A Tractable Approach to Base Station Sleep Mode Power Consumption and Deactivation Latency

We consider an idealistic scenario where the vacation (no-load) period of a typical base station (BS) is known in advance such that its vacation time can be matched with a sleep depth. The latter is the sum of the deactivation latency, actual sleep period and reactivation latency. Noting that the power consumed during the actual sleep period is a function of the deactivation latency, we derive an accurate closed-form expression for the optimal deactivation latency for deterministic BS vacation time. Further, using this expression, we derive the optimal average power consumption for the case where the vacation time follows a known distribution. Numerical results show that significant power consumption savings can be achieved in the sleep mode by selecting the optimal deactivation latency for each vacation period. Furthermore, our results also show that deactivating the BS hardware is sub-optimal for BS vacation less than a particular threshold value

Energy-Efficient and Load-Proportional eNodeB for 5G User-Centric Networks:A Multilevel Sleep Strategy Mechanism

Today, dense network deployment is being considered as one of the effective strategies to meet the capacity and connectivity demands of the fifth-generation (5G) cellular system. Among several challenges, energy consumption will be a critical consideration in the 5G era. In this direction, base station (BS) on/off operation (sleep mode) is an effective technique for mitigating the excessive energy consumption in ultradense cellular networks. However, the current implementation of this technique is unsuitable for dynamic networks with fluctuating traffic profiles because of coverage constraints, quality-of-service (QoS) requirements, and hardware switching latency. To address this, we propose an energy/load proportional approach for 5G BSs with control/data plane separation. The proposed approach depends on a multistep sleep mode profiling and predicts the BS vacation time in advance. Such a prediction enables selecting the best sleep mode strategy while minimizing the effect of BS activation/reactivation latency, resulting in significant energy savings

sustainable wireless broadband access to the future internet the earth project

In a world of continuous growth of economies and global population eco-sustainability is of outmost relevance. Especially, mobile broadband networks are facing an exponential growing traffic volume and so the sustainability of these networks comes into focus. The recently completed European funded Seventh Framework Programme (FP7) project EARTH has studied the impact of traffic growth on mobile broadband network energy consumption and carbon footprint, pioneering this field. This chapter summarizes the key insights of EARTH on questions like "How does the exploding traffic impact the sustainability?", "How can energy efficiency be rated and predicted?", "What are the key solutions to improve the energy efficiency and how to efficiently integrate such solutions?" The results are representing the foundation of the maturing scientific engineering discipline of Energy Efficient Wireless Access, targeting the standardisation in IETF and 3GPP, strongly influencing academic research trends, and will soon be reflected in products and deployments of the European telecommunications industry

Coverage Analysis and Cooperative Hybrid Precoding for 5G Cellular Networks

5G innovations have been made in both the network deployment and the transceiver architectures in order to increase coverage, energy- and spectrum-efficiency. Future base stations (BSs) are expected to be densely deployed in places such as walls and lamp posts and cover a smaller area compared to current macro BS systems. Using large spectrum at millimeter-wave (mmWave) frequency bands and highly directional beamforming with large antenna arrays, 5G will bring gigabit-per-second data rate and low-latency communications and enable many novel services such as high-speed mmWave wireless interconnections between devices, vehicular communications, etc.. Moreover, mmWave communication systems will be based on novel hybrid beamforming architectures which have reduced hardware power consumption and cost. Thus, for better understanding of 5G performance and limitations, one of the main goals in this thesis is to analyze new models that give tractable performance metrics for dense small BS networks. Another goal in this thesis is to study mmWave hybrid beamforming schemes which enable joint transmissions in multi-cell multi-user systems. In the thesis, we show the advantages of small cells in increasing the coverage probability and reducing the path loss and shadowing, and we show the value of cooperation in terms of power consumption and outage. In [Paper A] we derive analytical expressions for the successful reception probability of the equal gain combining receiver in a network where interfering transmitters are distributed according to a Poisson point process and interfering signals are spatially correlated. The results show that the spatial correlation reduces the successful reception probability and the effect of the spatial correlation increases with the number of antennas.\ua0[Paper B] follows to study the performance of a partial zero forcing receiver. The results are simulated in an environment with blockages and are analyzed under both Rayleigh and Rician channels. The coverage probability is shown to be maximized when using a subset of antennas\u27 degree-of-freedom for useful signal enhancement and using the remaining degrees of freedom for canceling the interference from strongest interferers. Finally, in [Paper C], we propose a hybrid beamforming scheme which minimizes the total power consumption of a multi-cell multi-user network, subject to per-user quality-of-service constraints. The proposed scheme is based on decoupling the analog precoding and digital precoding. The analog precoders are only dependent on the local channel state information at each BS. Then, the digital precoders are obtained by solving a relaxed convex optimization for given analog precoders. Simulation results show that the proposed algorithm leads to almost the same RF transmit power as that of fully digital precoding, while saving considerable hardware power due to the reduced number of RF chains and digital-to-analog converters

