Control information exchange through UWB in cognitive radio networks

This paper describes a design effort in a smart tracking prototype, in an attempt to provide real-time, mobile and seamlessly indoor/outdoor location tracking for medical staff, patient or instruments in hospital environment, by integrating GPS (Global Position System) with UWB (Ultra Wideband). Ultra Wideband (UWB) technology can locate a person or asset to the accuracy of centimeters. While UWB technology is becoming popular for indoor position tracking, GPS technology has been around for some time for use in outdoor position tracking with an accuracy of 1 to 100 meters depending on the type of equipment used. It is envisaged that it is possible to combine GPS with UWB to give accurate position regardless of where the person or object is in indoor or outdoor. In view of the strong need for indoor-outdoor tracking in many environments such as hospitals, we looked into the possibility of integrating UWB and GPS to provide the target with seamless indoor-outdoor navigation capability and have developed a prototype. The system structure and experimental results will be illustrated in this paper. To our knowledge, no same system is reported yet.

The emerging feature-rich and high-data-rate wireless applications have put increasing demand on radio spectrum. The scarcity of spectrum and the inefficiency in its usage, as caused by the current radio spectrum regulations, necessitate the development of new dynamic spectrum allocation policies to better exploit the existing spectrum. The current spectrum allocation regulations assign specific bands to particular services, and grant licensed band access to licensed users only. Cognitive Radio (CR) will revolutionize the way spectrum is allocated. In a CR network, the intelligent radio part allows unlicensed users (secondary users) to access spectrum bands licensed to primary users, while avoiding interference with them. In this scheme, a secondary user can use spectrum sensing hardware/software to locate spectrum portions with reduced primary user activity or idle spectrum slots, select the best available channel, coordinate access to this channel with other secondary users, and vacate the channel when a primary user needs it. To achieve this, the transceiver in a CR system should have awareness of the radio environment in terms of spectrum usage, power spectral density of transmitted/received signals, and wireless protocol signaling, should be able to adaptively tune system parameters such as transmit power, carrier frequency, and modulation strategy. The transceiver should also be ended with an antenna system that can simultaneously operate over a wide frequency band (sensing) and a chosen narrow band (communication), or operate over an ultra-wide frequency band while possibly blocking signals in a narrow frequency range. Ultra-wideband (UWB) is a transmission technique that uses pulses with a very short time duration across a very large frequency portion of the spectrum. UWB is different from other radio frequency communication techniques in that it does not use RF carriers, but instead employs modulated high frequency pulses of low power with a duration of less than 1 nanosecond. From the perspective of other communication systems, the UWB transmissions are part of the low power background noise. Therefore UWB promises to enable the usage of licensed spectrum without harmful interference to primary communication systems, and can be used as an enabling technology for implementing CR. In this chapter, we offer a general overview of Cognitive Radio and dynamic spectrum access, discuss the advantages of using UWB as an enabling technology for CR, and give

Cognitive Radio Networks (CRN) are opportunistic networks which aim to harness the white space in the television frequency spectrum, on a need-to-need basis, without interfering the incumbent, called the Primary User (PU). Cognitive radios (CR) that sense the spectrum periodically for sensing the PU activity, are called Secondary Users (SU). CRNs are susceptible to two major attacks, Byzantine attacks and Primary User Emulation Attack (PUEA). Both the attacks are capable of rendering a CRN useless, by either interfering with the PU itself or capturing the entire channel for themselves. Byzantine attacks detection and mitigation is an important security issue in CRN. Hence, the current work proposes using a multi-Hidden Markov Model system with an aim to detect different types of random-Byzantine attacks. Simulation results show good detection rate across all the attacks.

