Software
Femtocells
A throughput simulation framework for LTE-A systems
One of out latest projects is a MATLAB simulation framework for throughput calculation in every possible point within a LTE Advanced macrocell range. The framework allows user to add buildings in the topology and deploy femto base stations (BSs) in custom spots. Buildings' walls are taken into account for the path loss estimation, derived in case they interfere between the BS and the user equipment (UE), while the rest of BSs cause interference. The user may be attached to the macro or a femto BS. Simulation calculates throughput and other userful information like the SINR and the path loss, for every possible user location.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text.
A simulation framework for power management over femtocells in LTE-A systems
This is an extension of the MATLAB simulation framework for LTE-A systems, which allows power management over user-defined deployed femtocells. The framework is able to perform power control over the existing femtocells according to various integrated power management schemes. For each case, the resulting throughput is calculated for every point of the integrated femtocell/macrocell LTE-A network and is presented in a user-friendly GUI.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text or BibTex.
Simulator for interference mitigation techniques in heterogeneous networks
This user-friendly simulation framework is designed to reproduce custom heterogeneous networks and examine the cross-tier interference behavior between macrocells and femtocells. It allows the user to study and evaluate available interference mitigation techniques. The methods integrated include power control and frequency allocation. It provides the user the capability to control the transmission power of every Femtocell Base Station in order to achieve constant coverage femtocell radius. It can also simulate Inter-Cell Interference Cancellation (ICIC) coordinated macrocell environments, specifically Integer Frequency Reuse (IFR) and Soft Frequency Reuse and enforce frequency allocation between femtocells and the underlying macrocell.
Download source code.
An admission control simulation framework for LTE-A OFDMA Small Cell networks
Our research team has developed a Matlab simulation environment for OFDMA small cell networks. Firstly, we propose a novel power control mechanism that performs power selection and adaptation among the most efficient power algorithms in LTE-A small cells. The latter results in the most efficient and beneficial usage of energy radio resources. Secondly, with the possibility to co-function together with a Quality-of-Service (QoS) traffic provisioning algorithm, besides power control, real-time multimedia traffic, for instance High-Definition, becomes optimally differentiated. Plotting results show that total throughput capacity is augmented, and delay is minimized.
Download source code.
A throughput simulation framework for LTE-A systems
One of out latest projects is a MATLAB simulation framework for throughput calculation in every possible point within a LTE Advanced macrocell range. The framework allows user to add buildings in the topology and deploy femto base stations (BSs) in custom spots. Buildings' walls are taken into account for the path loss estimation, derived in case they interfere between the BS and the user equipment (UE), while the rest of BSs cause interference. The user may be attached to the macro or a femto BS. Simulation calculates throughput and other userful information like the SINR and the path loss, for every possible user location.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text.
A simulation framework for power management over femtocells in LTE-A systems
This is an extension of the MATLAB simulation framework for LTE-A systems, which allows power management over user-defined deployed femtocells. The framework is able to perform power control over the existing femtocells according to various integrated power management schemes. For each case, the resulting throughput is calculated for every point of the integrated femtocell/macrocell LTE-A network and is presented in a user-friendly GUI.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text or BibTex.
Simulator for interference mitigation techniques in heterogeneous networks
This user-friendly simulation framework is designed to reproduce custom heterogeneous networks and examine the cross-tier interference behavior between macrocells and femtocells. It allows the user to study and evaluate available interference mitigation techniques. The methods integrated include power control and frequency allocation. It provides the user the capability to control the transmission power of every Femtocell Base Station in order to achieve constant coverage femtocell radius. It can also simulate Inter-Cell Interference Cancellation (ICIC) coordinated macrocell environments, specifically Integer Frequency Reuse (IFR) and Soft Frequency Reuse and enforce frequency allocation between femtocells and the underlying macrocell.
Download source code.
An admission control simulation framework for LTE-A OFDMA Small Cell networks
Our research team has developed a Matlab simulation environment for OFDMA small cell networks. Firstly, we propose a novel power control mechanism that performs power selection and adaptation among the most efficient power algorithms in LTE-A small cells. The latter results in the most efficient and beneficial usage of energy radio resources. Secondly, with the possibility to co-function together with a Quality-of-Service (QoS) traffic provisioning algorithm, besides power control, real-time multimedia traffic, for instance High-Definition, becomes optimally differentiated. Plotting results show that total throughput capacity is augmented, and delay is minimized.
Download source code.
Multicast Broadcast Single Frequency Network
Tool for MBSFN area optimization based on spectral efficiency and cost
We have developed a tool (in Lua programming language) which optimizes the MBSFN area configuration based on one of the following two parameters: spectral efficiency and total telecommunication cost. In the first case the tool estimates the spectral efficiency (SE) of each cell, the resource efficiency (RE) of the network and gradually formulates the optimal network deployment that maximizes the network's RE. The algorithm starts with an arbitrary distribution of MBSFN cells (for a given interested UE drop area) and then makes random changes to it. For every change it calculates the RE of the system, if it has decreased, it rolls back to the best-known configuration. In many cases though, the changes happen to be beneficial for the RE and thus they are accepted. Gradually, this procedure leads to better configurations. In the case of optimizing the MBSFN area configuration based on the total telecommunication cost, the main idea is to sequentially compare the intermediate calculated costs until we find the minimum total one.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text or BibTex.
