Joint Design of Digital and Analog Processing for Downlink C-RAN with Large-Scale Antenna Arrays

In millimeter-wave communication systems with large-scale antenna arrays, conventional digital beamforming may not be cost-effective. A promising solution is the implementation of hybrid beamforming techniques, which consist of low-dimensional digital beamforming followed by analog radio frequency (RF) beamforming. This work studies the optimization of hybrid beamforming in the context of a cloud radio access network (C-RAN) architecture. In a C-RAN system, digital baseband signal processing functionalities are migrated from remote radio heads (RRHs) to a baseband processing unit (BBU) in the "cloud" by means of finite-capacity fronthaul links. Specifically, this work tackles the problem of jointly optimizing digital beamforming and fronthaul quantization strategies at the BBU, as well as RF beamforming at the RRHs, with the goal of maximizing the weighted downlink sum-rate. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraints on the RF beamforming matrices. An iterative algorithm is proposed that is based on successive convex approximation and on the relaxation of the constant modulus constraint. The effectiveness of the proposed scheme is validated by numerical simulation results

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

This paper studies multi-user 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 30% average sum rate performance gain over conventional schemes

Decentralized Deadlock-free Trajectory Planning for Quadrotor Swarm in Obstacle-rich Environments -- Extended version

This paper presents a decentralized multi-agent trajectory planning (MATP) algorithm that guarantees to generate a safe, deadlock-free trajectory in an obstacle-rich environment under a limited communication range. The proposed algorithm utilizes a grid-based multi-agent path planning (MAPP) algorithm for deadlock resolution, and we introduce the subgoal optimization method to make the agent converge to the waypoint generated from the MAPP without deadlock. In addition, the proposed algorithm ensures the feasibility of the optimization problem and collision avoidance by adopting a linear safe corridor (LSC). We verify that the proposed algorithm does not cause a deadlock in both random forests and dense mazes regardless of communication range, and it outperforms our previous work in flight time and distance. We validate the proposed algorithm through a hardware demonstration with ten quadrotors.Comment: 11 pages, extended version of conference versio

Parallax : an implementation of ELGDF (Extended Large Grain Data Flow)

The major obstacle to widespread parallel programming of multiprocessors is the lack of a convenient parallel programming system. PPSE (Parallel Programming Support Environment) is a unified approach to parallel programming. Parallax is developed as a component of PPSE based on ELGDF which is a graphical language for designing parallel programs. The goals of Parallax are to solve the following problems: 1) How to represent parallelism naturally in an application, 2) How to make a parallel program portable across different parallel computers, 3) How to remove time-dependent problems from the programmer's concern, 4) How to provide a standard software description format that can be used by other tools such as automated schedulers and performance analyzers, and 5) How to increase programmer's productivity. Our approach to these problems are: 1) ELGDF notation: Parallax uses ELGDF notation that allows a wide representation of a variety of parallel programs in a natural way for both shared-memory and message-passing models using higher level parallel abstractions. Program details such as synchronization are handled by the system using reusable libraries specific to each target system. Parallax is a CASE tool which supports common Software­Engineering techniques such as hierarchical design concepts that support both top-down and bottom-up design using visual­-programming techniques. 2) PP Design File: Parallel program designs are stored in a standard format in a PP (Parallel Program) Design File. This representation can be transformed into a task graph representation at different levels of granularity. The task graph of the problem can be used to estimate execution time and performance of the resulting parallel program. Parallax is implemented on Macintosh as part of the PPSE project. It has been used to represent the design for both shared and distributed memory machines, with individual programs written in FORTRAN and C, with and without Linda support, and it has successfully produced program designs which can be analyzed by other tools such as TaskGrapher[5] and SuperGlue[6]. Parallax currently does not automatically produce a task graph, nor does it fully represent programs written in high level languages such as FORTRAN. Finally, ELGDF currently lacks formal semantics for representing computations