Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.

UAV Communications for 5G and Beyond: Recent Advances and Future Trends

Pacific Place Jakarta is one of the largest shopping malls in Jakarta which has a lot of tenants. Some of them have their own Wi-Fi devices. This causes a problem for Wi-Fi connection. If the tenants have some devices operating on 2.4 GHz wave, it can cause signal interference that can affect the stability of Wi-Fi connection. Full Wi-Fi network evaluation is required at Pacific Place Jakarta in terms of signal performance and availability. In this paper, we analyze the current existing network using some network monitoring tools and we propose a solution based on capacity requirement that can be used to improve Wi-Fi performance. The solution is to arrange a 2.4 GHz channel configuration to minimize interference and output power settings. Simulation results show that there is an improvement in download and upload speed after the solution is implemented. The results also indicate the channel usage decrease of the 2.4 GHz channel and the surrounding noise floor level.

Performance Evaluation of Dense Wi-Fi Network Based on Capacity Requirement

Herein, we present a recirculating (RC) cyclic Vernier time-to-digital converter (TDC) using dynamic element matching and a register-based time amplifier (TA). The RC TDC reuses a single conversion stage for multiple conversions that are suitable for low power and compact realization. Good matching is achieved between multiple conversions because the same stage is reused. Processing the amplified residue further enhances the TDC resolution. We propose a new TA based on a time register that achieves a well-controlled time magnification over a wide input range. Unlike conventional approaches based on regeneration or meta-stability, the register-based TA does not suffer from a narrow input range. The proposed TA achieves a relatively high gain accuracy (< 1.5%) over a wide input range from 40 to 400 ps. Compact TDC implementation is further facilitated using the multiphase operation of a gated ring oscillator (GRO) in a cyclic Vernier structure. Using the phase-preserving characteristic of the GRO, the mismatch is first order shaped by data weight averaging. The RC TDC fabricated using a 28-nm CMOS process is realized in a small area of 0.0096 mm2. Measurements show that the TDC can be successfully configured into four gain modes with a resolution of 11.5, 7.7, 5.8, and 4.7 ps in a 12-bit dynamic range. The full range linearity measurement shows an integral nonlinearity of less than 1.48 LSB. The power consumption is 0.188 mW at a conversion rate of 3.51 MHz, leading to a favorable figure of merit of 0.032 pJ/conv.-step. The results show the effectiveness of the RC structure in realizing a high-performance, compact TDC.

A 4.7-ps Resolution Recirculating Cyclic Vernier TDC Using DWA-Based Mismatch Correction and a Register-Based Time Amplifier

The combination of 5G and Multi-access Edge Computing (MEC) can significantly reduce application delay by lowering transmission delay and bringing computational capabilities closer to the end user. Therefore, 5G MEC could enable excellent user experience in applications like Mobile Augmented Reality (MAR), which are computation-intensive, and delay and jitter-sensitive. However, existing 5G handoff algorithms often do not consider the computational load of MEC servers, are too complex for real-time execution, or do not integrate easily with the standard protocol stack. Thus they can impair the performance of 5G MEC. To address this gap, we propose Comp-HO, a handoff algorithm that finds a local solution to the joint problem of optimizing signal strength and computational load. Additionally, Comp-HO can easily be integrated into current LTE and 5G base stations thanks to its simplicity and standard-friendly deployability. Specifically, we evaluate Comp-HO through a custom NS-3 simulator which we calibrate via MAR prototype measurements from a real-world 5G testbed. We simulate both Comp-HO and several classic handoff algorithms. The results show that, even without a global optimum, the proposed algorithm still significantly reduces the number of large delays, caused by congestion at MECs, at the expense of a small increase in transmission delay.

5G MEC Computation Handoff for Mobile Augmented Reality.

Herein, we present a recirculating (RC) cyclic Vernier time-to-digital converter (TDC) using dynamic element matching and a register-based time amplifier (TA). The RC TDC reuses a single conversion stage for multiple conversions that are suitable for low power and compact realization. Good matching is achieved between multiple conversions because the same stage is reused. Processing the amplified residue further enhances the TDC resolution. We propose a new TA based on a time register that achieves a well-controlled time magnification over a wide input range. Unlike conventional approaches based on regeneration or meta-stability, the register-based TA does not suffer from a narrow input range. The proposed TA achieves a relatively high gain accuracy (< 1.5%) over a wide input range from 40 to 400 ps. Compact TDC implementation is further facilitated using the multiphase operation of a gated ring oscillator (GRO) in a cyclic Vernier structure. Using the phase-preserving characteristic of the GRO, the mismatch is first order shaped by data weight averaging. The RC TDC fabricated using a 28-nm CMOS process is realized in a small area of 0.0096 mm2. Measurements show that the TDC can be successfully configured into four gain modes with a resolution of 11.5, 7.7, 5.8, and 4.7 ps in a 12-bit dynamic range. The full range linearity measurement shows an integral nonlinearity of less than 1.48 LSB. The power consumption is 0.188 mW at a conversion rate of 3.51 MHz, leading to a favorable figure of merit of 0.032 pJ/conv.-step. The results show the effectiveness of the RC structure in realizing a high-performance, compact TDC. 

