Throughput Requirements Research Articles

Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment

Electricity demand response is proved to be a viable approach to address the peak demand, improve the power grid reliability, and facilitate the grid integration of renewable resources. The implementation of demand response management in the manufacturing sector, which is one of the main energy consumers, has received significant attention. The existing studies in this field have traditionally focused on maximizing the benefits of demand response through machine control and scheduling. However, due to the widespread adoption of fast-charging infrastructure for material handling equipment (MHE), the high-power demand of fast charging and the close coordination between the machines and MHE pose much-increased complexity in the energy management, which necessitates a more comprehensive demand response scheduling method for the integrated manufacturing and material handling system. In this study, an analytical manufacturing and material handling system model is proposed to obtain cost-effective production schedules under demand response. The interactions between machines and MHE, production throughput requirement, battery charging characteristic, and time-varying electricity pricing are jointly considered in the integrated model to help manufacturers reap substantial benefits of demand response. The effectiveness of the proposed method is validated through numerical case studies, where the results indicate a 15.1% energy cost reduction compared to the benchmark scheduling scheme.

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

Wireless mobile communications have experienced tremendous growth in the number of users, data rate requirements, and coverage in recent years. As the data rate and system throughput requirements increase, researchers and system designers need to develop efficient methods to meet these requirements with reasonable effort and cost. In this paper, we discuss an efficient approach to deal with diminishing the overhead of Downlink (DL) and feedback using Machine Learning (ML) for 802.11ax. In particular, the antennas that are related to the router were divided into two gatherings. We used good samples of Channel State Information (CSI) that were taken from an open-access dataset and used it to train our linear regression model. The first group of antennas was used as input to our model and the second group was used as the output of the model. In the online mode, we need to estimate only one group of antennas and for the second group, we can predict it using the trained linear regression model by using the estimated CSI group as input to the model. Therefore, the output of the model using as the CSI for the second group. In this way, we can reduce the overhead of the DL in the router as shown in the result table so the router will work more efficiently compared to the existing systems. From the results table in the last section, the average sum rate has increased between 20% and 30%.

Hybrid Multi Beamforming and Multi-User Detection Technique for MU MIMO System

Multi User—Multiple-input multiple-output based wireless communication system has several advantages over conventional MIMO systems such as high data rate and channel capacity which has drawn great attention recently and is prominently preferred for 5G systems. The other side interferences due to the Multi-User mobile environment such as co-channel interference and multiple access interference the overall system performance will be degraded. Highly reliable techniques need to be incorporated to improve the Quality of services. Moreover, the energy efficiency and compactness requirement of 5G systems present new challenges to investigate techniques for reliable communications. Introducing a novel low-complexity radix factorization based Fast Fourier transform multi beam former and maximal likelihood –multi-user detection techniques. As signal detector tailored with optimal sub-detector systems which results in considerable complexity reduction with intolerable error rate performance. The proposed radix factorized Fast Fourier transform—multi-beam forming architectures have the potential to reduce both hardware complexity and energy consumptions as compared to its state-of-the-art methods while meeting the throughput requirements of emerging 5G devices. Here through simulation results, the efficiency of the scaled ML sub-detector system at the downlink side is compared with the conventional ML detectors. Through experimental results, it is well proved that the proposed detector offers significant hardware and energy efficiency with the least possible error rate performance overhead.

CoHA-NTT: A Configurable Hardware Accelerator for NTT-based Polynomial Multiplication

In this paper, we introduce a configurable hardware architecture that can be used to generate unified and parametric NTT-based polynomial multipliers that support a wide range of parameters of lattice-based cryptographic schemes proposed for post-quantum cryptography. Both NTT and inverse NTT operations can be performed using the unified butterfly unit of our architecture, which constitutes the core building block in NTT operations. The multitude of this unit plays an essential role in achieving the performance goals of a specific application area or platform. To this end, the architecture takes the size of butterfly units as input and generates an efficient NTT-based polynomial multiplier hardware to achieve the desired throughput and area requirements. More specifically, the proposed hardware architecture provides run-time configurability for the scheme parameters and compile-time configurability for throughput and area requirements. This work presents the first architecture with both run-time and compile-time configurability for NTT-based polynomial multiplication operations to the best of our knowledge. The implementation results indicate that the advanced configurability has a negligible impact on the time and area of the proposed architecture and that its performance is on par with the state-of-the-art implementations in the literature, if not better. The proposed architecture comprises various sub-blocks such as modular multiplier and butterfly units, each of which can be of interest on its own for accelerating lattice-based cryptography. Thus, we provide the design rationale of each sub-block and compare it with those in the literature, including our earlier works in terms of configurability and performance.

