Mobile Networks


Our research team conducts substantial research activity within Computer Engineering & Informatics Department of the University of Patras and the Lab of Distributed Systems and Telematics.

We conduct research in the domain of mobile networks including the design, implementation, simulation and testing of existing and innovative technologies. In the past, we focused on Universal Mobile Telecommunications System (UMTS) and High Speed Packet Access (HSPA) networks and specifically on the field of multicasting over UMTS, through the Multimedia Broadcast/Multicast Service (MBMS). We also developed novel analytical models and simulations in order to examine the coverage, capacity and spectral efficiency of MBSFN (MBMS over a single frequency network), proposed to deliver multicast services over Long Term Evolution (LTE) systems. Currently, we focus on improving the overall performance of Long Term Evolution Advanced (LTE-A) networks through frequency reuse schemes and femtocell technology.

Additionally, our team has already started preliminary research in the area of the 5th generation of mobile networks (mobile 5G). For more information on our current research interests around 5G, please visit our page for 5G under Research tab.


5G will constitute the next major breakthrough into mobile telecommunications standards. Preliminary research on the main features of future wireless networks has proposed capabilities that are substantially beyond those defined in the current mobile technologies. Future wireless networks intend to offer solutions addressing to the explosive growth in video traffic, the acute shortage of spectrum, the growing need to minimize the energy requirements of mobile devices and network infrastructure and to cater to the insatiable desire for higher data rates.

LDST is following the latest developments in the fields of mobile telecommunications and continues its current research with main target to contribute to future mobile systems. Specifically, the research work and the experience of LDST team on radio resource management, multicast/broadcast services and small cells could contribute to both the standardization and development phase of future mobile networks. More specifically, it will include the following directions:

  • Network architectures and advanced low-cost wireless solutions including ultra-dense small cell deployment that span at a room-level, facilitate D2D communication, and achieve ultra-high bandwidth and minimum delay while at the same time they increase the radio access network capacity achieving at least a ten times increase in frequency reuse
  • Heterogeneous multi-tier networks that combine macrocellular infrastructure with small cells and Wi-Fi deployments as well as advanced interference mitigation techniques, efficient spectrum sharing, energy efficient access and smart distributed mobility management that also considers the network heterogeneity, e.g., the existence of small cells or Wi-Fi access points
  • Multi-hop routing through relay nodes in an opportunistic manner as well as location-independent access through small cell deployment in planes, high-speed trains and ships
  • Optimization of small cells performance and connectivity through efficient backhaul integration and multi-cell OAM functions
  • Combination of small cells and relay nodes under Self Organizing Networks (SON) concept, utilizing innovative schemes to solve deployment in heterogeneous networks
  • Massive distributed high-order spatial multiplexing (MIMO) also for small cells, achieving multi Giga-bps data rates towards the support of an anticipated mobile traffic increase
  • Techno-economic analysis and proposal of business-models for heterogeneous networking
  • Efficient mobile TV, video and radio services provision over mobile networks broadcasting and multicasting solutions with content caching and features approximating DVB
  • Extension of existing system level and link level simulation software to experiment and evaluate the above technologies also with propagation and mobility models adapted to Internet-of-Things

Focusing on the above directions, LDST is interested in establishing collaboration with pertinent organizations in the frame of the forthcoming calls of H2020.


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DUDe (Downlink and Uplink Decoupling)