In recent times, spectrum sensing and spectrum management become a crucial design issue in cognitive radio networks (CRN). To improve the spectrum utilization in CRN, the secondary users (SUs) will try to utilize the spectrum resource when it is unoccupied by the authorized primary users (PUs). At the same time, blockchain principle has been introduced to efficiently identify the legitimate SUs and allocate the spectrum resource as per the demand specified by the SUs. In this view, this article presents a new machine learning (ML) with blockchain‐based spectrum management technique in CRN. The proposed model undergoes three processes, namely spectrum sensing, blockchain‐based spectrum access, and malicious user (MU) identification. Initially, ML‐based extreme learning machine (ELM) technique is applied for spectrum sensing. Then, the presented blockchain approach provides secured spectrum allocation for SUs. Finally, the MUs are identified and to be blocked from accessing the available spectrum resource. An extensive simulation analysis is carried out to ensure the goodness of the proposed model. The obtained results indicated that the proposed model has offered better performance compared with other methods. The experimental outcome stated that under the presence of −20 dB SNR, the proposed method has attained a maximum detection rate of 0.68, whereas the KNN and OR rule methods have demonstrated a minimum detection rate of 0.58 and 0.5, respectively.

Ultra-wideband (UWB) technology offers a promising solution to the RF spectrum drought by allowing new services to coexist with current radio systems with minimal or no interference. This coexistence brings the advantage of avoiding the expensive spectrum licensing fees that providers of all other radio services must pay. In Ultra Wide Band technology, the conventional analog waveform is a simple pulse which in general is directly radiated to the air. These short pulses have typical widths of less than 1 nanosecond and thus bandwidth of over 1 GHz. Due to this wide bandwidth, Federal Communication Commission(FCC) has imposed certain restrictions on the emission limits of UWB systems. Inorder to fit well to the FCC mask, the basic pulse shapes have to be modified. So pulse shaping is very much relevant in UWB domain. The immediate objective of this paper is to design an optimal pulse shaping technique. This paper proposes a hybrid approach for UWB pulse shaping which utilises ideas from existing two techniques.

Ultra wideband (UWB) technology has been widely researched for both radar and communication systems. However, detecting the presence of a signal is essential and challenging for many applications. In this paper, we propose a novel detector for UWB radar signals with UWB communication interference by exploiting the signal cyclic features. A single-cycle detector based on fundamental cyclic frequency of radar signal is proposed to detect UWB radar signal. Simulation results illustrate the possibility to detect the UWB radar signal under the coexistence of a UWB communication signal and the proposed scheme is compared with conventional detectors for UWB radar systems.

Spectrum sensing is a significant issue in cognitive radio networks which enables estimation of the frequency spectrum and hence provides frequency reuse. In the large‐scale cognitive radio networks, secondary users cannot share a common spectrum since the coverage area of primary users is limited. In this study, the authors suggest a diffusion adaptive learning algorithm based on correntropy cooperation policy, which first categorises received data of secondary users into several groups, and then learns a common spectrum inside each group. The mean‐square performance of proposed algorithm is analysed and supported by simulations. Experimental results show that, in a multitask cognitive network, the proposed algorithm can achieve a better mean‐square deviation learning performance both in transient and steady‐state regimes in comparison with other conventional algorithms.

Adaptive signal identification has been an important issue in cognitive radio networks (CRNs). Most existing techniques require high-level signal-to-noise ratio (SNR) for signal identification. This study presents an intelligent technique that focuses on a theoretical and experimental study of the signal identification by using manifold learning algorithm in CRNs. The authors pose the problem of signal identification in CRNs as signal classification by using manifold learning on high dimensions, and a novel manifold learning algorithm named as SIEMAP is proposed, which is able to identify signals in a low-dimensional space. Simulation results indicate that SIEMAP outperforms classical methods in low dimensions and is capable of identifying signal types from the received signals.

