Infotech Oulu Annual Report 2014 - Radio Access Technologies (RAT)

Professor Matti Latva-aho
Department of Communications Engineering and the Centre for Wireless Communications, University of Oulu
matti.latva-aho(at)ee.oulu.fi
http://www.infotech.oulu.fi/rat
 

 

Background and Mission

The data rates as well as quality of service (QoS) requirements for rich user experience in wireless communication services is continuously growing. More and more devices will be connected to the global ubiquitous information network with the Wireless World Research Forum (WWRF) vision of seven trillion wireless devices serving seven billion people by 2020. The diversity of the devices and services will increase. While the demand of high data rates to provide multimedia services, such as video transmission, is increasing, the demand for lower rate machine-to-machine (M2M) communications to enable the so-called Internet of Things (IoT) is also increasing rapidly. A key part of the vision of such a large number of radio devices assumes that low cost and low power sensors are widely spread in various devices, such as home appliances and also in dedicated sensor nodes.

To enable the visions of improved connectivity, the network control and the technology applied in the nodes and devices need to make major leaps. One of the key concerns is the overall power and energy consumption of the devices and the whole network infrastructure. Some of the devices will be plugged into electricity supply network, while others will not. Those may be battery powered or they may harvest energy from the environment, e.g., by solar panel, from wind, from vibrations etc. The energy efficiency is a major issue from the network and device operation and life-time perspectives, but also relates to sustainable development when the complete system is concerned. Therefore, in addition to the more conventional target of area-spectral efficiency and increasing the data rates, also the power and energy efficiency of the evolving wireless systems is of major concern. The issue is complicated by the fact that also the infrastructure and user devices have also a cost both in terms of financial expenditure and usage of natural resources, which also implies a certain carbon footprint. The past research has addressed many aspects of the grand challenge – how to enable cost and energy-efficient future wireless connectivity - but densification of networks and more and more wirelessly connected devices pose totally new challenges requiring research and optimization across the Open System Interconnect (OSI) model layers and also beyond.

Scientific Progress

Major innovations in wireless systems and building components are needed to answer to the overall 1000-fold traffic growth within the next 10–15 years predicted by several major mobile operators and equipment vendors. Although the available frequency spectrum will most likely increase, massive improvements in spectral efficiency, new spectrum allocations towards 100 GHz, as well as drastically new ways for sharing the spectrum amongst different systems are unquestionably necessary. At the same time, the total power and energy consumption of the wireless systems must be minimised due to ever-increasing cost of energy and environmental sustainability. Additional challenges for wireless connectivity are put forward by the developing Internet of Things (IoT) and smart services as the data traffic between devices grows in volume and significance necessitating renewal of the current networking paradigms. In addition to capacity and coverage increase, improved area spectral efficiency, and low energy consumption, low human radio frequency (RF) radiation exposure is the third key driver for developing future technologies. Although no conclusive evidence of the harmfulness of radio waves on human organs exists, general concerns do. Therefore having an impact on the consumer behaviour, the issue merits to be addressed. Last but not least, the future solutions must be highly cost efficient as the number of devices and network nodes is expected to grow tremendously. The choice of materials and components in future mobile devices plays a crucial role in the drastically needed reduction of costs.

A viable approach to solve these problems is through the concept of Heterogeneous Networks (HetNets), see Figure 1. HetNets represent a novel networking paradigm based on the idea of deploying short-range, low-power and low-cost smallcell base stations operating in conjunction with the macrocellular network. The use of HetNets allows wireless networks to provide high data rates, alleviate network congestion by offloading macrocell traffic and by providing dedicated capacity to homes, enterprises, or urban hotspots. HetNets encompass a broad variety of cells and technologies, such as microcells, metrocells, picocells, femtocells, as well as advanced wireless relays, distributed antenna systems (DAS), radio remote heads (RRHs) and Wi-Fi. With the sharp increase in data demands in cellular networks due to the popularity of smartphone devices, these new types of deployments utilising low power nodes are receiving significant attention in both industry and academia, as a promising and cost-effective way to boost the system capacity and cope with the unprecedented (and unrelenting) challenge of spectrum and capacity crunch.

Figure 1. Heterogeneous Network (HetNet) illustration example.

