10 research outputs found
Spectral and Energy Efficiency in Cognitive Radio Systems with Unslotted Primary Users and Sensing Uncertainty
This paper studies energy efficiency (EE) and average throughput maximization
for cognitive radio systems in the presence of unslotted primary users. It is
assumed that primary user activity follows an ON-OFF alternating renewal
process. Secondary users first sense the channel possibly with errors in the
form of miss detections and false alarms, and then start the data transmission
only if no primary user activity is detected. The secondary user transmission
is subject to constraints on collision duration ratio, which is defined as the
ratio of average collision duration to transmission duration. In this setting,
the optimal power control policy which maximizes the EE of the secondary users
or maximizes the average throughput while satisfying a minimum required EE
under average/peak transmit power and average interference power constraints
are derived. Subsequently, low-complexity algorithms for jointly determining
the optimal power level and frame duration are proposed. The impact of
probabilities of detection and false alarm, transmit and interference power
constraints on the EE, average throughput of the secondary users, optimal
transmission power, and the collisions with primary user transmissions are
evaluated. In addition, some important properties of the collision duration
ratio are investigated. The tradeoff between the EE and average throughput
under imperfect sensing decisions and different primary user traffic are
further analyzed.Comment: This paper is accepted for publication in IEEE Transactions on
Communication
Radio resource allocation in collaborative cognitive radio networks based on primary sensing profile
In this paper, we present a novel power allocation scheme for multicarrier cognitive radio networks. The proposed scheme performs subchannel power allocation by incorporating primary users activity in adjacent cells. Therefore, we first define the aggregated subchannel activity index (ASAI) as an average indicator which characterizes the collective networkwide primary users' communication activity level. The optimal transmit power allocation is then obtained with the objective of maximizing a total utility function at the secondary base station (SBS), subject to the maximum SBS transmit power, and collision probability constraint at the primary receivers. Utilizing ASAI, we further obtain an energy efficient power allocation for the secondary system. Optimal energy efficiency (EE) and spectral efficiency (SE) are contradicting objectives, and thus, there is a tradeoff between these two performance metrics. We also propose a design approach to handle this tradeoff as a function of the ASAI, which provides quantitative insights into efficient system design. In addition to a lower signaling overhead, the simulation results confirm that the proposed scheme achieves a significantly higher achievable rate. Simulation results further indicate that using ASAI enables obtaining an optimal operating point based on the tradeoff between EE and SE. The optimal operating point can be further adjusted by relaxing/restricting the sensing parameters depending on the system requirements
Opportunistic Ambient Backscatter Communication in RF-Powered Cognitive Radio Networks
In the present contribution, we propose a novel opportunistic ambient
backscatter communication (ABC) framework for radio frequency (RF)-powered
cognitive radio (CR) networks. This framework considers opportunistic spectrum
sensing integrated with ABC and harvest-then-transmit (HTT) operation
strategies. Novel analytic expressions are derived for the average throughput,
the average energy consumption and the energy efficiency in the considered set
up. These expressions are represented in closed-form and have a tractable
algebraic representation which renders them convenient to handle both
analytically and numerically. In addition, we formulate an optimization problem
to maximize the energy efficiency of the CR system operating in mixed ABC
and HTT modes, for a given set of constraints including primary
interference and imperfect spectrum sensing constraints. Capitalizing on this,
we determine the optimal set of parameters which in turn comprise the optimal
detection threshold, the optimal degree of trade-off between the CR system
operating in the ABC and HTT modes and the optimal data transmission
time. Extensive results from respective computer simulations are also presented
for corroborating the corresponding analytic results and to demonstrate the
performance gain of the proposed model in terms of energy efficiency
Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems with Limited Feedback Using ESPAR Antennas
We consider an opportunistic cognitive radio (CR) system consisting of a
primary user (PU), secondary transmitter (SUtx), and secondary receiver (SUrx),
where SUtx is equipped with an electrically steerable parasitic array radiator
(ESPAR) antenna with the capability of choosing one beam among M beams for
sensing and communication, and there is a limited feedback channel from SUrx to
SUtx. Taking a holistic approach, we develop a framework for integrated
sector-based spectrum sensing and sector-based data communication. Upon sensing
the channel busy, SUtx determines the beam corresponding to PU's orientation.
Upon sensing the channel idle, SUtx transmits data to SUrx, using the selected
beam corresponding to the strongest channel between SUtx and SUrx. We formulate
a constrained optimization problem, where SUtx-SUrx link ergodic capacity is
maximized, subject to average transmit and interference power constraints, and
the optimization variables are sensing duration, thresholds of channel
quantizer at SUrx, and transmit power levels at SUtx. Since this problem is
non-convex we develop a suboptimal computationally efficient iterative
algorithm to find the solution. Our results demonstrate that our CR system
yields a significantly higher capacity, and lower outage and symbol error
probabilities, compared with a CR system that its SUtx has an omni-directional
antenna.Comment: This paper has been submitted to IEEE Transactions on Cognitive
Communications and Networkin
Ambient Backscatter Communication Based Cooperative Relaying for Heterogeneous Cognitive Radio Networks
In this paper, a new network model is proposed to improve the performance of the secondary channel in cognitive radio networks (CRNs) based ambient backscatter communication systems. This model is considered as a cooperative system with multi-secondary transmitter (ST) and multi-relay. The ST backscatters data to both the secondary receiver (SR) and relay. Also it harvests energy from the signal emitted by the primary transmitter (PT) during the busy period. The relay activated by the ST user forwards the information from ST to SR. During the idle period, the PT broadcast is interrupted and ST also performs active data transmission using the energy it has harvested. We aim to maximize the number of data transmitted to the SR. Therefore, how long the ST will perform backscattering, energy harvesting and active data transmission is a problem to be solved. In such cooperative systems with multiple users, the solution of the problem becomes more complex. Therefore, the system model has been mathematically modeled and transformed into an optimization problem, considering that users are transmitting data using time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) techniques. Numerical results showed that higher data rates were achieved in NOMA. Additionally, It has been seen that the proposed model performs better when compared to the existing approaches in the literature, where the ST can only harvest energy and transmit data actively or only transmit data with ambient backscatter communication
Cognitive Radio Systems: Performance Analysis and Optimal Resource Allocation
Rapid growth in the use of wireless services coupled with inefficient utilization of scarce spectrum resources has led to the analysis and development of cognitive radio systems. Cognitive radio systems provide dynamic and more efficient utilization of the available spectrum by allowing unlicensed users (i.e., cognitive or secondary users) to access the frequency bands allocated to the licensed users (i.e., primary users) without causing harmful interference to the primary user transmissions. The central goal of this thesis is to
conduct a performance analysis and obtain throughput- and energy-efficient optimal resource allocation strategies for cognitive radio systems. Cognitive radio systems, which employ spectrum sensing mechanisms to learn the channel occupancy by primary users, generally operate under sensing uncertainty arising due to false alarms and miss-detections. This thesis analyzes the performance of cognitive radio systems in a practical setting with imperfect spectrum sensing.
