Ubiquitous Cell-Free Massive MIMO Communications

Since the first cellular networks were trialled in the 1970s, we have witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic growth has been managed by a combination of wider bandwidths, refined radio interfaces, and network densification, namely increasing the number of antennas per site. Due its cost-efficiency, the latter has contributed the most. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses massive antenna arrays to provide a very high beamforming gain and spatially multiplexing of users, and hence, increases the spectral and energy efficiency. It constitutes a centralized solution to densify a network, and its performance is limited by the inter-cell interference inherent in its cell-centric design. Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive MIMO system implementing coherent user-centric transmission to overcome the inter-cell interference limitation in cellular networks and provide additional macro-diversity. These features, combined with the system scalability inherent in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO from prior coordinated distributed wireless systems. In this article, we investigate the enormous potential of this promising technology while addressing practical deployment issues to deal with the increased back/front-hauling overhead deriving from the signal co-processing.Comment: Published in EURASIP Journal on Wireless Communications and Networking on August 5, 201

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

Self-organised multi-objective network clustering for coordinated communications in future wireless networks

The fifth generation (5G) cellular system is being developed with a vision of 1000 times more capacity than the fourth generation (4G) systems to cope with ever increasing mobile data traffic. Interference mitigation plays an important role in improving the much needed overall capacity especially in highly interference-limited dense deployment scenarios envisioned for 5G. Coordinated multi-point (CoMP) is identified as a promising interference mitigation technique where multiple base stations (BS) can cooperate for joint transmission/reception by exchanging user/control data and perform joint signal processing to mitigate inter-cell interference and even exploit it as a useful signal. CoMP is already a key feature of long term evolution-advanced (LTE-A) and envisioned as an essential function for 5G. However, CoMP cannot be realized for the whole network due to its computational complexity, synchronization requirement between coordinating BSs and high backhaul capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters. This PhD thesis aims to investigate optimum dynamic CoMP clustering solutions in 5G and beyond wireless networks with massive small cell (SC) deployment. Truly self-organised CoMP clustering algorithms are investigated, aiming to improve much needed spectral efficiency and other network objectives especially load balancing in future wireless networks. Low complexity, scalable, stable and efficient CoMP clustering algorithms are designed to jointly optimize spectral efficiency, load balancing and limited backhaul availability. Firstly, we provide a self organizing, load aware, user-centric CoMP clustering algorithm in a control and data plane separation architecture (CDSA) proposed for 5G to maximize spectral efficiency and improve load balancing. We introduce a novel re-clustering algorithm for user equipment (UE) served by highly loaded cells and show that unsatisfied UEs due to high load can be significantly reduced with minimal impact on spectral efficiency. Clustering with load balancing algorithm exploits the capacity gain from increase in cluster size and also the traffic shift from highly loaded cells to lightly loaded neighbours. Secondly, we develop a novel, low complexity, stable, network-centric clustering model to jointly optimize load balancing and spectral efficiency objectives and tackle the complexity and scalability issues of user-centric clustering. We show that our clustering model provide high spectral efficiency in low-load scenario and better load distribution in high-load scenario resulting in lower number of unsatisfied users while keeping spectral efficiency at comparably high levels. Unsatisfied UEs due to high load are reduced by $68.5\%$ with our algorithm when compared to greedy clustering model. In this context, the unique contribution of this work that it is the first attempt to fill the gap in literature for multi-objective, network-centric CoMP clustering, jointly optimizing load balancing and spectral efficiency. Thirdly, we design a novel multi-objective CoMP clustering algorithm to include backhaul-load awareness and tackle one of the biggest challenges for the realization of CoMP in future networks i.e. the demand for high backhaul bandwidth and very low latency. We fill the gap in literature as the first attempt to design a clustering algorithm to jointly optimize backhaul/radio access load and spectral efficiency and analyze the trade-off between them. We employ 2 novel coalitional game theoretic clustering methods, 1-a novel merge/split/transfer coalitional game theoretic clustering algorithm to form backhaul and load aware BS clusters where spectral efficiency is still kept at high level, 2-a novel user transfer game model to move users between clusters to improve load balancing further. Stability and complexity analysis is provided and simulation results are presented to show the performance of the proposed method under different backhaul availability scenarios. We show that average system throughout is increased by 49.9% with our backhaul-load aware model in high load scenario when compared to a greedy model. Finally, we provide an operator's perspective on deployment of CoMP. Firstly, we present the main motivation and benefits of CoMP from an operator's viewpoint. Next, we present operational requirements for CoMP implementation and discuss practical considerations and challenges of such deployment. Possible solutions for these experienced challenges are reviewed. We then present initial results from a UL CoMP trial and discuss changes in key network performance indicators (KPI) during the trial. Additionally, we propose further improvements to the trialed CoMP scheme for better potential gains and give our perspective on how CoMP will fit into the future wireless networks