HetNets can mainly be seen as an evolutionary path towards future wireless networks. Besides networks evolution, also revolutionary solutions are needed as the capacity needs cannot be solved alone with natural evolution of current technologies. Totally novel solutions can be sought in the following areas: system design for new spectrum allocations up to 100 GHz, new air-interface technologies and cognitive self-organized networking features.

The research themes covered with RAT group and related challenges are discussed in the sequel.

New Spectrum Allocations

Besides the spectrum bands typically used for mobile communications, in particular the UHF band (300 MHz - 3 GHz) and the lower SHF band (3 GHz - 30 GHz), the remaining part of the SHF band and parts of 30 GHz - 300 GHz, also known as millimeter wave -MMW- band) are currently attracting interest for communication purposes. These bands offer partly large continuous intervals of not yet used spectrum that allow implementation of very high data-rate systems. On-going research, initial system proposals and first implementations indicate that the technological challenges resulting from the usage of this spectrum can be addressed. However, almost all system solutions developed for this spectrum are standalone concepts not interacting with classical mobile communication systems. There is a need to investigate how the specific characteristics of these frequency bands can be exploited, which services can be efficiently supported by solutions using this spectrum, and how these system solutions can be integrated in an overall wireless communication concept. This will result in an understanding on how the use of beyond 6 GHz spectrum influences spectrum needs of future mobile systems.

Channel Models

Radio propagation below 6 GHz in urban environment has been extensively modelled for various habitats from macro to microcells.  Several propagation models for GSM and LTE exist that can be easily extended to cover the channel behaviour down to picocell level. However, these models are missing fine resolution 3D angular domain characteristics (in azimuth and elevation domains) that are crucial for large scale MIMO and high capacity approaches. They also lack full description of the channel at higher frequencies. Ray tracing techniques can be used to simulate the key aspects of the radio propagation in femtocell environment. However, new spectrum allocations beyond 6 GHz with intensified use of elevation domain call for more knowledge on the channel, which can only be gained and verified via measurements in real environments.

Novel Air-interface Technologies

The air interface is the foundation on which any wireless-communication infrastructure is based. The properties of the different air-interface protocol layers (physical layer, MAC layer, retransmission protocols, etc.), and how these operate together, are thus critical for the quality-of-service, spectral and energy efficiency, robustness, and flexibility of the entire wireless system. One of the key drivers for the evolution of the air interface is the paradigm shift from larger coverage cells to smaller and smaller, less and less regularly deployed cells dominating network architectures. Hence, this viable change (for example, in link geometry distribution) has to be carefully studied as an enabler for novel technical solutions to provide the expected services despite of the fact that the available spectrum does not increase in the same proportion.

Basic Transmission Technologies

Orthogonal Frequency Division Multiplexing (OFDM) is the transmission technology used in the most recently developed wireless technologies, such as LTE/LTE-Advanced and WiMAX. Being a kind of multi-carrier transmission scheme, OFDM provides low-complexity means to handle, and even take advantage of, radio-channel frequency-selectivity, due to the small sub-carrier spacing and the possibility for scheduling in the frequency domain. At the same time, OFDM spectral efficiency, flexibility, and robustness are a compromise with the need for a cyclic prefix and the required guard bands. Different means to further enhance spectral efficiency and flexibility/robustness, e.g. improved spectral containment allowing better coexistence with services in adjacent bands and thus efficient implementation of cognitive radios (CRs), beyond that of conventional OFDM, should thus be pursued. This includes more general multi-carrier transmission schemes, as well as other transmission approaches that may not be based on the multi-carrier principle.

Advanced Multi Antenna Transmission/Reception

The use of multiple antennas at the transmission and/or receiver side is an important way of greatly enhancing the efficiency and robustness of the air interface. Although multi-antenna transmission/reception is today an established technology component in state-of-the-art mobile-broadband technologies, such as HSPA and LTE, much can still be done to fully exploit all its potential, both on the link and the system level. This includes more robust multi-antenna transmission (e.g., in terms of limited channel knowledge), as well as extending the capabilities of the multi-antenna transmission schemes to provide efficient and flexible multi-user multiplexing.

