# Digitala Vetenskapliga Arkivet

Change search

Cite
Citation style
• apa
• ieee
• modern-language-association-8th-edition
• vancouver
• Other style
More styles
Language
• de-DE
• en-GB
• en-US
• fi-FI
• nn-NO
• nn-NB
• sv-SE
• Other locale
More languages
Output format
• html
• text
• asciidoc
• rtf
Distributed Detection and Its Applications with Energy Harvesting Wireless Networks
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems. (Signals and Systems Division)
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

Tackling the evergrowing energy requirement has been the other major challenge for wireless communication. To address this issue, a significant amount of research has been dedicated to the idea of incorporating the capability of energy harvesting in wireless devices. Further research in this domain has also introduced the idea of wireless energy sharing, where individual users are additionally capable of sharing energy with each other. The problem with such systems is the inherently stochastic nature of the energy harvesting process. Furthermore, there are practical limitations of the size of the battery for each user, which limits the amount of energy that can be stored at a particular time instant.

Motivated by these two factors, the work presented in this thesis has its focus on cognitive radio networks with energy harvesting capability. In the aforementioned network, unlicensed users are concerned with achieving two fundamental goals. Firstly, they want to efficiently utilize the radio spectrum when the licensed user is not active, which results in the sum-throughput maximization problem with energy harvesting constraint. We have also investigated this problem where individual unlicensed users are capable of sharing energy with each other. Secondly, they want to detect the change in the activity in the licensed user spectrum as soon as possible. Motivated by this goal, we have investigated the problem of change point detection delay minimization in wireless sensor networks with energy harvesting constraints in a decentralized setting. Furthermore, we have explored the detection delay parameter for the decentralized settings with local decisions with similar constraints of energy availability due to energy harvesting.

##### Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2020. , p. 80
##### Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1942
##### Keywords [en]
Energy harvesting, Wireless Sensor Networks, Distributed Detection, Cognitive Radio Networks
##### National Category
Signal Processing
##### Research subject
Electrical Engineering with specialization in Signal Processing
##### Identifiers
ISBN: 978-91-513-0960-6 (print)OAI: oai:DiVA.org:uu-409528DiVA, id: diva2:1425881
##### Public defence
2020-06-05, Siegbahnssalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 15:00 (English)
##### Supervisors
Available from: 2020-05-15 Created: 2020-04-22 Last updated: 2020-06-04
##### List of papers
1. Sensing throughput optimization in fading cognitive multiple access channels with energy harvesting secondary transmitters.: Extended version of conference paper
Open this publication in new window or tab >>Sensing throughput optimization in fading cognitive multiple access channels with energy harvesting secondary transmitters.: Extended version of conference paper
##### Place, publisher, year, edition, pages
Uppsala universitet, 2016
##### National Category
Engineering and Technology
##### Identifiers
urn:nbn:se:uu:diva-302637 (URN)
Available from: 2016-09-07 Created: 2016-09-07 Last updated: 2020-04-22
2. Sensing Throughput Optimization in Cognitive Fading Multiple Access Channels With Energy Harvesting Secondary Transmitters
Open this publication in new window or tab >>Sensing Throughput Optimization in Cognitive Fading Multiple Access Channels With Energy Harvesting Secondary Transmitters
2016 (English)In: 2016 24Th European Signal Processing Conference (EUSIPCO), 2016, p. 577-581Conference paper, Published paper (Refereed)
##### Abstract [en]