Mobility-sensitive tool for MBSFN area optimization based on power control
The tool for MBSFN area optimization has been extended to support moving users and to parse simulation scenaria written in an extendable scripting language. This tool is able to achieve reduction of the overall power consumption while maintaining a certain amount of satisfied users in terms of bitrate. It uses a random walk optimization algorithm with tweaks for semi-directed changes so that optimization is faster. It is important to mention that the algorithm is written in such a way that applying it to a live network requires only a few changes.
Download source code.
Tool for MBSFN area optimization based on spectral efficiency and cost
We have developed a tool (in Lua programming language) which optimizes the MBSFN area configuration based on one of the following two parameters: spectral efficiency and total telecommunication cost. In the first case the tool estimates the spectral efficiency (SE) of each cell, the resource efficiency (RE) of the network and gradually formulates the optimal network deployment that maximizes the network's RE. The algorithm starts with an arbitrary distribution of MBSFN cells (for a given interested UE drop area) and then makes random changes to it. For every change it calculates the RE of the system, if it has decreased, it rolls back to the best-known configuration. In many cases though, the changes happen to be beneficial for the RE and thus they are accepted. Gradually, this procedure leads to better configurations. In the case of optimizing the MBSFN area configuration based on the total telecommunication cost, the main idea is to sequentially compare the intermediate calculated costs until we find the minimum total one.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text or BibTex.
Mobility-sensitive tool for MBSFN area optimization based on power control
The tool for MBSFN area optimization has been extended to support moving users and to parse simulation scenaria written in an extendable scripting language. This tool is able to achieve reduction of the overall power consumption while maintaining a certain amount of satisfied users in terms of bitrate. It uses a random walk optimization algorithm with tweaks for semi-directed changes so that optimization is faster. It is important to mention that the algorithm is written in such a way that applying it to a live network requires only a few changes.
Download source code.
AL-FEC integration on ns-3
In order to simulate the application of an AL-FEC protection scheme over 3GPP MBMS environments, we utilize the ns-3 network simulator.The AL-FEC protection (based on Raptor codes) is modeled on the application source before the transmitted data being forwarded to the core network. More precisely, according to the specified Source Block Length of the FEC block the transmitted packets are organized in AL-FEC source blocks and thereafter the redundant AL-FEC symbols are produced for each source block. The number of the generated additional AL-FEC symbols is determined by the transmission overhead a multicast sender introduces to the transmission. Thereafter, the generated source and repair symbols are transmitted through an IP multicast flow to multiple recipients.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text
Fractional Frequence Reuse
Implementation in Matlab for FFR optimization in OFDMA networks
Our team has developed a Matlab simulation for OFDMA networks. We propose a dynamic FFR mechanism that selects the optimal frequency allocation and inner cell radius based on the cell total throughput and a custom metric which is called user satisfaction (US). In detail, the mechanism divides the cell into two regions (inner and outer). For each potential frequency allocation (FA), the mechanism calculates the per-user throughput, the cell total throughput and US. This procedure is repeated for successive inner cell radius. Afterwards, the mechanism selects the optimal FFR scheme that maximizes the cell total throughput and US.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text or BibTex.
Implementation in Matlab for FFR optimization in OFDMA networks
Our team has developed a Matlab simulation for OFDMA networks. We propose a dynamic FFR mechanism that selects the optimal frequency allocation and inner cell radius based on the cell total throughput and a custom metric which is called user satisfaction (US). In detail, the mechanism divides the cell into two regions (inner and outer). For each potential frequency allocation (FA), the mechanism calculates the per-user throughput, the cell total throughput and US. This procedure is repeated for successive inner cell radius. Afterwards, the mechanism selects the optimal FFR scheme that maximizes the cell total throughput and US.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text or BibTex.
Simulation Framework for power control in UMTS Manhattan Grid Environments
Our team has developed a Matlab simulation for power control in UMTS Microcell environment (Manhattan grid environment) that takes into consideration moving and non moving users. The tool calculates the transmitted power from the Node Bs that serve a number of MBMS users. It examines three different cases. The first case ("Group of UEs in the same spot") assumes that all UEs are static and are placed at the same spot. The second case ("UEs with random coordinates") assumes that all UEs are static and each UE has its own coordinates. Finally, the third case ("UEs with random coordinates and moving UE") assumes that all UEs but one are static and have their own coordinates. It also assumes that one UE is moving.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text or BibTex.
Congestion Control
Additions for NS-2 simulator
We have developed the following mechanisms: 1) A multicast packet forwarding mechanism for UMTS supporting join/leave requests as well as handover and relocation procedures 2) Two mechanisms (PGMCC and TFMCC) for adaptive multicast data transmission of MBMS services in UMTS.
It should be mentioned that due to the fact that ns-2 does not include the implementation of the UMTS architecture, in our work, we use the Eurane extension, which supports the UMTS architecture.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text or BibTex.
Additions for NS-2 simulator
We have developed the following mechanisms: 1) A multicast packet forwarding mechanism for UMTS supporting join/leave requests as well as handover and relocation procedures 2) Two mechanisms (PGMCC and TFMCC) for adaptive multicast data transmission of MBMS services in UMTS.
It should be mentioned that due to the fact that ns-2 does not include the implementation of the UMTS architecture, in our work, we use the Eurane extension, which supports the UMTS architecture.
Download source code.
If you are using the simulator for your scientific work, please use the reference in plain text or BibTex.