Centralized and partial decentralized design for the Fog Radio Access Network

Fog Radio Access Network (F-RAN) has been shown to be a promising network architecture for the 5G network. With F-RAN, certain amount of signal processing functionalities are pushed from the Base Station (BS) on the network edge to the BaseBand Units (BBU) pool located remotely in the cloud. Hence, partially centralized network operation and management can be achieved, which can greatly improve the energy and spectral efficiency of the network, in order to meet the requirements of 5G. In this work, the optimal design for both uplink and downlink of F-RAN are intensively investigated

Intelligent on-demand radio resource provisioning for green ultra-small cell networks

This thesis studies intelligent on-demand radio resource provisioning involving sleep mode operation in ultra Small Cell Networks (SCNs). Sleep modes are low power states of base stations. The purpose of the research is to investigate how appropriate traffic information can be adopted in sleep mode operation schemes for SCNs with different architectures. A novel protocol-friendly sleep mode operation algorithm based on Adaptive Traffic Perception is proposed for distributed SCN architectures. It is proved robust to different SCN layouts with the reduction in the average power consumption of base stations being more than 35% while maintaining the Quality of Service. The Traffic-aware Cell Management scheme adopting Direction of Arrival information is particularly designed to eliminate the necessity of computation for sleeping base stations. This scheme is shown to significantly reduce the side effects associated with the sleep mode operation, including system overheads and the increasing user transmission power. For SCNs using centralised architectures, such as Cloud Radio Access Networks, Hotspot-oriented Green Frameworks are proposed for different information availabilities, which achieve almost 80% reduction in power consumption of Remote Radio Heads at low traffic levels. A clustering technique is utilised for the optimisation of the placement of active Remote Radio Heads, lowering the average user transmission power. The amount of reduction depends on the completeness of the information and can exceed 70% compared with the state-of-the-art. A type II Matern Hard-core Point Process is used for modelling SCNs. The derivation and approximation of its distance distributions are also proposed. The distance distributions are used for the probabilistic theoretical analysis of some metrics of the sleep mode operation

Emerging Communications for Wireless Sensor Networks

Wireless sensor networks are deployed in a rapidly increasing number of arenas, with uses ranging from healthcare monitoring to industrial and environmental safety, as well as new ubiquitous computing devices that are becoming ever more pervasive in our interconnected society. This book presents a range of exciting developments in software communication technologies including some novel applications, such as in high altitude systems, ground heat exchangers and body sensor networks. Authors from leading institutions on four continents present their latest findings in the spirit of exchanging information and stimulating discussion in the WSN community worldwide

