Channel assembling policies for heterogeneous fifth generation (5G) cognitive radio networks.

Submitted to the School of Engineering in fulfillment of the academic requirements for the degree of Doctor of Philosophy in Electronic Engineering.

Plagiarism

Publications

Evolution of Wireless Technologies toward 5G-Cognitive Radio Networks

To improve the disadvantages of the previous technologies, a switching system was introduced that works on the design of 1G and 2G. Due to subscriber demand, with many drawbacks from 2.5G to 1G, the 3G was announced and launched in late 2001.

Figure 1.3 Overview of 5G-cognitive radio network architecture [2]

Research Problems

Thesis Organization

Chapter seven concludes the study in this research with a detailed assessment of the contributions, significance and recommendations.

Thesis Contributions

It is hoped that this discussion will provide insight into expanding the work on this thesis and based on the lessons learned so far. The work done in this thesis resulted in several published papers as shown in the next section.

Publications in Journals and Conference Proceedings from the Research work

Srivastava, “Performance Analysis of Heterogeneous Channel Assembly Strategies in Cognitive Radio Networks,” published in International Journal of Engineering and Technology Innovation (IJETI, Scopus-Elsevier), vol. Srivastava, "Primary User Impact on Secondary Channels in a Cognitive Radio Network: An Overlap Performance Analysis" (pending final acceptance upon payment of previous article) in International Journal on Communications Antennas and Propagation (IRECAP, Scopus-Elsevier), Italy, January 2017.

Cognitive Radio Networks (CRN): An Overview

The primary users are the spectrum owners (licensed users) with the highest priority for spectrum access. This is made possible by the SUs sensing the spectrum with corresponding feedback information (sensed information) sent to the SUs central node.

Cognitive Cycle of Cognitive Radio

• Spectrum Access
• Spectrum Access Models
• PU Spectrum Opportunity Usage Pattern
• Spectrum Sensing
• Spectrum Decision
• Spectrum Sharing
• Spectrum Mobility

This has a direct relationship with the anticipated period of spectrum availability and package configuration. In addition, the PU patterns should be considered in the weighted importance of spectrum hole selection.

Figure 2.2 PU spectrum utilization pattern

Channel Assembling in Cognitive Radio Networks

In the greedy strategy, an SU will continue aggregating channels up to the upper limit, if any channels are available at the time it accessed. Due to the delay-sensitive traffic, a higher priority is given to the inelastic users, unlike the elastic traffic with a lower priority.

Conclusion

The simplified flow chart/algorithm for the ON/OFF behavior of the different primary users is presented in Section 3.4, while numerical results and discussion are provided in Section 3.5. The geometric ON/OFF distribution is a stochastic process which depends on the probability of the ON/OFF epoch [108].

Figure 2.7 Aggregating of TV white space (spectrum holes) for SU services

System Description and Investigations

Next, it is necessary to examine and compare the commonly assumed PU's ON/OFF distributions achieved by this part of the thesis. The contribution of this research work provides insight into the spectrum availability pattern in different ON/OFF activity models (distribution).

Primary Users ON/OFF Processes

• Markovian ON/OFF Process
• Exponential ON/OFF process
• Geometric ON/OFF process

The Markovian ON/OFF process is guided by two factors, namely the state transition matrix and the state transition probabilities. Nevertheless, the state transition matrix and state transition probabilities remain constant for the similar distribution, but the ON/OFF slot capacity varies for a given distribution as observed.

Figure 3.2 A Busy/Idle pattern of PUs 3.3.1 Markovian ON/OFF Process

System Algorithm/Flow-Chart for the ON/OFF Processes

Consequently, how far they are spread is captured by their variances, which are alternatively accounted for by their standard deviation or the square root of the variance. In addition, the standard deviation is one of the factors used to assess the stability of the three commonly assumed ON/OFF PU activities.

Numerical Results and Discussion

Conclusion

Assuming a Markov on/off on/off process as the preferred process, the next section will be devoted to investigating the impact of primary user activity on secondary user channels in cognitive radio networks. The second part of this work will focus on the impact of primary user behavior (activity) on the SU channel in CRN.

