoff state when the channel is detected to be busy, and further possible ISS in the defer period and backoff slot time (BST) of eCCA can be considered. Based on the proposed model, the expressions of collision probabilities and throughput under three backoff mechanisms based LAA schemes are derived to deeply analyze their performance by comparisons. A large number of experimental and analytical results prove the validity of the proposed model. The analytical results also show that not only the backoff mechanisms but also ISS can greatly affect the network performance. In the respect of fairness (i.e. the impact on WiFi users), the WiFi collision probability always increases with the decrease of the false alarm probability (FAP). This means the WiFi collision probabilities under three LAA schemes may be higher than the baseline level for the graceful coexistence when FAP is very little. In this sense, the LAA schemes cannot be referred to as being graceful though they have been proven to be deterministic graceful coexistence schemes under perfect spectrum sensing (PSS). In the respect of throughput, LAA throughput always decreases and WiFi throughput increases with the growth of FAP, and total throughput always decreases. Therefore, based on the fixed SNR and sampling rate in actual scenario, the network performance can be tuned by energy threshold, as is consistent with the viewpoint in other literatures.