Efficient energy management in ultra-dense wireless networks

The increase in demand for more network capacity has led to the evolution of wireless networks from being largely Heterogeneous (Het-Nets) to the now existing Ultra-dense (UDNs). In UDNs, small cells are densely deployed with the goal of shortening the physical distance between the base stations (BSs) and the UEs, so as to support more user equipment (UEs) at peak times while ensuring high data rates. Compared to Het-Nets, Ultra-dense networks (UDNs) have many advantages. These include, more network capacity, higher flexibility to routine configurations, and more suitability to achieve load-balancing, hence, fewer blind spots as well as lower call blocking probability. It should be noted that, in practice, due to the high density of deployed small cells in Ultra-Dense Networks, a number of issues, or rather concerns, come with this evolution from Het-Nets. Among these issues include problems with efficient radio resource management, user cell association, inter- and intra-cell interference management and, last but not least, efficient energy consumption. Some of these issues which impact the overall network efficiency are largely due to the use of obsolete algorithms, especially those whose resource allocation is based solely on received signal power (RSSP). In this paper, the focus is solely on the efficient energy management dilemma and how to optimally reduce the overall network energy consumption. Through an extensive literature review, a detailed report into the growing concern of efficient energy management in UDNs is provided in Chapter 2. The literature review report highlights the classification as well as the evolution of some of the Mobile Wireless Technologies and Mobile Wireless Networks in general. The literature review report provides reasons as to why the energy consumption issue has become a very serious concern in UltraDense networks as well as the various techniques and measures taken to mitigate this. It is shown that, due to the increasing Mobile Wireless Systems’ carbon footprint which carries serious negative environmental impact, and the general need to lower operating costs by the network operators, the management of energy consumption increases in priority. By using the architecture of a Fourth Generation Long Term Evolution (4G-LTE) UltraDense Network, the report further shows that more than 65% of the overall energy consumption is by the access network and base stations in particular. This phenomenon explains why most attention in energy efficiency management in UDNs is largely centred on reducing the energy consumption of the deployed base stations more than any other network components like the data servers or backhauling features used. Furthermore, the report also provides detailed information on the methods/techniques, their classification, implementation, as well as a critical analysis of the said implementations in literature. This study proposes a sub-optimal algorithm and Distributed Cell Resource Allocation with a Base Station On/Off scheme that aims at reducing the overall base station power consumption in UDNs, while ensuring that the overall Quality of Service (QoS) for each User Equipment (UE) as specified in its service class is met. The modeling of the system model used and hence formulation of the Network Energy Efficiency (NEE) optimization problem is done viii using stochastic geometry. The network model comprises both evolved Node B (eNB) type macro and small cells operating on different frequency bands as well as taking into account factors that impact NEE such as UE mobility, UE spatial distribution and small cells spatial distribution. The channel model takes into account signal interference from all base stations, path loss, fading, log normal shadowing, modulation and coding schemes used on each UE’s communication channels when computing throughout. The power consumption model used takes into account both static (site cooling, circuit power) and active (transmission or load based) base station power consumption. The formulation of the NEE optimization problem takes into consideration the user’s Quality-of-service (QoS), inter-cell interference, as well as each user’s spectral efficiency and coverage/success probability. The formulated NEE optimization problem is of type Nondeterministic Polynomial time (NP)-hard, due to the user-cell association. The proposed solution to the formulated optimization problem makes use of constraint relaxation to transform the NP-hard problem into a more solvable, convex and linear optimization one. This, combined with Lagrangian dual decomposition, is used to create a distributed solution. After cellassociation and resource allocation phases, the proposed solution in order to further reduce power consumption performs Cell On/Off. Then, by using the computer simulation tools/environments, the “Distributed Resource Allocation with Cell On/Off” scheme’s performance, in comparison to four other resource allocation schemes, is analysed and evaluated given a number of different network scenarios. Finally, the statistical and mathematical results generated through the simulations indicate that the proposed scheme is the closest in NEE performance to the Exhaustive Search algorithm, and hence superior to the other sub-optimal algorithms it is compared to

