20 research outputs found
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resource allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, there exists an important challenge in "how to determine the
configuration that maximizes the long-term average number of served IoT devices
at each Transmission Time Interval (TTI) in an online fashion". Given the
complexity of searching for optimal configuration, we first develop real-time
configuration selection based on the tabular Q-learning (tabular-Q), the Linear
Approximation based Q-learning (LA-Q), and the Deep Neural Network based
Q-learning (DQN) in the single-parameter single-group scenario. Our results
show that the proposed reinforcement learning based approaches considerably
outperform the conventional heuristic approaches based on load estimation
(LE-URC) in terms of the number of served IoT devices. This result also
indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve
almost the same performance with much less training time. We further advance
LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative
Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario,
thereby solve the problem that Q-learning agents do not converge in
high-dimensional configurations. In this scenario, the superiority of the
proposed Q-learning approaches over the conventional LE-URC approach
significantly improves with the increase of configuration dimensions, and the
CMA-DQN approach outperforms the other approaches in both throughput and
training efficiency
Traffic pattern prediction in cellular networks.
PhDIncreasing numbers of users together with a more use of high bit-rate services complicate radio resource management in 3G systems. In order to improve the system capacity and guarantee the QoS, a large amount of research had been carried out on radio resource management. One viable approach reported is to use semi-smart antennas to dynamically change the radiation pattern of target cells to reduce congestion.
One key factor of the semi-smart antenna techniques is the algorithm to adjust the beam pattern to cooperatively control the size and shape of each radio cell. Methods described in the literature determine the optimum radiation patterns according to the current observed congestion. By using machine learning methods, it is possible to detect the upcoming change of the traffic patterns at an early stage and then carry out beamforming optimization to alleviate the reduction in network performance.
Inspired from the research carried out in the vehicle mobility prediction field, this work learns the movement patterns of mobile users with three different learning models by analysing the movement patterns captured locally. Three different mobility models are introduced to mimic the real-life movement of mobile users and provide analysable data for learning.
The simulation results shows that the error rates of predictions on the geographic distribution of mobile users are low and it is feasible to use the proposed learning models to predict future traffic patterns. Being able to predict these patterns mean that the optimized beam patterns could be calculated according to the predicted traffic patterns and loaded to the relevant base stations in advance
Optimization of the interoperability and dynamic spectrum management in mobile communications systems beyond 3G
The future wireless ecosystem will heterogeneously integrate a number of overlapped Radio
Access Technologies (RATs) through a common platform. A major challenge arising from the
heterogeneous network is the Radio Resource Management (RRM) strategy. A Common RRM
(CRRM) module is needed in order to provide a step toward network convergence. This work
aims at implementing HSDPA and IEEE 802.11e CRRM evaluation tools.
Innovative enhancements to IEEE 802.11e have been pursued on the application of cross-layer
signaling to improve Quality of Service (QoS) delivery, and provide more efficient usage of
radio resources by adapting such parameters as arbitrary interframe spacing, a differentiated
backoff procedure and transmission opportunities, as well as acknowledgment policies (where
the most advised block size was found to be 12). Besides, the proposed cross-layer algorithm
dynamically changes the size of the Arbitration Interframe Space (AIFS) and the Contention
Window (CW) duration according to a periodically obtained fairness measure based on the Signal
to Interference-plus-Noise Ratio (SINR) and transmission time, a delay constraint and the
collision rate of a given machine. The throughput was increased in 2 Mb/s for all the values of
the load that have been tested whilst satisfying more users than with the original standard. For
the ad hoc mode an analytical model was proposed that allows for investigating collision free
communications in a distributed environment.