A more radical technology step is to extend current multi-antenna schemes, typically consisting of just a few antenna ports at each transmitter/receiver node, towards massive multi-antenna configurations, in the extreme case consisting of several hundred antenna ports. In theory, this would provide a path towards enormous enhancements in terms of system efficiency. However, the introduction of such massive antenna configurations for wireless communication requires extensive work, both in terms of the antenna technology itself and in terms of transmission and reception algorithms needed to efficiently utilize the antenna system.

Advanced Interference Handing

The link performance in mobile broadband systems is today often limited by interference from other nodes of the system. Relatively primitive means to suppress such interference at the receiver side have already been introduced in state-of-the-art wireless-communication systems. However, the ever-increasing ability for computationally-intensive signal processing, even in hand-held terminals, opens up new opportunities for more advanced methods in interference suppression/elimination or even utilization. Research is needed to realize such schemes, both on their basic principles and on their integration into wireless networks. A very specific type of interference impacting the receiver of a radio unit is its own transmit signal. In all systems of today (apart from simple repeaters), such interference is handled by separation of transmission and reception either in frequency (frequency division duplex) or in time (time division duplex). If the interference of the transmitted signal could be suppressed by other means, such as advanced receiver processing and specific antenna configurations, this could, at least in theory, double the capacity. Means to achieve this have been demonstrated in academia and the scheme becomes of particular interest in smallcells scenarios, when the transmit and receive signal level difference is smaller than in other settings (e.g., macro-cellular). However, significant work is still needed to make such schemes practically useful. In particular, extending the bandwidth and dynamic range of such systems are a priority. The implications of such schemes extend beyond the physical layer and could potentially transform, even revolutionize, higher layers of the network and allow previously intractable problems to be solved.

Radio Access Networking

Future wireless networks will face diverse challenges, amongst which are efficiencies in cost and resources including the growing but still scarce spectrum resource. All aspects of mobile networking still can and, more importantly, have to be improved to meet future requirements. For many years, capacity and coverage have been continuously enhanced, but have never matched completely the increasing demands. An always important topic is the resource efficiency, both in terms of installed/active equipment and in terms of shared resources to harvest on statistical multiplexing. Moreover, in the time of climate change and operational cost, energy efficiency has become more and more important. 

To continue the process of improvement and innovation with respect to these wireless networking characteristics, many key enabling technologies are available, but there are still many issues unsolved and require in-depth investigation in a holistic manner before they can be deployed. The following sections will go into more details with respect to these.

Inter Layer Network Optimization

Undoubtedly, future wireless communication networks must provide a large range of services, including voice, data and streamed multimedia, at reasonable cost and QoS, comparable to competing wire-line technology. This increased demand may lead to a need for employing new network topologies, such as multi-hop wireless networks, mobile ad-hoc networks and deeper integration of wire-line and wireless networks.

A fundamental problem in designing such complex systems is the derivation of a network control mechanism, comprizing of flow control, routing, scheduling and physical resources management that can provide QoS guarantees and ensure the network stability under a large set of service demands. Traditionally, these control decisions have been optimized independently at different network layers. Every layer controls a subset of the decision variables and observes a subset of parameters and variables from the other layers. Thus, each layer in the protocol stack hides the complexity of the layers below and provides a set of services to the layer above. While the general principle of layering is widely recognized as one of the key reasons for the enormous success of wire-line data networks, there is now a worldwide recognition that it is no longer efficient, especially in the case of wireless networks. Globally-efficient designs of wireless networks cannot be achieved without crossing the boundaries of the standard Open Systems Interconnection (OSI) layers.                    

Radio Access Network Operation

The tendency in radio network management is to allow system optimization at local level as much as possible: the systems are getting more and more decentralized. The long-lasting dilemma has thus been on finding a right balance between centralized control and self-organized networking. As smallcells will have a more important role in the future and optical fiber backhaul is becoming more feasible to deploy, the system architectures based on local centralized clusters of smallcells become of interest for global optimization of the cluster operation (e.g., following the NUM framework). An alternative approach is to allow full flexibility at each smallcell, leading to true self-organized networking behavior.