DUDe technology in 5G mobile networks enables a UE to connect to two different Base Stations (BSs), one for the Uplink and a separate one for the Downlink needs. This approach changes the traditional structure and relationship between the BSs and the UEs and leads to enhanced and increased throughput, energy efficiency and fairness. However, this also leads to challenges for the new mobile networks system, regarding changes in BS user carrying capacity.
Cell association has traditionally been based only on the downlink received signal power, despite the fact that the transmission power for the uplink and downlink networks, differs significantly. This approach has been proven adequate for homogeneous networks with macro BSs all having similar transmission power levels. However, heterogeneous networks consist of multiple stations of different types, with big disparity in the transmission power, and this approach seems highly inefficient. DUDe can be based on multiple parameters, and a dominant notion of DUDe is for the downlink cell association to be based on the downlink received power while the uplink to be based on the pathloss
The next step in DUDe is to try to improve general network coverage by taking into consideration the physical frequency resources. Satisfying the augmented needs and requirements dictated by 5G networks, can be achieved by applying the 5G NR radio interface protocol at the physical layer of the network in question. Applied mechanisms need to preserve QoS and target at maximizing the spectral efficiency, thus leading to higher data rates. By taking into consideration the demands of each user and begin with satisfying the needs of users with the minimum requirements, mechanisms can satisfy a large amount of the users sharing the network’s resources.



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MU-MIMO is the next step of SU-MIMO or simply MIMO, that refers to sending and receiving more than one data signals simultaneously over the same radio. It gives the ability to a wireless AP to transmit to multiple client devices at the same time. The transceiver and the receiver ought to have multiple antennas/radio chains to support MIMO connectivity. Each spatial stream is transmitted from a different radio/antenna chain in the same frequency as the transmitter and the receiver then reconstructs the original stream as it knows the phase offsets of its antennas.
The MU-MIMO technology is generally applied in many areas and is massively exploited in many rising technologies. For example, the 3rd Generation Partnership Project (3GPP) and LTE exploit the MU-MIMO technology. Thanks to this technology, available radio spectrum 3GPP LTE networks could achieve higher spectral efficiency than the 3G networks. MU-MIMO systems have also received widespread success in wireless networks.
There are currently no universally established ways of dictating the exact channel capacity of MU-MIMO systems. Researches showed the performance of such systems, regarding capacity measurements. This metric can be defined in the usual Shannon sense and dictates the highest rates that can be achieved with arbitrarily small error probability. At first, the capacity has to be evaluated for each user. Then, the capacity region is determined for the entire region, where which maximum achievable rates are reached. Evaluating the capacity region is related to some constraints and should be set in conjunction with the performed communication scenario. The different scenarios that can be addressed include a) UL-MU-MIMO with single antenna users, b) UL-MU-MIMO with multiple antenna users and c) DL-MU-MIMO with multiple antenna users and single antenna BS.
Mu-MIMO next step is to achieve higher data rates, reliability and traffic demands that concerns the 5G and beyond era.



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Machine Learning

ML technique’s purpose is to confront the huge amount of data and traffic caused by the rapid uptake of mobile devices and the rising popularity of mobile applications and services in the 5G system. This huge quantity of mobile data cannot be managed by a single machine. A distributed network-based solution has to be applied, performing data mining and big data analysis to the data generated by the users and their devices, possibly combining different modalities (e.g. speech, text, GPS data, traffic data, etc.) and produce real-time response enabled by an autonomous and intelligent network.
The network itself cannot be considered a static infrastructure anymore. In order to cope with the increased traffic demand it has to become a flexible, adaptive, dynamic infrastructure able to self-configure its own parameters, even its perceived topology and automate its management, operation, and maintenance tasks, limiting direct human intervention as much as possible. ML techniques, like deep learning, can analyze extremely complex wireless networks with many nodes and dynamic link quality to find the network dynamics (such as hotspots, interference distribution, congestion points, spectrum availability, etc.) based on the analysis of a large amount of network parameters (such as delay, loss rate, link SNR, etc.). ML algorithms can be used for different network layers, including physical layer modulation/coding, data link layer access control/resource allocation, and routing layer path search and traffic balancing.
ML techniques and learning paradigms can be applied on key problems in networking such as traffic prediction, resource and fault management, routing and classification, QoS and network security. Several ML optimization methods have also been proposed to address similar problems, along with deep learning with artificial neural networks (ANNs).
ML can also facilitate advanced application requirements. For instance, it can help analyze users' mobility by capturing different patterns of user movement, either individually or when they are in groups. It is possible to detect users indoors or outdoors through various signals received either from the devices or from the channel. This is also an important opportunity for mobile network resource management. The use of ML to enhance other network functions, such as network security is also possible. ML algorithms can analyze "signatures" and patterns of attacks and generalize them to prevent future attacks and perceptions of patterns that are completely different from normal behaviors, thus resulting in a reduction in the effort to create rules to avoid attacks.