The paper investigates the problem of maximizing the expected achievable sum rate in a fading multiple access cognitive radio network when secondary user (SU) transmitters have energy harvesting capability, and perform cooperative spectrum sensing. We formulate the problem as maximization of throughput of the cognitive multiple access network over a finite time horizon subject to a time averaged interference constraint at the primary user (PU) and almost sure energy causality constraints at the SUs. The problem is a mixed integer non-linear program with respect to two decision variables, namely, spectrum access decision and spectrum sensing decision, and the continuous variables sensing time and transmission power. In general, this problem is known to be NP hard. For optimization over these two decision variables, we use an exhaustive search policy when the length of the time horizon is small, and a heuristic policy for longer horizons. For given values of the decision variables, the problem simplifies into a joint optimization on SU transmission power and sensing time, which is non-convex in nature. We present an analytic solution for the resulting optimization problem using an alternating convex optimization problem for non-causal channel state information and harvested energy information patterns at the SU base station (SBS) or fusion center (FC) and infinite battery capacity at the SU transmitters. We formulate the problem with causal information and finite battery capacity as a stochastic control problem and solve it using the technique of dynamic programming. Numerical results are presented to illustrate the performance of the various algorithms.

##### Series
European Signal Processing Conference, ISSN 2076-1465
##### National Category
Electrical Engineering, Electronic Engineering, Information Engineering
##### Identifiers
urn:nbn:se:uu:diva-316230 (URN)10.1109/EUSIPCO.2016.7760314 (DOI)000391891900469 ()9780992862657 (ISBN)
##### Conference
24th European Signal Processing Conference (EUSIPCO), AUG 28-SEP 02, 2016, Budapest, HUNGARY
Available from: 2017-02-27 Created: 2017-02-27 Last updated: 2020-04-22Bibliographically approved
3. Quantized non-Bayesian quickest change detection with energy harvesting
Open this publication in new window or tab >>Quantized non-Bayesian quickest change detection with energy harvesting
2018 (English)Conference paper, Published paper (Refereed)
##### Abstract [en]

This paper focuses on the analysis of an optimal sensing and quantization strategy in a multi-sensor network where each individual sensor sends its quantized log-likelihood information to the fusion center (FC) for non-Bayesian quickest change detection. It is assumed that the sensors are equipped with a battery/energy storage device of finite capacity, capable of harvesting energy from the environment. The FC is assumed to have access to either non-causal or causal channel state information (CSI) and energy state information (ESI) from all the sensors while performing the quickest change detection. The primary observations are assumed to be generated from a sequence of random variables whose probability distribution function changes at an unknown time point. The objective of the detection problem is to minimize the average detection delay of the change point with respect to a lower bound on the rate of false alarm. In this framework, the optimal sensing decision and number of quantization bits for information transmission can be determined with the constraint of limited available energy due to finite battery capacity. This optimization is formulated as a stochastic control problem and is solved using dynamic programming algorithms for both non-causal and causal CSI and ESI scenario. A set of non-linear equations is also derived to determine the optimal quantization thresholds for the sensor log-likelihood ratios, by maximizing an appropriate Kullback-Leibler (KL) divergence measure between the distributions before and after the change. A uniform threshold quantization strategy is also proposed as a simple sub-optimal policy. The simulation results indicate that the optimal quantization is preferable when the number of quantization bits is low as its performance is significantly better compared to its uniform counterpart in terms of average detection delay. For the case of a large number of quantization bits, the performance benefits of using the optimal quantization as compared to its uniform counterpart diminish, as expected.

IEEE, 2018
##### Series
IEEE Global Communications Conference, E-ISSN 2576-6813
##### National Category
Signal Processing
##### Identifiers
urn:nbn:se:uu:diva-363299 (URN)10.1109/GLOCOM.2018.8647715 (DOI)000465774303093 ()978-1-5386-4727-1 (ISBN)
##### Conference
IEEE Global Communications Conference (GLOBECOM), 9-13 December 2018, Abu Dhabi, United Arab Emirates
Available from: 2018-10-16 Created: 2018-10-16 Last updated: 2020-04-22Bibliographically approved
4. Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting
Open this publication in new window or tab >>Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting
2019 (English)In: IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, ISSN 2332-7731, Vol. 5, no 2, p. 382-399Article in journal (Refereed) Published
##### Abstract [en]

