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This section identifies technologies that are the building blocks of sustainable information and communications technology (ICT).

In 2015, the United Nations (UN) defined 17 intertwined sustainable development goals (SDG), while calling on society to meet them by 2030. The ICT and wireless networks industry is one of the most prominent industries to positively contribute towards all established goals, facilitating the creation and access to new services and reduced CO₂ emissions [42]. In this context, sustainable connectivity is fundamental toward increasing efficiency in using resources in future networks, as pointed out by a group of international experts led by the 6G Flagship framework [43]. The UN indicates in [44] that the ICT sector is instrumental in making a significant impact on emissions reduction.

Recently, the European Commission announced an overarching objective towards a green future that is economic growth decoupled from resource use [45, 46]. Furthermore, the European Commission has prioritized digitization [45] and achieving carbon neutrality through the Green Deal [46] as crucial development goals for a sustainable Europe. The European Union is setting ambitious targets for climate change and sustainability for the coming decades, accompanied by many other countries worldwide, as evinced by the 2020 Climate Change Performance Index Results report [47].

In line with this, in 2021, the Finnish Ministry of Transport and Communications announced its climate strategy for the ICT sector [48], which comprises six objectives and measures paving the path to achieve ecologically sustainable digitization. Interestingly, some of those objectives resonate with the ideas in this book, namely: 1. increase of the efficiency of resource use, 2. boost of the devices lifetime, and 3. reduction of energy consumption by the ICT sector.

The ICT sector consumes from 4% to 10% of global electric power, and it is responsible for 3% to 5% of greenhouse gas emissions [48]. Moreover, a recent study indicates that devices and infrastructure are responsible for about 70% of the total energy consumption in the ICT sector [49].

In light of the SDG and climate action, many stakeholders in the ICT sector have taken action. For instance, two of the largest manufacturers, namely Nokia [50, 51] and Ericsson [52], have set clear targets towards sustainable ICT. Moreover, along with the UN’ forecast [44] concerning the use of ICT solutions, Ericsson’s research [52] recently predicted a 15% reduction on global carbon emissions by 2030 and decrease to less than 2% on ICT’s global carbon footprint.

All in all, ICT plays a pivotal role in sustainability and climate action. However, the ICT section is undergoing a sustainable transformation as well, evinced by energy consumption reduction, energy efficient designs, inclusion of renewable sources [44, 51, 52]. However, devices and infrastructure are still critical contributors to the energy footprint of the ICT sector. Therefore, it is essential to identify technologies and methods that provide energy-saving solutions lowering their footprint. In this line, we identity key technology enablers toward sustainable ICT. We briefly summarize some of these fundamental technologies, and then in Chapter 2, we delve into technical details and analysis.

Intelligent Reflective Surface (IRS). The IRS emerges as a promising alternative solution to reconfigure the wireless propagation environment via software-controlled reflective meta-materials. The IRS builds on numerous low-cost, passive, reflective meta-material elements. These elements induce controlled phase and amplitude variations on an incident signal. Hence the IRS enables a programmable and controllable wireless environment. This controllability empowers communication engineers to better combat the impairments of the wireless channel in a completely new form [53-55]. Moreover, the IRS does not require a complete transceiver chain. Besides, assembly is flexible and can cover arbitrarily shaped surfaces. These two characteristics impact directly cost and energy consumption.

The transition towards high-frequency bands, namely millimeter and sub-millimeter-wave bands, is underway in cellular systems. In this context, the IRS IRS is an option to provide low-cost link diversity or strengthen line-of-sight (LOS) channel components.

Nonetheless, to achieve scalable performance and overcome competing technologies, the number of elements in an IRS needs to be large. This approach is unbearable in a fully programmable system, since each element requires an individual (phase and amplitude) control, and channel measurement, thus imposing communication overheads. AI-based solutions can help mitigating such challenges [54]. Another option is based on channel state information (CSI)-free solutions (discussed in Chapter 2) to reduce the signaling and instantaneous estimation of controllable features.

Tiny Machine Learning. Tiny machine learning, known as TinyML, is a flourishing field devoted to extremely low power, always-on, battery operated devices, which is stereotypical of many MTDs. TinyML objective is to integrate machine learning mechanisms into microcontroller-equipped objects. The idea comes from the fact that as the network and technology evolve, information processing and AI become more distributed. Therefore, it moves from the cloud to the edge, and now from edge to devices. Notably, it goes toward extremely low power devices, which can potentially make AI ubiquitous for many massive IoT use-cases. Besides, TinyML opens up opportunities to novel services and applications that do not require centralized (cloud) processing, which alleviates energy consumption and reduces the risk related to security and privacy [56].

For instance, [57] introduces a flexible toolkit that generates energy efficient and lightweight artificial neural networks for a class of microcontrollers. The authors discuss the different optimizations in energy efficiency and computational power. Besides, they test the applicability of their results in a self-sustainable wearable.

Moreover, [56] provides a comprehensive review of the TinyML ecosystem and frameworks for integrating algorithms into microcontrollers, and proposes a multi-RAT architecture suitable for MTDs comparing the performance of different classification algorithms. The results show that these algorithms achieve good accuracy and speed. However, the authors warn about careful design needed due to memory restrictions at the microcontroller. All in all, these recent findings evince a promising research area.

