Intelligent LTE Systems

7.2.1 Intelligent Fractional Frequency Reuse (FFR) ... 151 7.2.2 Intelligent Connection Mobility Control: ... 152 7.3 Intelligent Topology Management ... 153 7.4 Intelligent Power Control ... 154

7.1 Introduction

This chapter proposes some of the future research work possibilities based on the accomplishments of the work in this thesis. Dynamic Spectrum Access (DSA) has a central role for ultra-dense cognitive radio networks within 5G communication systems. The proposed Quantum Reinforcement Learning (QRL) technique demonstrated an ultra-high learning speed in certain circumstances. These specific criteria might solve problematic aspects within many learning systems when facing dynamic environments. It also supports fully distributed learning strategies that include a large number of agents learning together with a high possibility of reducing conflicts or collisions. The resulting fast learning showed that by relying exclusively on local information gained by the learning agent within the Access Base Station (ABS) it is possible to some extent to not lose the benefit of the high learning speed.

7.2 Intelligent LTE Systems

The most important differences between LTE and former systems like the 3G system are

like a RNC (Radio Network Controller) in 3G for example. The central node needed to

control all the radio resources and mobility over multiple NodeB (3G base stations) underneath. NodeBs functions are based on the commands of RNC through Iub interface. In LTE, on the other hand, Radio Resource Management is carried out in the eNBs (evolved NodeB), with signalling information exchange within the control plane over X2 interface as shown in figure 7.1. The eNBs in this case are allowed to use the entire frequency band. They manage the frequency allocations as described earlier in section 2.3.2.2 in the cell and sector to optimize all the UE’s communication.

Figure 7. 1. E-UTRAN Architecture in LTE Systems

Server Server

X2

X2

X2

S1

S1

S1

S1

S1

S1

MME/S-GW

MME/S-GW

eNB

eNB

eNB

According to overview of 3GPP Release 8, the eNB functions include Radio Resource

Management (RRM) which in turn includes for example:

 Radio Admission Control

 Connection Mobility Control

 Dynamic Spectrum Access (DSA) for UEs in both uplink and downlink (scheduling)

The performance of LTE eNB is highly affected by the radio resource management

algorithm and its implementation. Based on the above mentioned functions of eNBs, learning

techniques might be implemented within LTE systems and within eNBs as follows:

7.2.1 Intelligent Fractional Frequency Reuse (FFR)

FFR, is used within LTE as a frequency planning strategy to avoid interference among

adjacent cells. It divides coverage space around eNBs into inner and outer zones to ensure full

frequency reuse within inner zones (described in section 2.3.2.2). However, such strategy

imposes frequency constraints that might limit the system capability to deal with dynamic

environments with different spectrum demands from cell to another.

The mobility of UEs might be continuous and rapid. Their locations might change from

outer to inner frequency allocation zones (or vice versa) around each eNBs. As a result, the

number of users served by outer and inner zones might change which might need temporary

frequency re-planning that does not cause interference with other cells which might suffer

from the same fluctuation. In such a case, learning becomes a necessity to keep all UEs within

each cell coverage area well served through removing the possibility of the lack of channel

availability. The Learning agent within each eNB in this case has to learn how many and

which channels to be allocated to each zone. Interaction between eNB and the surrounding

environment through assigning channels to UEs and observing the resulting performance will

As has been discussed in chapter 6, QRL has been shown to be able to make the system

more capable to be adaptable to fluctuations in traffic demand. Such capability might reduce

the period of any possible drop in QoS due to rapid and dramatic change in traffic. Each

fractional zone might have a learning engine within it. This will help to form a QRL-based

FFR that is able to change frequency allocation policy and bandwidth according on location

and amount of demand. In other words, QRL can be used to control the frequency reuse

policy. The local interference environment can be learned fast and thus the frequency

allocation can be changed accordingly.

7.2.2 Intelligent Connection Mobility Control:

Connection mobility control (CMC) is the function that is responsible for the management of radio resources in both connection (Handover) and idle modes of the UE. Handover decisions might be based on the following:

 UE mobility.

 eNodeB measurements.  Neighbour cell load.  Traffic distribution.  Hardware resources.  Operator defined policies.

Handover happens as a result of EU mobility between two different coverage areas of two cells that requires handing over to maintain QoS due to interference or signal strength fading. In this case, the cell where the UE starts from is referred to as the source cell while the cell that the UE ends being served by is referred to as the target cell. One of the most important points upon which the decision about the determination of the target cell is based on is the cell availability (i.e. can accommodate an additional UE). This case is very similar to the case

presented in this thesis of choosing a channel by the ABS. The other point is the QoS provided to the user by the target cell compared to that delivered by the source cell and other surrounding cells. Such information can be reported back to the source cell afterward.

A learning agent within each eNodeB in this case can be used to form an intelligent cell behaviour in choosing the best target cell in reference to UE location and used channel. A register (Q-table/Amplitude table) can be used within each cell to record and update the result of handing over each UE to a specific target cell through comparison between the performance delivered to the UEs in both cells. The accumulative experience over time can cause improvements in choosing target cells by source cells. Common knowledge among eNBs is gradually formed about performance of different channels by different cells. Such result will create a target cell preference list within each cell for each channel. An intelligent handover might present an improvement to the overall network service in case of dense and dynamic network.