Monolithic topological honeycomb lens for achromatic focusing and imaging

Wavelength dispersion is a universal phenomenon in refractive optics that degrades optical performances. Current mitigation methods are being greatly challenged by the demanding requirements of miniaturization, low aberrations, high throughput, and accurate manufacture. Here, we propose an unconventional monolithic honeycomb-like lens concept with topological degrees of design freedom, capable of near diffraction-limited achromatic performance. To automate the design process, a methodological framework is presented that directly evolves the honeycomb lens from a flat window, instead of a knowledge-based starting geometry. The honeycomb lens performance at the full field of view is remarkably better than either traditional aspheric lenses or commercial cascaded lenses. It also overcomes limitations of flat metalenses, which have extremely small aperture sizes and suffer from multi-order aberrations. This topological honeycomb lens concept may pave the way to inexpensive and compact achromatic optics for focusing and imaging applications in consumer cameras, wearable optics, virtual reality, etc.

RAN Slicing Performance Tradeoffs: Timing Versus Throughput Requirements

The coexistence of diverse services with heterogeneous requirements is a fundamental feature of 5G. This necessitates efficient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Radio Access Network (RAN) slicing</i> , defined as sharing of the wireless resources among diverse services while guaranteeing their throughput, timing, and/or reliability requirements. In this paper, we investigate RAN slicing for an uplink scenario in the form of multiple access schemes for two user types: (1) broadband users with throughput requirements and (2) intermittently active users with timing requirements, expressed as either Latency-Reliability (LR) or Peak Age of Information (PAoI). Broadband users transmit data continuously, hence, are allocated non-overlapping parts of the spectrum. We evaluate the trade-offs between the achievable throughput of a broadband user and the timing requirements of an intermittent user under Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA), considering capture. Our analysis shows that NOMA, in combination with packet-level coding, is a superior strategy in most cases for both LR and PAoI, achieving a similar LR with only a slight 2% decrease in throughput with respect to the case where an independent channel is allocated to each user. The latter solution leads to the upper bound in performance but requires double the amount of resources than the considered OMA and NOMA schemes. However, there are extreme cases where OMA achieves a slightly greater throughput than NOMA at the expense of an increased PAoI.

Use of sockets technology in the network system of a virtual laboratory based on machine learning algorithms

The use of virtual laboratory systems has become a modern trend in many areas of human activity. Modern networked virtual laboratories use virtual reality technology to implement laboratory simulations and interactive environment technology to control simulations. The work examines the advantages, principles of building a virtual laboratory and existing problems in the communication mechanism. In particular, when exchanging data between node machines in a network virtual laboratory system, the network layer cannot ensure the same transmission of packet data. The article reviews modern methods of machine learning: supervised, unsupervised, semi-supervised and reinforcement learning. It was determined that the main characteristics of the virtual laboratory system architecture are openness, modularity and real-time communication. The communication mechanism of the virtual laboratory system does not meet the requirements of high bandwidth and real-time two-way communication. To solve the problem of technical requirements of high throughput and ultra-low delay of the virtual laboratory system, a communication planning technology based on a deep machine learning algorithm is proposed. The work uses the Socket mechanism to implement communication based on the TCP/IP protocol. In accordance with the technical requirements of high throughput and ultra-low latency of the network system of the virtual experiment, a technology of self-tuning and optimal planning of the communication network based on the deep machine learning algorithm is proposed. The proposed technology makes it possible to make the optimal decision regarding resource planning and implement optimization of the indirect selection of system parameters.

