The rapid evolution of cellular networks has made them a critical infrastructure for modern society, but it has also led to quickly increasing energy consumption. Open Radio Access Network (O-RAN), with its disaggregated architecture, offers significant potential for improving energy efficiency (EE) in next-generation networks. This survey presents a comprehensive review of state-of-the-art O-RAN energy efficiency research, structured around three key dimensions: energy measurement, energy modeling, and energy optimization. We examine both software- and hardware-based measurement techniques, highlighting their strengths, limitations, and applicability on different network functions. The survey then categorizes existing energy consumption models into theoretical and empirical approaches, with a discussion of their methodologies and constraints. Finally, we explore energy optimization strategies defined by O-RAN specifications and beyond, revealing diverse technical directions in academia and industry. The survey also highlights emerging research challenges, including the lack of high-fidelity and generic datasets, the need for standardized evaluation frameworks and methodologies, and the impact of continuous O-RAN evolution on the validity of existing studies. Overall, this work aims to serve as a foundational reference for researchers and engineers to build sustainable and energy-aware O-RAN networks.

The challenge of evaluating and modeling Quality of Experience (QoE) for content streaming video services is discussed in this paper. Based on the QoE evaluation methodology proposed by the EU-funded TRIANGLE project, a novel no-reference QoE model is presented to predict the user Mean Opinion Score (MOS) for videos streamed services such as YouTube. The study has been performed in a testbed that can control radio and network parameters, enabling real-life scenarios for mobile applications and devices in a controllable and repeatable manner. This work takes advantage of Amazon Mturk as a crowdsourcing platform to collect user opinions at scale, efficiently and quickly harvesting opinion scores from a much more diverse population than it would be possible from a local human panel. The performance of the proposed no-reference model is compared with PEVQ-S, a full-reference standardized algorithm used in the industry, showing better performance but with a much simpler approach. The performance has also been evaluated in SQoE-III, a publicly available adaptive video streaming database, to ensure the robustness of the model.

Software-Defined Radios (SDRs) enable more flexible connectivity solutions than traditional systems, but still face several challenges hindering their widespread adoption. General-Purpose Processor (GPP) based SDRs have generally been too slow for low-latency protocols. Meanwhile Field Programmable Gate Array (FPGA)-based SDR setups suffer from high prices and a steep learning curve for developers. This paper investigates the feasibility of implementing Bluetooth Low Energy (BLE) in a GPP based Peripheral Component Interconnect Express (PCIe) connected SDR. In particular, we focus on adhering to the timing requirements of BLE in a practical SDR implementation. For this, we propose a multi-threaded implementation based on a subset of the open-source BLE library BTLE. Using a signal generator and oscilloscope, we show that the SDR is able to achieve a response time down to 105 μs and can accurately respond in the required$150\pm 2\ \upmu\mathrm{s}$Inter Frame Space (IFS) time window. Furthermore, we also validate that channel hopping is supported by the SDR-based platform. To the best of our knowledge, this is the first SDR implementation able to meet the IFS requirements of BLE, hereby leading the way for more complete fully software based BLE protocol stacks.

Internet of Things (IoT) is one of the main features in 5G. Low-power wide-area networking (LPWAN) has attracted enormous research interests to enable large-scale deployment of IoT, with the design objectives of low cost, wide coverage area, as well as low-power consumption. In particular, a long battery lifetime is essential since many of the IoT devices will be deployed in hard-to-access locations. Prediction of the battery lifetime depends on the accurate modeling of energy consumption. This article presents a comprehensive power consumption model for battery lifetime estimation, which is based on user equipment (UE) states and procedures, for two cellular IoT technologies: 1) Narrowband IoT (NB-IoT) and 2) long-term evolution for machines (LTE-M). A measurement testbed has been set up and the proposed model has been tested and validated via extensive measurements under various traffic patterns and network scenarios, achieving the modeling inaccuracy within 5%. The measurement results show that the battery lifetime of an IoT device can reach up to ten years as required by 3GPP, with proper configuration of the traffic profile, the coverage scenario, as well as the network configuration parameters.

The NB-IoT radio technology is a key enabler of the wireless Internet of Things. A competitive parameter is device battery lifetime, in addition to wide-area coverage and low device cost. In this paper, we present the first publicly available empirical power consumption measurements on two NB-IoT devices. The target is to provide a power consumption model for Internet of Things battery lifetime estimation. The measurement results show that the two devices consume 710-840 mW when transmitting at 23 dBm, due to a power amplifier efficiency of about 37 %. Receiving control and data channels require 210- 240 mW, while the Discontinuous Reception and Power Save Mode sleep states consume 21-23 mW and 14-35 uW, respectively. In general, the power consumption levels are higher than the 3GPP estimates, and we thus estimate the battery lifetime is 21-85 % shorter, with these first generation devices.