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New wireless standards such as 3GPP's High Speed Packet Access (HSPA) and Long Term Evolution (LTE) achieve considerable advancements in system capacity and throughput, but the deployment of macro cells results in high operational and capital expenditures. A way to increase cost-capacity of the networks is to deploy a large number of smaller and cheaper cells, i.e. femtocells.

Femtocells will improve coverage in indoors, contributing to offload the macro network, yet very important considering that a large amount of wireless traffic is originated in indoors. In addition, femtocells will use a cheaper backhaul connection: internet.

Not surprisingly, the case of femtocells has gained enormous support from the industry since it can represent a more cost-effective solution for wireless network operators than traditional deployments.

Deployment of femtocells represents a promising solution to increase cost-capacity benefits for network operators and provide higher data rates to end-users. Femtocells are conceived to provide indoor wireless access to a cellular network through a Home Base Station, which is connected via internet to the operator's core network, helping to improve coverage in indoors, offload the macrocell and reduce costs for operators.

However, large scale deployment of femtocells can severely interfere with the existing macrocell within which they are deployed, particularly when operating in co-channel or in immediate adjacent channels with respect to the macrocell and when using a closed access policy. For instance, macrocell coverage holes in the downlink will appear, i.e. zones in the vicinity of a home base station where interference from home base station signals will prevent macrocell users to receive the desired service from the macrocell network.


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Fractional Frequence Reuse

Long Term Evolution (LTE) networks offer high capacity and are specified and designed to accommodate small, high performance, power-efficient end-user devices. The investigation of inter-channel interference mitigation techniques has become a key focus area in achieving dense spectrum reuse in next generation cellular systems. Fractional Frequency Reuse (FFR) has been proposed as a technique to overcome this problem, since it can efficiently utilize the available frequency spectrum.

In FFR the cell space is divided into two regions: inner, which is close to the Base Station (BS) and outer, which is situated to the borders of the cell.

The whole frequency band is divided into several sub-bands, and each one is differently assigned to inner and outer region of the cell respectively. As a result of FFR, intra-cell interference is eliminated, and inter-cell interference is substantially reduced. At the same time the system throughput is enhanced. Various reuse factors and interference mitigation levels can be achieved by adjusting either the bandwidth proportion assigned to each region or the transmission power of each band.

In FFR, in order to ensure that the mutual interference between users and BSs remains below a harmful level, adjacent cells use different frequencies. In fact, a set of different frequencies are used for each cluster of adjacent cells. Cluster patterns and the corresponding frequencies are reused in a regular pattern over the entire service area. The closest distance between the centers of two cells using the same frequency (in different clusters) is determined by the choice of the cluster size and the layout of the cell cluster. This distance is called the frequency reuse distance.

One of the main objectives of LTE is to achieve high spectral efficiency, meaning the use of the whole of the system’s bandwidth in all cells. This approach is called Frequency Reuse 1 and is considered as the simplest scheme: all sub-bands of the available bandwidth are allocated to each cell. In Frequency Reuse 3, the system bandwidth is divided into 3 equal sub-bands; each one of these is allocated to cells in a manner that no other surrounding cell is using the same sub-band. Full frequency reuse in each cell can exempt the necessity of advance frequency planning among different cells, and the frequency reuse patterns can be dynamically adapted on a frame-by-frame basis in each cell.