This paper focuses on the problem of sensing throughput optimization in a fading multiple access cognitive radio (CR) network, where the secondary user (SU) transmitters participate in cooperative spectrum sensing and are capable of harvesting energy and sharing energy with each other. We formulate the optimization problem as a maximization of the expected achievable sum-rate over a finite horizon, subject to an average interference constraint at the primary receiver, peak power constraints, and energy causality constraints at the SU transmitters. The optimization problem is a non-convex, mixed integer non-linear program (MINLP) involving the binary action to sense the spectrum or not, and the continuous variables, such as the transmission power, shared energy, and sensing time. The problem is analyzed under two different assumptions on the available information pattern: 1) non-causal channel state information (CSI), energy state information (ESI), and infinite battery capacity and 2) the more realistic scenario of the causal CSI/ESI and finite battery. In the non-casual case, this problem can be solved by an exhaustive search over the decision variable or an MINLP solver for smaller problem dimensions, and a novel heuristic policy for larger problems, combined with an iterative alternative optimization method for the continuous variables. The causal case with finite battery is optimally solved using a dynamic programming (DP) methodology, whereas a number of sub-optimal algorithms are proposed to reduce the computational complexity of DP. Extensive numerical simulations are carried out to illustrate the performance of the proposed algorithms. One of the main findings indicates that the energy sharing is more beneficial when there is a significant asymmetry between average harvested energy levels/channel gains of different SUs.

##### Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
##### National Category
Communication Systems Telecommunications Signal Processing
##### Identifiers
urn:nbn:se:uu:diva-389809 (URN)10.1109/TCCN.2019.2908860 (DOI)000471115000016 ()
##### Funder
Swedish Research Council, 2017-04053 Available from: 2019-07-30 Created: 2019-07-30 Last updated: 2020-04-22Bibliographically approved
5. Asymptotic Performance Analysis of Distributed Non-Bayesian Quickest Change Detection with Energy Harvesting Sensors
Open this publication in new window or tab >>Asymptotic Performance Analysis of Distributed Non-Bayesian Quickest Change Detection with Energy Harvesting Sensors
##### Abstract [en]

This paper focuses on the distributed non-Bayesian quickest change detection of the probability distribution of a random process in a wireless senor network (WSN), where the distributions before and after the change point is assumed to be known. The individual sensors are capable of harvesting energy from their surroundings. Each sensor decides to sense the observation signal depending on the available energy at its disposal. Once, a sensor decides to sense, it takes a sample of the observation signal and computes the log-likelihood ratio (LLR) of the aforementioned two distributions, if enough energy is available in its battery for sensing and processing the sample. On the other hand, if enough energy is not available, the sensor decides to abstain from the sensing process during that time slot and waits until a future time slot when it accumulates enough energy to perform the sensing and processing. Once a sensor computes the LLR, it uses that information to calculate the Cumulative Sum (CUSUM) test statsistic to arrive at a local decision about the change point. When a change is detected, these decisions are then sent to the FC (provided the transmitting sensor has enough energy to transmit the decision to the fusion centre successfully), where they are collated to form a single decision about the detection of the change point based on some pre-decided fusion rule. In this work, using asymptotic results on the detection delay for CUSUM tests for a single sensor, we have derived asymptotic results for the expected detection delay (when the change occurs) for three common fusion rules, namely, OR, AND and $r$ out of $N$ rule respectively. These results are analyzed for the scenario when the average harvested energy ($\mathit{\bar{H}}$) at each sensor is greater than or equal to the amount of energy required for sensing ($E_{s}$). We show that in such cases, the standard existing asymptotic results for CUSUM test holds for the local decisions. Consequently, we have determined corresponding results for the detection delay for decisions taken at the FC with the three aforementioned fusion rules by using the theory of order statistics. Numerical results are provided to support the theoretical claims.