Self-Organizing Radio Networks

Wireless networks in the legacy and the Future Internet context will have to satisfy an increasing volume of applications (e.g., various new concepts such as smart infrastructures, various information flows such as video and data apart from voice, various end-points, namely, machines and smartphones, etc.) and an increasing volume of associated demand and traffic. Moreover, as there is fierce and intense competition in telecommunications, there is pressing need for cost efficiency in the satisfaction of the application/demand/traffic requirements. Cost-efficiency requires savings in various cost components, e.g., the capital expenditures (CAPEX), the operational expenditures (OPEX), etc. Self-organizing radio networks have the potential of becoming a main contributor in this direction. This derives from their self-management/awareness/organization capabilities, as well as from the knowledge they can obtain through an inherent machine learning functionality. In the light of the aspects above, self-organizing radio networks can efficiently complement/extend and add sophistication to a wireless communication ecosystem. 

A self-organizing radio network is based on opportunities regarding the links (i.e., radio access technologies, spectrum, transmission power, etc.), as well as the nodes (i.e., devices, terminals, relays, access points serving cells of various sizes, etc.) that can be used for its formation. They can be created, in a particular location and time period, for the efficient application provision and the resolution of capacity and coverage problems, either in the wireless access or in the backhaul. Efficiency will stem from the higher resource utilization that can be achieved (opportunity exploitation), the lower energy consumption of the infrastructure, the handling of situations through adaptations and without needing to resort to worst-case oriented planning, the capability for automated and knowledge-based handling of situations. All these have the potentials to lead to decreases in the CAPEX and the OPEX, as well as to fast and reliable management.

Advanced intelligence should be developed for realizing cognitive radio networks. The intelligence of a self-organizing radio network requires research work for yielding capabilities for the perception and reasoning regarding the context of operation, decision-making regarding its creation/maintenance/ release, as well as learning regarding the contexts encountered, the decisions taken to handle them, and the alternate ways they could be handled. The context of operation refers to the traffic requirements, radio environment conditions, mobility levels, the status and capabilities of devices and potential links.

The research on self-organizing radio networks will necessitate the precision and evaluation of scenarios and requirements regarding their role in extending wireless access infrastructures. Next in the agenda can be the development/refinements of functional and system architectures, also taking into account the integration with the overall wireless world. In order to complement the architecture work, there needs to be elaboration and ultimately specification of control channels for the cooperation of the self-organizing and management components. Efficient, stable and scalable algorithmic solutions are needed. Special emphasis needs to be placed on validation, especially, by means of experiments, trials and pilots. Concrete links with standardization and regulation should be evolved and exploited for producing the framework that can lead to exploitation.

User Context

Modern smartphones may allow predicting user behavior including user location and data transfers using their sensors such as: GPS, gyroscope or accelerometer combined with calendar information and past activities. This allows for several possibilities for radio resource management optimization such as:

send or receive emails with large attachments, send or download large files not necessarily immediately but at the ideal time / position for the wireless network (requiring less spectrum and energy), still in time to satisfy user expectations and possibly even before the user request. I.e., resource allocation can be optimized based on the user context. Important research questions to facilitate this approach include:

  • Algorithms to determine and predict user context/activity
  • Evaluating the potential of this approach, regarding both more efficient spectrum and energy utilization.
  • Defining a generic framework to be introduced in future systems/standards for enabling this approach

This technology can be combined with various other approaches of system optimization for future wireless systems by advancing not abstract low level performance figures but focusing on end-to-end context specific user requirements and directing scarce rescores (spectrum, energy, ...) as well as optimizing the network on all levels towards where most value is created for the users in their respective contexts.

Besides targeting at breakthroughs for future mobile communication systems, the research group is focusing also on how to utilize existing and forthcoming wireless solutions for communications and control for critical infrastructures, termed as dependable wireless systems. Such solutions are needed, e.g., for any abnormal operation of mobile communication networks which are vital for today’s societies. In the event of manmade or natural catastrophe, it is important to autonomously adjust the network operation such that vital communications can be carried out. As all wireless systems require electricity to be operated, in some cases co-design of power grids and wireless networks is needed. This opens new interesting possibilities for multidisciplinary wireless systems optimization where also human behavior and economics models need to be taken into account.

 

Personnel

professors

3

senior research fellows

5

postdoctoral researchers

7

doctoral students

33

other research staff

10

total

58

person years for research

48

 

 

External Funding

Source

EUR

Academy of Finland

740 000

Tekes

1 611 000

other domestic public

236 000

domestic private

395 000

international

177 000

total

3 159 000

 

Doctoral Theses

Höyhtyä, M (2014) Adaptive power and frequency allocation strategies in cognitive radio systems. VTT Science 61.

