Software Defined Radio Environment Research Articles

Deep improved bidirectional long short‐term memory‐based antenna selection for multiple input multiple output software defined radio system

SummaryMultiple input multiple output (MIMO) constitutes a wireless approach that employs several senders and receivers in order to send numerous data at once. Due to the rapid increase of cellular mobile device usage and the limitation of computing power, antenna selection (AS) has attracted more and more attention recently. AS can keep a balance between communication performance and computational complexity. In this paper, a novel deep learning (DL)‐based AS for MIMO software defined radio (SDR) system is proposed. The main objective of this work is to select the optimal antennas in MIMO systems to balance the efficiency of communication and maximize the secrecy capacity in massive MIMOME channels. First, we set up a DL‐based AS‐aided MIMO SDR environment that ensures intelligent decisions for AS. Accordingly, the hybrid loss function induced in bidirectional long short term memory (HLFI‐Bi‐LSTM) model is proposed to build the decision server, in which it takes some estimated features as input. The features such as channel state information, received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and antenna gain are considered accordingly as the input to HLFI‐Bi‐LSTM. The HLFI‐Bi‐LSTM model is the extended version of Bi‐LSTM, which is improved by inducing the hybrid loss function, which is the combination of two functions namely, combo loss and inverse mean square error. The proposed HLFI‐Bi‐LSTM attained the greater accuracy value of 0.968 at 80% for optimal selection of the antenna.

Real-Time Data Transfer Based on Software Defined Radio Technique using Gnu radio/USRP

Software Defined Radio (SDR) offers a extensive radio communication platform that uses software updates to make use of fresh technology. From SDR, the idea of an Orthogonal frequency division multiplexing (OFDM) has evolved to personalize SDRs. The channel dispersiveness causes Inter Symbol Interference (ISI) but OFDM is more resistant at these condition because of this reason it is widely used in wireless communication systems. OFDM is having a good performance in terms of Bit Error Rate (BER) and high spectrum efficiency, so it is considered as a key role for next generation wireless communication system. In this paper, three different types of data are transferred in a real time SDR of OFDM transceiver using GNURadio/Universal Software Radio Peripheral (USRP). OFDM is extremely sensitive for synchronization errors such as time and frequency offsets and to estimate channel condition. Therefore, a standard algorithm is applied to solve synchronization and channel estimation problems in SDR based OFDM system. This testbed is implemented using two USRPs of model N210 as transmitter and receiver with an open source of GNURadio as a software. The implementation of OFDM is evaluated for different types of information like text, audio and Image. This evaluates the BER v/s SNR for real time data transmission in SDR Environment.

HackRF + GNU Radio: A software-defined radio to teach communication theory

In this paper, an alternative to the traditional methodology related to signal processing-like subjects is proposed. These are subjects that require a deep mathematical and theoretical basis, but the practical goal is not often emphasized, which drives students to lose interest in the subject. Thus, a software-defined radio environment is proposed to provide a more practical view of the subject. This solution consists of an open hardware–software platform able to capture and process a wide range of frequencies. HackRF is the hardware component, while GNU Radio will provide the graphical support to this device. The tests performed with a set of 36 students have revealed that they are more satisfied with this framework than just employing a traditional equation-based environment as Matlab. Furthermore, their scores in the exams also support the suitability of the proposed platform.

Integration of GPU Computing in a Software Radio Environment

This article studies the integration of Graphics Processing Units in a Software Defined Radio environment. Two main solutions are considered, based on two levels of granularity for the parallelization. First, a fine grain parallelism solution, which is an extension of the existing solutions but adapted to operations of large computational complexity, is proposed. Second, an original solution based on coarse grain approach allowing better usage of the computing resources and easier parallelism extraction is described. For both solutions, scheduling and communication design as well as implementation are given, along with integration in the environment. Both solutions have been implemented and compared on different operations types and on multi-operations sequences. It is clearly shown that using the second solution can provide performance improvement, while the first one is not adapted to SDR applications.

A Reconfigurable ASIP for Convolutional and Turbo Decoding in an SDR Environment

Future mobile and wireless communication networks require flexible modem architectures to support seamless services between different network standards. Hence, a common hardware platform that can support multiple protocols implemented or controlled by software, generally referred to as software defined radio (SDR), is essential. This paper presents a family of dynamically reconfigurable application-specific instruction-set processors (ASIPs) for channel coding in wireless communication systems. As a weakly programmable intellectual property (IP) core, it can implement trellis-based channel decoding in a SDR environment. It features binary convolutional decoding, and turbo decoding for binary as well as duobinary turbo codes for all current and upcoming standards. The ASIP consists of a specialized pipeline with 15 stages and a dedicated communication and memory infrastructure. Logic synthesis revealed a maximum clock frequency of 400 MHz and an area of 0.11 mm2 for the processor's logic using a low power 65-nm technology. Memories require another 0.31 mm2 . Simulation results for Viterbi and turbo decoding demonstrate maximum throughput of 196 and 34 Mb/s, respectively. The ASIP hence outperforms state-of-the-art decoder architectures targeting software defined radio by at least a factor of three while consuming only 60% or less of the logic area.

Modular Software-Defined Radio

In view of the technical and commercial boundary conditions for software-defined radio (SDR), it is suggestive to reconsider the concept anew from an unconventional point of view. The organizational principles of signal processing (rather than the signal processing algorithms themselves) are the main focus of this work on modular software-defined radio. Modularity and flexibility are just two key characteristics of the SDR environment which extend smoothly into the modeling of hardware and software. In particular, the proposed model of signal processing software includes irregular, connected, directed, acyclic graphs with random node weights and random edges. Several approaches for mapping such software to a given hardware are discussed. Taking into account previous findings as well as new results from system simulations presented here, the paper finally concludes with the utility of pipelining as a general design guideline for modular software-defined radio.