Next-generation Wireless Access Networks Research Articles

Communication-Computing Built-in-Design in Next-Generation Radio Access Networks: Architecture and Key Technologies

Communication-Computing Built-in-Design in Next-Generation Radio Access Networks: Architecture and Key Technologies

Reconfigurable optical crosshaul architecture for 6G radio access networks

The radio access network (RAN) architecture is undergoing a significant evolution to support the next-generation mobile networks and their emerging applications. To realize scalable and sustainable deployment and operations, RAN needs to consider the requirements of 6G and beyond wireless technologies such as ultra densification of cells, higher data rates, ubiquitous coverage, and new radio spectrum in the millimeter-wave band. This calls for a careful redesign of every aspect of RAN, including its crosshaul. The crosshaul is an important network segment in future RAN, capable of transporting diverse traffic types with varying stringent requirements within RAN. The crosshaul towards 6G is envisioned to be highly intelligent, reconfigurable, and adaptable to dynamic service requirements and network conditions. To this end, we propose a software defined network (SDN)-enabled reconfigurable optical crosshaul architecture (ROCA) that supports heterogeneous crosshaul transport technologies and dynamic functional splittings. ROCA enables efficient and intelligent control of the crosshaul data plane. The proposed architecture with a set of the next-generation RAN (NG-RAN) transport interfaces is evaluated using network models built on the ns-3 network simulator. Simulation results demonstrate the strengths and weaknesses of different crosshaul interfaces in agreement with the understanding of respective NG-RAN interfaces from the literature, which validates the modeling accuracy. We then demonstrate the reconfigurability of the architecture using a dynamic scenario with different reconfiguration strategies for meeting the user and network demands. The results indicate that ROCA serves as a scalable and flexible foundation for supporting high-capacity delay-stringent RAN that can be used in 6G and beyond wireless technologies.

Simple and low-latency multipoint-to-point aggregation architecture using commercial optical transceivers for uplink fronthaul.

The increasing demand of real-time applications poses a huge challenge to building next-generation radio access network (NG-RAN) with higher stability and lower system complexity. Parallel signal detection (PSD), which aggregates signals of different intermediate frequencies (IFs) on different wavelengths with a single photodiode (PD), becomes a promising candidate for uplink mobile fronthaul with the advantage of low-latency. However, high requirements on the transmitters inhibit the large-scale deployment of radio units (RU). In this paper, we propose an economical, low-latency, multipoint-to-point (MP2P) uplink fronthaul architecture capable of aggregating four end-users with commercial 25G-class optical modules and a single PD. With delta-sigma modulation (DSM), commercial off-the-shelf optical modules can replace analog transmitters in traditional systems. As a demonstration, we aggregated 4 × 380.16-MHz 5 G new radio (NR) orthogonal frequency division multiplexing (OFDM) signals in an IF band with a fixed interval of 400 MHz over 20 km fiber with 4 users.

OPlaceRAN—A Placement Orchestrator for Virtualized Next-Generation of Radio Access Network

The fifth-generation mobile evolution enables Next-Generation Radio Access Networks (NG-RAN) transformations. The RAN protocol stack is split into eight disaggregated options combined in three network units, i.e., Central, Distributed, and Radio. Further advances allow the RAN functions to be virtualized on top of general-purpose hardware using the virtualized RAN (vRAN). The combination of NG-RAN and vRAN results in vNG-RAN, enabling the management of the disaggregated units and protocols as a set of radio functions. However, the orchestration-based placement of these radio functions is challenging since the best decision can be determined by multiple constraints involving RAN disaggregation, crosshaul network requirements, availability of computational resources, etc. This article proposes OPlaceRAN, a vNG-RAN deployment orchestrator framed within the NFV reference architecture and aligned with the Open RAN initiative. OPlaceRAN supports the dynamic placement of radio functions focusing on vNG-RAN planning and is designed to be agnostic to the placement optimization solution. We developed a prototype based on cloud-native tools to deploy RAN using containerized virtualization and the OpenAirInterface emulator. The evaluation is analyzed considering two different approaches as a proof-of-concept. First, we applied two placement solutions in a controlled real computing infrastructure with a crosshaul network. Second, we investigated the orchestrator’s scalability with a real and larger-scale topology. Our results show that OPlaceRAN is an effective cloud-native solution for containerized network function placement and agnostic to the placement solution, handling scale-out well. OPlaceRAN is up-to-date with the most advanced vNG-RAN design and development approaches, contributing to the evolution of fifth-generation networks.