Introduction

The PU being the licensed owner has the highest priority on the channel while the SU accesses the primary channel opportunistically when the PUs are absent (idle). The NP being the licensed user is unaware of the presence of the SUs and hence no prior notice is given before occupying it.

Related Works

In their study, the Pareto distribution model was adopted due to the characteristic of the behavior of PUs. Alternatively, the SU can harvest RF energy for packet transmission when the channel is busy.

System Model and Assumptions

Nevertheless, cognitive users continue to probe the channel slot by keeping the packet to be sent at the beginning of the queue and rescan the channels at the next slot, see Figure 3.11. A cognitive user operates in an overload/saturation state where a packet is ready and waiting to be served.

Figure 3.11 ON/OFF slots diagram showing SU probing and transmitting

System Analytical Model

The secondary users can be real-time traffic/users (packaged voice calls, eg Skype, WhatsApp, Viber, etc.) or non-real-time traffic (file downloads, browsing and so on).

Performance Measures

The saturated throughput 𝑇𝑝 of the SU over the primary channel is defined as the number of secondary packets (data) successfully transmitted on the primary channel per time slot. While the saturated throughput 𝑇𝑠 for the primary users over the secondary channel is defined as the number of primary packets (data) successfully transmitted on the secondary channel per time slot.

System Algorithm/Flow Chart for the OSA Scheme

The SU probes the PU channel and deposits its packets at the beginning (header position) of the PU slot, as shown in Figure 3.13. Adaptation and synchronization of SU with channel enables and ensures smooth transaction between SU ​​and PU for opportunistic spectrum access.

Numerical Results and Discussion

The result in Figure 3.16 shows that the throughput 𝑇𝑝 of SUs over the primary channel increases regardless of the occupancy statistics as stated earlier. In Figure 3.17, the result of the total throughput 𝑇 which is a summation of the throughput of PU over the secondary channel and SU over the primary channel, respectively shows that as 𝑙 increases, the total throughput (total number of successfully transmitted packets) increases. regardless of occupancy statistics.

Figure 3.15 SU Throughput vs time-slot

Conclusion

The system model and assumptions are formulated in Section 4.3 with two channel assembly strategies in Section 4.4. Performance measurements of the system model are described in Section 4.5, while numerical results and discussion are presented in Section 4.6.

Related Works

However, the unused PU channel slots need some organization to meet the demand from the SUs and therefore the need for channel aggregation. This section of the thesis develops and compares two channel assembling strategies (IBS and RBS) which incorporate the dynamics of a wireless connection with adaptive modulation and coding (AMC) against the No-assembling strategy (NA) in a homogeneous SU traffic.

System Model and Assumptions

• Wireless Channel Model and AMC
• Channel Slot Capacity

This is due to its ability to capture a wide range of fading characteristics of the wireless connection. The total capacity of the system is given by (𝑀 ∗ 𝑆) where 𝛿𝑖 = 𝛽𝑖 (𝛽⁄ 𝑖+ 𝛼𝑖) is the channel utilization ratio.

Figure 4.2. SU Wireless frame utilization schemes

Channel Assembling Strategies

• Immediate Blocking Strategy (𝜃 𝑝𝑢 , 𝜃 𝑠𝑢 , 𝜃 𝑛 )
• Readjustment Based Strategy (𝜃 𝑝𝑢 , 𝜃 𝑠𝑢 , 𝜃 𝑛 𝑚𝑖𝑛 , 𝜃 𝑛 𝑚𝑎𝑥 )
• No-Assembling strategy (NAS)

Then, the PU channel slot capacity 𝜃𝑝𝑢 upon arrival and based on the utilization ratio is given as;. When 𝑆𝑈𝑖 requests a service, the CRBS-based assembly algorithm checks the availability of the resource in a similar way to the IBS system.

System Model Performance Measures

Numerical Results and Discussions

Moreover, in Figure 4.5(b), with the arrival of more primary users, the free channel slots will be occupied, therefore, more secondary users will not be given access to use the resources. Similarly, Figure 4.6(b), throughput improves when primary users leave because channel slots would be available for use by secondary users.

