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Machine-to-Machine Communications

                                                                                               MIMO Classification

[1] L. Hanzo, O. Alamri, M. El-Hajjar and N. Wu, Near-Capacity Multi-Functional MIMO Systems: Sphere-Packing, Iterative Detection and Cooperation, John Wiley & Sons, May 2009.
[2] Near-Capacity Wireless Transceivers and Cooperative Communications in the MIMO Era: Evolution of Standards, Waveform Design, and Future Perspectives
[3] Wireless myths, realities, and futures: from 3G/4G to optical and quantum wireless
[4] Multifunctional MIMO systems: A combined diversity and multiplexing design perspective
[5] MIMO-aided near-capacity turbo transceivers: taxonomy and performance versus complexity
[6] Spatial Modulation for Generalized MIMO: Challenges, Opportunities and Implementation.
[7] MIMO stochastic model and capacity evaluation of on-body channels 
[More Publications]

Data traffic demand in cellular networks today is increasing at an exponential rate. As people use more electronic devices at home and in the office and with the proliferation of smart connected peripherals including actuators and sensors, there is an ability to impart a central nervous system on our planet, in what is often referred to as the “Internet of Things” (IoT) supporting Machine-to-Machine (M2M) communications. IoT is an evolution of the existing internet, which will form an integral part of the future internet which is expected to be a dynamic global network infrastructure with self configuring capabilities. The number of things connected to the Internet exceeded the number of people connected in year 2009, which marked the birth of the IoT. It is predicted that the number of M2M connected devices will range from 10 billion to 50 billion devices by the year 2020, including different kinds of low bandwidth sensor and actuator applications to high bandwidth applications such as wireless HDMI multimedia transmission. According to Cisco, global mobile data traffic will increase 13-fold between 2012 and 2017 reaching 11.2 ExaBytes per month by 2017 and mobile network connection speeds will increase 7-fold by 2017. The average mobile network connection speed (526 Kilobits per second (kbps) in 2012) will exceed 3.9 Megabits per second (Mbps) in 2017, according to Cisco.

This technology will allow us to measure quantities and control systems on a global scale, whilst at the same time offer a hitherto unprecedented access to detailed information. The connection of 'things' relying on advanced signal processing and connectivity capabilities can be thought of as an evolution of our current digital world, which has the potential of changing the way we interact with our environment in what can be thought of as the integration of the physical and the digital worlds. The IoT could allow anyone and anything to be connected with anyone and anything, anywhere, using any network and any service.

                                                                                                                                                   MIMO Classification

[1] Pilot contamination elimination for large-scale multiple-antenna aided OFDM systems
[2] Opportunistic relay selection for co-operative relaying in cochannel interference contaminated networks
[3] Performance analysis of high-speed railway communication systems subjected to co-channel interference and channel estimation errors
[4] Detect-and-Forward Relaying Aided Cooperative Spatial Modulation for Wireless Networks
[5] Layered Steered Space–Time-Spreading-Aided Generalized MC DS-CDMA
[6] Layered steered space-time codes using multi-dimensional sphere packing modulation
[7] Downlink Steered Space-Time Spreading For Multi-Carrier Transmission Over Frequency Selective Channels
[8] Layered Steered Space-Time Codes and their capacity
[9] Coherent and Differential Downlink Space-Time Steering Aided Generalised Multicarrier DS-CDMA
[10] Area spectral efficiency of soft-decision space–time–frequency shift-keying-aided slow-frequency-hopping multiple access
[11] Soft-Demodulation of Space-Time-Frequency Shift Keying for Iterative Detection
[12] A universal space-time architecture for multiple-antenna aided systems
[13] Reduced-complexity iterative-detection aided generalized space-time shift keying
[14] Effects of Channel Estimation on Spatial Modulation 
[15] Spatial modulation and space-time shift keying: optimal performance at a reduced detection complexity
[16] Generalised pre-coding aided spatial modulation
[More Publications]

In the context of the multi-billion M2M communications market, system requirements are going to substantially differ from the current communication systems, where the M2M communication model should be reconfigurable and adaptive in order to cope with the limited resources of the devices and with the heterogeneity of these devices. Current wireless mobile networks are typically homogeneous, using a network of macro Base Stations (BSs) having similar transmit power levels and antenna patterns. Moreover, all BSs offer unrestricted access to the user terminals in the network and serve roughly the same number of user terminals having similar data and performance requirements. On the other hand, in the M2M context, different applications will have different requirements in terms of their bandwidth, data rate, transmission power, Quality of Service (QoS), mobility, memory and size as well as how often data is transmitted. This ranges from the low-cost connected devices of sensor networks to more complex and smarter devices, such as surveillance cameras around the house or in public places. Other applications, such as medical or safety applications, require a high reliability. The connected objects will have data rate requirements ranging from a few Kbps to a few Mbps and might be communicating over a just a few meters or to a few Kilometres. These objects will have different computational power, memory requirement, size, energy and storage capacity. For example smart electricity metering and health surveillance will have opposing needs in terms of availability, mobility and bandwidth. Additionally, it is essential to consider the high density and the scalability of the network, while always considering both the energy versus bandwidth resources, especially when the devices are mobile. Hence, the major challenge of research in M2M communications is the ability to allow all types of devices to communicate within the IoTs, from bit-level communication to continuous data streams, from sporadic connections to connections that are always on, from standard services to emergency modes, spanning applications from local to global, based on either a single device or on distributed sets of devices.