Heterogeneous Access and Resource Management. Conventionally, ortho-gonal access and radio resource management ensure that users connect to the network and convey information. Nonetheless, with mMTC, the number of users scales to a point beyond the availability of orthogonal resources, which may incur performance losses, extended delays, large energy consumption. In this context, non-orthogonal solutions emerge at both levels, access and resource management. To combat such losses, non-orthogonal multiple access (NOMA) multiplexes users in either of the two most prominent solutions: power [58] or code [59] domain. This strategy can allocate more users through sophisticated successive interference cancellation (SIC) techniques than the available resources, which outweighs the cost of increased complexity at the receiver. A more recent development called rate-splitting multiple access [60] promises even further improvements in spectral and energy efficiencies than other non-orthogonal counterparts [61, 62]. At the same time, [63] discusses the information-theoretical limits of massive access.

Non-orthogonality plays an essential role in other domains, such as resource management. In [64], the authors introduce and analyze the heterogeneous coexistence of different service modes, referred to as non-orthogonal slicing, from an information-theoretical perspective. Later, this idea is expanded or combined with other (non-)orthogonal access solutions [65-68]. These recent works evince the importance of non-orthogonal massive access, primarily when associated with heterogeneous resource management techniques.

Distributed Antennas and Networks. Distributed antenna system (DAS) comprises geographically separated antennas interconnected to a central processing unit, which handles the signal processing. DAS increases diversity in the network [69]. This theoretical concept permeates the wireless communications community since the early 2000s. However, only recently, it is becoming a reality in practice due to many advancements in signal processing, distributed systems, cloud and edge computing [70-73].

In a cell-free DAS⁵, known as cell-free massive multiple-input multiple-output (mMIMO), the access points (APs) (equipped with massive number of antennas) connect via backhaul to central processing units. These processing nodes may belong to a centralized or distributed architecture, for instance [73] evaluates pros and cons of each approach. Together these interconnected APs serve the devices under the coverage area [70], thus reducing interference and achieving higher data rates.

A pivotal distinction to previous solutions is scalability and the ability to [72] 1. provide (almost) uniformly high signal-to-noise ratio (SNR) and data rates across the network; 2. manage interference joint processing multiple APs compared to other solutions that face inter-cell interference; 3. increase SNR via coherent transmissions since APs with weaker channels contribute to joint processing.

Hardware imperfections at the user side and backhaul availability may hamper these gains and are yet open challenges. Incorporating backhaul capacity and processing ability at the edge becomes critical as well [73]. Advances in this area may lead to further improvement in network access, processing time, and energy efficiency.

Short Length Codes. Legacy communication systems design and optimization rely on channel capacity, which assumes infinite long block lengths. Thanks to the law of the large numbers, a code averages out the stochastic variations imposed by the wireless channel. The notion of capacity is a reasonable benchmark in many practical systems since packets comprise a long block of several bits (in the order of thousands of bits). This fundamental result arose in the seminal work of Claude Shannon [74].

However, in many massive IoT applications, the information payload of the message is relatively short, only a few bytes. In this case, current state-of-the-art codes perform poorly, which results in wastage of resources and poor performance.

Only recently, new information-theoretic results appeared related to the performance of short-block length communication. These results show that the achievable rate depends not only on the channel quality of the communication link but also on the actual block length error probability tolerable at the receiver [75]. Please refer to Appendix A for an overview of finite block length coding.

Cellular systems rely on low-density parity-check and polar codes, however tuned to serve broadband users. mMTC on the other hand demands many changes on, for instance, 1. the optimization of coding schemes for a short block of length - ranging from a few hundred to a thousand bits; 2. robust error detection in order to avoid the need for outer codes; and 3. novel decoding algorithms able to work with limited or even without CSI at the receiver.

In addition, in the context of WET, the message block length impacts the energy and information reliability, thus raising an interesting trade-off between energy-information reliability and resource allocation [76, 77].

CSI Free Solutions. Traditionally, CSI is used to compensate the impairments imposed by the wireless channel while consequently improving the communication performance. However, CSI acquisition becomes costly in massive IoT due to number of devices, heterogeneous traffic patterns, latency requirements [78]. In light of these constraints, novel CSI-free and CSI-limited (when, for instance, only long term statistics are available) solutions are needed taking also into account environmental conditions and side information to spare energy, time and spectrum resources.

The subsequent chapters carefully discuss promising CSI-free solutions for massive IoT.

Energy Harvesting, Zero-energy Devices and Backscatter. Incorporating energy harvesting (EH) capabilities into the MTDs will significantly impact the next generation of mMTC since MTDs will operate batteryless [78, 79]. Zero-energy devices will require a rethinking of the air interface and network architecture. Pundits forecast more than 40 years battery life and even continuous battery-less operation of zero-energy devices [35, 80].

Whenever the energy transfer happens through radio frequency (RF), the devices can either harness the ambient energy from neighboring transmissions, known as ambient energy from neighboring transmissions, known as ambient RF–EH (see Chapter 3) or be served by a dedicated carrier, know as WET. The former is discussed within Chapter 2, while the latter is the scope of this book whose objective is to promote the most recent, and in our view, the most promising solutions towards sustainable ICT in massive IoT networks.