We present a random access method inspired on Bloom filters that is suited for Machine-Type Communications (MTC). Each accessing device sends a signature during the contention process. A signature is constructed using the Bloom filtering method and contains information on the device identity and the connection establishment cause. We instantiate the proposed method over the current LTE-A access protocol. However, the method is applicable to a more general class of random access protocols that use preambles or other reservation sequences, as expected to be the case in 5G systems. We show that our method utilizes the system resources more efficiently and achieves significantly lower connection establishment latency in case of synchronous arrivals, compared to the variant of the LTE-A access protocol that is optimized for MTC traffic. A dividend of the proposed method is that it allows the base station (BS) to acquire the device identity and the connection establishment cause already in the initial phase of the connection establishment, thereby enabling their differentiated treatment by the BS.

IEEE 802.11ah is a novel WiFi-based protocol, aiming to provide an access solution for the machine-to-machine (M2M) communications. In this paper, we propose an adaptive access mechanism that can be seamlessly incorporated into IEEE 802.11ah protocol operation and that supports all potential M2M reporting regimes, which are periodic, on-demand, and alarm reporting. The proposed access method is based on a periodically reoccurring pool of time slots, whose size is proactively determined on the basis of the reporting activity in the cell. We show that it is possible to both efficiently and reliably resolve all reporting stations in the cell, within the limits of the allowed deadlines. As a side result, we also provide a rationale for modeling the interarrival time in alarm events by using the Beta distribution, a model that is considered in the 3GPP standardization.

While LTE has been widely rolled out for human-type services, it is also a promising solution for cost-efficient connectivity of the smart grid monitoring equipment. This is a type of machine-to-machine (M2M) traffic that consists mainly of sporadic uplink transmissions. In such a setting, the amount of traffic that can be served in a cell is not constrained by the data capacity, but rather by the signaling constraints in the random access channel and control channel. In this paper, we explore these limitations using a detailed simulation of the LTE access reservation protocol (ARP). We find that 1) assigning more random access opportunities may actually worsen performance and 2) the additional signaling that follows the ARP has very large impact on the capacity in terms of the number of supported devices; we observed a reduction in the capacity by almost a factor of 3. This suggests that a lightweight access method, with a reduced number of signaling messages, needs to be considered in standardization for M2M applications. Additionally we propose a tractable analytical model to calculate the outage that can be rapidly implemented and evaluated. The model accounts for the features of the random access, control channel, and uplink and downlink data channels, as well as retransmissions.

A canonical scenario in Machine-Type Communications (MTC) is the one featuring a large number of devices, each of them with sporadic traffic. Hence, the number of served devices in a single LTE cell is not determined by the available aggregate rate, but rather by the limitations of the LTE access reservation protocol. Specifically, the limited number of contention preambles and the limited amount of uplink grants per random access response are crucial to consider when dimensioning LTE networks for MTC. We propose a low-complexity model of LTE's access reservation protocol that encompasses these two limitations and allows us to evaluate the outage probability at click-speed. The model is based chiefly on closed-form expressions, except for the part with the feedback impact of retransmissions, which is determined by solving a fixed point equation. Our model overcomes the incompleteness of the existing models that are focusing solely on the preamble collisions. A comparison with the simulated LTE access reservation procedure that follows the 3GPP specifications, confirms that our model provides an accurate estimation of the system outage event and the number of supported MTC devices.

Machine-to-Machine (M2M) communications are one of the major drivers of the cellular network evolution towards 5G systems. One of the key challenges is on how to provide reliability guarantees to each accessing device in a situation in which there is a massive number of almost-simultaneous arrivals from a large set of M2M devices. The existing solutions take a reactive approach in dealing with massive arrivals, such as non-selective barring when a massive arrival event occurs, which implies that the devices cannot get individual reliability guarantees. In this paper we propose a proactive approach, based on a standard operation of the cellular access. The access procedure is divided into two phases, an estimation phase and a serving phase. In the estimation phase the number of arrivals is estimated and this information is used to tune the amount of resources allocated in the serving phase. Our results show that the proactive approach is instrumental in delivering high access reliability to the M2M devices.