Cognitive networking for next generation of cellular communication systems

This thesis presents a comprehensive study of cognitive networking for cellular networks with contributions that enable them to be more dynamic, agile, and efficient. To achieve this, machine learning (ML) algorithms, a subset of artificial intelligence, are employed to bring such cognition to cellular networks. More specifically, three major branches of ML, namely supervised, unsupervised, and reinforcement learning (RL), are utilised for various purposes: unsupervised learning is used for data clustering, while supervised learning is employed for predictions on future behaviours of networks/users. RL, on the other hand, is utilised for optimisation purposes due to its inherent characteristics of adaptability and requiring minimal knowledge of the environment. Energy optimisation, capacity enhancement, and spectrum access are identified as primary design challenges for cellular networks given that they are envisioned to play crucial roles for 5G and beyond due to the increased demand in the number of connected devices as well as data rates. Each design challenge and its corresponding proposed solution are discussed thoroughly in separate chapters. Regarding energy optimisation, a user-side energy consumption is investigated by considering Internet of things (IoT) networks. An RL based intelligent model, which jointly optimises the wireless connection type and data processing entity, is proposed. In particular, a Q-learning algorithm is developed, through which the energy consumption of an IoT device is minimised while keeping the requirement of the applications--in terms of response time and security--satisfied. The proposed methodology manages to result in 0% normalised joint cost--where all the considered metrics are combined--while the benchmarks performed 54.84% on average. Next, the energy consumption of radio access networks (RANs) is targeted, and a traffic-aware cell switching algorithm is designed to reduce the energy consumption of a RAN without compromising on the user quality-of-service (QoS). The proposed technique employs a SARSA algorithm with value function approximation, since the conventional RL methods struggle with solving problems with huge state spaces. The results reveal that up to 52% gain on the total energy consumption is achieved with the proposed technique, and the gain is observed to reduce when the scenario becomes more realistic. On the other hand, capacity enhancement is studied from two different perspectives, namely mobility management and unmanned aerial vehicle (UAV) assistance. Towards that end, a predictive handover (HO) mechanism is designed for mobility management in cellular networks by identifying two major issues of Markov chains based HO predictions. First, revisits--which are defined as a situation whereby a user visits the same cell more than once within the same day--are diagnosed as causing similar transition probabilities, which in turn increases the likelihood of making incorrect predictions. This problem is addressed with a structural change; i.e., rather than storing 2-D transition matrix, it is proposed to store 3-D one that also includes HO orders. The obtained results show that 3-D transition matrix is capable of reducing the HO signalling cost by up to 25.37%, which is observed to drop with increasing randomness level in the data set. Second, making a HO prediction with insufficient criteria is identified as another issue with the conventional Markov chains based predictors. Thus, a prediction confidence level is derived, such that there should be a lower bound to perform HO predictions, which are not always advantageous owing to the HO signalling cost incurred from incorrect predictions. The outcomes of the simulations confirm that the derived confidence level mechanism helps in improving the prediction accuracy by up to 8.23%. Furthermore, still considering capacity enhancement, a UAV assisted cellular networking is considered, and an unsupervised learning-based UAV positioning algorithm is presented. A comprehensive analysis is conducted on the impacts of the overlapping footprints of multiple UAVs, which are controlled by their altitudes. The developed k-means clustering based UAV positioning approach is shown to reduce the number of users in outage by up to 80.47% when compared to the benchmark symmetric deployment. Lastly, a QoS-aware dynamic spectrum access approach is developed in order to tackle challenges related to spectrum access, wherein all the aforementioned types of ML methods are employed. More specifically, by leveraging future traffic load predictions of radio access technologies (RATs) and Q-learning algorithm, a novel proactive spectrum sensing technique is introduced. As such, two different sensing strategies are developed; the first one focuses solely on sensing latency reduction, while the second one jointly optimises sensing latency and user requirements. In particular, the proposed Q-learning algorithm takes the future load predictions of the RATs and the requirements of secondary users--in terms of mobility and bandwidth--as inputs and directs the users to the spectrum of the optimum RAT to perform sensing. The strategy to be employed can be selected based on the needs of the applications, such that if the latency is the only concern, the first strategy should be selected due to the fact that the second strategy is computationally more demanding. However, by employing the second strategy, sensing latency is reduced while satisfying other user requirements. The simulation results demonstrate that, compared to random sensing, the first strategy decays the sensing latency by 85.25%, while the second strategy enhances the full-satisfaction rate, where both mobility and bandwidth requirements of the user are simultaneously satisfied, by 95.7%. Therefore, as it can be observed, three key design challenges of the next generation of cellular networks are identified and addressed via the concept of cognitive networking, providing a utilitarian tool for mobile network operators to plug into their systems. The proposed solutions can be generalised to various network scenarios owing to the sophisticated ML implementations, which renders the solutions both practical and sustainable