CO-CHANNEL INTERFERENCE MITIGATION IN 4G CELLULAR NETWORKS USING ADAPTIVE EQUALIZATION TECHNIQUE FOR IMPROVED PERFORMANCE

3.7 Data Collected On Interference and Antenna Power at the Period of Call 

3.8 Values of Packet Loss, Calculated Congestion and the Dates of Data 

3.9 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate 

3.10 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate and 

3.11 Values of Packet Loss, Calculated Congestion and the Dates of Data 

3.12 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate and 

3.13 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate 

3.14 Values of Packet Loss, Calculated Congestion and the Dates of Data

3.15 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate and 

3.16 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate and 

3.17 Values of Packet Loss, Calculated Congestion and the Dates of Data Collection for 

3.18 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate and 

3.19 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate and 

3.20 Values of Packet Loss, Calculated Congestion and the Dates of Data Collection 

3.21 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate 

3.22 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate 

3.23 Values of Packet Loss, Calculated Congestion and the Dates of Data 

3.24 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate and 

3.25 Values of Packet Loss, Calculated Congestion, Calculated Bit Error Rate and 

4.1 Analysis on Conventional and Adaptive Equalizer Interference for the 

4.2 Analysis on Conventional and Adaptive Equalizer Interference for the 

4.3 Analysis on Conventional and Adaptive Equalizer Interference for the 

4.4 Analysis on Conventional and Adaptive Equalizer Interference for the 

4.5 Analysis on Conventional and Adaptive Equalizer Interference for the 

4.6 Analysis on Conventional and Adaptive Equalizer Interference for the 

4.1 Conventional Interference and Interference with Adaptive Equalizer for the Month of June 62

4.2 Conventional Interference and Interference with Adaptive Equalizer for the Month of July 64

4.3 Conventional Interference and Interference with Adaptive Equalizer for the Month of August 66

4.4 Conventional Interference and Interference with Adaptive Equalizer for the Month of September 67

4.5 Conventional Interference and Interference with Adaptive Equalizer for the Month of October 69

4.6 Conventional Interference and Interference with Adaptive Equalizer for the Month of November 70

4.7 Graph Showing the Effect of Co-channel Interference of the Network under Study in the Months of June to November. 71

4.8 Graph Showing the Mitigation Effect on Co-channel Interference using Adaptive Equalization Technique in the Months of June to November. 72

Wireless communication has experienced a significant growth in terms of transmitted information as a result of mobile internet, new wireless services and smart phones traffics. New radio access technologies provide new services and high data rate everywhere. In the wireless communication systems, the information is sent over radio frequency bands which are characterized by their carrier frequency, bandwidth, propagation conditions, and interference conditions. 4G Wireless Networks meant for broadband services is based on Orthogonal Frequency Division Multiplexing Access (OFDMA), (Gonzalez, 2013).

 Recent technologies use multiple antennas at the base station and radio spectrum as a common resource hence the use of it is strictly regulated by National Governments and Agencies like the National Communication Commission and International Telecommunication Union. The regulation, ensure that various systems may coexist without interfering with each other. Major disadvantage of this regulation is that spectrum allocation is rigid and is done over large geographical areas and in particular, over long time periods whereas spectrum is accessed locally and over short time periods hence the cost of obtaining new frequency bands for exclusive use is high. If the rules of accessing radio spectrum would be more flexible such bands could be taken into secondary use locally. Such secondary use could coexist with the primary if the interference among the two is managed dynamically and locally. As the usage of wireless systems keeps increasing at a rapid rate, both in terms of the data rate per user and in number of users, the wireless systems need to be continuously developed further to keep up with capacity demand, (Saquib, et al, 2013).

In general, higher system capacity may be achieved by improving the spectral efficiency, increasing the bandwidth, and deploying more base stations.  However, these improvements come with a cost. Higher spectral efficiency at the physical layer through the use of multiple antenna techniques and spectrally efficient waveforms increases the cost of the devices. The same is true for increasing the bandwidth. Supporting multiple frequency bands known as carrier aggregation in a device requires careful design of the radio frequency which is also expensive. Limited availability of newly paired frequency bands hinders the deployment of new frequency division duplex systems hence, the decreasing reuse factor means that the interference among the reusing radio links increases, (Saquib, et al, 2013).

