Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks

Conventional cellular wireless networks were designed with the purpose of providing high throughput for the user and high capacity for the service provider, without any provisions of energy efficiency. As a result, these networks have an enormous Carbon footprint. In this paper, we describe the sources of the inefficiencies in such networks. First we present results of the studies on how much Carbon footprint such networks generate. We also discuss how much more mobile traffic is expected to increase so that this Carbon footprint will even increase tremendously more. We then discuss specific sources of inefficiency and potential sources of improvement at the physical layer as well as at higher layers of the communication protocol hierarchy. In particular, considering that most of the energy inefficiency in cellular wireless networks is at the base stations, we discuss multi-tier networks and point to the potential of exploiting mobility patterns in order to use base station energy judiciously. We then investigate potential methods to reduce this inefficiency and quantify their individual contributions. By a consideration of the combination of all potential gains, we conclude that an improvement in energy consumption in cellular wireless networks by two orders of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843

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

Uplink CoMP Capability Improvements In Heterogeneous Cellular Networks

LTE-Advanced meets the challenge raised by powerful, mobile devices and bandwidth-hungry applications by investing in solutions such as carrier aggregation, higher order MIMO, relay nodes and Coordinated Multipoint (CoMP) transmission/reception. The latter, in particular, is envisioned to be one of the most important techniques in LTE-Advanced to improve the throughput and functionality of cell borders. CoMP allows users to have multiple data transmission and reception from/toward multiple cooperating eNodeBs (eNBs), increasing the utilization factor of the network. Resource allocation in the uplink is especially beneficial because more sophisticated algorithms can leverage the availability of additional connection points where the signal from the User Equipment (UE) is processed, ultimately providing UEs with increased throughput. Additionally, a significant part of the interference caused by neighboring cells can be seen as a useful received signal thanks to CoMP, provided those cells are part of the Coordinated Reception Point (CRP) set. This is especially important in critical regions, in terms of interference, like cell edges. Finally, in the case of joint multi-cell scheduling, CoMP introduces a reduction in the backhaul load by requiring only scheduling data to be transferred between coordinated eNBs. Arguably, CoMP is most appealing in the uplink direction since it does not require UE modifications: indeed, users need not be aware that there is any kind of cooperation among receiving eNBs. UEs are merely scheduled for transmission on a set of frequencies that happens to be split among different eNBs, although they still retain standard signaling channels through only one of these eNBs, usually referred to as the serving cell. In this work we focus on uplink CoMP from a system point of view. Specifically, we are interested in comparing through simulation the performance of uplink CoMP in various scenarios with different user participation to CoMP transmissions and CoMP margins. Some works have already investigated uplink CoMP both in simulation and through field trials. Our contribution confirms the findings of previous works as far as the throughput gain for edge users is concerned, but introduces three novel observations that can spur future investigations on CoMP systems, in both downlink and uplink regime, and lead to the design of new resource allocation algorithms: • We look at Heterogeneous scenario where there is no restriction in the type of cells that can be in the CRP set, but simultaneously we introduce clustering option included limited number of Macro and small cells to be acted independently from other clusters in CoMP process. • We introduce a parameter called CoMP Pool Percentage (CPP), which quantifies the fraction of PRBs that are reserved for UEs using a specific eNB as CRP (out of the resources nominally available to that eNB). Our algorithm show that the setting of CPP must be carefully gauged depending on the number of CoMP users and the scenario. • We proposed an innovative dynamic algorithm to make decision of the CPP value in order to improve the gain for CoMP users while considering the whole network gain. Combination of the three above mentioned routine and algorithms, according to simulations, confirms an average gain of at least 20% percent for the CoMP users, (average over various population) locating in cell boarder, while the whole network benefits by average of 5% gain for all the users (see results section). The algorithm also guarantees more gain for more values of CoMP margin. In other words, the more the population of CoMP users locating in cell borders the more would be the achievable gain. Objectives of this PhD thesis are concluded as follows: • Design a Network-level simulator whose features are close to a real LTE network, including advanced capabilities and innovations • Observe the response of the network to parameters changes • Increase the throughput gain (using CoMP vs. non using it) and the quality of service • Design and evaluate the Novel Scheduling Algorithm • Compare the obtained results with real case

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

Distributed Optimization of Multi-Cell Uplink Co-operation with Backhaul Constraints

We address the problem of uplink co-operative reception with constraints on both backhaul bandwidth and the receiver aperture, or number of antenna signals that can be processed. The problem is cast as a network utility (weighted sum rate) maximization subject to computational complexity and architectural bandwidth sharing constraints. We show that a relaxed version of the problem is convex, and can be solved via a dual-decomposition. The proposed solution is distributed in that each cell broadcasts a set of {\em demand prices} based on the data sharing requests they receive. Given the demand prices, the algorithm determines an antenna/cell ordering and antenna-selection for each scheduled user in a cell. This algorithm, referred to as {\em LiquidMAAS}, iterates between the preceding two steps. Simulations of realistic network scenarios show that the algorithm exhibits fast convergence even for systems with large number of cells.Comment: IEEE ICC Conference, 201