In cognitive radio networks (CRNs), perfect knowledge of a dynamic radio environment is hard to know due to hardware limitation, short sensing time and network connectivity issues in CRNs. In this paper, we study the dynamic resource allocation problem for heterogeneous services in CRNs with imperfect channel sensing. We formulate the optimization problem as a mixed integer programming problem under constraints. Then we solve the optimal joint power and channel allocation problem using discrete stochastic optimization method. The proposed algorithm has low computation complexity and fast convergence to approximate to the optimal solution under imperfect channel sensing. Another advantage of this method is that it can track the changing radio environment to allocate the resources dynamically. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

Spread spectrum-based coordination design for spectrum-agile wireless ad hoc networks

In recent years, ultra wideband (UWB) technology has been drawing attention as a promising technology in order to materialize ubiquitous network. The third-generation (3G) and the fourth-generation (4G) smartphones which employ the UWB technology have been hot. This study reports on a multiband antenna, which covers 800 MHz for 4G, 2.0 GHz for 3G and 3.1-10.6 GHz for UWB, built into such a smart phone. In the results from investigation, both of the multiband antennas, designed and made on an experimental basis, showed the VSWR less than 2.0 at 800 MHz, 2 GHz and UWB bands. In conclusion, the multiband antenna developed in this paper is enough in practical use.

Routing is one of the most important and fundamental issues in Cognitive Radio Networks (CRNs). In this paper, we propose an effective routing scheme. Our main contributions are threefold. First, we propose a spectrum-aware Semi-Structure Routing (SSR) framework which incorporates power control. By employing forwarding zones and routing zones, SSR can effectively utilize the local real-time spectrum dynamics and meanwhile guarantee the global routing performance. Second, considering the lack of analytical models for routing protocol performance [1] in CRNs, we analyze the upper bound of the induced latency and scalability of SSR. Finally, extensive simulation results are presented to validate the performance of SSR.

An ultra wideband (UWB) technology is a potential candidate for implant body area networks (BANs), where wireless communications are established between inside and outside of a human body. The UWB can accomplish higher data rate than the other frequency band for the implant communication. However, due to its high frequencies, the UWB signals suffer from quite large attenuation in the implant communication link, which makes it difficult to achieve reliable communications. For achieving reliable communication, it is well known that a spatial diversity technique is efficient without any frequency extension. In our previous works, we developed a transmit polarization diversity antenna for the UWB implant communication. However, optimal UWB modulation scheme for transmit diversity were rarely discussed. In this paper, in order to investigate the optimal UWB modulation schemes for implant communication with transmit diversity, we compare the communication performances of UWB-impulse radio (UWB-IR) and multiband-orthogonal frequency division multiplexing (MB-OFDM). For this purpose, we first analyze the propagation characteristics in the implant UWB channel, which ranges from 3.4 GHz to 4.8 GHz, using a finite difference time domain (FDTD) numerical analysis technique. Then, we evaluate and discuss the communication performances of both modulation schemes for the transmit polarization diversity from the viewpoint of the BER and the required transmit power.

Spectrum sharing and power allocation are interesting issues in cognitive radio networks. In our paper, we introduce a pricing scheme to demonstrate the spectrum sharing scenario between primary users and secondary users. We demonstrate the joint optimal allocation scheme of both spectrum and power for secondary users buying spectrum from multiple primary users. The joint optimal allocation scheme maximizes secondary users' utilities while primary users' utilities meet certain requirements. Moreover, we illustrate a distributed resource allocation scheme to jointly optimize usage of power and spectrum when each secondary user does not know others' buying strategies. Our simulation results show that under some utility requirements, the joint optimized resource allocation scheme has 3dB gain over that under even resource allocation.

Spectrum sensing is a critical and challenging issue in cognitive radio networks. Prior research has demonstrated that using spatial diversity or temporal diversity can lead to more efficient spectrum sensing. Based on Neyman-Pearson criterion, this paper derives a novel spectrum sensing algorithm which exploits spatial diversity among multiple cognitive sensors and temporal diversity among consecutive time slots jointly. The numerical results not only verify the improvement of the sensing performance comparing with singular (spatial or temporal) diversity is applied, but also show the reduction of sensing overhead in low signal-noise-ratio (SNR) regime.