Furthermore, the interference experienced in a system can be classified into different types, since it is caused by different subjects. In a wireless system we can classify these interferences; self-interference, multiple access interference, co-channel interference and adjacent channel interference. Self-interference includes interference that occurs among signals that are transmitted from a single transmitter. The specific mechanism and amount of self-interference depends on the modulation type. For instance, in OFDM there may be inter-carrier interference (ICI) among the subcarriers due to carrier frequency offsets caused by oscillator mismatches, and the Doppler effects and fast fading caused by motion of the transceivers. Inter-symbol interference (ISI) occurs in OFDM when the delay spread of the channel exceeds the length of the cyclic prefix or guard interval, and also when the receiver is not accurately time-synchronized to the transmission. The inter stream interference in a multi-stream MIMO transmission may be also considered as a form of self-interference. Multiple access interference is interference among the transmissions from multiple radios utilizing the same frequency resources to a single receiver (Moray, 2008). When multiple transmissions in cellular uplink take place simultaneously, they interfere with each other, though the physical layer would allow orthogonal (in the time, frequency, code, or spatial domain) multiple accesses in theory, orthogonality may not be maintained in practice due to synchronization errors, radio frequency (RF) circuitry non-idealities, and the effects of wireless propagation channel. Cellular systems employ several mechanisms in order to maintain sufficient orthogonality in multiple access scenarios. Firstly, power control is essential. Since the terminals transmitting to the Base-stations (BS) are distributed over the cell area, there is a large variation in the path loss between the BS and the terminals. If all terminals would transmit with the same power, the difference of the received powers can be high enough for the stronger signals to mask the weaker signals due to the limited dynamic range of the receiver. (Gessner, 2008)

In order to meet the ever increasing demand for the mobile broadband applications and services, next generation cellular systems target aggressive frequency reuse due to the scarcity of frequency spectrum, (Cho et al, 2013). The frequency reuse of one (Reuse-1) is an example of such aggressive frequency reuse, where all the available radio resources are allocated at every cell of the network. Such frequent frequency reuse increases the spatial spectrum efficiency and the network capacity, at the expense of increased channel interference (Himayatet al, 2010). Therefore, co channel interference is a major limiting factor which affects the users’ ability to achieve the desired quality-of-service. Due to the above mentioned challenge, co-channel interference mitigation is the primary interest of both the academic and industry communities hence this research mainly focuses on the co-channel interference in 4G cellular network. The model is selected as a multiple access technique in the downlink as it offers the flexibility while allocating the frequency spectrum resources based on the channel quality, through its inherent feature of multi-user diversity hence, the need to develop a co-channel mitigation technique using adaptive equalization technique, which considers sectoring and dynamic spectrum partitioning for overall improved performance for the irregular cellular geometry ((López-Pérez, 2011).

The aim of this research is co-channel interference mitigation in 4G cellular network using adaptive equalization technique 

The objectives of this study are:

The proposed network model for the irregular geometry based cellular system can contribute towards realizing self-organizing networks. This is because the proposed network model is able to adapt to the network variations and automatically implement the proposed spectrum allocation according to the requirement when the current spectrum partition is no longer valid. Note that the deployment of the proposed self-organized spectrum assignment scheme is not limited for single tier macro-cell network. The proposed scheme can be an excellent fit to the successful deployment of the Heterogeneous Network. The number of users and devices around a particular network can continuously vary. Classical network planning tools would not be able to configure and optimize the proposed network. Therefore, the adaptive equalization techniques need to be self-organized in order to autonomously integrate into the radio access network. Moreover, to efficiently utilize the available resources in the proposed network, frequency spectrum is shared. However, this type of deployment results in cross-tier interference because of the co-channel deployment.

 

The proposed interference mitigation scheme can be applied to avoid the interference by allocating orthogonal spectrum band to macro and femto users (shahegul, 2017). Therefore, the proposed scheme is feasible in the successful deployment of the Heterogeneous Networks. The proposed interference mitigation scheme can also be applied to Device-to-Device (D2D) communications, where the co-channel deployment will cause interference that would limit the performance gain of this technology. In D2D communication, the implementation of the proposed self-organized spectrum allocation schemes is feasible by ensuring the orthogonal sub-band allocation in the multi-tier devices. To improve the spectral efficiency, the same spectrum utilized for H2H (Human-to-human) communications can be reused for M2M communications. This will increase the spatial spectrum efficiency and network capacity at the expense of increased interference. The proposed network with adaptive equalization scheme can be utilized to mitigate the interference in the M2M communication and 4G technology by dynamically allocating the spectrum resources in the self-organized fashion.

 The Frequency division duplex mode of the network downlink transmission for cellular network model is assumed in this thesis. It focuses on the use of adaptive equalization technique in the mitigation of 4G cellular network co-channel interference. Communication devices are simulated using simulink package in MATLAB to design the model with data collected from the Base Station under study to ascertain if there is an improvement in the network after the use of the adaptive equalization technique in the designed model. The users are assumed to be connected to the nearest BSs. Consequently, the coverage region of each cell is irregular. Self-organized mitigation schemes are developed for the irregular geometry based cellular network. The proposed schemes are self-organized in the scope that each cell of the network autonomously decides its spectrum partition based on the load condition, spectrum requirement and channel conditions. Therefore, the proposed interference mitigation schemes are aware of the diverse user traffic demands and the channel quality.

This research work is arranged in five chapters;

Chapter 1 presents the introduction and background information of the study.

Chapter 2 presents the literature review of evolution of the 4G network, technology used for interference mitigation in 4G communication system. 

In chapter 3, the methodology and processes leading to the development of adaptive equalization technique are presented. It also describes the simulation carried out in MATLAB.

Chapter 4 presents the results and discussion obtained from the conventional and adaptive equalization technique.

Finally, in chapter 5, conclusion and recommendations for future work is presented.