UP-GDBF: A 19.3 Gbps Error Floor Free 4KB LDPC Decoder for NAND Flash Applications

An error floor phenomenon, decoding performance, and throughput are three major concerns for LDPC decoders in NAND Flash applications. With a penalty method and an active iteration mechanism, we present a Unified Penalty Gradient Descent Bit Flipping (UP-GDBF) decoding algorithm, which not only possesses error-floor free property but also improves convergence speed in decoding performance. To fulfill the high-throughput requirement while maintaining reliable error correction capability, we propose an energy-based backtracking scheme to reduce 40% latency with a negligible 0.8% area overhead. Implemented in TSMC 16nm process, the proposed 4KB LDPC decoder can achieve a throughput of 19.3 Gbps with 0.120 mm2 area to satisfy ONFI 5.0 throughput requirement. Compared to existing approaches, our decoder architecture provides superior data rate and decoding performance in both 1KB and 4KB LDPC codes.

Scheduling of Heterogeneous Services by Resolving Conflicts

Fifth generation (5G) new radio introduced flexible numerology to accommodate heterogeneous services. However, optimizing the scheduling of heterogeneous services with differing delay and throughput requirements over 5G new radio is a challenging task. In this paper, we investigate near optimal, low complexity scheduling of radio resources for ultra-reliable low-latency communications (URLLC) when coexisting with enhanced mobile broadband (eMBB) services. We demonstrate that maximizing the sum throughput of eMBB services while servicing URLLC users over a fixed length time-frequency grid, is, in the long-term, equivalent to minimizing the number of URLLC placements in the time-frequency grid; this is the consequence of reducing the number of infeasible placements for eMBB, to which we refer to as “conflicts.” To meet this new objective, we evaluate the performance of new, conflict-aware heuristics, consisting of a family of “greedy” and a lightweight heuristic inspired by bin packing optimization, all of near optimal performance. Moreover, having shed light on the impact of conflict in layer-2 scheduling, we investigate non-orthogonal multiple access (NOMA) as a potential approach for conflict resolution leveraging superposition coding. The superior performance of NOMA with respect to OMA, thanks to resolving conflicts, is showcased by extensive numerical results.

AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

Wireless IoT networks have seen an unprecedented rise in number of devices, heterogeneity and emerging use cases which led to diverse throughput, reliability and latency (Quality of Service) requirements. Fulfilling these diverse requirements in a rapidly changing and dynamic wireless environment is a complex and challenging task. On top of including new technologies and wireless standards, one solution is to deploy cross-layer Design (CLD) and multiple Radio Access Technologies (Multi-RAT) to develop scalable QoS-aware IoT networks. However, the complexity of such solutions is high as it involves complex inter-layer interactions and dependencies and inter-application dependencies in multi-RAT networks. Moreover, the wireless environment is very dynamic, so having an optimal constellation of parameters is a challenging task. In this paper, we address the possibilities of using Artificial Intelligence (AI) and Machine Learning (ML) to address these high dimensional and dynamic problems. Based on our findings, we have proposed a distributed network management framework employing AI & ML for studying inter-layer dependencies and developing cross-layer design, traffic classification and traffic prediction at the edge devices for reliable QoS in multi-RAT IoT networks. A thorough discussion on future directions and emerging challenges related to our proposed framework has also been provided for further research in this field.

DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training

On-chip DNN inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy and flexibility requirements. Heterogeneous clusters are promising solutions to meet the challenge, combining the flexibility of DSP-enhanced cores with the performance and energy boost of dedicated accelerators. We present Darkside, a System-on-Chip with a heterogeneous cluster of 8 RISC-V cores enhanced with 2-b to 32-b mixed-precision integer arithmetic. To boost performance and efficiency on key compute-intensive Deep Neural Network (DNN) kernels, the cluster is enriched with three digital accelerators: a specialized engine for low-data-reuse depthwise convolution kernels (up to 30 MAC/cycle); a minimal overhead datamover to marshal 1-b to 32-b data on-the-fly; a 16-b floating point Tensor Product Engine (TPE) for tiled matrix-multiplication acceleration. Darkside is implemented in 65nm CMOS technology. The cluster achieves a peak integer performance of 65 GOPS and a peak efficiency of 835 GOPS/W when working on 2-b integer DNN kernels. When targeting floating-point tensor operations, the TPE provides up to 18.2 GFLOPS of performance or 300 GFLOPS/W of efficiency – enough to enable on-chip floating-point training at competitive speed coupled with ultra-low power quantized inference.

