Throughput and Collision Analysis of Multi-Channel Multi-Stage Spectrum Sensing Algorithms

Multi-stage sensing is a novel concept that refers to a general class of spectrum sensing algorithms that divide the sensing process into a number of sequential stages. The number of sensing stages and the sensing technique per stage can be used to optimize performance with respect to secondary user throughput and the collision probability between primary and secondary users. So far, the impact of multi-stage sensing on network throughput and collision probability for a realistic network model is relatively unexplored. Therefore, we present the first analytical framework which enables performance evaluation of different multi-channel multi-stage spectrum sensing algorithms for Opportunistic Spectrum Access networks. The contribution of our work lies in studying the effect of the following parameters on performance: number of sensing stages, physical layer sensing techniques and durations per each stage, single and parallel channel sensing and access, number of available channels, primary and secondary user traffic, buffering of incoming secondary user traffic, as well as MAC layer sensing algorithms. Analyzed performance metrics include the average secondary user throughput and the average collision probability between primary and secondary users. Our results show that when the probability of primary user mis-detection is constrained, the performance of multi-stage sensing is, in most cases, superior to the single stage sensing counterpart. Besides, prolonged channel observation at the first stage of sensing decreases the collision probability considerably, while keeping the throughput at an acceptable level. Finally, in realistic primary user traffic scenarios, using two stages of sensing provides a good balance between secondary users throughput and collision probability while meeting successful detection constraints subjected by Opportunistic Spectrum Access communication

Spectrum measurement, sensing, analysis and simulation in the context of cognitive radio

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated locally by national agencies. Spectrum has been assigned to different services and it is very difficult for emerging wireless technologies to gain access due to rigid spectmm policy and heavy opportunity cost. Current spectrum management by licensing causes artificial spectrum scarcity. Spectrum monitoring shows that many frequencies and times are unused. Dynamic spectrum access (DSA) is a potential solution to low spectrum efficiency. In DSA, an unlicensed user opportunistically uses vacant licensed spectrum with the help of cognitive radio. Cognitive radio is a key enabling technology for DSA. In a cognitive radio system, an unlicensed Secondary User (SU) identifies vacant licensed spectrum allocated to a Primary User (PU) and uses it without harmful interference to the PU. Cognitive radio increases spectrum usage efficiency while protecting legacy-licensed systems. The purpose of this thesis is to bring together a group of CR concepts and explore how we can make the transition from conventional radio to cognitive radio. Specific goals of the thesis are firstly the measurement of the radio spectrum to understand the current spectrum usage in the Humber region, UK in the context of cognitive radio. Secondly, to characterise the performance of cyclostationary feature detectors through theoretical analysis, hardware implementation, and real-time performance measurements. Thirdly, to mitigate the effect of degradation due to multipath fading and shadowing, the use of -wideband cooperative sensing techniques using adaptive sensing technique and multi-bit soft decision is proposed, which it is believed will introduce more spectral opportunities over wider frequency ranges and achieve higher opportunistic aggregate throughput.Understanding spectrum usage is the first step toward the future deployment of cognitive radio systems. Several spectrum usage measurement campaigns have been performed, mainly in the USA and Europe. These studies show locality and time dependence. In the first part of this thesis a spectrum usage measurement campaign in the Humber region, is reported. Spectrum usage patterns are identified and noise is characterised. A significant amount of spectrum was shown to be underutilized and available for the secondary use. The second part addresses the question: how can you tell if a spectrum channel is being used? Two spectrum sensing techniques are evaluated: Energy Detection and Cyclostationary Feature Detection. The performance of these techniques is compared using the measurements performed in the second part of the thesis. Cyclostationary feature detection is shown to be more robust to noise. The final part of the thesis considers the identification of vacant channels by combining spectrum measurements from multiple locations, known as cooperative sensing. Wideband cooperative sensing is proposed using multi resolution spectrum sensing (MRSS) with a multi-bit decision technique. Next, a two-stage adaptive system with cooperative wideband sensing is proposed based on the combination of energy detection and cyclostationary feature detection. Simulations using the system above indicate that the two-stage adaptive sensing cooperative wideband outperforms single site detection in terms of detection success and mean detection time in the context of wideband cooperative sensing

Networked Collaborative Sensing using Multi-domain Measurements: Architectures, Performance Limits and Algorithms

