FPGA Acceleration for Computationally Efficient Symbol-Level Precoding in Multi-User Multi-Antenna Communication Systems

Abstract

In this paper, we demonstrate an FPGA-accelerated design of the computationally efficient symbol-level precoding (SLP) for high-throughput communication systems. The SLP technique recalculates the optimal beam-forming vectors by solving a non-negative least squares problem per every set of transmitted symbols. It exploits the advantages of constructive inter-user interference to minimize the total transmitted power and increase service availability. The benefits of using SLP come with a substantially increased computational load at the gateway. The FPGA design enables the SLP technique to perform in real-time operation mode and provide a high symbol throughput for the multiple receive terminals. We define the SLP technique in a closed-form algorithmic expression and translate it to hardware description language (HDL) and build an optimized HDL core for an FPGA. We evaluate the FPGA resource occupation, which is required for the high throughput multiple-input-multiple-output (MIMO) systems with sizeable dimensions. We describe the algorithmic code, the I/O ports mapping, and the functional behavior of the HDL core. We deploy the IP core to an actual FPGA unit and benchmark the energy efficiency performance of the SLP. The synthetic tests demonstrate a fair energy efficiency improvement of the proposed closed-form algorithm compared to the best results obtained through the MATLAB numerical simulations. INDEX TERMS Convex programming, field programmable gate arrays, hardware resources, multicast communication, MIMO, optimization, precoding, power minimization, interference, wireless channels.

Learning-Assisted Eavesdropping and Symbol-Level Precoding Countermeasures for Downlink MU-MISO Systems

“…where W is the bandwith, R c is the rate (equals 2 in the case of QPSK), and BER a is the effective BER, where a can be either intended user or Eve. Similar to the performance metric used in [41], we define the secure rate as R sec = R int − R eve (25) where R int is the effective rate at the intended user and R eve is the one at the Eve. Last but not least, we define a new metric that we call Energy Efficiency for Secure Transmission (EEST) η eest that combines both the secure bits transmitted and the transmit power consumed, so it will be [secure − bits/Joule], and defined as…”

Weighted Sum-SINR and Fairness Optimization for SWIPT-Multigroup Multicasting Systems With Heterogeneous Users

“…Another promising technology to meet the challenges described is transmit precoding for multi-user MISO systems, which aims at the enhancement of channel capacity and diversity [11], [12]. In order to substantially improve the system performance, Multi-group (MG) Multicasting (MC) has emerged as another potentially viable technique, whose benefits were demonstrated in [13], [14].…”

M-QAM Modulation Symbol-Level Precoding for Power Minimization: Closed-Form Solution

“…By inserting (13) into (12) we derive an approximate closedform solution to calculate the perturbation vector for the optimization problem (11) aŝ…”

“…Nevertheless, the main contribution of these works was in algorithmic complexity of SLP and CI-VP techniques, the practical implementation stayed out of their scope. In [13], [14] we demonstrated the feasibility to deliver low computationally complexity of SLP technique [7] and implement it on actual hardware processing the baseband in a real-time basis for a downlink transmission.…”