Lightweight Multifactor Authentication Scheme for NextGen Cellular Networks

With increased interest in 6G (6th Generation) cellular networks that can support intelligently small-cell communication will result in effective device-to-device (D2D) communication. High throughput requirement in 5G/6G cellular technology requires each device to act as intelligent transmission relays. Inclusion of such intelligence relays and support of quantum computing at D2D may compromise existing security mechanisms and may lead towards primitive attacks such as impersonation attack, rouge device attack, replay attack, MITM attack, and DoS attack. Thus, an effective yet lightweight security scheme is required that can support existing low computation devices and can address the challenges that 5G/6G poses. This paper proposes a Lightweight ECC (elliptic curve cryptography)-based Multifactor Authentication Protocol (LEMAP) for miniaturized mobile devices. LEMAP is the extension of our previous published work TLwS (trust-based lightweight security scheme) which utilizes ECC with Elgamal for achieving lightweight security protocol, confidentiality, integrity, and non-repudiation. Multi-factor Authentication is based on OTP (Biometrics, random number), timestamp, challenge, and password. This scheme has mitigated the above-mentioned attacks with significantly lower computation cost, communication cost, and authentication overhead. We have proven the correctness of the scheme using widely accepted Burrows-Abadi-Needham (BAN) logic and analyzed the performance of the scheme by using a simulator. The security analysis of the scheme has been conducted using the Discrete Logarithm Problem to verify any quantum attack possibility. The proposed scheme works well for 5G/6G cellular networks for single and multihop scenarios.

Interference and QoS-Aware Resource Allocation Considering DAS Behavior for C-RAN Power Minimization

This work aims to minimize the power consumption of cloud virtualization components by addressing the important challenge of designing allocation schemes that cater for both radio and computational resources in a virtualized cloud-based radio environment. Unlike previous efforts, we consider the realistic behavior of radio resource heads (RRHs) associated with one baseband unit (BBU), acting as a DAS. We first formulate the admission control (AC) and the RRH–BBU mapping problem, subject to constraints on user throughput requirements, computational capacity in the BBU pool, and according to the assumption of DAS behavior. As the optimal solution is practically intractable for large-scale dynamic networks, we propose a two-level resource allocation framework, based on two developed algorithms: a first one for AC at the radio level and a second one for RRH–BBU mapping at the computational level. The two algorithms are designed to allow both the levels to consider each other’s constraints and particularities. Results obtained from an extensive simulation-based performance evaluation show the high performance of our proposal in terms of radio-related metrics, number of accepted users, power saving, and energy and spectrum efficiency, compared with three baseline reference schemes.

High Spectral Efficiency and Low Error Rate MIMO-GFDM for Next-Generation Communication Systems

Multiple-input multiple-output (MIMO) technology with high order signal modulation method has been proposed recently as a scheme to achieve high throughput requirements of next-generation communication applications. However, the use of high-order modulation signals degrades the error rate performance. Generalized frequency division multiplexing (GFDM) has attracted enormous consideration for deploying in new communication scenarios due to its advantages in terms of out-of-band (OOB) emission, spectral efficiency (SE), and latency. Index modulation (IM) methods convey digital information by utilizing transmission entities with adjustable index parameters to provide flexible system design and offer attractive advantages such as energy and SE without increasing the computational complexity. The Multiple-Mode (MM) method permits multiple subcarriers to transmit multiple modes and employs the whole permutation of these modes to convey additional information bits. The proposed MIMO MM-GFDM-IM scheme improves the bit error rate and the spectral efficiency compared to the existing schemes. The proposed method can select the modulation order and the number of modes to achieve the design goals. Furthermore, a system with high modulation order can improve the spectral efficiency when the constellation point is fixed. This paper also proposes a practical and effective MM selection strategy and two low-complexity detectors.

