Research On Key Radio Resource Management Technology In 3G LTE

Rapid development of mobile communication and the merging of mobile network and Internet have put dramatically growing demands for the wide-band mobile communication. Therefore, the 3^rd Generation Partnership Project (3GPP) has proposed a new mobile communication system evoluted from the 3^rd Generation system, which is called as the 3G long term evolution (3G LTE). It will provide higher bandwidth, lower latency, and better quality of service (QoS) guarantees.The most significant improvement of 3G LTE uplink system is that it employs many novel air-interface techniques. Among them, the single carrier-frequency division multiple access (SC-FDMA) and virtual multiple input multiple output (Virtual MIMO) are one of the most important ones. SC-FDMA is not only easy to be realized but also can overcome the inter-symbol interference due to the multi-path mobile channel propagation. Compared to orthogonal frequency division multiple access (OFDMA), SC-FDMA has the lower peak-to-average power ratio (PAPR) at the transmitter. On the other hand, the Virtual MIMO can increase more channel capacity than the single input multiple output (SIMO) significantly.When employing these two techniques, the schedulable resource of the system becomes three dimensions including space, time and frequency domain. So more multi-user diversity gain can be exploited by utilizing the time-variant channel fading. Thus the system capacity can be further improved by the advanced radio resource management (RRM) technologies and the QoS of each user can also be guaranteed. Therefore, this dissertation focuses on the radio resource management based on these two techniques. There are four major schemes proposed in this dissertation.Scheme 1: For SC-FDMA system, the inter-cell co-channel interference is the bottleneck of the system capacity. A distributed power control scheduling algorithm is proposed to solve this problem. In this algorithm, the scheduling and power control are carried out in each cell independently in order to avoid the complexity of joint multi-cell scheduling. The simulation results show that this algorithm can reduce inter-cell interference and improve the system capacity with providing the reasonable fairness.Scheme 2 and 3: When employing Virtual MIMO, how to pair users to acheive high Virtual MIMO channel capacity is the key to improve the system performance. In Virtual MIMO, there are two types of user paring scheduling for the radio resource managements:Type 1: The system schedules one user, according to a traditional scheduling algorithm which is not related with Virtual MIMO channel, and then chooses another pairing user to maximize the Virtual MIMO channel capacity. This type is simple, but it can't provide enough QoS guarantees. A pairing algorithm is proposed in this dissertation based on signal to noise ratio (SNR) of the users. The users are divided into two groups, i.e. high-SNR group and low-SNR group, each group has its individual pairing metric. The co-group users of the scheduled user have the pairing priority, and a marginal utility function is provided for crossover-group pairing. The simulation results show that the algorithm can provide higher system throughput and maintain fairness for low SNR users.Type 2: The scheduling algorithm is combined with Virtual MIMO user pairing; the system chooses the user pair, whose utility function value is the biggest, from all possible user pair candidates. This type can provide QoS guarantees, but it is complex. For this case, a cross-layer scheduling algorithm is proposed. The algorithm utilizes the queue information from radio link control (RLC) layer, and the channel fading information of physical layer. The simulation results show that the algorithm can guarantee the QoS latency request of users, and provides fairness in a manner.Scheme 4: In Virtual MIMO scheduling type 2, the computing complexity of exhaustive searching best user pair is very high. In this dissertation, the scheduling factor of users in SIMO mode are calculated, and the scheduler chooses some users whose scheduling factor are big to form a group. Then, the scheduler uses this group as the initial population of the genetic algorithm to find the best pair. Another method is to search best pair in this local group directly. The simulation results show that the performance ot the genetic scheme with the chosen initial population, can approach the performance of the exhaustive search scheme. As for the local group searching scheme, though the scheduling precision is lower, it can further reduce the computing complexity. So they can be chosen to use according to the request of complexity and precision of scheduling.