In the M2M environment, transmissions will occur in diverse radio propagation environments supporting high-velocity terminals and sharing the radio spectrum by many terminals. The availability of new spectrum for next-generation broadband wireless services is presently uncertain. Any development of the communications systems to accommodate the IoTs will have to build on the communication and network structures defined by such standards. The evolution and pervasive nature of present communication technologies has to take into account the unprecedented number of devices for the developing IoTs. The choice of modulation and coding schemes can significantly influence both the performance and the cost for a given range of bit rates. Hence, these devices can be heterogeneous in diverse aspects, including functionality; their grade of mobility; synchronous, asynchronous, multi hop or broadcast nature; communication medium including wired and wireless; transmission frequency as well as the type of applications. On the other hand, the current wireless communications systems are rely on diverse different standards including digital TV, digital radio, GSM, 3G and LTE, where the single-link spectral efficiency has approached the theoretical limits, but not the area-spectral efficiency (ASE) limits. Hence, the next generation of developments in the area of wireless communications is about improving the ASE.

It has been shown that further improvements in the ASE are possible by increasing the node deployment density. However, in the already dense deployments of macro cells, the attainable cell splitting gains are limited owing to the severe inter-cell interference in addition to the site acquisition costs in capacity-limited dense urban areas. Therefore, in order to achieve further improvements, the 3GPP has been working on including Heterogeneous Networks (HetNets) in LTE Advanced (LTE-A), where low power BSs can be employed alongside the high-power macro BSs. These low power BSs can be classified as pico, femto and relay nodes, where HetNets form a promising technique for improving the ASE.

The currently available communications systems use a variety of TDMA, FDMA, CDMA and OFDMA as their multiple access techniques. On the other hand, the massive M2M communications systems offering high quality ubiquitous and seamless wireless access cannot be managed by the current infrastructure, which will not scale to large collections of nodes and is destined to be plagued with unmanageable interference and network congestion. Nonetheless, we have to build on the currently available systems rather than discard them. Hence, to develop such scalable and dynamically pervasive wireless access, there is a need for fundamentally new techniques to support all these terminals using the different degrees of freedom, including the time-, frequency-, space-, code- and power-domains. Given the different requirements of the M2M devices identified earlier, these degrees of freedom can be exploited for the multiple access and scheduling of users, for attaining diversity gain, for multiplexing gains as well as for a combination of all of these.

We have holistically designed efficient resource allocation techniques utilising all available degrees of freedom, including space, time, frequency, code, etc., including the design of Multi-Functional Antenna Arrays (MFAAs) utilising space and frequency, space and time, space, time and frequency as well as space and code domains in order to achieve hitherto unattainable performance gains. Additionally, the team has extensive experience in the design of receivers for interference-limited scenarios as well as in the development of hitherto unexplored degrees of freedom for improving the communications systems' performance, such as the design of radically new space-time shift keying and space-time-frequency shift keying.

In a M2M network, a great deal of flexibility is needed in terms of how networks are constructed and operated, how spectrum is shared most efficiently among several operators and technologies for maintaining flexibility and fairness in terms of the throughput per user, whilst controlling the latency. Furthermore, a high cell edge capacity has to be maintained at a modest power consumption. This is a fundamental paradign change in the way the resources are allocated to an operator, network, cell and to users. Hence, our goal is to develop future-proof integrated systems in a unified manner.

Explicitly, M2M wireless networks will grow largely by adding vast amounts of small devices with minimum hardware, software and intelligence, which limits their resilience to any imperfections, such as the ubiquitous interference.

[1] A Systematic Luby Transform Coded V-BLAST System
[2] Energy-efficient dynamic resource allocation for opportunistic-relaying-assisted SC-FDMA using turbo-equalizer-aided soft decode-and-forward 
[3] Cross-layer aided energy-efficient opportunistic routing in ad hoc networks
[4] A spectrum leasing cooperative medium access protocol and its stability analysis
[5] Multi-objective routing optimization using evolutionary algorithms
[6] Achieving maximum energy-efficiency in multi-relay OFDMA cellular networks: a fractional programming approach
[7] Dynamic human behaviour based epidemic content dissemination in mobile social networks
[8] Energy-Conscious Turbo Decoder Design: A Joint Signal Processing and Transmit Energy Reduction Approach
[More Publications]

In the context of M2M communications, direct communication between devices should be enabled without the need to go through a BS. Hence, routing is a major challenge in such a system design. A critical issue in designing M2M networks might be to analytically understand and model their connectivity, i.e. the number of neighbouring nodes each node communicates with, since the connectivity is critical for the design of routing algorithms. Routing is a challenging task and has received a tremendous amount of attention from researches. This has led to the development of diverse routing protocols. However, generally the existing routing techniques require end-to-end routing information for establishing their routes and this is not always feasible in large-scale M2M networks with billions of devices. Furthermore, energy efficiency is a main consideration in M2M networks and hence in any cross-layer operation aided energy-efficient routing design, the network lifetime is a major factor to consider.

The team has an extensive experience in cross-layer design relying on bio-inspired techniques conceived for designing routing designs in various types of networks having various diverse topologies. Furthermore, the team has designed and developed several cross-layer optimisation techniques for maximising the network lifetime of wireless sensor networks, where the optimisation criteria include reducing both the dissipated energy and the end-to-end delay as well as a range of other important network design criteria.