15 research outputs found
A Trade-off Analysis of Energy Detectors and Partitioned Search for Primary Detection
Cognitive radios aim to coexist in the unused spectrum bands which are licensed to primary users without harming the primary transmission/reception. For a cognitive radio, it is important to detect the band in which the primary is operating as fast as possible and with high reliability in order to adapt its transmission. In this work, we propose P-partitioning method in combination with energy detectors for the search of the band that the primary user is operating. In the P-partitioning method, the spectrum bands are categorized into P groups and the group that the primary band belongs to is detected in a recursive fashion. The energy detector operates on each group and the test statistics is the total energy received in the bands belonging to the group. The proposed search technique has detection time PlogP(N), where N is the number of bands in the spectrum. When P = N, the proposed scheme is equivalent to linear search with detection time N. We study the performance of the proposed scheme for a single non-cooperative radio and also for multiple cooperating radios. For a single cognitive radio, we provide an upper bound on the probability of correct detection which presents two different regimes of operation. In the low SNR regime, although it is counter-intuitive the partitioning improves the probability of detection. This is due an averaging effect when the signal energy in different bands are accumulated to obtain the energy contribution from a group. In the high SNR regime, performance degrades with partitioning. In addition, we observe that user cooperation improves the performance in the high SNR regimes
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
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
Over-the-air computation for cooperative wideband spectrum sensing and performance analysis
For sensor network aided cognitive radio, cooperative wideband spectrum sensing can distribute the sampling and computing pressure of spectrum sensing to multiple sensor nodes (SNs) in an efficient way. However, this may incur high latency due to distributed data aggregation, especially when the number of SNs is large. In this paper, we propose a novel cooperative wideband spectrum sensing scheme using over-the-air computation. Its key idea is to utilize the superposition property of wireless channel to implement the summation of Fourier transform. This avoids distributed data aggregation by computing the target function directly. The performance of the proposed scheme is analyzed with imperfect synchronization between different SNs. Furthermore, a synchronization phase offset (SPO) estimation and equalization method is proposed. The corresponding performance after equalization is also derived. A working prototype based on universal software radio periphera (USRP) and Monte Carlo simulation is built to verify the performance of the proposed scheme
Wideband Autonomous Cognitive Radios: Spectrum Awareness and PHY/MAC Decision Making
The cognitive radios (CRs) have opened up new ways of better utilizing the scarce wireless spectrum resources. The CRs have been made feasible by recent advances in software-defined radios (SDRs), smart antennas, reconfigurable radio frequency (RF) front-ends, and full-duplex RF front-end architectures, to name a few. Generally, a CR is considered as a dynamically reconfigurable radio capable of adapting its operating parameters to the surrounding environment. Recent developments in spectrum policy and regulatory domains also allow more flexible and efficient utilization of wider RF spectrum range in the future. In line with the future directions of CRs, a new vision of a future autonomous CR device, called Radiobots, was previously proposed. The goals of the proposed Radiobot surpass the dynamic spectrum access (DSA) to achieve wideband operability and the main features of cognition. In order to ensure the practicality and robust operation of the Radiobot structure, the research focus of this dissertation includes the following aspects: 1) robust spectrum sensing and operability in a centralized CR network setup; 2) robust multivariate non-parametric quickest detection for dynamic spectrum usage tracking in an alien RF environment; 3) joint physical layer and medium access control layer (PHY/MAC) decision-making for wideband bandwidth aggregation (simultaneous operation over multiple modes/networks); and 4) autonomous spectrum sensing scheduling solutions in an alien ultra wideband RF environment. The major contribution of this dissertation is to investigate the feasibility of the autonomous CR operation in heterogeneous RF environments, and to provide novel solutions to the fundamental and crucial problems/challenges, including spectrum sensing, spectrum awareness, wideband operability, and autonomous PHY/MAC protocols, thus bringing the autonomous Radiobot one step closer to reality
Spectrum Sensing in Cognitive Radio: Multi-detection Techniques based Model
Cognitive radio (CR) paradigm is a new radio technology proposed to solve spectrum scarcity and underutilization. Central to CR is spectrum sensing (SS), which is responsible for detecting unoccupied frequencies. Since Detection techniques differ in their performance, selecting the optimal detection method to locally perform SS has received significant attention. This research work aims to enhance the reliability of local detection decisions, under low SNR, by developing a spectrum sensing that can take advantage of multiple detection techniques. This model can either select the optimal technique or make these techniques cooperate with one another to achieve better sensing performance. The model performance is measured with respect to detection and false alarm probability as well as sensing time. To develop this model, the performance of three detection techniques is evaluated and compared. Furthermore, the voting and the maximum a posteriori probability (MAP) fusion models were developed and employed to combine spectrum sensing results obtained from the three techniques. It is concluded that the cyclostationary feature detection technique is a superior detector in low SNR situations. MAP fusion model is found to be more reliable than the voting model