Perpetual Reconfigurable Intelligent Surfaces Through In-Band Energy Harvesting: Architectures, Protocols, and Challenges

Reconfigurable intelligent surfaces (RISs) are considered a key enabler of highly energy-efficient 6G and beyond networks. This property arises from the absence of power amplifiers in the structure, in contrast to active nodes, such as small cells and relays. However, a certain amount of power is still required for RIS operation. To improve their energy efficiency further, we propose the notion of perpetual RISs, which secure the power needed to supply their functionalities through wireless energy harvesting (EH) of impinging transmitted electromagnetic (EM) signals. Toward this, we initially explain the rationale behind such RIS capability and proceed with a presentation of the main RIS controller architecture that can realize this vision under an in-band EH consideration. Furthermore, we present a typical EH architecture, followed by two harvesting protocols. Subsequently, we study the performance of the two protocols under a typical communications scenario. Finally, we elaborate on the main research challenges governing the realization of large-scale networks with perpetual RISs.


I. INTRODUCTION
Although using millimeter-wave (mmWave) bands, in order to prevent the capacity crunch of sub-6 GHz bands, has been envisioned and standardized for 5G networks, widescale network deployment on these bands is expected to be realized in their 6G counterparts.The large bandwidth offered in mmWave bands is essential not only for boosting the communication rates, but also for achieving sub-meter localization that is required in several challenging use cases with a high societal impact, such as autonomous driving in urban areas [1], highly accurate localization of Intenet of Things (IoT) devices in a smart factory [2], and indoor navigation of people with impaired vision [3].
However, mmWave bands are more susceptible to fixed and moving blockages in comparison with their sub-6-GHz counterparts.A straightforward solution to counteract this bottleneck is the large network densification with small cells and relays so that line-of-sight (LoS) connections between them and the end users are achieved with very high probability.However, such a solution may be prohibitive from a cost and energy consumption point of view [4].
To counteract the aforementioned bottleneck, reconfigurable intelligent surfaces (RISs) are widely believed to be a viable alternative due to their capability for conformal designs and notably lower power consumption compared with active nodes that are equipped with power amplifiers (PAs).This is due to the lack of PAs in the RIS case, which is the most power consuming electronic component [5].By adjusting the impedance of their unit cells (UCs), RISs are able to perform a variety of functions, such as reflection, absorption, diffraction, and polarization change of the incident electromagnetic wave.Owing to their ease of deployment, RISs are expected to be ubiquitously deployed in both indoor and outdoor scenarios in the forthcoming 6G and beyond networks, especially for mmWave bands, so as to provide numerous alternative transmitter-RIS and RIS-receiver LoS routes in case of blockages.They can assist not only communications, but also localization simultaneously [6].

Why Do We Need Perpetual RISs?
Current RIS prototypes base their reconfigurability on fieldprogrammable gate array (FPGA) controllers that normally exhibit power consumption levels that require the RIS to be constantly plugged onto the power grid.This need could impair the requirement for a pervasive RIS deployment due to difficulty of massively wiring them to the grid.In particular, deploying cables involves planning and notable maintenance costs that can immensely grow for massive deployments [7].Moreover, requests to local authorities for the permission of installing the required wired infrastructure would be needed in several occasions, which are usually time consuming processes.In addition, there are places that the power grid would not be allowed to reach to for preventing urban visual pollution.
Additionally, supplying the energy needs of RISs with single-use batteries is also not a viable solution either because this would give rise to a large effort for regular replacements of a massive number of single-use batteries, let alone the constant monitoring of their level that would be required.Based on the aforementioned powering issues that a massive RIS deployment would induce, the following question arises: Could RISs perpetually operate by means of wireless energy harvesting from the impinging electromagnetic (EM) signals that are used for communications and localization.
In the remainder of this article, we first present the two main RIS controller architectures and explain why only one of these can potentially result in perpetual operation.Subsequently, we introduce an energy-harvesting (EH) architecture together with two in-band EH protocols.Furthermore, their performance is compared.Finally, we identify a number of research challenges for the realization of perpetual RISs and conclude this article with the main takeaways.

CONTROLLER ARCHITECTURES
Let us now present the two basic RIS controller architectures, namely the conventional FPGA-based architecture and the integrated architecture.In addition, we will elaborate on why the integrated architecture is the only viable approach for perpetual RIS operation.

FPGA-based architecture
As depicted in Fig. 1, in this architecture the FPGA acts as an external controller and adjusts the bias voltages of the tuning elements that are attached to the UCs.This, in turn, alters the impedance of the UCs so that the desired metasurface function is realized.The tuning elements normally comprise varactors or variable resistors, positive intrinsic negative diodes or switches, microelectromechanical systems, mechanical parts, or advanced materials, such as graphene, or liquid crystals [8].The FPGA-based architecture is the conventional architecture with which several proof-of-concept RISs have been designed and manufactured.It offers the advantage of separate design of the metasurfaces and FPGAs.On the other hand, FPGA-based architectures are usually bulky and exhibit a significant power consumption that make perpetual operation challenging [8].

