Special Issue on Efficiency in Wireless Software-Defined Radio Systems

In this paper, we present a general method to reconfigure a software defined radio (SDR) using a set of intelligent algorithms called a cognitive engine (CE) to create a cognitive radio (CR) that provides a user with a required quality of service (QoS). The proposed architecture relies on the CE to generate an XML document that describes the SDR behavior. An Application Programming Interface (API) translates the XML document to the platform-specific commands to adjust the SDR. The API structure developed here will find initial applications in public safety and military communications to provide low-cost, multi-band, multi-mode operation for interoperability with SDR technology.

Software Defined Radios (SDRs) are wireless communication systems in which many of the radios' important functional elements, including filtering, frequency translation and modulation/demodulation, are implemented in software via Digital Signal Processing (DSP) techniques using embedded processors and Field Programmable Gate Arrays (FPGA) as described by Harris and Lowdermilk in 2010 [1]. SDRs differ from traditional radios that typically employ custom silicon to implement such functions. SDRs exhibit several advantages over traditional radios, including re-configurability and reduced development costs and time-to-market since they do not require custom silicon. SDRs also enjoy long life cycles, since they can readily be adapted to meet future needs. Cognitive Radios (CRs) are a special class of SDRs that employ adaptive DSP technologies to implement spectral management. This enables CRs to deliver improved effective bandwidth, quality of service, and enhanced security, which have become increasingly important in modern commercial and military wireless communications applications.

The Software-defined radio (SDR) technology is considered as a promising solution to address the issue of spectrum scarcity by providing a high level of flexibility and configurability to wireless communication systems. This technology, widely used in cognitive radio (CR) networks, aims at optimizing the spectrum efficiency. However, to reach this objective, a new conception of spectrum sensing methods needs to be adopted. Indeed, in the emerging wireless communications systems, telecom researchers face the challenge of detecting the presence of energy with a very low signal-to-noise ratio (SNR) in a very complex environment. For this purpose, for complex environments such as CR networks, machine-learning (ML) algorithms seem to be a suitable solution allowing a smart spectrum-sensing scheme. This scheme enables the increase of the signal detection probability and the decrease the false detection probability of signal detection. In this paper, four supervised ML models are compared: Naive Bayes classifier, Support Vector Machine, Gradient Boosting Machine and Distributed Random Forest. Furthermore, a principal component analysis (PCA) is performed to reduce the dimensionality of the complex dataset while preserving the useful variability. Their performance is subsequently evaluated by computing their receiver operating characteristic (ROC) curves. The entire algorithms are then implemented and demonstrated using the GNU (GNU’s Not Unix) Radio platform and an SDR-based electronic card, HackRF One.

Electromagnetic spectrum is a natural resource and at present its use is regulated by fixed spectrum assignment policy. That is, spectrum is assigned to license holders or services on a long term basis for large geographical regions. This fixed channel allocation served well in the past but the rapid and tremendous growth in wireless communication changed the entire scenario. Introduction of every new communication application, either military or civilian makes the spectrum more and more congested. Meanwhile, the spectrum allocated to specific users does not ensure it is being used effectively at all times. Measurement studies reveal that 15 to 85 percent of the licensed radio spectrum is relatively unutilized or under utilized in most of the geographical locations for a considerable amount of time. The limited available spectrum and its inefficient usage encouraged a new communication paradigm to exploit the radio frequency spectrum opportunistically. This new concept is termed Dynamic Spectrum Access (DSA). The technology that makes DSA possible is Cognitive Radio (CR) which was first suggested by Joseph Mitola. In fact, CR is an all-encompassing term for a wide variety of technologies that enable radios to achieve various levels of self configuration and different functionalities ranging from ubiquitous wireless access to DSA for a device centric interference management. DSA networks define the already existing licensed users as primary users or legacy users and CR as secondary user. CR, sits on top of Software Defined Radio (SDR) is an intelligent wireless system that adapts to its environments to utilize the spectrum by its secondary usage in a 'do no harm' fashion. In simple words, it senses the radio environment, detects vacant spectrum bands, decides the best spectrum and chooses the transmission scheme which insures minimum interference to adjacent legacy users. As DSA networks targets mainly the wideband spectrum, the transmission technology should mitigate multi-path frequency selective fading channel impairments and should have multi-user support. Thus DSA demands for CR transmission scheme of high flexibility, adaptivity, multi-user support and moderate error performance in fading channels.

This paper investigates the design issues of spectrum sensing in the cognitive radio (CR) networks of opportunistic unlicensed spectrum access. The cognitive radios can perform a communication using the incumbent user spectrum band without the interference caused by the cognitive radio users. In this case, the cognitive radios must know the real-time radio environments of the incumbent user spectrum band using the spectrum sensing, beacon signal, and geo-location database access. Then in this paper, we are going to provide spectrum sensing issues which include the sensing techniques, the regulatory requirements, the analysis of DTV detection threshold, and main considerations associated with the spectrum sensing design in cognitive radio systems. Also, this paper introduces design trade-offs in order to optimize the sensing parameters such as sensing time and sensing complexity.

