Wireless body area network (WBAN) has become a promising technology, which can be widely applied in health monitoring, and so on. However, the performance of a practical WBAN may severely suffer from the degradation caused by dynamic nature of wireless channels with the movements of human body. Traditional communication frameworks cannot catch up with the channel variation of dynamic WBANs, which may severely degrade the performance, so an accurate channel prediction model is necessary for developing an efficient transmission strategy. In this paper, we propose a DeepBAN communication framework for dynamic WBANs. In our proposed framework, a temporal convolution network (TCN) based deep learning approach is adopted for channel prediction, the computationally intensive task of which is processed by mobile edge computing (MEC), to reduce the response time. Given the predicted channel conditions, we propose a joint power control, time-slot allocation, and relay selection algorithm to maximize the energy efficiency of the system, taking into account the transmission reliability and end-to-end latency requirements. We evaluate the performance of DeepBAN, and the results show that it can achieve energy-efficient, reliable, and low-latency data transmission in dynamic WBANs, which can improve the system energy efficiency by 15% compared with the stochastic scheduling scheme.

In dynamic scenarios such as walking, wireless body area networks (WBANs) have unique dynamic features, for instance, the sensor nodes placed on the limbs have approximately periodic link qualities. To realize energy-efficient and reliable communication in dynamic WBANs, this paper focuses on designing a scheduling strategy, that exploits the dynamic channel periodicity. In walking scenarios, we first construct a WBAN testbed, then conduct experiments to show the channel periodicity between the sensor nodes deployed at different positions and quantify the correlation between the received signal strength indication (RSSI) and the acceleration signals. Next, we propose a gait cycle detection algorithm to detect the periodic motion and calculate the acceleration periodicity and phase. Then, we propose a channel periodicity based scheduling (CPBS) strategy that exploits the periodic fluctuations of link qualities, to improve energy efficiency and reliability. With the CPBS strategy, the transmission time is allocated according to the motion state and the rate requirement of each sensor node, which can avoid channel collision. Finally, we evaluate the performance of the CPBS strategy in both periodic and realistic (approximately 23% of the time for aperiodic movements) scenarios. The results show that, compared to stochastic scheduling (SS) strategy, the proposed CPBS strategy can save 17.61% and 13.56% energy consumption in periodic and realistic scenarios, respectively. In addition, the packet loss rate (PLR) can be made as low as zero in both scenarios. Therefore, the proposed CPBS strategy can achieve energy-efficient and reliable communication in dynamic WBANs.

User-centric ultra-dense network (UUDN) has become a promising paradigm with the advantages over traditional cell-centric structure with respect to spectrum and power usage. This paper introduces full duplex (FD) technology into UUDN and presents a joint scheme of user access, subchannel allocation and power control for FD UUDN. The scheme firstly judges the feasibility of the constraints of the transmit power and rate requirements. If they are feasible, the total network capacity is maximized under the transmit power constraints. Otherwise, the minimal total power of the network is found to satisfy the rate requirements. The sum rate maximization is solved by multiplicative linear fractional programming (MLFP) based power allocation (MAPEL) algorithm and particle swarm optimization (PSO) algorithm, respectively. The power minimization problem is transformed by averaging the total rate in each subchannel of the user, and then solved by time-sharing method. Simulation demonstrates the enhancement of spectrum efficiency and the reduction of consumed power for UUDN compared with cell-centric UDN. Moreover, the advantage of FD over half duplex (HD) is shown in terms of spectrum efficiency and power consumption.

Feature points loss and images mismatch in the variation of light intensity, weak texture and large angle rotation for the feature points extraction of ORB-SLAM2 are severe. To deal with the problem, a feature points extraction algorithm based on adaptive information entropy, i.e., Adaptive Information Entropy Feature (AIEF) algorithm is proposed. According to the information entropy, the image blocks with less information are removed and those with more texture image information and larger gradient are selected. Then an adaptive algorithm is used to automatically calculate the optimal threshold of the image information entropy. The image blocks are homogenized to avoid that the extracted feature points are too dense and getting stuck is prevented, which makes the algorithm more robust. Finaly validation is performed using the Oxford standard data set and the performances of the AIEF algorithm are compared with those of the SIFT, SURF, and ORB-SLAM2 algorithms. Experimental results on the Oxford standard data set demonstrate that the AIEF algorithm outperforms the traditional counterparts in terms of processing time, number of feature points, correct matching number and correct matching rate.

Wireless body area network (WBAN) can be applied to a variety of scenarios, such as physical training, patient care and health monitoring for the aged. To prolong the lifetime of WBANs and respond to an emergency, energy efficiency and age of information (AOI) become two most important performance indicators. The dynamic characteristic of WBANs makes the link quality of wireless communication vary intensely, which brings challenges to the reliability of data transmission. Conventional scheduling policies do not take into account the AOI and that the life time of the data packets from some sensor nodes may exceed the time limit, which are energy inefficient and may cause an accident when unforeseen emergency happens. As a remedy, first, we adopt a deep learning method based on neural basis expansion analysis for interpretable time series (N-BEATS) for channel prediction. The transmit power according to channel prediction results is adjusted, to improve the energy efficiency of data transmission. Then, we propose an AOI-aware scheduling (AOI-AS) strategy which takes into account energy efficiency and data freshness. Simulation experiments show that our proposed scheduling strategy can reduce the AOI by 7% and the average energy consumption by 6% compared with the stochastic scheduling (SS) mechanism.

With practical finite alphabet inputs, usually the throughput of a practical communication system cannot reach the capacity based on assumption of Gaussian inputs. In this paper, we study the resource allocation strategy for multiple-input multiple-output (MIMO) full-duplex backscatter assisted wireless-powered communication network (FD-BAWPCN) with finite alphabet inputs, to maximize the sum-throughput. Firstly, we propose a gradient-based resource allocation strategy (GBRA), which alternately optimizes the precoder and time allocation based on the two-block alternating direction method of multipliers (ADMM). Secondly, to reduce the computational complexity and improve the feasibility of the strategy in practical applications, we further propose a codebook-based resource allocation strategy (CBRA), which can run more than three orders of magnitude faster than GBRA at the expense of a small sum-rate degradation. Then, the performance of the full-duplex (FD) and half-duplex (HD) system with or without backscatter assistance using GBRA are compared and analyzed. Numerical results demonstrate that the GBRA strategy has great robustness under various conditions but suffers a high computational complexity, and the CBRA strategy is effective and converges speedily.