You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey 
Chapter Title:  Context-Awareness. 
1. Abstract of Cited Paper (BIB001):  This paper considers the problem of fingerprinting localization in wireless networks based on received-signal-strength (RSS) observations. First, the performance of static localization using power maps (PMs) is improved with a new approach called the base-station-strict (BS-strict) methodology, which emphasizes the effect of BS identities in the classical fingerprinting. Second, dynamic motion models with and without road network information are used to further improve the accuracy via particle filters. The likelihood-calculation mechanism proposed for the particle filters is interpreted as a soft version (called BS-soft) of the BS-strict approach applied in the static case. The results of the proposed approaches are illustrated and compared with an example whose data were collected from a WiMAX network in a challenging urban area in the capitol city of Brussels, Belgium. 
2. Abstract of Cited Paper (BIB002):  Base stations have been identified to be the most power consuming part in current mobile networks. Since their power profile only depends to a small fraction on the actual traffic load, putting some base stations in sleep mode has been identified as a solution to scale the network power consumption with the actual load. This paper presents a new system architecture based on the paradigm of “cell on-demand”, which is currently studied in the Beyond Cellular Green Generation (BCG2) project of the GreenTouch consortium. We first outline the key characteristics of the new system architecture, then, we present a framework for position information processing in order to predict the channel quality between a user and a set of switched-off base stations that can potentially be activated by the system to serve the user. Experimental evaluation on a small scale testbed shows the viability of the proposed framework by achieving estimation errors of 20%. 
3. Abstract of Cited Paper (BIB003):  Mobile edge computing (a.k.a. fog computing) has recently emerged to enable \emph{in-situ} processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in renewable-powered mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning. 
4. Abstract of Cited Paper (BIB004):  In order to cope with the relentless data tsunami in 5G wireless networks, current approaches such as acquiring new spectrum, deploying more BSs, and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost, and flexibility. In this regard, context- aware 5G networks with edge/cloud computing and exploitation of big data analytics can yield significant gains for mobile operators. In this article, proactive content caching in 5G wireless networks is investigated in which a big-data-enabled architecture is proposed. In this practical architecture, a vast amount of data is harnessed for content popularity estimation, and strategic contents are cached at BSs to achieve higher user satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours worth of mobile data traffic is collected from a major telecom operator in Turkey, and big-data-enabled analysis is carried out, leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved in terms of both user satisfaction and backhaul offloading. For example, in the case of 16 BSs with 30 percent of content ratings and 13 GB storage size (78 percent of total library size), proactive caching yields 100 percent user satisfaction and offloads 98 percent of the backhaul.

Literature Review:
A system is said to be context-aware if it uses context to provide relevant information and/or services to its customers, where relevancy depends on the user's task; see BIB001 . Context information can be divided into two groups: (i) network context and (ii) user context. The former describes the status of the network, for example, type and position of devices, activity status, energy consumption, capacity, and current load. Such information is operatorrelated and it can be obtained via the backhauling/EPC network. Then, the user context information is the information on the user profile in terms of mobility and service requirements. This information is used for resource allocation, for example, the position and quality of the available channels to be allocated to the user BIB003 . The authors in BIB003 offer an overview of context management in future wireless networks, and they focus on developing energy efficient resource management policies and using user position information for channel quality prediction. The considered strategies are based on radio signal strength intensity (RSSI) estimation obtained through propagation models and measurements provided by mobile terminals. To produce accurate RSSI estimates, a fingerprinting approach BIB002 using power maps is proposed, whereby shadowing, fading, and non-line-of-sight (NLOS) effects are captured. The power maps are constructed by storing the RSSI measurements for a terminal located at a certain position during an offline phase. To increase the estimation accuracy, the fingerprints were collected under various environmental conditions (time of day, weather conditions, etc.). Although this approach provides accurate databases, it is time consuming and expensive. An improvement to it involves mobile users in the creation and maintenance of the power map; that is, users with positioning capabilities (e.g., GPS) report their position and RSSI observations to a database. The collection of data from a limited number of physical (hardware) and virtual (software) sensors, towards the development of network solutions, is of great importance. However, as the number of devices being