Uplink Resource Allocation Techniques For Massive Accesses For M2M Communications

Machine-to-Machine (M2M) communications is quickly becoming the vital force to support the Internet of Things (IoT) in the Long Term Evolution (LTE) systems and the future Fifth Generation (5G) systems.With the wide application for M2M communications in the security monitoring, cargo tracking, intelligent pay, health care and remote monitoring, the number of M2M communication terminals is massively expanding. However, when the massive M2M communication terminals access the existing mobile communication network designed for Human-to-Human (H2H) communications, the Radio Access Network(RAN) overload happens, which will reduce the successful accesses,increase the access delay and energy consumption, and even lead to the suspension of M2M communications service. Therefore, the network access capacity becomes the main restricting factor for the massive M2M communications. Due to the limited wireless resource, how to improve the network access capacity is essentially a problem of how to make more efficient use of the wireless resource. Therefore, in order to improve the network access capacity for massive M2M communications, the key point is to design a reasonable way of resource allocation. Since the M2M communications has main businesses in the uplink, the resource allocation between the random access channel and data transmission channel is the main research content in this paper. Generally speaking, the research results in this paper mainly include the following aspects:1. To realize the compromise between the performance of access capacity and the complexity of uplink resource allocation, this paper proposes an uplink resource allocation method based on the limited number of terminal interval. The expression of access capacity is first derived. To maximize the access capacity, the relationship between the access capacity and traffic load, as well as resource allocation between random access channel and data transmission channel are analyzed, the limited number of terminal interval and the corresponding resource allocation as well as the optimal traffic load are decided. Then, based on the current traffic load, the Access Class Barring (ACB) factor is adjusted to control the number of terminals for reducing the collision probability and increasing the access capacity. Then, the terminal interval that the controlled terminals belong is determined, as well as the corresponding resource allocation. In addition, this paper proposes an uplink resource allocation method that the new arriving terminals are controlled. The controlled new arriving terminals and retransmission terminals as the actual terminals access the network according to the proposed uplink resource allocation based on the limted number of terminal intervals.2. Considering the variety of M2M businesses and the difference of business characteristic, this paper proposes a dynamic resource allocation method with Quality of Service (QoS) guarantee for multiple M2M businesses. In view of two kinds of delay-sensitive M2M businesses competing uplink resource, traffic load is effectively controlled and uplink resource allocation is optimized under the premise of satisfying the access delay requirements for each business. The relationship among the access capacity, ACB factors, resource allocation for different business and channel resource allocation proportion is derived. To meet QoS requirements for M2M businesses, and to maximize the access capacity and resource efficiency, the solutions for ACB factors, the resource allocation for different business and the channel resource allocation proportion are transformed into the mathematical model solving the random access channel resource for different business. Upon the optimal random access channel resource for different business is solved, the optimal ACB factors, the optimal resource allocation for different business and the optimal channel resource allocation proportion are determind.3. In the actual system, the current traffic load needs to be predicted for overload control and uplink resource allocation. This paper proposes a joint traffic load prediction and uplink resource allocation method. The mathematical model for traffic load prediction is first derived, and then the resource allocation between the random access channel and data transmission channel is optimized based on the predicted traffic load.Dual ACB mechanisms are introduced, and the mathematical model for traffic load prediction is relative to the first ACB factor and the number of collision preambles. The first ACB factor can be directly determined.Through the designed random access process, whether the terminal sends the collision preamble can be confirmed by judging the terminal identification (ID) and preamble ID, therefore, the traffic load prediction method is simple in implementation. In addition, according to the relationship among the access capacity, ACB factors, the predicted traffic load and resource allocation, the resource allocation between random access channel and data transmission channel is optimized to improve the access capacity and resource efficiency.4. For the demand of massive accesses and high access capacity in the 5G wireless network, Non-Orthogonal Multiple Access (NOMA) is seen as the breakthrough in the next generation mobile communications,which realizes higher access capacity and higher efficiency in the limited spectrum resource. From the perspective of multiple access and resource allocation, this paper proposes a hybrid multiple access and resource allocation method. The performance advantage of Orthogonal Multiple Access (OMA) and the spectrum efficiency advantage of NOMA are combined to maximize the number of terminal connections on the same time-frequency resource. A hybrid multiple access and resource allocation is designed, including the resource allocation between NOMA and OMA,as well as the reource allocation between random access channel and data transmission channel. The algorithm of terminal pairing and power allocation for hybrid multiple access is designed. Before M2M terminal accesses the network, the appropriate multiple access is decided according to the channel state information, the data rate of business and transmit power. According to the algorithm, traffic load for OMA and NOMA can be decided. If all the terminals in the cell select the right multiple access, the access capacity and resource efficiency is improved.