In the first part of the thesis, optimal power adaptation schemes that maximize the achievable rates of cognitive users with arbitrary input distributions in underlay cognitive radio systems subject to transmit and interference power constraints are studied. Simpler approximations of optimal power control policies in the low-power regime are determined. Low-complexity optimal power control algorithms are proposed.
Next, energy efficiency is considered as the performance metric and power allocation strategies that maximize the energy efficiency of cognitive users in the presence of time-slotted primary users are identified. The impact of different levels of channel knowledge regarding the transmission link between the secondary transmitter and secondary receiver, and the interference link between the secondary transmitter and primary receiver on the optimal power allocation is addressed. In practice, the primary user may change its status during the transmission phase of the secondary users. In such cases, the assumption of time-slotted primary user transmission no longer holds. With this motivation, the spectral and energy efficiency in cognitive radio systems with unslotted primary users are analyzed and the optimal frame duration and energy-efficient optimal power control schemes subject to a collision constraint are jointly determined.
The second line of research in this thesis focuses on symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. General formulations for the optimal decision rule and error probabilities for arbitrary modulation schemes are provided. The optimal decision rule for rectangular quadrature amplitude modulation (QAM) is characterized, and closed-form expressions for the average symbol error probability attained with the optimal detector under both transmit power and interference constraints are derived.
Furthermore, throughput of cognitive radio systems for both fixed-rate and variable-rate transmissions in the finite-blocklength regime is studied. The maximum constant arrival rates that the cognitive radio channel can support with finite blocklength codes while satisfying statistical quality of service (QoS) constraints imposed as limitations on the buffer violation probability are characterized.
In the final part of the thesis, performance analysis in the presence of QoS requirements is extended to general wireless systems, and energy efficiency and throughput optimization with arbitrary input signaling are studied when statistical QoS constraints are imposed as limitations on the buffer violation probability. Effective capacity is chosen as the performance metric to characterize the maximum throughput subject to such buffer constraints by capturing the asymptotic decay-rate of buffer occupancy. Initially, constant-rate source is considered and subsequently random arrivals are taken into account
Spectrum sharing systems for improving spectral efficiency in cognitive cellular network
Since spectrum is the invisible infrastructure that powers the wireless communication, the demand has been exceptionally increasing in recent years after the implementation of 4G and immense data requirements of 5G due to the applications, such as Internet-of-Things (IoT). Therefore, the effective optimization of the use of spectrum is immediately needed than ever before. The spectrum sensing is the prerequisite for optimal resource allocation in cognitive radio networks (CRN). Therefore, the spectrum sensing in wireless system with lower latency requirements is proposed first. In such systems with high spatial density of the base stations and users/objects, spectrum sharing enables spectrum reuse across very small regions. The proposed method in this Thesis is a multi-channel cooperative spectrum sensing technique, in which an independent network of sensors, namely, spectrum monitoring network, detects the spectrum availability. The locally aggregated decision in each zone associated with the zone aggregator (ZA) location is then passed to a decision fusion centre (DFC). The secondary base station (SBS) accordingly allocates the available channels to secondary users to maximize the spectral efficiency. The function of the DFC is formulated as an optimization problem with the objective of maximizing the spectral efficiency. The optimal detection threshold is obtained for different cases with various spatial densities of ZAs and SBSs. It is further shown that the proposed method reduces the spectrum sensing latency and results in a higher spectrum efficiency. Furthermore, a novel power allocation scheme for multicell CRN is proposed where the subchannel power allocation is performed by incorporating network-wide primary system communication activity. A collaborative subchannel monitoring scheme is proposed to evaluate the aggregated subchannel activity index (ASAI) to indicate the activity levels of primary users. Two utility functions are then defined to characterize the spectral efficiency (SE) and energy efficiency (EE) as a function of ASAI to formulate a utility maximization problem. The optimal transmit power allocation is then obtained with the objective of maximizing the total utility at the SBS, subject to maximum SBS transmit power and collision probability constraint at the primary receivers. Since optimal EE and SE are two contradicting objectives to obtain the transmit power allocation, the design approach to handle both EE and SE as a function of common network parameter, i.e., ASAI, is provided which ultimately proves the quantitative insights on efficient system design. Extensive simulation results confirm the analytical results and indicate a significant improvement in sensing latency and accuracy and a significant gain against the benchmark models on the rate performance, despite the proposed methods perform with lower signalling overhead