Latency reduction by dynamic channel estimator selection in C-RAN networks using fuzzy logic

Due to a dramatic increase in the number of mobile users, operators are forced to expand their networks accordingly. Cloud Radio Access Network (C-RAN) was introduced to tackle the problems of the current generation of mobile networks and to support future 5G networks. However, many challenges have arisen through the centralised structure of C-RAN. The accuracy of the channel state information acquisition in the C-RAN for large numbers of remote radio heads and user equipment is one of the main challenges in this architecture. In order to minimize the time required to acquire the channel information in C-RAN and to reduce the end-to-end latency, in this paper a dynamic channel estimator selection algorithm is proposed. The idea is to assign different channel estimation algorithms to the users of mobile networks based on their link status (particularly the SNR threshold). For the purpose of automatic and adaptive selection to channel estimators, a fuzzy logic algorithm is employed as a decision maker to select the best SNR threshold by utilising the bit error rate measurements. The results demonstrate a reduction in the estimation time with low loss in data throughput. It is also observed that the outcome of the proposed algorithm increases at high SNR values

Energy-Efficient Spectrum Sensing for Cognitive Radio Enabled Remote State Estimation Over Wireless Channels

The performance of remote estimation over wireless channels is strongly affected by sensor data losses due to interference. Although the impact of interference can be alleviated by applying cognitive radio technique which features in spectrum sensing and transmitting data only on clear channels, the introduction of spectrum sensing incurs extra energy expenditure. In this paper, we investigate the problem of energy-efficient spectrum sensing for remotely estimating the state of a general linear dynamic system, and formulate an optimization problem which minimizes the total sensor energy consumption while guaranteeing a desired level of estimation performance. We model the problem as a mixed integer nonlinear program and propose a simulated annealing based optimization algorithm which jointly addresses when to perform sensing, which channels to sense, in what order and how long to scan each channel. Simulation results demonstrate that the proposed algorithm well balances the sensing energy and transmission energy expenditure and can achieve the desired estimation performance

Underwater localization and node mobility estimation

In this paper, localizing a moving node in the context of underwater wireless sensor networks (UWSNs) is considered. Most existing algorithms have had designed to work with a static node in the networks. However, in practical case, the node is dynamic due to relative motion between the transmitter and receiver. The main idea is to record the time of arrival message (ToA) stamp and estimating the drift in the sampling frequency accordingly. It should be emphasized that, the channel conditions such as multipath and delay spread, and ambient noise is considered to make the system pragmatic. A joint prediction of the node mobility and speed are estimated based on the sampling frequency offset estimation. This sampling frequency offset drift is detected based on correlating an anticipated window in the orthogonal frequency division multiplexing (OFDM) of the received packet. The range and the distance of the mobile node is predicted from estimating the speed at the received packet and reused in the position estimation algorithm. The underwater acoustic channel is considered in this paper with 8 paths and maximum delay spread of 48 ms to simulate a pragmatic case. The performance is evaluated by adopting different nodes speeds in the simulation in two scenarios of expansion and compression. The results show that the proposed algorithm has a stable profile in the presence of severe channel conditions. Also, the result shows that the maximum speed that can be adopted in this algorithm is 9 km/h and the expansion case profile is more stable than the compression scenario. In addition, a comparison with a dynamic triangular algorithm (DTN) is presented in order to evaluate the proposed system

Decoding the `Nature Encoded\u27 Messages for Wireless Networked Control Systems

Because of low installation and reconfiguration cost wireless communication has been widely applied in networked control system (NCS). NCS is a control system which uses multi-purpose shared network as communication medium to connect spatially distributed components of control system including sensors, actuator, and controller. The integration of wireless communication in NCS is challenging due to channel unreliability such as fading, shadowing, interference, mobility and receiver thermal noise leading to packet corruption, packet dropout and packet transmission delay. In this dissertation, the study is focused on the design of wireless receiver in order to exploit the redundancy in the system state, which can be considered as a `nature encoding\u27 for the messages. Firstly, for systems with or without explicit channel coding, a decoding procedures based on Pearl\u27s Belief Propagation (BP), in a similar manner to Turbo processing in traditional data communication systems, is proposed to exploit the redundancy in the system state. Numerical simulations have demonstrated the validity of the proposed schemes, using a linear model of electric generator dynamic system. Secondly, we propose a quickest detection based scheme to detect error propagation, which may happen in the proposed decoding scheme when channel condition is bad. Then we combine this proposed error propagation detection scheme with the proposed BP based channel decoding and state estimation algorithm. The validity of the proposed schemes has been shown by numerical simulations. Finally, we propose to use MSE-based transfer chart to evaluate the performance of the proposed BP based channel decoding and state estimation scheme. We focus on two models to evaluate the performance of BP based sequential and iterative channel decoding and state estimation. The numerical results show that MSE-based transfer chart can provide much insight about the performance of the proposed channel decoding and state estimation scheme. In this dissertation, the study is focused on the design of wireless receiver in order to exploit the redundancy in the system state, which can be considered as a `nature encoding\u27 for the messages. Firstly, for systems with or without explicit channel coding, a decoding procedures based on Pearl\u27s Belief Propagation (BP), in a similar manner to Turbo processing in traditional data communication systems, is proposed to exploit the redundancy in the system state. Numerical simulations have demonstrated the validity of the proposed schemes, using a linear model of electric generator dynamic system. Secondly, we propose a quickest detection based scheme to detect error propagation, which may happen in the proposed decoding scheme when channel condition is bad. Then we combine this proposed error propagation detection scheme with the proposed BP based channel decoding and state estimation algorithm. The validity of the proposed schemes has been shown by numerical simulations. Finally, we propose to use MSE-based transfer chart to evaluate the performance of the proposed BP based channel decoding and state estimation scheme. We focus on two models to evaluate the performance of BP based sequential and iterative channel decoding and state estimation. The numerical results show that MSE-based transfer chart can provide much insight about the performance of the proposed channel decoding and state estimation scheme

Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems

In many Cyber-Physical Systems, we encounter the problem of remote state estimation of geographically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors have to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant one needs to decide which sensors have network access and which ones do not. To address this scheduling problem, we formulate an associated Markov decision process (MDP). This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others. Our algorithm is shown to significantly outperform these algorithms for many example scenarios