Heterogeneous Cellular Networks Mixed with LoS and NLoS Transmissions

In the last decades, the rapid increase of user traffc demand for better user experience has pushed the traditional macrocell-only networks being evolving to modern heterogeneous networks(HetNets) with a multi-tier structure. The dense deployment of small-cell base stations (BSs) implies short distances between BSs and users. It is therefore likely that users will see line-of-sight (LoS) links from its serving BS and even nearby interfering BSs, which has not been considered in performance analysis for multi-tier HetNets yet. In this thesis, the dense multi-tier HetNet with LoS and non-line-of-sight (NLoS) transmissions based on a multi-slope path loss model is analyzed. The spatial locations of BSs of any given network tier and those of mobile users are modeled as independent spatial Poisson point processes (PPPs). The expressions of downlink coverage probability are divided for a multi-tier HetNet, based on that the calculations of the area spectral effciency (ASE) and energy effciency (EE) are further proposed. The results demonstrate that in an extremely dense HetNet, both the ASE and EE of the HetNet will drop quickly with further increase of the small-cell density due to the dominance of LoS interfering small-cell links. Following that, the investigation is moved to the probabilistic events of LoS and NLoS transmissions. Four transmission scenarios are simulated with different path loss models, including a linear LoS probability function, a suburban area, a millimetre wave transmission and a 3D path loss model. Accordingly, a user-centric BS clustering strategy is proposed for a non-coherent joint transmissions (JTs) in dense small-cell networks, based on the idea of grouping the BSs with their LoS probabilities to such user above a predefined threshold. The proposed BS clustering strategy is evaluated in the above four transmission environments. Our simulation results show that the coverage probability and spectrum effciency (SE) achieved by the proposed user-centric BS clustering strategy achieve a rapid growth rate with the increasing BS density, and even at extremely high BS densities in all four considered environments. Lastly, following the proposed BS clustering strategy above, a further developed clustering strategy called multi-BS multi-user-equipment (UE) clustering is proposed to allow multiple BSs to serve multiple UEs simultaneously. The main idea of this clustering strategy is to boost network performance in terms of coverage probability and SE at high BS density without sacrificing the ASE. Utilizing stochastic geometry, the closed form expressions of the network performance in terms of coverage probability, SE, ASE and EE are derived in a downlink small-cell network. The results show that the proposed clustering strategy achieves high coverage probability and linear increasing SE and ASE in ultra dense networks at same time

Asymptotic Analysis of the Downlink in Cooperative Massive MIMO Systems

We consider the downlink of a cooperative cellular communications system, where several base-stations around each mobile cooperate and perform zero-forcing to reduce the received interference at the mobile. We derive closed-form expressions for the asymptotic performance of the network as the number of antennas per base station grows large. These expressions capture the trade off between various system parameters, and characterize the joint effect of noise and interference (where either noise or interference is asymptotically dominant and where both are asymptotically relevant). The asymptotic results are verified using Monte Carlo simulations, which indicate that they are useful even when the number of antennas per base station is only moderately large. Additionally, we show that when the number of antennas per base station grows large, power allocation can be optimized locally at each base station. We hence present a power allocation algorithm that achieves near optimal performance while significantly reducing the coordination overhead between base stations. The presented analysis is significantly more challenging than the uplink analysis, due to the dependence between beamforming vectors of nearby base stations. This statistical dependence is handled by introducing novel bounds on marked shot-noise point processes with dependent marks, which are also useful in other contexts

Heterogeneous LTE/ Wi-Fi architecture for intelligent transportation systems

Intelligent Transportation Systems (ITS) make use of advanced technologies to enhance road safety and improve traffic efficiency. It is anticipated that ITS will play a vital future role in improving traffic efficiency, safety, comfort and emissions. In order to assist the passengers to travel safely, efficiently and conveniently, several application requirements have to be met simultaneously. In addition to the delivery of regular traffic and safety information, vehicular networks have been recently required to support infotainment services. Previous vehicular network designs and architectures do not satisfy this increasing traffic demand as they are setup for either voice or data traffic, which is not suitable for the transfer of vehicular traffic. This new requirement is one of the key drivers behind the need for new mobile wireless broadband architectures and technologies. For this purpose, this thesis proposes and investigates a heterogeneous IEEE 802.11 and LTE vehicular system that supports both infotainment and ITS traffic control data. IEEE 802.11g is used for V2V communications and as an on-board access network while, LTE is used for V2I communications. A performance simulation-based study is conducted to validate the feasibility of the proposed system in an urban vehicular environment. The system performance is evaluated in terms of data loss, data rate, delay and jitter. Several simulation scenarios are performed and evaluated. In the V2I-only scenario, the delay, jitter and data drops for both ITS and video traffic are within the acceptable limits, as defined by vehicular application requirements. Although a tendency of increase in video packet drops during handover from one eNodeB to another is observed yet, the attainable data loss rate is still below the defined benchmarks. In the integrated V2V-V2I scenario, data loss in uplink ITS traffic was initially observed so, Burst communication technique is applied to prevent packet losses in the critical uplink ITS traffic. A quantitative analysis is performed to determine the number of packets per burst, the inter-packet and inter-burst intervals. It is found that a substantial improvement is achieved using a two-packet Burst, where no packets are lost in the uplink direction. The delay, jitter and data drops for both uplink and downlink ITS traffic, and video traffic are below the benchmarks of vehicular applications. Thus, the results indicate that the proposed heterogeneous system offers acceptable performance that meets the requirements of the different vehicular applications. All simulations are conducted on OPNET Network Modeler and results are subjected to a 95% confidence analysis

Positioning in 5G and 6G Networks—A Survey

Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios