Feilong Lin

A novel spectrum sharing scheme for industrial cognitive radio networks: From collective motion perspective

Spectrum sharing is a promising technique responsible for providing efficient and fair spectrum allocation. Considering the unevenness phenomenon of spectrum usage in industrial wireless networks, a novel spectrum sharing scheme, in the framework of industrial cognitive radio network (ICRN), is proposed in this paper via an autonomous switching technique to equalize the spectrum usage and increase the spectrum access possibility of new requests. The autonomous switching technique borrows the idea from the fact that specific collective motion can be achieved by local actions of individuals in many biological systems. The accessed nodes sense the limited spectrum range around their central frequency and then make the decision of channel switching autonomously. Several sensing report based rules are presented to facilitate the switching decision in order to equalize the channel usage among the sensing range of each node. It is demonstrated that by using these rules the spectrum usage becomes more even, and thus the spectrum utilization and fairness are both improved. Numerical examples are given to show the effectiveness of the proposed spectrum sharing scheme.

Distributed Load Shedding for Microgrid with Compensation Support via Wireless Network

Due to the limited generation and finite inertia, microgrid suffers from the large frequency and voltage deviation which can lead to system collapse. Thus, reliable load shedding to keep frequency stable is required. Wireless network, benefiting from the high flexibility and low deployment cost, is considered as a promising technology for fine-grained management. In this paper, for balancing the supply-demand and reducing the load-shedding amount, a distributed load shedding solution via wireless network is proposed. Firstly, active power coordination of different priority loads is formulated as an optimisation problem. To solve it, a distributed load shedding algorithm based on subgradient method (DLSS) is developed for gradually shedding loads. Using this method, power compensation can be utilised and has more time to lower the power deficit so as to reduce the load-shedding amount. Secondly, to increase the response rate and enhance the reliability of our method, a multicast metropolis schedule based on TDMA (MMST) is developed. In this protocol, time slots are dedicatedly allocated and a checking and retransmission mechanism is utilised. Finally, the proposed solution is evaluated by NS3-Matlab co-simulator. The numerical results demonstrate the feasibility and effectiveness of our solution.

State Estimation Oriented Wireless Transmission for Ubiquitous Monitoring in Industrial Cyber-Physical Systems

State estimation over wireless sensor networks (WSNs) plays an important role for the ubiquitous monitoring in industrial cyber-physical systems (ICPSs). However, the unreliable wireless channels lead to the transmitted measurements arriving at the remote estimator intermittently, which will deteriorate the estimation performance. Question of how to improve the transmission reliability in the hostile industrial environment to guarantee the pre-defined estimation performance for ICPSs is largely unexplored. This paper is concerned with a redundant transmission strategy to meet the reliability requirement for state estimation. This strategy incorporates the ISM channels with the opportunistically harvested channels to provide adequate spectrum opportunities for redundant transmissions. First, we explore the relationship between the estimation performance and the transmission reliability, based on which a joint optimization of channel allocation and power control is then developed to guarantee the estimation performance and maximize the sum rate of the WSN. Second, we formulate the optimization into a mix-integer nonlinear programming problem, which is solved efficiently by decomposing it into the channel allocation and power control subproblems. Ultimately, simulation study demonstrates that the proposed strategy not only ensures the required state estimation performance, but also increases the sum rate of the WSN.

SDP: Separate Design Principle for Multichannel Scheduling in Priority-Aware Packet Collection

Industrial wireless sensor networks (IWSNs) are expected to play a key role in the next generation of Industry Internet, which present a flexible and low-cost way to comprehensively perceive process systems in industrial field. Priority-aware packet collection is of vital importance for IWSNs to gather the data packets according to their differentiated transmission requirements. In multichannel IWSNs, this is still a challenging problem due to the complexity of collision avoidance design and transmission delay optimization. In this paper, we consider the packet collection with two priority classes, and propose a separate design principle (SDP) for multichannel scheduling based on IEEE 802.15.4e protocol. In SDP, we separately design two multichannel superframes for high and low priority sensors, where each superframe fully uses the time slots on all channels. Then, a concession strategy is devised to resolve the possible transmission collisions. Simulation results demonstrate that SDP based multichannel scheduling achieves lower transmission delay to both high and low priority sensors comparing with the IEEE 802.15.4e protocol.

MUBFP: Multi-user beamforming and partitioning in downlink MIMO systems

Multi-user beamforming (MUBF) scheme has been studied extensively as a linear precoding technique to achieve better performance of system capacity in downlink MIMO systems. Due to mutual interference between users, the objective of maximizing average sum capacity and fair service provisioning to different users should be well balanced in the framework of designing MUBF scheme. To this end, we consider the problem of joint optimization of multi-user beamforming and partitioning (MUBFP), which attempts to find the optimal partitioning of users and their corresponding beamforming weight vectors such that the average sum capacity is maximized. A decomposition approach is developed to decouple this problem into the user partitioning and beamformer design subproblems, which are solved using the agglomerative hierarchical clustering algorithm and sequential parametric convex approximation approach, respectively. An iterative algorithm is proposed to search the optimal partitioning number by incorporating the results of these two subproblems. Simulation results are provided to show the effectiveness of the proposed MUBFP scheme.