Capacity Analysis of a Full Duplex Device-to-Device Wireless Network using Voronoi diagrams and Distance Distributions

Full duplex (FD) and Device-to-Device (D2D) communication are two revolutionary protocols that have enabled better spectrum utilization and more reliable data delivery in wireless networks.  In addition, stochastic geometry tools have become necessary to characterize the randomness in the present networks with respect to the irregular architecture and the competing access schemes. This work analyses the performance of a mobile network comprising nodes which are randomly distributed in a square area, which are equipped with FD radios, and can communicate using D2D. The base station (BS) nodes and user nodes in the network are modelled as points of a homogenous binomial point process (BPP) and a homogeneous Poisson point process (PPP) respectively. The network area is tessellated into cells using Voronoi diagrams which approximates to a nearest BS-to-user node association policy. The user nodes can cache popular file objects which are available in a centralized server in the network and other nodes in proximity can request for such objects and receive them using D2D. Using well known distance distribution expressions and stochastic geometry analysis, the distribution of the signal-to-interference ratio (SIR), the D2D and FD collaboration probabilities and the average coverage probability are derived. It is shown that a network-wide quality of service is maintained without additional spectrum utilization when the user nodes can be intelligently tuned to transmit and receive using FD and/or D2D modes. Keywords— Device-to-Device Communication, Full Duplex Communication, stochastic geometry analysis, Voronoi diagrams, Distance    Distribution

A Centralized Routing for Lifetime and Energy Optimization in WSNs Using Genetic Algorithm and Least-Square Policy Iteration

Q-learning has been primarily used as one of the reinforcement learning (RL) techniques to find the optimal routing path in wireless sensor networks (WSNs). However, for the centralized RL-based routing protocols with a large state space and action space, the baseline Q-learning used to implement these protocols suffers from degradation in the convergence speed, network lifetime, and network energy consumption due to the large number of learning episodes required to learn the optimal routing path. To overcome these limitations, an efficient model-free RL-based technique called Least-Square Policy Iteration (LSPI) is proposed to optimize the network lifetime and energy consumption in WSNs. The resulting designed protocol is a Centralized Routing Protocol for Lifetime and Energy Optimization with a Genetic Algorithm (GA) and LSPI (CRPLEOGALSPI). Simulation results show that the CRPLEOGALSPI has improved performance in network lifetime and energy consumption compared to an existing Centralized Routing Protocol for Lifetime Optimization with GA and Q-learning (CRPLOGARL). This is because the CRPLEOGALSPI chooses a routing path in a given state considering all the possible routing paths, and it is not sensitive to the learning rate. Moreover, while the CRPLOGARL evaluates the optimal policy from the Q-values, the CRPLEOGALSPI updates the Q-values based on the most updated information regarding the network dynamics using weighted functions

In vivo range of motion of the lumbar spinous processes

The study design included an in vivo laboratory study. The objective of the study is to quantify the kinematics of the lumbar spinous processes in asymptomatic patients during un-restricted functional body movements with physiological weight bearing. Limited data has been reported on the motion patterns of the posterior spine elements. This information is necessary for the evaluation of traumatic injuries and degenerative changes in the posterior elements, as well as for improving the surgical treatment of spinal diseases using posterior procedures. Eight asymptomatic subjects with an age ranging from 50 to 60 years underwent MRI scans of their lumbar segments in a supine position and 3D models of L2–5 were constructed. Next, each subject was asked to stand and was positioned in the following sequence: standing, 45° flexion, maximal extension, maximal left and right twisting, while two orthogonal fluoroscopic images were taken simultaneously at each of the positions. The MRI models were matched to the osseous outlines of the images from the two orthogonal views to quantify the position of the vertebrae in 3D at each position. The data revealed that interspinous process (ISP) distance decreased from L2 to L3 to L4 to L5 when measured in the supine position; with significantly higher values at L2–3 and L3–4 compared with L4–5. These differences were not seen with weight-bearing conditions. During the maximal extension, the ISP distance at the L2–3 motion segment was significantly reduced, but no significant changes were detected at L3–4 and L4–5. During flexion the ISP distances were not significantly different than those measured in the MRI position at all segments. Going from the left to right twist positions, the L4–5 segment had greater amounts of ISP rotation, while all segments had similar ranges of translation in the transverse plane. The interspinous process distances were dependent on body posture and vertebral level