Figure 4.5 (b) Blocking probability of SU as a function of 𝜆 𝑝

Conclusion

Introduction

These research efforts and recommendations are due to the emerging broadband applications and services on today's wireless communication systems. On the one hand, the remaining part of the secondary spectrum is being stretched by emerging wireless applications and multimedia services, resulting in spectrum scarcity.

Related Works

System Model

• Wireless Channel Model and AMC
• Channel Slot Capacity

The CRBs schedule and initiate spectrum utilization with overall transactions between the SU of the various classes. Also, the different states of the wireless link were characterized using a finite state Markovian process, whose.

Figure 4.9 Network architecture

Channel Assembling Strategies

• Immediate Blocking Strategy (𝜃 𝑝𝑢 , 𝜃 𝑠𝑢 , 𝜃 𝑛𝑖 , 𝜃 𝑛𝑗 )
• Readjustment based strategy (𝜃 𝑝𝑢 , 𝜃 𝑠𝑢 , 𝜃 𝑛𝑖,𝑗 𝑚𝑖𝑛 , 𝜃 𝑛𝑖,𝑗 𝑚𝑎𝑥 )
• Queuing Based Strategy (QBS)

CRBS check wireless link 𝜸 ;// CRBS check signal to noise ratio Check CRBs 𝜽𝒔𝒖 ;// CRBS check available resources for SUs. When a user 𝑆𝑈1 𝑖 requests service, the assembly algorithm implemented in CRBS checks for the accessibility of resources similar to the IBS scheme.

System Model Performance Measures

If the common SU of classes 1 and 2 is forcibly interrupted and received 𝜂𝑇𝑠1,2 and 𝜁𝑇𝑠1,2, respectively, then it is expressed as,. However, in this section it can be expressed as "not-blocked", which can be expressed as;.

Numerical Results and Discussion

However, the response of the probability of forced outage as a result of the arrival of the primary user is shown in Figures 4.12 (a) and (b) respectively. As predicted, SU will experience forced disruption in all schemes due to the arrival of UP.

Figure 4.10 (b) Blocking probability of SU class 1 vs PU arrival rate

Conclusion

A real-world wireless scenario can be characterized by many end states [142]. This presents a more practical scenario of wireless conditions that contrasts with the novel.

Related Works

Therefore, a realistic CAS should take into account the different nature of wireless connectivity and this builds part of the foundations of the present study. Developed an analytical framework to evaluate the performance of the proposed channel assembly strategies used in this chapter.

System Model and Resource Utilization

Furthermore, the licensed user (SU) opportunistically utilizes the PU channels by deploying one of the proposed schemes. The CRBS makes the decision to block, drop or allow. The SUs on the basics of resource accessibility are initiated by CAS, but the SU capacity is a function of the wireless link conditions (SNR).

Wireless Resource Capacity Modelling and AMC

• SU wireless channel model and AMC
• AMC and SU frame configuration
• SU channel-slot configuration

The amount of channel slots in the static frame 𝑆 depends on the variable signal-to-noise ratio (γ). The number of channel slots combined by CRBS for the secondary user decreases as the SNR improves and vice versa, as covered in Table I.

Figure 5.2 Channel-slot configuration which depends on SNR/mode pair .

Proposed Channel Assembling Strategies

• Immediate blocking strategy (𝜔, 𝜃 𝑠𝑢 , 𝜃 𝑛 )
• Readjustment based strategy (𝜔, 𝜃 𝑠𝑢 , 𝜃 𝑛 𝑚𝑖𝑛 , 𝜃 𝑛 𝑚𝑎𝑥 )

CRBS checks wireless link 𝜸𝒔𝒖 //CRBS checks wireless link for PU absence If 𝑷𝑼𝒊_Channel-slots=Idle (free); // free PU slots. CRBS check wireless link 𝜸𝒔𝒖 // cognitive radio base station check wireless link state (SNR) of SUs.

System Analytical Models

• CTMC Analysis for IBS
• Transition Table from Present State (𝜔, 𝑉 𝑔 , 𝑉 𝑚 , 𝑉 𝑏 ) to Other States
• Performance Measures
• Immediate Blocking Strategy IBS
• CTMC Analysis for Readjustment Based Strategy
• Performance Measures of the Readjustment Based Strategy

SU in moderate state with minimum number of channel slots (𝜃𝑛𝑚𝑖𝑛) uses the freed channel slots to reach the upper limit (𝜃𝑛𝑚𝑎𝑥). SU in moderate state with minimum/maximum number of channel slots (𝜃𝑛𝑚𝑖𝑛/𝜃𝑛𝑚𝑎𝑥) state is left by the system after service completion (releases channel slots).

