Uplink Spectrum Overlay Coverage Enhancement Algorithm in 5G Network

The imbalance between the uplink and downlink rates and coverage of the 5G network has led to limited vertical industry services. Aiming at breaking the imbalance between the uplink and downlink rates and improving the coverage of 5G network, a uplink coverage enhancement algorithm is designed from the aspects of networking mode, bandwidth, uplink and downlink subframe ratio, etc. It uses high- and low-frequency time-frequency joint scheduling to enable uplink full-time slot scheduling, thereby improving uplink coverage and rate. According to the actual test on the live network, the results show that the super-uplink algorithm can increase the near-point uplink rate by 15% to 30%, increase the uplink rate for indoor midpoint scenarios by 40% to 80%, and increase the uplink rate for outdoor and indoor weak spot scenarios by 100% to 400%.

Resource scheduling based on routing tree and detection matrix for Internet of things

Effective scheduling of limited communication resources is one of the critical methods for data transmission in the Internet of things. However, the time slot utilization rate of many existing resource scheduling methods of Internet of things is not high. This article proposes a new efficient resource scheduling based on routing tree and detection matrix for Internet of things. In heterogeneous Internet of things, according to the different working modes and functions, the nodes are divided into Internet of things devices, routing nodes, and base station. We use time slot multiplexing to improve the time slot utilization of continuous transmission in Internet of things. First, the time slot allocation table in a round is obtained by the time slot scheduling based on the routing tree. Then, the collision matrix and the transmission matrix are established based on the time slot allocation table in a round. Finally, the minimum time slot scheduling in continuous rounds is determined based on the routing tree and the detection matrix. The experimental results show that the resource scheduling based on routing tree and detection matrix effectively improves the utilization of time slots and improves the throughput of the Internet of things.

An Efficient Dynamic Slot Scheduling Algorithm for WSN MAC: A Distributed Approach

In the current scenario, the growth of IoT based solutions gives rise to the rapid utilisation of WSN. With energy constraint sensor nodes in WSN, the design of energy efficient MAC protocol along with timeliness requirement to handle collision is of paramount importance. Most of the MAC protocols designed for a sensor network follows either contention or scheduled based approach. Contention based approach adapts well to topology changes, whereas it is more costly in handling collision as compared to a schedule based approach. Hence, to reduce the collision along with timeliness, an effective TDMA based slot scheduling algorithm needs to be designed. In this paper, we propose a TDMA based algorithm named DYSS that meets both the timeliness and energy efficiency in handling the collision. This algorithm finds an effective way of preparing the initial schedule by using the average two-hop neighbors count. Finally, the remaining un-allotted nodes are dynamically assigned to slots using a novel approach. The efficiency of the algorithm is evaluated in terms of the number of slots allotted and time elapsed to construct the schedule using the Castalia simulator.

Congestion Avoidance and Delay Minimization in Energy Aware Routing of Dynamic ieee 802.11s WMN

Minimization of delay in collecting the data at any base stations is one of the major concerns in cluster based Wireless Mesh Networks. several researches have proposed algorithms to control congestion considering static nature of a node. Mobility of a node results in high congestion due to frequent link breakages and high energy consumption due to re-establishment of route during routing process. Hence, the authors consider dynamic nodes with single hop inside the static cluster. The proposed model includes four modules namely, Cluster head selection, slot allocation, slot scheduling and data collection process. the cluster head selection is based on the maximum energy, number of links and link duration. Slot allocation is based on the available energy () and the required energy (). Slot scheduling is carried out based on the link duration. Data at the base station will be collected as they are scheduled. Model is tested using Network Simulator-3 (NS3) and results indicate that the proposed model achieves least delay besides reducing the congestion compared to the existing methods.