##### Keywords
Energy harvesting, Sensor Networks, Distributed Change-Point Detection
##### National Category
Signal Processing
##### Research subject
Electrical Engineering with specialization in Signal Processing
##### Identifiers
urn:nbn:se:uu:diva-409526 (URN)
Available from: 2020-04-22 Created: 2020-04-22 Last updated: 2020-04-22
6. On Optimal Quantized Non-Bayesian Quickest Change Detection with Energy Harvesting
Open this publication in new window or tab >>On Optimal Quantized Non-Bayesian Quickest Change Detection with Energy Harvesting
(English)In: IEEE Transactions on Green Communications and Networking, E-ISSN 2473-2400Article in journal (Other academic) Epub ahead of print
##### Abstract [en]

In this paper, we consider a problem of decentralized non-Bayesian quickest change detection using a wireless sensor network where the sensor nodes are powered by harvested energy from the environment. The underlying random process being monitored by the sensors is subject to change in its distribution at an unknown but deterministic time point and the sensors take samples (sensing) periodically, compute the likelihood ratio based on the distributions before and after the change, quantize it and send it to a remote fusion centre (FC) over fading channels for performing a sequential test to detect the change. Due to the unpredictable and intermittent nature of harvested energy arrivals, the sensors need to decide whether they want to sense, and at what rate they want to quantize their information before sending them to the FC, since higher quantization rates result in higher accuracy and better detection performance, at the cost of higher energy consumption. We formulate an optimal sensing and quantization rate allocation problem (in order to minimize the expected detection delay subject to false alarm rate constraint) based on the availability (at the FC) of non-causal and causal information of sensors’ energy state information, and channel state information between the sensors and the FC. Motivated by the asymptotically inverse relationship between the expected detection delay (under a vanishingly small probability of false alarm) and the Kullback-Leibler (KL) divergence measure at the FC, we maximize an expected sum of the KL divergence measure over a finite horizon to obtain the optimal sensing and quantization rate allocation policy, subject to energy causality constraints at each sensor. The optimal solution is obtained using a typical dynamic programming based technique, and based on the optimal quantization rate, the optimal quantization thresholds are found by maximizing the KL information measure per slot. We also provide suboptimal threshold design policies using uniform quantization and an asymptotically optimal quantization policy for higher number of quantization bits. We provide an asymptotic approximation for the loss due to quantization of the KL measure, and also consider an alternative optimization problem with minimizing the expected sum of the inverse the KL divergence measure as the cost per time slot. Numerical results are provided comparing the various optimal and suboptimal quantization strategies for both optimization problem formulations, illustrating the comparative performance of these strategies at different regimes of quantization rates.

##### Keywords
Energy harvesting, Sensor Networks, Decentralized Change-Point Detection, Quantization
##### National Category
Signal Processing
##### Research subject
Electrical Engineering with specialization in Signal Processing
##### Identifiers
urn:nbn:se:uu:diva-409392 (URN)10.1109/TGCN.2019.2961113 (DOI)
Available from: 2020-04-20 Created: 2020-04-20 Last updated: 2020-04-22

#### Open Access in DiVA

##### File information
File name FULLTEXT01.pdfFile size 650 kBChecksum SHA-512
ace629f1de9460474406e7ff4ee2d74d5034a4c1ab939b05b6e3ea7b023556f5829d244d713e3923cbe3460252ff985bfdd61cf248e3ec9287aecd8a2bff5d01
Type fulltextMimetype application/pdf

#### Search in DiVA

Biswas, Sinchan
##### By organisation
Signals and Systems
##### On the subject
Signal Processing

#### Search outside of DiVA

The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available
isbn
urn-nbn

#### Altmetric score

isbn
urn-nbn
Total: 179 hits

Cite
Citation style
• apa
• ieee
• modern-language-association-8th-edition
• vancouver
• Other style
More styles
Language
• de-DE
• en-GB
• en-US
• fi-FI
• nn-NO
• nn-NB
• sv-SE
• Other locale
More languages
Output format
• html
• text
• asciidoc
• rtf
v. 2.35.10
| |