Integrated architecture
In contrast to the FPGA-based architecture, the integrated architecture relies on the integration of a network of communicating chips within the metasurface containing tuning elements, control circuits, and even sensors.As it is pointed out in [8], integrated architectures are custom-made and are therefore Fig. 2: Top-level diagram of the ASIC used in [9] as the controling chip.
much more optimized than FPGA-based architectures.This means that the control sub-system is less intrusive in terms of EM interference, less bulky, and potentially exhibits lower power consumption.Hence, perpetual operation is envisioned as a possibility for the integrated RIS architecture by means of wireless energy harvesting [8].The metasurface controlling chips, that would wirelessly receive reconfiguration commands under perpetual operation, may consist of circuitry that reads the UC state and digital-to-analog (DAC) converters that adjust the bias voltage to the tuning elements.
A possible architecture for such a controlling chip, based on application-specific integrated circuits (ASICs) for simultaneously controlling the response of 4 UCs, is depicted in Fig. 2 [9].According to the particular example1 , the ASIC comprises: i) the control circuit, ii) the DACs, and iii) the radio frequency (RF) tunable loading elements (LEs).The control circuit is responsible for the communication operations of the ASIC by wirelessly receiving reconfiguration commands 2 and sending/receiving communication data to/from its neighboring controllers.In the particular implementation of [9], the control circuit consists of an internal memory with 64 cells that store the reconfiguration data that are required by the LEs for adjusting the impedance of the UCs.In addition, the control circuit integrates another internal memory with 18 cells for storing the data for networking among the ASICs.In turn, the cells that store the RIS reconfiguration data drive the inputs of the 8 DACs.Furthermore, the output of the DACs drives the input of the LEs.The LEs consist of a metal-oxide-semiconductor field-effect transistor (MOSFET) varistor that adjusts the real part of the UC impedance and a MOSFET varactor that adjusts its imaginary part.Finally, we note that an important feature of the ASIC proposed in [9] is its asynchronous operation, which can result in a notably lower circuit consumption compared with a synchronous implementation.

Energy Harvesting Architecture
The EH architecture is depicted in Fig. 3.The absorbed power of subsets of UCs is combined in the RF domain and the combined outputs drive an equal number of rectifying circuits that transform the RF energy to a direct current (DC) one.A DC combining network combines the DC powers and its output charges a battery that is used to power the ASICs.
The presented architecture is a compromise between the two extreme cases of: i) combining in the RF domain the absorbed powers of all the UCs and ii) enabling each UC to drive a single rectifying circuit.The first case may result in substantial insertion losses due to RF combining, if the absorbed powers are not perfectly phase aligned, whereas the absorbed power of each UC in the second case might not be sufficient to turn on the rectifying circuit [10].Hence, the architecture presents a flexible design and the amount of chains is subject to optimization, based on the specific application and electronic packaging considerations.Finally, as far as the rectifying circuit is concerned, which is a passive device, the three main options for its realization are a diode, where a Schottky diode is the most common implementation, a bridge of diodes, and a voltage rectifier-multiplier [10].

Power Consumption Model
Due to the fact that the RF/DC combiners and rectifying circuits in the presented energy harvesting architecture are passive devices, the only source of power consumption in the RIS is the ASIC.As with any electronic device, this power consumption consists of the summation of a static and a dynamic part.The latter part is due to the wireless reception of reconfiguration commands, the switching operations and the resulting charging/discharging of internal capacitances each time the impedance of the UCs needs to be reconfigured, and the internal communication among the ASICs.Hence, by denoting the number of reconfigurations of UC i in a time window T (this can be the frame duration) by N reci and the energy cost for such reconfigurations by E reci , for the dynamic power consumption P dyn of the RIS it holds [11, Eq. (4.7)] On the other hand, the static power consumption is mainly attributed to the power consumption of the DACs, as [9] reveals.