Computation-aware delay optimal mobility management (MM) is an important problem in ultra-dense network (UDN) with mobile edge computing (MEC). Since the additional time delay caused by task computation is not taken into consideration, traditional radio access-oriented mobility management scheme cannot guarantee the overall delay performance of delay-sensitive user equipment (UE). In this paper, we propose a novel dynamic programming-based mobility management (DPMM) scheme to minimize the average delay under an energy consumption constraint. DPMM makes MM decisions using statistic information to handle the inaccurate state information. Cooperative data transmission is adopted to improve the delay performance. Simulation shows that the proposed DPMM scheme can achieve delay performance close to optimal and reduce the frequency of handover. However, the wireless link, computation resources and UE’s location in UDN environment is dynamic, which leads to information uncertainties. We further propose an MM scheme based on deep Q-network (DQN) to learn the system information from the environment. In this scheme, UE takes the current and past observed delay as experience, learning the optimal mobility management strategy through DQN training. Simulation shows that DQN-based MM can learn from experience and reduce the handover frequency to a certain degree.

/pdf/reinforcement-learning-based-computation-aware-mobility-2z9e9piurs.pdf

Reinforcement learning based computation-aware mobility management in ultra dense networks

Two major mismatches in SR latch-based time amplifiers (TA) include input skew, which causes gain imbalance, and loading mismatch, which reduces gain accuracy. Accordingly, we propose an all-digital mismatched self-calibrator and compensator for an SR latch-based variable-gain TA. Tunable matching cells and variable capacitors are built into TAs to compensate for input skew (gain imbalance) and loading mismatch (gain inaccuracy). To ensure that the proposed calibration works efficiently and accurately, the TA must provide at least high and low gains where the low gain calibrates the most significant bit (MSB) and the high gain calibrates the least significant bit (LSB). This self-calibrator costs 4375 gates, and the power consumption is 2.8 mA for the TA gain with 2 and 3.2 mA for the TA gain with 16 at a sampling rate of 10 MHz.

/pdf/all-digital-mismatched-calibrator-and-compensator-for-sr-3u0etpdxn0.pdf

All-Digital Mismatched Calibrator and Compensator for SR Latch-Based Variable-Gain Time Amplifier

For high-speed communications need, Fronthaul, a new mobile architecture related to RAN-based networks, has been widely used in 4G/5G mobile network. Compared with the traditional architecture of the baseband station, Fronthaul is divided into baseband unit (BBU) and remote radio head (RRH). Due to the separation, the interface between BBU and RRH requires more effort to handle the data rate transmission. In this paper, we propose the fiber Fronthaul to achieve the high bandwidth requirement. The BBU includes the SoftPHY OAI running on Intel I7-6700 CPU and the data collector implemented by Xilinx VC707. The RRH is implemented by Xilinx ZC706. And the RF front-end is implemented by Analog Devices ADFMCOMMS5-EBZ. The interface between the SoftPHY OAI and the data collector is PCIe, and the interface between the data collector and the RRH is optical fiber. It is important that we design the synchronization scheme to synchronize and calibrate the real-time clock (RTC) of different RRH. Therefore, the proposed fiber Fronthaul with SoftPHY OAI can achieve the long distance, stability and high speed transmission.

Design and Integration of Fiber Fronthaul with Soft PHY for OAI-BASED RAN

A step-down multi-mode switching DC-DC converter for battery-powered portable IoT products is proposed, and it was implemented by using a 0.35-μm CMOS mixed-signal 2P4M polycide 5V/3.3V process. The load capacitor at converter output can be removed from the dynamic circuit which helps during transients. The output stage is designed with both the static and dynamic controls to suppress the possible large overshoot/undershoot voltage during load change. Moreover, a novel adaptive switching frequency (ASF) technique is also proposed in this work to optimize the converter performance at different input voltages. There are three operational modes in the proposed regulator, and, thus, a wide input voltage range, 2.1-5 V, is achieved. The output voltage is set to 1.8 V, and the maximal load current is 100mA. The measured conversion efficiency may go up to 87.6%.

High-Efficiency Step-Down Multi-Mode Switching DC-DC Converter for IoT Devices

this paper explains a new efficiency architecture of scalable-DU as Wireless Processing Unit. It is proposed that how to accelerate the computing capability of eNB. The legacy multiprocessor architecture always is master-slave which need a control center to balance the usage of resource and making job assignments efficiently. Scalable-DU as Wireless Processing Units no need to have one control center to do these things and can execute jobs by themselves. It can be more adjustable and efficient for the assignment of the jobs and resource management. Besides, this thesis also designs the architecture of ALU which to support the various arithmetic calculations for the purpose of communication.

Terng-Yin Hsu

Papers

Reinforcement learning based computation-aware mobility management in ultra dense networks

All-Digital Mismatched Calibrator and Compensator for SR Latch-Based Variable-Gain Time Amplifier

Design and Integration of Fiber Fronthaul with Soft PHY for OAI-BASED RAN

High-Efficiency Step-Down Multi-Mode Switching DC-DC Converter for IoT Devices

Scalable DU Architecture for IoT massive connection