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Forward Error Correction

A crucial point on the provision of reliability over mobile multicast delivery is the use of a Forward Error Correction (FEC) scheme on the application layer. FEC, unlike the common methods for error control is not based on lost or corrupted packets retransmission, since the error correction is "forward" in the sense that redundant data are transmitted in advance with the source information, in order to obtain the receivers the ability to overcome packet losses. The application of FEC on ptm reliability protocols provides particular advantages. The most important property of FEC codes is the ability to use the same FEC packets to repair simultaneously different independent packet losses at multiple receivers, without the need of the costly or impossible procedure of packets retransmission. In order to meet the error free transmission requirement of demanding applications, 3GPP recommends the use of the systematic, fountain Raptor code as an Application Layer FEC (AL-FEC) protection mechanism exclusively for MBMS.

The 3GPP multicast services standard, named MBMS, is a unidirectional ptm service in which data are transmitted from a single source to a group of multiple mobile endpoints in a specific service area. 3GPP defines two delivery methods namely, download and streaming.

Download uses the FLUTE protocol which is carried over UDP/IP and is independent of the IP version and the underlying link layers used. In order to apply AL-FEC protection on the MBMS download delivery, the transmitted file is partitioned in one or several source blocks each consisting of k source symbols. For each source block, redundant repair symbols are generated through FEC encoding with a unique ID assigned on each resulting encoding symbol. Subsequently, one or more encoding symbols are placed in each FLUTE packet payload with the resulting packets encapsulated in UDP and distributed over the IP multicast flow.

On streaming delivery, RTP is the application layer protocol which provides means for sending real-time or streaming data over UDP transport layer. The MBMS AL-FEC streaming framework operates on RTP/UDP flows. A copy of the source packets is forwarded to the Raptor encoder and arranged in a source block with each packet occupying a new row of T bytes. The source block is filled up to k rows, where the value of k can be different for each source block. After forming a FEC source block from the packets to be protected together, the Raptor encoder generates the desired repair symbols which are then sent using the FEC repair packet format.


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Multicast Broadcast Single Frequency Network

The evolved Multimedia Broadcast and Multicast Services (e-MBMS) feature constitutes the evolutionary successor of MBMS for Long Term Evolution (LTE) systems. The key motivation for integrating multicast and broadcast extensions into mobile communication systems is to enable efficient group related data distribution services, especially on the radio interface.

To improve the multimedia data delivery, LTE has exploited the Orthogonal Frequency-Division Multiplexing (OFDM) radio interface to transmit MBMS data as a multicell transmission over a synchronized Single Frequency Network (MBSFN).

A key new feature of LTE is the possibility to exploit the OFDM radio interface to transmit multicast or broadcast data as a multicell transmission over a synchronized Single Frequency Network: this is known as Multimedia Broadcast Single Frequency Network (MBSFN) operation.

MBSFN transmission enables a more efficient operation of the MBMS service, allowing over-the-air combining of multi-cell transmissions towards the User Equipment (UEs).

In MBSFN operation, MBMS data is transmitted simultaneously over the air from multiple tightly time-synchronized cells. A UE receiver will therefore observe multiple versions of the signal with different delays due to the multicell transmission. Provided that the transmissions from the multiple cells are sufficiently tightly synchronized for each to arrive at the UE within the cyclic prefix at the start of the symbol, there will be no Inter Symbol Interference (ISI). In effect, this makes the MBSFN transmission appear to a UE as a transmission from a single large cell, and the UE receiver may treat the multicell transmissions in the same way as multipath components of a single-cell transmission without incurring any additional complexity. The UE does not even need to know how many cells are transmitting the signal.

This Single Frequency Network reception leads to significant improvements in spectral efficiency compared to UMTS Release 6 MBMS, as the MBSFN transmission greatly enhances the SINR. This is especially true at the cell edge, where transmissions which would otherwise have constituted inter-cell interference are translated into useful signal energy - hence the received signal power is increased at the same time as the interference power being largely removed.