The addition of extra frequency spectrum bands and an integrated CRRM that enables spectrum
aggregation was also addressed. RAT selection algorithms allow for determining the gains obtained
by using WiFi as a backup network for HSDPA. The proposed RAT selection algorithm
is based on the load of each system, without the need for a complex management system. Simulation
results show that, in such scenario, for high system loads, exploiting localization while
applying load suitability optimization based algorithm, can provide a marginal gain of up to
450 kb/s in the goodput. HSDPA was also studied in the context of cognitive radio, by considering
two co-located BSs operating at different frequencies (in the 2 and 5 GHz bands) in the
same cell. The system automatically chooses the frequency to serve each user with an optimal
General Multi-Band Scheduling (GMBS) algorithm. It was shown that enabling the access to
a secondary band, by using the proposed Integrated CRRM (iCRRM), an almost constant gain
near 30 % was obtained in the throughput with the proposed optimal solution, compared to a
system where users are first allocated in one of the two bands and later not able to handover
between the bands. In this context, future cognitive radio scenarios where IEEE 802.11e ad hoc
modes will be essential for giving access to the mobile users have been proposed
Multimedia in mobile networks: Streaming techniques, optimization and User Experience
1.UMTS overview and User Experience
2.Streaming Service & Streaming Platform
3.Quality of Service
4.Mpeg-4
5.Test Methodology & testing architecture
6.Conclusion
Laajakaistaisen CDMA solukkoverkon kapasiteetti makrosoluympäristössä
Tämän diplomityön tarkoitus on tutkia kuormitetun laajakaistaiseen koodijakomenetelmään perustuvan makrosoluverkon kapasiteettia.
Työn tavoitteena on selvittää simulointien avulla, miten solukkoverkon palvelemien matkaviestinten käyttäjien eri tiedonsiirtonopeudet ja liikkumisnopeudet vaikuttavat järjestelmän kokonaiskapasiteettiin.
Tehonsäädön ja samalla taajuudella olevien solujen välisen kanavananvaihdon toiminnallisuuksien ja parametrien vaikutuksia tutkitaan verkon kapasiteetin kannalta Tutkimus alkaa hajaspektriteknologian perusteiden ja UMTS:n (Universal Mobile Telecommunications System) FDD (Frequency Division Duplex) osan esittelyillä.
Laajakaistaisen koodijakoon perustuvan verkon kapasiteetin ominaisuudet esitellään.
Makrosoluympäristö määritellään ja sen perusominaisuudet esitellään.
Laajakaistaista koodijakoon perustuvaa UMTS-järjestelmää mallintava simulointiohjelmisto kuvaillaan ennen tämän diplomityön simulointien ja tulosten esittelyä ja analysointia.
Tämän tutkimuksen simuloinnit suoritetaan yksinkertaisessa esikaupunkityyppisessä makrosoluympäristössä.
Laajakaistaisen koodijakoon perustuvan järjestelmän kapasiteetti käyttäytyy hyvin dynaamisesti.
Kapasiteetti riippuu käyttäjien jakaumasta verkossa, tiedonsiirtonopeuksista, liikkumisnopeudesta, häiriön määrästä ja tehonsäädön ja samalla taajuudella olevien solujen kanavanvaihdon parametrien valinnasta.
Puhekäyttäjien lisäys vaikuttaa enemmän suuria tiedonsiirtonopeuksia kuin alhaisia tiedonsiirtonopeuksia käyttävien tilaajien palvelun laatuun.
Solujen väliset häiriöt vaikuttavat merkittävästi verkon kapasiteettiin.
Alhaiset tehonsäädön virheet eivät vaikuta merkittävästi hitaasti liikkuvien käyttäjien saamaan palvelun laatuun.
Samalla taajuudella olevien solujen kanavanvaihdon kynnysarvo tulisi optimoida, jotta voitaisiin saavuttaa verkon suurin mahdollinen kapasiteetti ja minimoida häiriöt
Modelling and Optimisation of GSM and UMTS Radio Access Networks
The size and complexity of mobile communication networks have increased in the last years making network management a very complicated task. GSM/EDGE Radio Access Network (GERAN) systems are in a mature state now. Thus, non-optimal performance does not come from typical network start-up problems, but, more likely, from the mismatching between traffic, network or propagation models used for network planning, and their real counterparts. Such differences cause network congestion problems both in signalling and data channels. With the aim of maximising the financial benefits on their mature networks, operators do not solve anymore congestion problems by adding new radio resources, as they usually did. Alternatively, two main strategies can be adopted, a) a better assignment of radio resources through a re-planning approach, and/or b) the automatic configuration (optimisation, in a wide sense) of network parameters. Both techniques aim to adapt the network to the actual traffic and propagation conditions. Moreover, a new heterogenous scenario, where several services and Radio Access Technologies (RATs) coexist in the same area, is now common, causing new unbalanced traffic scenarios and congestion problems. In this thesis, several optimisation and modelling methods are proposed to solve congestion problems in data and signalling channels for single- and multi-RAT scenarios