Today's wireless networks are characterized by fixed spectrum assignment policy. The spectral scarcity and the inefficiency in the spectrum usage necessitate new communication paradigms to exploit the existing wireless spectrum, opportunistically. Software Defined Radio (SDR) and Cognitive Radio (CR) are the very paradigms for wireless communication, in which either a network or a wireless node reconfigures its transmission or reception parameters to communicate efficiently, avoiding interference with licensed or unlicensed users. CR adapts itself to the newer environment on the basis of its intelligent sensing and captures the best available spectrum to meet user communication requirements. When the radio link features are extended to the network layer, the cognitive radios form the cognitive radio network. This chapter is focused on software defined radio, its architecture, its limitations, evolution to cognitive radio network, architecture of the CR, and its relevance in wireless and mobile ad-hoc networks.

In the past, radio systems were designed to communicate using one or two wave forms. As a result, two groups of people with different types of traditional radio were not able to communicate due to incompatibility. The need to communicate with people using different types of equipment can only be solved by using software defined radio (SDR). SDR is a wireless device that works with any communication system, be it a cellular phone, a pager, a WI-FI transceiver an AM or FM radio, a satellite communications etc. In both hardware and software Digital Signal Processing (DSP) techniques are used to design a real time system. For SDR applications involving high complexity, it becomes impossible to stay with single sampling rate and so altering the sampling rate at different stages is required for low cost DSP hardware. Thus variable sampling rates i.e. Multirate Digital Signal Processing is required.

Motion estimation in videos is a computationally intensive process. A popular strategy for dealing with such a high processing load is to accelerate algorithms with dedicated hardware such as graphic processor units (GPU), field programmable gate arrays (FPGA), and digital signal processors (DSP). Previous approaches addressed the problem using accelerators together with a general purpose processor, such as acorn RISC machines (ARM). In this work, we present a co-processing architecture using FPGA and DSP. A portable platform for motion estimation based on sparse feature point detection and tracking is developed for real-time embedded systems and smart video sensors applications. A Harris corner detection IP core is designed with a customized fine grain pipeline on a Virtex-4 FPGA. The detected feature points are then tracked using the Lucas---Kanade algorithm in a DSP that acts as a co-processor for the FPGA. The hybrid system offers a throughput of 160 frames per second (fps) for VGA image resolution. We have also tested the benefits of our proposed solution (FPGA + DSP) in comparison with two other traditional architectures and co-processing strategies: hybrid ARM + DSP and DSP only. The proposed FPGA + DSP system offers a speedup of about 20 times and 3 times over ARM + DSP and DSP only configurations, respectively. A comparison of the Harris feature detection algorithm performance between different embedded processors (DSP, ARM, and FPGA) reveals that the DSP offers the best performance when scaling up from QVGA to VGA resolutions.

Cognitive Radio networks (CRN) enable cognitive users to use portions of the licensed spectrum of the primary users (PU). Dynamic spectrum access (DSA) is a design aspect that facilitates the transmission of cognitive radio (CR) users over idle channels. In many cases, it is not preferable that CR users to be treated equally. Provisioning quality of service (QoS) to some CR users is a challenge in DSA design. In this situation, The CR user traffic is divided into high and low priority classes. Sub-channel reservation (SCR) scheme is the most traditional scheme to provide priority to CR users in admission. In this paper, a new scheme named “low priority bounding” (LPB) scheme is used to provide QoS for CR users. The LPB scheme provides priority by restricting the number of low priority CR users by a threshold. This scheme maintains a suitable level of service to high priority users (as provided by SCR scheme) while improve the level of service for low priority ones. So the revenue gained from LPB scheme is optimized in comparison with SCR scheme. The impact of LPB scheme in the provided QoS is evaluated in terms of blocking probability, service termination rate, and service completion rate. Simulation results are compared with SCR scheme and it shows that the LPB scheme can significantly improve the revenue gained over the traditional SCR scheme.

The fear of spectrum scarcity in licensed bands or the poor coexisting behavior in unlicensed bands demands the exploration of new resource sharing strategies. As a solution, dynamic spectrum access (DSA) is being addressed to allocate already assigned but sparsely used frequency bands to secondary users without causing any significant interference to primary users. It exploits opportunities in temporal dimension rather than in spectral dimension. Cyclic data transmission is preferred in wireless factory automation systems. We propose the exploitation of this cyclic transmission to implement a DSA based cognitive radio (CR). The CR, working as a secondary user, will operate in the presence of primary users and avoids any harmful interference among coexisting systems. We implemented this idea using both software defined radio (SDR), and conventional hardware defined radio (HDR) based architectures. The experimental results show satisfactory robustness against cyclic interferers.