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Modulation and Codind Scheme

Spectral Efficiency (SE) refers to the data rate that can be transmitted over a given bandwidth in a communication system. Several studies have shown that SE is directly related to the modulation and coding scheme (MCS) selected for the transmission. Additionally, the most suitable MCS is selected according to the measured SINR as a certain block error rate (BLER) target to be achieved. It is important to focus on a dynamic user distribution, with users distributed randomly in the MBSFN area and therefore experiencing different SINRs. On the basis of the measured SINRs, we investigate the selection of the MCS that better suits each examined user deployment and should be used by the base stations when transmitting the MBMS data.

To select the MCS and calculate the SE in the case of a single receiver, we use the following four-step procedure:

  • Step 1: signal-to-interference plus noise ratio calculation
  • Step 2: modulation and coding scheme selection
  • Step 3: throughput estimation
  • Step 4: single-user spectral efficiency

TheMCS selection and the SE evaluation in the multiple-users case are deduced from the single-user case described in the previous section. In general, when multiple users are located in the MBSFN area, the value of the total SE depends on the selected MCS. This section examines four approaches for the selection of the MCS during MBSFN transmissions. These approaches are carefully selected so as to match different users' deployments and media traffic conditions that could be realized in real word scenarios. More specifically, the selected approaches are listed below:

  • The first approach selects the MCS that ensures that all users, even those with the lowest SINR, receive the MBSFN service (bottom-up approach).
  • The second approach selects the MCS that ensures the maximum SE in the MBSFN area (top-down approach).
  • The third approach sets a predefined SE threshold for the area and selects the MCS that ensures that the average SE over the MBSFN area exceeds this threshold (area-oriented approach).
  • The fourth approach selects the MCS that ensures that at least the 95% of the users receive the MBSFN service with a predefined target SE (user-oriented approach).


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Cost Analysis

Cost analysis is currently a somewhat controversial set of methods in program evaluation. One reason for the controversy is that these terms cover a wide range of methods, but are often used interchangeably. In mobile research we use this term in order to describe a process that includes all the costs in a mobile deployment. For example, a cost analysis of the MBMS service is investigated based on the transmission cost over all the interfaces and nodes of the LTE architecture. During the evaluation, we take into account the total transmission cost that consists of the packet delivery cost at the network nodes and interfaces and the cost for control procedures

In a cost analysis procedure, it is critical that an effective metric will be chosen so that all the important parameters will be included. The cost metric used in the study for the MBMS service in LTE, includes the telecommunication cost for both packet deliveries and control signal transmissions. Based on the e-MBMS operation an analysis is performed for each type of cost that has to be taken into account for the calculation of the total telecommunication cost for the entire session.

In this case, the cost analysis includes the following parts of the total telecommunication cost:

  • Polling cost
  • Air interface cost
  • Core network cost
  • Synchronization cost


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Power Control

Power in mobile networks is the most limited resource and may lead to significant capacity decrease when misused. Providing multicast or broadcast services to a meaningful proportion of a cell coverage area may require significant amounts of power dedicated to the multicast or broadcast transmission. Several techniques, such as Dynamic Power Setting (DPS), Macro Diversity Combining (MDC) and Rate Splitting (RS) have been introduced in order to minimize the base station's total E-MBMS transmission power.

Power control is one of the most critical aspects in MBMS due to the fact that downlink transmission power in UMTS networks is a limited resource and must be shared efficiently among all MBMS users in a cell. Power control aims at minimizing the transmitted power, eliminating in this way the intercell interference. However, when misused, the use of power control may lead to a high level of wasted power and worse performance results.