Model of microwave link channel with adaptive modulation under the fading conditions

Higher throughput requirements of the radio access network (RAN) of 5th generation (5G) network lead in turn to even more higher capacity require-ments of wireless backhaul segment. Current research proposes a model of microwave link channel with adaptive modulation under the fading condi-tions. This model allows to estimate maximum possible throughput with re-quired bit error probability (BEP) for channel with adaptive modulation un-der fading conditions. The contribution of the current research is the consid-eration of the 5G requirements on higher throughput and fading conditions in microwave link. Whereby this fading is assumed as a process with Rice distribution.

Energy-Efficient Beamforming and Resource Allocation for Multi-Antenna MEC Systems

This paper studies an energy-efficient beamforming and resource allocation for multi-access edge computing (MEC) systems consisting of multi-antenna access points (APs) and single-antenna users. We consider maximizing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">energy efficiency</i> (EE) of a MEC system, defined as the total computed bits per total energy consumption of the MEC system. To enhance the EE performance, we employ multiple antennas at APs to exploit multiplexing- or (receive) beamforming-gain in the uplink and perform download beamforming for transmitting computation results in the downlink. We consider both spatial-division multiple access (SDMA) based MEC system and time-division multiple access (TDMA) based MEC system and compare their EE performance. We formulate EE maximization problems for the SDMA-based and TDMA-based MEC systems, which are nonconvex and thus cannot be solved by standard convex optimization techniques. We first transform the problems by applying semidefinite relaxation (SDR). Then, we solve the relaxed problems for the SDMA-based MEC system and TDMA-based MEC system by using Dinkelbach method and difference-of-concave programming algorithm. We observe that the SDMA-based MEC system outperforms the TDMA-based MEC system in EE performance most of the cases. However, for the special case when i) only users’ energy consumption is counted (i.e., energy consumption of AP and MEC server is not considered) for EE, and ii) there is no minimum throughput requirement, we show that the TDMA-based MEC system outperforms the SDMA-based MEC system. Simulation results demonstrate that the proposed schemes significantly enhance the EE of MEC systems.

Expert-Knowledge-Based Data-Driven Approach for Distributed Localization in Cell-Free Massive MIMO Networks

Massive Multiple-Input Multiple-Output (MaMIMO) communication networks are recently being investigated for hltheir high potential for localisation services. This is enabled by the high-dimensional channel state information (CSI) captured by the many antennas in the system. Previously, it has been shown that these systems can achieve a very high localisation accuracy. However, many challenges still remain, we identified two of them. First, the recent trend towards cell-free MaMIMO with many highly distributed Access Points (AP), leads to the question of how this impacts the localisation methods. Current localisation methods process the signals in a central processing unit (CPU), resulting in a high fronthaul requirement when deploying these algorithms in a distributed network, limiting the deployment and scalability. Second, there exists a trade-off between using model-driven and data-driven localisation methods. In this work, we propose two new localisation methods which employ a distributed processing strategy and compare them against two centralised localisation methods. In addition, the four analysed methods explore the trade-off between being model- and data-driven. Moreover, the proposed ML-MUSIC method blurs the lines between the two by combining Machine Learning and traditional signal processing. Next to comparing the localisation accuracy, we evaluate the performance in a dynamic setting, the scalability and fronthaul requirement of the methods. The proposed Machine Learning-enhanced Multiple Signals Classification method, ML-MUSIC, reaches a median error of 34.2 mm on the test set while only using 500 training samples. Due to ML-MUSICs distributed design, the fronthaul throughput requirement is reduced 1200-fold in comparison to the centralised methods. Furthermore, ML-MUSIC has the lowest computational complexity of all analysed methods, making it an ideal method to localise users in upcoming distributed cell-free MaMIMO networks.