As a promising 6G technology, integrated sensing and communication (ISAC) gains growing interest. ISAC provides integration gain via sharing spectrum, hardware, and software. However, concerns exist regarding its sensing performance when compared to dedicated radar systems. To address this issue, the advantages of widely deployed networks should be utilized, and this paper proposes networked collaborative sensing (NCS) using multi-domain measurements (MM), including range, Doppler, and two-dimension angle of arrival. In the NCS-MM architecture, this paper proposes a novel multi-domain decoupling model and a novel guard band-based protocol. The proposed model simplifies multi-domain derivations and algorithm designs, and the proposed protocol conserves resources and mitigates NCS interference. To determine the performance limits, this paper derives the Cram\'er-Rao lower bound (CRLB) of three-dimension position and velocity in NCS-MM. An accumulated single-dimension channel model is used to obtain the CRLB of MM, which is proven to be equivalent to that of the multi-dimension model. The algorithms of both MM estimation and fusion are proposed. An arbitrary-dimension Newtonized orthogonal matched pursuit (AD-NOMP) is proposed to accurately estimate grid-less MM. The degree-of-freedom (DoF) of MM is analyzed, and a novel DoF-based two-stage weighted least squares (TSWLS) is proposed to reduce equations without DoF loss. The numerical results show that the performances of the proposed algorithms are close to their performance limits

Analysis Framework for Opportunistic Spectrum OFDMA and its Application to the IEEE 802.22 Standard

We present an analytical model that enables throughput evaluation of Opportunistic Spectrum Orthogonal Frequency Division Multiple Access (OS-OFDMA) networks. The core feature of the model, based on a discrete time Markov chain, is the consideration of different channel and subchannel allocation strategies under different Primary and Secondary user types, traffic and priority levels. The analytical model also assesses the impact of different spectrum sensing strategies on the throughput of OS-OFDMA network. The analysis applies to the IEEE 802.22 standard, to evaluate the impact of two-stage spectrum sensing strategy and varying temporal activity of wireless microphones on the IEEE 802.22 throughput. Our study suggests that OS-OFDMA with subchannel notching and channel bonding could provide almost ten times higher throughput compared with the design without those options, when the activity and density of wireless microphones is very high. Furthermore, we confirm that OS-OFDMA implementation without subchannel notching, used in the IEEE 802.22, is able to support real-time and non-real-time quality of service classes, provided that wireless microphones temporal activity is moderate (with approximately one wireless microphone per 3,000 inhabitants with light urban population density and short duty cycles). Finally, two-stage spectrum sensing option improves OS-OFDMA throughput, provided that the length of spectrum sensing at every stage is optimized using our model

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

Cooperative wideband spectrum sensing with multi-bit hard decision in cognitive radio

Cognitive radio offers an increasingly attractive solution to overcome the underutilization problem. A sensor network based cooperative wideband spectrum sensing is proposed in this paper. The purpose of the sensor network is to determine the frequencies of the sources and reduced the total sensing time using a multi-resolution sensing technique. The final result is computed by data fusion of multi-bit decisions made by each cooperating secondary user. Simulation results show improved performance in energy efficiency

Sparsity Adaptive Compressive Sensing based Two-stage Channel Estimation Algorithm for Massive MIMO-OFDM Systems

Massive multi-input multioutput (MIMO) coupled with orthogonal frequency division multiplexing (OFDM) has been utilized extensively in wireless communication systems to investigate spatial diversity. However, the increasing need for channel estimate pilots greatly increases spectrum consumption and signal overhead in massive MIMO-OFDM systems. This paper proposes a two-stage channel estimation algorithm based on sparsity adaptive compressive sensing (CS) to address this issue. To estimate the channel state information (CSI) for pilot locations in Stage 1, we provide a geometry mean-based block orthogonal matching pursuit (GBMP) method. By calculating the geometric mean of the energy in the support set of the channel response, the GBMP method, when compared to conventional CS methods, can drastically reduce the number of iterations and effectively increase the convergence rate of channel reconstruction. Stage 2 involves estimating the CSI for nonpilot locations using a time-frequency correlation interpolation method, which can increase the accuracy of the channel estimation and is dependent on the estimated results from Stage 1. According to the simulation results, the proposed two-stage channel estimation algorithm greatly reduces the running time with little error performance degradation when compared to traditional channel estimating algorithms