On the PTP downlink transmissions, fast power control is used to maintain the quality of the link and thus to provide a reliable connection for the receiver to obtain the data with acceptable error rates. Transmitting with just enough power to maintain the required quality for the link also ensures that there is minimum interference affecting the neighboring cells. However, when a user consumes a high portion of power, more than actually is required, the remaining power, allocated for the rest of the users, is dramatically decreased, thus leading to a significant capacity loss in the system. During PTM downlink transmissions, Node B transmits at a power level that is high enough to support the connection to the receiver with the highest power requirement among all receivers in the multicast group. This would still be efficient because the receiver with the highest power requirement would still need the same amount of power in a unicast link, and by satisfying that particular receiver's requirement, the transmission power will be enough for all the other receivers in the multicast group. Consequently, the transmitted power is kept at a relatively high level most of the time, which in turn, increases the signal quality at each receiver in the multicast group. On the other hand, a significant amount of power is wasted and moreover intercell interference is increased.


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Multimedia Broadcast Multicast Services

Multimedia Broadcast Multicast Service (MBMS) is a novel framework, extending the existing UMTS infrastructure that constitutes a significant step towards the so-called Mobile Broadband. MBMS is intended to efficiently use network and radio resources, both in the core network and, most importantly, in the air interface of UMTS Terrestrial Radio Access Network (UTRAN), where the bottleneck is placed to a large group of users. Actually, MBMS is a Point-to-Multipoint service in which data is transmitted from a single source entity to multiple destinations, allowing the network resources to be shared. MBMS is an efficient way to support the plethora of the emerging wireless multimedia and application services, such as Mobile TV and Streaming Video by supporting both broadcast and multicast transmission modes.

In MBMS rich wireless multimedia data is transmitted simultaneously to multiple recipients, by allowing resources to be shared in an economical way. MBMS efficiency is derived from the single transmission of identical data over a common channel without clogging up the air interface with multiple replications of the same data. From the service and operators' point of view, the employment of MBMS framework involves both an improved network performance and a rational usage of radio resources, which in turns leads to extended coverage and service provision. In parallel, users are able to realize novel, high bit-rate services, experienced until today only by wired users.

As the term MBMS indicates, there are two types of service mode: the broadcast mode and the multicast mode. Each mode has different characteristics in terms of complexity and packet delivery. The broadcast service mode is a unidirectional Point-to-Multipoint (PTM) service. Actually, with broadcast, the network simply floods data packets to all nodes within the network. In the multicast operation mode, data is transmitted solely to users that explicitly request such a service. More specifically, the receivers have to signal their interest for the data reception to the network and then the network decides whether the user may receive the multicast data or not. Unlike the broadcast mode, the multicast mode generally requires a subscription to the multicast subscription group and then the user joining the corresponding multicast group. Moreover, due to the selective data transmission to the multicast group, it is expected that charging data for the end user will be generated for this mode, unlike the broadcast mode.

The MBMS framework requires minimal modifications in the current UMTS architecture. As a consequence, this fact enables the fast and smooth upgrade from pure UMTS networks to MBMS-enhanced UMTS networks. The major modification in the existing UMTS platform for the provision of the MBMS framework is the addition of a new entity called Broadcast Multicast-Service Center (BM-SC). Actually, BM-SC acts as entry point for data delivery between the content providers and the UMTS network and is located in the PS domain of the CN. The BM-SC entity communicates with existing UMTS/GSM networks and external PDNs.


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Congestion Control

Congestion control is a policy that regulates the source transmission rate according to the network congestion. In IP multicast, the User Datagram Protocol (UDP) is used for the transport layer. This protocol does not implement any congestion control. Instead, the Transmission Control Protocol (TCP) regulates its transmission rate according to network congestion. This means that the coexistence of multicast traffic and TCP traffic may lead to unfair use of network resources. In order to prevent this situation, the deployment of multicast congestion control is indispensable. This kind of congestion control is well-known as TCP-friendliness.

The adoption of a multicast congestion control in cellular networks poses an additional set of challenges which are related to the existence of radio links and mobile terminals. All the algorithms for congestion control treat the packet loss as a manifestation of network congestion. This assumption may not apply to networks with radio links, in which packet loss is often induced by reasons other than network congestion like noise or radio link error. As a consequence, the network reaction should not be a drastic reduction of the sender's transmission rate. Moreover, the mobile terminals' computing power cannot afford complicated statistics and traffic measurements. Consequently, such operations should not be executed on the mobile equipment.

