Time Division Multiple Access Mode Research Articles

Secure intelligent reflecting surface assisted mobile edge computing system with wireless power transfer

In this paper, we study an Intelligent Reflecting Surface (IRS) assisted Mobile Edge Computing (MEC) system under eavesdropping threats, where the IRS is used to enhance the energy signal transmission and the offloading performance between Wireless Devices (WDs) and the Access Point (AP). Specifically, in the proposed scheme, the AP first powers all WDs with the wireless power transfer through both direct and IRS-assisted links. Then, powered by the harvested energy, all WDs securely offload their computation tasks through the two links in the time division multiple access mode. To determine the local and offloading computational bits, we formulate an optimization problem to jointly design the IRS's phase shift and allocate the time slots constrained by the security and energy requirements. To cope with this non-convex optimization problem, we adopt semidefinite relaxations, singular value decomposition techniques, and Lagrange dual method. Moreover, we propose a dichotomy particle swarm algorithm based on the bisection method to process the overall optimization problem and improve the convergence speed. The numerical results illustrate that the proposed scheme can boost the performance of MEC and secure computation rates compared with other IRS-assisted MEC benchmark schemes.

Laser beam imaging via multiple mode operations of the extreme dynamic range CAOS camera.

Demonstrated for the first time, to the best of our knowledge, is laser beam imaging via multiple mode operations of the digital micro-mirror device-based coded access optical sensor (CAOS) camera. Specifically, outlined are novel modes of software programmable CAOS imaging, which include the time division multiple access (TDMA) mode, the code division multiple access (CDMA) mode, the CDMA-TDMA mode, the frequency division multiple access (FDMA)-TDMA mode, the frequency modulation (FM)-CDMA-TDMA mode, FM-TDMA mode, and the FDMA-CDMA-TDMA mode. Engagement of FDMA and CDMA modes enables simultaneous multi-pixel improved signal-to-noise ratio photo detection, while use of TDMA prevents optical point detector saturation. The use of the FDMA and FM modes creates high digital signal processing gain via temporal spectrum analysis to produce extreme dynamic range pixel-specific imaging. Using an un-attenuated 633nm He-Ne laser near-Gaussian focusing beam, experimentally acquired are 13.68μm spatial resolution laser beam map CAOS images with dynamic ranges of 44.78dB, 45.28dB, 83.90dB, and 94.9dB for the TDMA, CDMA, FM-CDMA-TDMA, and FM-TDMA modes, respectively. The FM-TDMA mode with its extreme dynamic ranging CAOS pixel-mapping capability experimentally measures the non-Gaussian spatially oscillatory irradiance behavior predicted by Huygens-Fresnel diffraction theory. The demonstrated CAOS camera through software control allows high-flexibility, robust imaging of laser beams across different wavelength bands with widely varying beam irradiance levels and spatial signatures, thus empowering optical system designers to match overall system requirements that are highly dependent on accurate and reliable laser beam metrology.

Resource Allocation Techniques for Wireless Powered Communication Networks With Energy Storage Constraint

This paper studies multiuser wireless powered communication networks, where energy constrained users charge their energy storages by scavenging energy of the radio frequency signals radiated from a hybrid access point (H-AP). The energy is then utilized for the users' uplink information transmission to the H-AP in time division multiple access mode. In this system, we aim to maximize the uplink sum rate performance by jointly optimizing energy and time resource allocation for multiple users in both infinite capacity and finite capacity energy storage cases. First, when the users are equipped with the infinite capacity energy storages, we derive the optimal downlink energy transmission policy at the H-AP. Based on this result, analytical resource allocation solutions are obtained. Next, we propose the optimal energy and time allocation algorithm for the case where each user has finite capacity energy storage. Simulation results confirm that the proposed algorithms offer about 30% average sum rate performance gain over conventional schemes.