Toward a Live BBU Container Migration in Wireless Networks

Cloud Radio Access Networks (Cloud-RANs) have recently emerged as a promising architecture to meet the increasing demands and expectations of future wireless networks. Such an architecture can enable dynamic and flexible network operations to address significant challenges, such as higher mobile traffic volumes and increasing network operation costs. However, the implementation of compute-intensive signal processing Network Functions (NFs) on the General Purpose Processors (General Purpose Processor) that are typically found in data centers could lead to performance complications, such as in the case of overloaded servers. There is therefore a need for methods that ensure the availability and continuity of critical wireless network functionality in such circumstances. Motivated by the goal of providing highly available and fault-tolerant functionality in Cloud-RAN-based networks, this paper proposes the design, specification, and implementation of live migration of containerized Baseband Units (BBUs) in two wireless network settings, namely Long Range Wide Area Network (LoRaWAN) and Long Term Evolution (LTE) networks. Driven by the requirements and critical challenges of live migration, the approach shows that in the case of LoRaWAN networks, the migration of BBUs is currently possible with relatively low downtimes to support network continuity. The analysis and comparison of the performance of functional splits and cell configurations in both networks were performed in terms of fronthaul throughput requirements. The results obtained from such an analysis can be used by both service providers and network operators in the deployment and optimization of Cloud-RANs services, in order to ensure network reliability and continuity in cloud environments.

A NOMA-UFMC PRECODED SYSTEM FOR 6G

Multi-Carrier Waveform (MCW) modeling and design are envisioned as one of the most important and challenging for the 6th generation (6G) communication networks. In oppose to Orthogonal Frequency Division Multiplexing (OFDM) waveforms, new and innovative design techniques for MCWs have been designed and proposed in recent literature because of their performance superiority. The typical OFDM waveforms have dominated the previous generation of communication systems and proven their potential in many real-time communication environments, but it may not be sufficient to meet the ambitious target of 6G communication systems. Hence, need for new solutions like flexible MCWs and relevant technological advancements in waveform design are needed. This paper proposes designing and evaluating a new MCW design to meet the 6G requirements for spectral efficiency, throughput, and overall system capacity. On the transmitter side, the MCW design proposed in this article employs power domain multiplexing, such as Non-Orthogonal Multiple Access (NOMA), and phase-rotations of the input signal to the Universal Filtered Multi-Carriers (UFMC) modulations, where the Base-Station (BS) assigns different power levels to each user while using the same frequency resources. MATLAB® simulations were performed to assess the proposed MCW performance. Detailed simulation data are employed for comparative performance analysis of the proposed MCW. The results have shown the superior performance of the proposed MCW approach compared to the conventional 5th generation (5G) NOMA-UFMC waveform.

Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning With Deep Unfolding

The year 2019 witnessed the rollout of the 5G standard, which promises to offer significant data rate improvement over 4G. While 5G is still in its infancy, there has been an increased shift in the research community for communication technologies beyond 5G. The recent emergence of machine learning approaches for enhancing wireless communications and empowering them with much-desired intelligence holds immense potential for redefining wireless communication for 6G. The evolving communication systems will be bottlenecked in terms of latency, throughput, and reliability by the underlying signal processing at the physical layer. In this position letter, we motivate the need to redesign iterative signal processing algorithms by leveraging deep unfolding techniques to fulfill the physical layer requirements for 6G networks. To this end, we begin by presenting the service requirements and the key challenges posed by the envisioned 6G communication architecture. We outline the deficiencies of the traditional algorithmic principles and data-hungry deep learning (DL) approaches in the context of 6G networks. Specifically, deep unfolded signal processing is presented by sketching the interplay between domain knowledge and DL. The deep unfolded approaches reviewed in this letter are positioned explicitly in the context of the requirements imposed by the next generation of cellular networks. Finally, this letter motivates open research challenges to truly realize hardware-efficient edge intelligence for future 6G networks. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>—In this letter, we discuss why the infusion of domain knowledge into machine learning frameworks holds the key to future embedded intelligent communication systems. Applying traditional signal processing and deep learning approaches independently entails significant computational and memory constraints. This becomes challenging in the context of future communication networks, such as 6G with significant communication demands where dense deployments of embedded Internet of Things (IoT) devices are envisioned. Hence, we put forth deep unfolded approaches as the potential enabling technology for 6G artificial intelligence (AI) radio to mitigate the computational and memory demands as well as to fulfill the future 6G latency, reliability, and throughput requirements. To this end, we present a general deep unfolding methodology that can be applied to iterative signal processing algorithms. Thereafter, we survey some initial steps taken in this direction and more importantly discuss the potential it has in overcoming challenges in the context of 6G requirements. This letter concludes by providing future research directions in this promising realm.