There are two well-known multicast congestion control schemes over UMTS networks. We analyze the applicability of the TCP-Friendly Multicast Congestion Control (TFMCC) and the Pragmatic General Multicast Congestion Control (PGMCC) to UMTS networks. Both schemes belong to the class of single-rate congestion control schemes. In this class of schemes, the receiver with the worst congestion level is selected as the representative and the transmissions rate is adjusted accordingly. Such schemes are simple enough, so as to meet a prime objective for UMTS multicast services, that is scalability to applications with thousands of receivers.

  • TFMCC (TCP-Friendly Multicast Congestion Control)
    TFMCC is a well-known equation-based multicast congestion control mechanism which is fair towards competing TCP flows. It uses a control equation derived from the TCP equilibrium equation which relates the long term throughput to the loss and Round-Trip Time (RTT) (this scheme is called "equation-based" for this reason). This equation directly controls the sender's transmission rate. The loss event rate and the RTT are the parameters that define this target throughput. Each receiver calculates its target throughput and considers it as the acceptable sending rate from the sender to itself. TFMCC uses a feedback scheme which assures that the feedback of the receiver calculating the slowest transmission rate, always reaches the sender. This scheme is based on the concept of the Current Limiting Receiver (CLR). Moreover, the TFMCC design ensures that the sender gets feedback from the receivers experiencing the worst network conditions without being overwhelmed by feedback (feedback implosion is suppressed).
  • PGMCC (Pragmatic General Multicast Congestion Control)
    PGMCC is a TCP-friendly scheme in which the receiver reports are sent to the sender in the form of Negative Acknowledgments (NAKs). This functionality helps the sender to continuously monitor the status of the receivers' group. The receiver with the worst throughput according to the control scheme is the group's representative, the acker. The receiver reports embedded into the NAKs help the sender to select and to track changes of the acker. A window-based congestion control scheme similar to TCP congestion control is run between the sender and the acker, which has the responsibility to send positive ACKs for each data packet. The window-based control used in PGMCC is partly different from TCP congestion control. The differences are: the use of distinct windows for rate and for reliability/flow control, the retransmission behavior, the use of sender-based RTT measurements for selecting the representative and the ACK clocking scheme when the representative switches over.

The degradation of the radio channels in the UTRAN causes malfunctions in the legacy TFMCC and PGMCC schemes. The innovation of LDST work stems from the fact that the original schemes are partly modified and extended in order to support the particularities of the UTRAN. Our proposals introduce minor modifications in the UMTS architecture. Additionally, complicated operations like statistics and traffic measurements are avoided to be performed on mobile equipment. Therefore, our schemes take into account the limited computing power of the mobile terminals. Last but not least, it was our motivation to study the modified TFMCC and PGMCC schemes in a comparative way.


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Mobile Apps

Today there are several different platforms for mobile application development. Each one of these operating systems has its own software developments kit, requires special tools for application development. They also require knowledge of various programming languages and they have a different way to access the functions of different devices and services for a specific operation. It is obvious that the main problem of developing applications for each platform separately is the use of different development kits and need for knowledge of different and often complex programming languages.

With the emergence of HTML5 these problems have started being addressed effectively. HTML5 allows that the application development is not targeted on each platform and therefore it is possible to create cross-platform applications that can operate in exactly the same way in all platforms. The application in this case runs through the browser, which is able to access and make use of most available functions in a mobile device and operating system. Another important advantage is that the application is implemented with web technologies (HTML, JavaScript, CSS, etc.) which of course is much more efficient and simple.

Our group has recognized the need for turning toward web technologies and, to this direction, we developed a demo web application that integrates all the major features of HTML5 including built-in video, canvas, local database, geo-location, device orientation, access to cameras, multi-threaded execution,etc. This demo application is available here as well as its demo video.


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