Celentano, U (2014) Dependable cognitive wireless networking: modelling and design. Acta Universitatis Ouluensis C 488.

Hekkala, A (2014) Compensation of transmitter nonlinearities using predistortion techniques. Case studies of envelope tracking amplifiers and radio-over-fibre links. VTT Science 53.

 

Selected Publications

[1] F. Pantisano, M. Bennis, W. Saad, M. Debbah, and M. Latva-aho, “Improving macrocell-small cell coexistence through adaptive interference draining,” IEEE Transactions on Wireless Communications, vol. 13, no. 2, pp. 942-955, Feb. 2014.

[2] N. S. Ferdinand, D. B. da Costa, A. L. F. de Almeida, and M. Latva-aho, “Physical layer secrecy performance of TAS wiretap channels with correlated main and eavesdropper channels,” IEEE Wireless Communications Letters, vol. 3, no. 1, pp. 86-89, Feb. 2014.

[3] H. Pennanen, A. Tölli, and M. Latva-aho, “Decentralized robust beamforming for coordinated multi-cell MISO networks,” IEEE Signal Processing Letters, vol. 21, no. 3, pp. 334-338, Mar. 2014.

[4] K. Jayasinghe, P. Jayasinghe, N. Rajatheva, and M. Latva-aho, “Secure beamforming design for physical layer network coding based MIMO two-way relaying,” IEEE Communications Letters, vol. 18, no. 7, pp. 1270-1273, July 2014.

[5] E. Bastug, M. Bennis, and M. Debbah, “Living on the edge: the role of proactive caching in 5G wireless networks,” IEEE Communications Magazine, vol. 52, no. 8, pp. 82-89, Aug. 2014.

[6] K. B. Shashika Manosha, M. Codreanu, N. Rajatheva, and M. Latva-aho, “Power-throughput tradeoff in MIMO heterogeneous networks,” IEEE Transactions on Wireless Communications, vol. 13, no. 8, pp. 4309-4322, Aug. 2014.

[7] P. H. J. Nardelli, M. Kountouris, P. Cardieri, and M. Latva-aho, “Throughput optimization in wireless networks under stability and packet loss constraints,” IEEE Transactions on Mobile Computing, vol. 13, no. 8, pp. 1883-1895, Aug. 2014.

[8] D. Nguyen, L.-N. Tran, P. Pirinen, and M. Latva-aho, “On the spectral efficiency of full-duplex small cell wireless systems,” IEEE Transactions on Wireless Communications, vol. 13, no. 9, pp. 4896-4910, Sep. 2014.

[9] Z. Khan, H. Ahmadi, E. Hossain, M. Coupechoux, L. A. DaSilva, and J. J. Lehtomäki, “Carrier aggregation/channel bonding in next generation cellular networks: methods and challenges,” IEEE Network, vol. 28, no. 6, pp. 34-40, Nov./Dec. 2014.

[10] K. Jayasinghe, P. Jayasinghe, N. Rajatheva, and M. Latva-aho, “Linear precoder-decoder design of MIMO device-to-device communication underlaying cellular communication,” IEEE Transactions on Communications, vol. 62, no. 12, pp. 4304-4319, Dec. 2014.

[11] J. Wildman, P. H. J. Nardelli, M. Latva-aho, and S. Weber, “On the joint impact of beamwidth and orientation error on throughput in directional wireless Poisson networks” IEEE Transactions on Wireless Communications, vol. 13, no. 12, pp. 7072-7085, Dec. 2014.

[12] H. Alves, C. H. M. de Lima, P. H. J. Nardelli, R. D. Souza, and M. Latva-aho, “On the secrecy of interference-limited networks under composite fading channels,” IEEE Signal Processing Letters, to appear, 2015.

[13] H. Alves, G. Brante, R. D. Souza, D. B. da Costa, and M. Latva-aho, “On the performance of secure full-duplex relaying under composite fading channels,” IEEE Signal Processing Letters, to appear, 2015.

[14] P. Luoto, P. Pirinen, M. Bennis, S. Samarakoon, S. Scott, and M. Latva-aho, “Co-primary multi-operator resource sharing for small cell networks,” IEEE Transactions on Wireless Communications, to appear, 2015.

Last updated: 9.4.2015