Harvesting Protocols
We now report two protocols for energy harvesting that are based on either a time-splitting or a UC-splitting approach [12].
Time-splitting protocol: A typical frame structure is depicted in Fig. 4. Based on it, the preamble interval, which is used for both synchronization and channel estimation of the TX-RIS and RIS-RX links, is followed by an energy harvesting interval in which all the UCs act as perfect absorbers.Finally, the payload transmission interval follows where all the UCs act as perfect reflectors towards the RX.The postpreamble functionality of the RIS is illustrated in Fig. 5.
Let us now denote the number of UCs in the RIS by M s .Regarding the number of UC impedance adjustments that are needed during each frame, apart from M s adjustments needed for power absorption and another M s adjustments for the payload transmission, based on the channel estimates, a number of UC adjustments is needed for channel estimation during the preamble interval.The reason for this becomes clear by considering that the RIS does not have active components to perform channel estimation in order to keep its design as simple and low-energy consuming as possible.Hence, channel estimation involves only estimation at either the TX or RX.The simplest protocol for channel estimation relies on activating only one UC at a time to act as perfect reflector while keeping the remaining ones off [13].Hence, such a channel estimation protocol requires in total M s UC impedance adjustments.Based on the above, during the transmission of one frame in total 3M s UC reconfigurations are needed for channel estimation, wireless power absorption, and payload transmission.

UC-splitting protocol:
The frame structure is depicted in Fig. 4.After the preamble transmission, simultaneous wireless power transfer and information transmission is realized by dedicating a subset of UCs for harvesting through perfect absorption and the rest for information transmission by acting as perfect reflectors towards the RX.Illustratively, the functionality of the RIS for the post-preamble frame interval is depicted in Fig. 6.
Regarding the total number of UC reconfigurations needed in the UC-splitting protocol during the transmission of one frame, M s reconfigurations are needed for channel estimation and another M s reconfigurations for impedance adjustment related to the simultaneous wireless power transfer and payload transmission interval.Hence, 2M s reconfigurations are needed in total, which are smaller by M s reconfigurations compared with the time-splitting case.
Finally, we note that for the allocation of the time and UC resources in the time-and UC-splitting harvesting protocols, respectively, average metrics can be considered as the easiest implementation so that the allocation does not depend on instantaneous channel estimates, but only on the channel statistics.

PERFORMANCE COMPARISON OF THE TIME AND UC-SPLITTING PROTOCOLS
Let us now indicatively compare the performance of the time-and UC-splitting protocols in a typical communicationsonly scenario in which a mobile user is targeted via an RIS and the average rate maximization is the metric of interest.The simulation parameters are presented in Table I.In addition, the energy-harvesting model and the harvesting circuit parameters of [14] are employed.
As far as the problem of the optimal resource allocation for the time-and UC-splitting protocols, we target the maximization of the average rate provided that the energy consumption requirements of the RIS are covered by the harvested energy.( On the other hand, in the case of the UC-splitting protocol ( Based on the solution of the presented problems, in Fig. 7 we illustrate the average rate vs. the static ASIC power consumption that is achieved by the two protocols.The depicted ASIC static power consumption range is in the order of the one achieved in [9].As we observe, in terms of average rate the UC-splitting protocol notably outperforms its time-splitting counterpart throughout the ASIC static power consumption range for which the solution of the two problems is feasible.This trend is justified by the fact that in the time-splitting case the factor corresponding to the reduction of time resources is a linear multiplicative factor of Shannon's formula.On the other hand, for the UC-splitting protocol case such a term is included inside the logarithm function of Shannon's formula (in the signal-to-noise ratio (SNR) expression) [14].
Finally, it is interesting to examine the ratio of ASIC dynamic power consumption over the static one for the two examined protocols.This is depicted in Fig. 8.As we observe, as the ASIC static power consumption increases it largely dominates over the dynamic part.This is a clear indication that the realization of perpetual RISs dictates the design of ASICs that exhibit a very low static power consumption.

CHALLENGES
In this section, we present the main research challenges regarding the realization of perpetual RISs and their deployment in future networks.

Low-Energy Consumption ASIC Design
A key feature in the feasibility of perpetually operating RISs is the design of ASICs that exhibit a very low static power consumption, as the simulation results revealed.This is arquably the greatest obstacle to overcome.According to the indicative simulation results, we saw that the ASICs of the RIS should not consume more than just few µW of static power consumption for the perpetual operation to be feasible.Instead, in the literature we observe that typical ASICs used in integrated architecture designs exhibit a static power consumption of few hundred of µW, which would render the perpetual RIS operation infeasible [9].More specifically, the most power consuming component of the ASICs considered in [9] is the DACs.In addition, apart from the static power consumption per DAC, the number of DACs and the number of UCs that each ASIC controls can be optimized so that the perpetual operation is realized, based on the estimated amount of impinging power.

Optimized Protocol Design for Energy Harvesting
We have proposed two protocol architectures for RIS energy harvesting, namely the time-and UC-splitting achitectures.As we saw in the previous section, the latter architecture achieves a higher communication rate at the cost of a reduced SNR, as revealed in [12], since a portion of the UCs is dedicated to energy harvesting while the rest simultaneously convey information.On the other hand, the time-splitting architecture achieves the maximum SNR since all the UCs are dedicated to the transmission of information.Besides this, having a relatively high SNR at the receiver would be also important for the localization accuracy.Hence, there should be a novel investigation of the most suitable energy harvesting architecture for facilitating the demands of both communication and localization.Most likely a stand-alone time-or UC-splitting architecture would not be the way forward, but a dynamic switching between the time-and UC-splitting architectures, depending on real-time demands would be needed in realworld scenarios if it can be supported by the hardware.