In this paper, we review some of the recent patents on spectrum sensing techniques for cognitive radio (CR) applications. The presented patents are categorized under: 1) spectrum sensing algorithms and architectures, 2) spectrum sensing and data communication and 3) cooperative spectrum sensing. The presented patents include special sensing algorithms that are based on the multi-resolution spectrum sensing (MRSS) approach, which ensures reliability of the spectrum sensing in wireless RF environments. We also discuss a CR patent that includes multiple RF chains to perform simultaneous cognitive tasks. Several patents that consider cooperative sensing are also presented as a solution to the hidden terminal problem inherent in wireless networks. We review the advantages and disadvantages of the presented patents and propose future directions for developing spectrum sensing techniques for CR’s. Keywords: Cognitive radio, cooperative spectrum sensing, dynamic spectrum access, energy detection, multi-resolution spectrum sensing, opportunistic spectrum access, spectrum sensing

Software Defined Radio (SDR)-powered cognitive radio network architectures are likely to play a prominent role in future 5G networks. Cognitive Radio (CR) and SDR appear to be feasible solutions for enhancing spectrum utilization and optimizing frequency band use among users. Indeed, CR is a type of wireless communication in which wireless devices detect and alter transmission properties in response to environmental sensing. We propose, in this work, a novel joint fully distributed spectrum management and hybrid routing protocol to arrange spectrum sharing among cognitive radio users. In our protocol, secondary users can alternate between the reactive channel selection phase and the proactive channel selection phase based on previous usage information maintained in their database tables about their list of available channels. Specifically, channel availability and utilization are established by investigating Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) transmission effects (pass or collision) and fed into a database table for each secondary user (SU). A java-based simulator is used to test the performance of our protocol. Throughput is utilized as an evaluation metric. According to simulation findings, our protocol considerably reduces collisions between secondary users while accessing spectrum opportunities for the transmission process and achieves higher throughput than the prior relevant protocol, namely, probabilistic and deterministic path selection protocol in the cognitive radio network.

A prototype Dynamic Spectrum Access (DSA) system is implemented, for ease of implementation, using the audio spectrum. In this setup, laptop PCs behave as two software defined radios (SDR), one a primary user (PU) and the other a secondary user (SU). The radios communicate digital information over the air, using the audio capabilities of standard soundcards. The PU, which is emulating the licensed user of an audio channel centered at 4 kHz, communicates using binary phase shift keying (BPSK) modulated data in periodic bursts. A cognitive radio (CR) based SU wishes to opportunistically occupy the PU channel during the inter-burst times, i.e. during the temporal ‘spectrum holes,’ while not interfering with the PU traffic. The PU link is implemented in MATLAB, while the SU is defined in GNU Radio with a separate Python control module defining its algorithm for spectrum sensing and hopping. The implementation of both communication links is described, as well as the initial performance evaluation of the preliminary system in terms of bit error rate performance of the PU with and without the presence of an active SU. The measured BER achieved for the best of the PU bursts with DSA was on the order of a few percent, just slightly higher than without DSA.

We consider a cognitive radio (CR) network that makes opportunistic access to a spectrum licensed to the primary users. During its operation, the CR network carries out spectrum sensing on a frame-by-frame basis to detect active primary users, thereby avoiding interfering with them. We formulate a collision-throughput tradeoff problem which, based on the sensing time requirement and the traffic pattern of primary users, finds optimal value for the frame duration of CR operation so that the throughput of the CR network is maximized, yet the collision probability of the primary users is not greater than a threshold. We derive the theoretical formula for achievable throughput of CR network and find the optimal solution for frame duration. Computer simulations are presented to evaluate the performance of our approach.

Cognitive radio (CR) technology is a potential solution to the spectrum scarcity problem. In CR systems, the availability of the licensed spectrum portion is monitored by the spectrum sensing process and strategies are applied to use this licensed portion without interfering with active primary users (PU). CR provides a flexible system that secondary users (SU) can make decisions about the spectrum usage at any time by simply configuring corresponding transmission parameters. However, implementation of CR systems can be a challenging task due to characteristic difficulties of the wireless channels introduced by fading. Especially spectrum sensing process is affected by changing channel conditions, which should be considered in order to create a high performance CR system. Robust spectrum sensing is essential for a CR system due to its vital role in the efficient usage of the spectrum. Therefore, several algorithms that are proposed for this issue should contain suitable properties considering realistic channel conditions. Implementation of these algorithms can be realized through software defined radios (SDRs). SDR is a core component of the CR technology and it allows a practical development process with modification on the software rather than hardware. Thus, SDR based approaches to CR problems are quite effective. In this chapter, the state of the art of CR systems are explained in detail by highlighting essential components of the existing studies. Effective approaches to the implementation using SDR systems are given. Moreover, an energy detection based spectrum sensing implementation for 2.4 GHz ISM band is given as an implementation example and channel based spectrum usage is analyzed by using SDR tools LabVIEW and NI USRP-2921 hardware in real-time.