introduced into the mobile space increases, it becomes difficult to process all the data that is collected. Therefore, context-awareness computing can play a crucial role in deciding what data needs to be collected and processed, acting as a filtering block. Such allows for the storage of context information linked to any piece of data, so that its interpretation can be done easily and be effective. From the context information, context reasoning is applied to deduce new knowledge and better understanding based on the available context  . The need for reasoning is due to the imperfection and uncertainty characteristics of raw context. In addition, context reasoning techniques can also be classified, similar to ML, into supervised and unsupervised. If supervised, training data is first collected and then labelled according to the expected results. A function that can generate the expected results using the training data is derived. Such learning techniques can be used for activity recognition, for example, public commuters and big events. Unsupervised techniques find hidden structures in unlabelled data. Since training data is not used, there is no error or reward signal involved in the evaluation of a solution. This technique can be used for situations where possible outcomes are unknown, such as the detection of unusual user behavior. Discussion. The network elements of future mobile networks should be appropriately configured with the aid of learning techniques. Operators can employ ML to exploit user, network, and mobile traffic datasets to better understand their subscriber base and to analyze network traffic for network management procedures. They may also apply these approaches to boost services or to identify why users do not adopt them. The applications of ML and DM techniques, coupled with context-awareness, are expected to be a point of strength of MEC platforms for edge network management. A possible model for information retrieval and processing is as follows. Through the RNIS function, the radio network data and other real time context information can be exposed to authorized MEC applications for network management purposes. In addition, the user location services (LOC, which provides UE location features) can obtain user mobility patterns at runtime and then share them with authorized applications for handover management procedures. Having the network visibility, combined with observed mobility patterns, proper resource planning can be achieved resulting in an efficient network. If the network service charge can be effected at the MEC platform, through a charging function (or application) linked with the TOF, traffic load statistics can be obtained and then, ML tools can be utilized to understand the traffic patterns within the network edge, thus availing adaptive resource allocation, for example, adaptive BS power transmission for conserving energy and MEC servers provisioning. We finally stress that while it is clear that ML, DM, and context-awareness will be among the most crucial elements of 5G and beyond-5G mobile networks, the current unavailability of mobile traffic datasets obtained from operators limits the progress towards research aimed at building effective management algorithms based on them. Energy Efficiency. Different machine learning frameworks have been proposed for imitating human intelligence, model complex relationships between inputs and outputs, extract statistical structure, and then identify patterns in observed data  . In the context of mobile networks, mainly in the EPC, network and user behavior prediction is required for designing efficient management strategies for computational resource allocation, content caching, improving quality of service and reliability. Application of network optimization tools is important towards EE improvement. The context and social information can be used to enable mobile edge caching and computing, thus reducing delay and in-network data traffic (content transmission), as well as improving EE in virtualized environment through VM consolidation and switching-off idle servers BIB004 . To realize optimal strategies for EE, ML tools can be used to predict users content request distributions, mobility patterns, and content request frequency. In addition, they can be used to learn about computing center workloads variation, and then allocate computing resources/tasks based on the learned information to minimize computation durations. This avails energy savings opportunities, as user behavior can be learned in advance, and then allocates the required computational resources to serve the predicted/inferred quantity of mobile users. The authors in BIB005 propose a new architecture where content popularity is estimated by applying machine learning tools in the harnessed big data. They also show how this architecture can enable caching at the edge and yield backhaul offloading gains, thus leaving some network nodes inactive. For example, deep spiking neural networks (SNNs) as part of ML optimization tools are shown to possess a potential for improving latency and EE through eventbased computation  . The authors in  introduce a novel technique for differentiating spike events using error backpropagation mechanism. In this work, the membrane potentials of spiking neurons are treated as differentiable signals and discontinuities as noise at spike time. Using this strategy, it is shown that the proposed method surpassed all previously reported results. Despite the promising strategies towards network learning procedures and usage, the access to big data, mainly call detail records, is still a limiting factor towards energy efficient procedures utilizing ML tools.