Channel Modeling for Various High Frequency Bands
Suitable high-frequency bands for all three purposes of RIS energy harvesting, communication, and localization, is another innovative concept to investigate.In particular, it is known that due to electronics energy harvesting becomes less efficient when going up the spectrum.However, very high frequency bands, such as THz bands, offer the advantage of a stronger LoS component due to the more directional transmissions and also finer resolution for localization due to the larger bandwidths.In addition, the multipath components that can also be harvested and importantly contribute to the absorbed energy on the RIS, apart from the direct LoS component, can importantly add to the required energy for supplying the energy needs of the RISs [14].Hence, accurate channel models for the different high frequency bands are required.These aspects create very interesting tradeoffs regarding the potential of different frequency bands for energy harvesting that need to be investigated.

Network Planning
The particular network planning will be based on achieving the requirements on communications and localization with a certain reliability, while at the same time the probability of not covering the RIS energy demands is lower than a certain threshold.For such a network planning reliable traffic models in a region are essential since these would determine the statistical availability of the small cells for supplying the energy needs of the RISs.For instance, apart from the energy supply that an RIS can receive during the information transmission of its associated small cells, other, possibly underutilized, small cells in that time instant could act as power beacons for adding to the total harvested energy by the RISs.

Multi-Band Energy Harvesting
The in-band energy-harvesting case examined in this work can be considered as a lower-bound scenario regarding the system performance considering that as the cost and size of electronics reduces eventually a perpetual RIS can host multiband circuitry for energy harvesting.For instance, even in 6G and beyond networks that will mostly rely on mmWave bands for communication and localization, sub-6 GHz bands will still exist in multi-band small cells as a backup solution and also as a prime solution for control signals towards the mobile users.Hence, an RIS could incorporate both mmWave and sub-6 GHz circuitry to capture the ambient RF energy in the latter case from the small-cell transmissions.Additionally, another added energy-harvesting layer could relate to capturing solar energy in outdoor scenarios.Hence, the potential of multiband energy harvesting should be investigated, taking also into account the cost and size of the resulting structure.

Communication-and Information-Theoretic Fundamental Limits
The possibility of random energy arrivals in the case of multi-band energy harvesting, on top of the deterministic inband harvesting that has been presented in this article, creates unique communication-and information-theoretic problems to be solved.Apart from the fact that in the presence of a ubiquitous RIS deployment the communication channel becomes programmable, with the existence of perpetual RISs the extent of its programmability depends on a random process that is related to the energy arrivals.From an information-theoretic point of view, a very interesting and challenging problem is the computation of the capacity of such a channel under finite-size batteries.In addition, channel coding theorems are of importance for such a novel system.Moreover, from a communications point of view, there is a need for practical adaptive modulation and coding schemes.

Real-Time Network Optimization
Accurate analytical models for optimizing the resources in large-scale networks that incorporate perpetual RISs would be intractable to obtain.This is due to the complexity increase with respect to conventional networks that rely on power-grid supplied RISs.In particular, taking into account the real-time energy demands of the RISs substantially increases the optimal resource allocation complexity.Hence, data-driven approaches can be leveraged for the optimization of the available network resources.However, obtaining the massive amount of realtime data for training in centralized servers with the required latency and network energy consumption seems a daunting task.For alleviating this, distributed artificial intelligence methods can be leveraged, but this alone may not be adequate.Consequently, to effectively tackle this issue offline data for training through the use of less reliable analytical models that rely, for instance, on stochastic-geometry approaches, can be examined [15].This way the amount of real-time training can be notably reduced.

CONCLUSIONS
The idea of perpetual RISs through RF in-band energy harvesting has been introduced in this article.For its realization, it was firstly explained why the integrated architecture is potentially the only viable enabling architecture.Subsequently, we presented a typical EH architecture together with the timeand UC-splitting protocols for in-band EH.An indicative performance comparison between these two protocols followed, under an optimal allocation of resources for maximizing the average rate, which revealed that the UC-splitting protocol largely outperforms its time-splitting counterpart.Moreover, it was revealed that the static power consumption would most likely be the main part of the total ASIC power consumption.Finally, from a hardware, link-level, and network perspective, several challenges, together with enablers to overcome them, have been identified towards the realization of large-scale communication networks with perpetual RISs.
For the time-splitting protocol, such a problem takes the following form:

TABLE I :
Parameter values used in the simulations.