Figure 5.3 Summarized System Transition Diagram [8]

Numerical Results and Discussions

However, in each of the wireless link conditions, RBS shows better improvement (lower congestion probability) than IBS due to its flexibility. However, the RBS scheme improved the SU capacity compared to the IBS scheme for all channel conditions.

Figure 5.5 SU blocking probability P b n vs PU arrival λ p

Conclusion

A robust channel assembly strategy must therefore take into account the dynamics (different nature) of the wireless link and the queuing regime, so this is part of the investigation. Channel assembly strategies have been proposed in the literature to mitigate various losses in a communication network.

Related Works

A finite buffer system (queuing regime) is proposed that will further reduce blocking and forced termination of the SU considering the varying nature of the wireless connection and different traffic classes, thus extending the CA strategies proposed therein. An analytical framework is developed to guide the implementation of the proposed joint scheme in CAS.

System Model and Assumptions

The licensed user (PU) has higher rights to use the channel and can interrupt an SU (unlicensed user) regardless of class at any time. The licensed user (SU) uses the PU channels (TV band) opportunistically through the implementation of any of the proposed schemes.

Wireless Channel Model

• AMC and Secondary User Frame Configuration
• Secondary User Mini-Slot Configuration

This is made possible by the AMC controller, which adjusts its transmission parameters. Third, it optimizes the speed of sending and delivering information units (bit or packet) with target error rates (TER) according to the state of the wireless connection.

Queuing-Based CAS (CAS+Q)

• Queuing Regime
• Queuing Strategy

𝑆𝑈𝑎 in good state is forced to abort, 𝑆𝑈𝑏 in good state, in queue 𝑄2 uses all idle channel slots. 𝑆𝑈𝑎 in moderate condition is forced off, 𝑆𝑈𝑏 with 𝜃𝑚𝑚𝑖𝑛 in good condition uses all idle channel slots.

Figure 6.2 Schematic of the proposed queuing regime deployed 6.5.2 Queuing Strategy

System Analytical Models and Assumptions

• CTMC Analysis for CAS+Q
• Performance Measures

A 𝑆𝑈𝑎 in good condition is forced to terminate, 𝑆𝑈𝑏 with 𝜃𝑔𝑚𝑖𝑛 in good condition uses all the available channel slots…. A 𝑆𝑈𝑎 in bad state is forced to terminate, 𝑆𝑈𝑏 with 𝜃𝑏𝑚𝑖𝑛 in good state uses all available channel slots.

Numerical Results and Discussion

From Figure 6.12, the spectrum utilization is improved using the proposed strategy for both classes of SUs. The result in Figure 6.13 shows that when access/admission is granted to SUs based on the previously mentioned criteria, the SU then accumulates the required channel slots.

Figure 6.3(a) 𝑆𝑈 𝑎 Blocking probability, 𝑃 𝑏(𝑎) vs Queue length, 𝑄 1𝑚𝑎𝑥

Conclusion

But, there is an interaction-effect between primary and secondary users in the channel of primary users due to the opportunistic spectrum access of SUs. The investigation of the proposed strategies showed that the integration of queuing technique and AMC in channel assembly strategies in cognitive radio.

Recommendations

It provided an insight into the effect of the licensed user on the secondary network in a dynamic wireless link in terms of their performance and the potentials of integrating AMC into channel assembly strategies. The point is to improve spectrum utilization, secondary network capacity, and more specifically, minimizing SU congestion and forced termination, and possibly allowing interrupted SU service to be queued and served later.

Future Works

Abouzeid, "Auction-Based Spectrum Sharing in Cognitive Radio Networks", in IEEE Information Theory and Applications Workshop (ITA), San Diego, 2013. Hossain, "Bronnentoewijzing voor spectrumonderlaag in cognitieve radionetwerken", IEEE Transactions on Wireless Communications, vol.