DRL-assisted joint allocation of antenna, radio, and front-haul resources in TWDM-PON-based NG-RANs with mMIMO-enabled beamforming

The high spectrum efficiency of massive multiple input multiple output (mMIMO)-enabled beamforming is attractive for solving the massive number of connections in next-generation radio access networks (NG-RANs). However, the beamforming based on multiple antenna sub-arrays to support multi-connections would introduce extra 2D antenna sub-array selection and radio resource block (RB) allocation; meanwhile, considering the front-haul bandwidth consumption would reduce as much as possible the redundant data transmitting over front-haul in the NG-RANs. Therefore, a key issue is how to coordinate 2D antenna sub-array selection and radio RB allocation to minimize the front-haul bandwidth and maximize the radio RB utilization when accommodating a set of beam antenna array (BAA) requests. In this paper, we first establish a 3D BAA mapping model to jointly optimize 2D antenna sub-array selection and radio RB allocation, which would in turn affect the allocation of the front-haul wavelength bandwidth in a time and wavelength division multiplexed passive optical network (TWDM-PON)-based front-haul with the mMIMO antenna system. An integer linear programming mathematical model is formulated, and a novel deep reinforcement learning (DRL)-based algorithm is proposed to optimize the front-haul bandwidth and radio RB utilization. Besides, two heuristic algorithms are developed with different antenna sub-array selection policies as the benchmarks. The extensive simulation results show that our proposed DRL-based algorithm can attain the lowest average cost (AC) for different numbers of BAA requests considered by seeking the optimal trade-off between the front-haul bandwidth consumption and the number of the used antennas. Compared with the inter-sub-array benchmark, the DRL-based algorithm can achieve up to 7.5% AC reduction, which is mainly attributed to the 21.2% reduction of the front-haul bandwidth without increasing the number of used antennas.

On the Benefits of vDU Standardization in Softwarized NG-RAN: Enabling Technologies, Challenges, and Opportunities

Softwarization of Next Generation-Radio Access Network (NG-RAN) comes along with virtualization of Network Functions (NFs) as purely software-based applications, distributed among a Central Unit (CU) and Distributed Units (DU). This article demonstrates the benefits of NG-RAN softwarization that enable Mobile Network Operators (MNOs) to adopt cutting-edge technologies and maximize network efficiency. In this article, we focus on the benefits of standardization of virtualized Distributed Unit (vDU), and describe NG-RAN features intended to boost network performance and maximize resource efficiencies in deployment, operation, and management, resulting in a reduced Total Cost of Ownership (TCO), particularly when it comes to addressing different needs of new services in NG.

Heterogeneous Computational Resource Allocation for NOMA: Toward Green Mobile Edge-Computing Systems

Mobile Edge Computing (MEC) is a viable solution in response to the growing demand for broadband services in the new-generation heterogeneous systems. The dense deployment of small cell networks is a key feature of next-generation radio access networks aimed at providing the necessary capacity increase. Nonetheless, the problem of green networking and service computing will be of great importance in the downlink, because the uncontrolled installation of too many small cells may increase operational costs and emit more carbon dioxide. In addition, given the resource and computational limitation of the user layer, energy efficiency (EE) and fairness assurance are critical issues in MEC-based cellular systems. Considering the user fairness criteria, this paper proposes a dynamic optimization model which maximizes the total UL/DL EE along with satisfying the necessary QoS constraints. Based on the non-convex characteristics of the EE maximization problem, the mathematical model can be divided into two separate subproblems, i.e., computational carrier scheduling and resource allocation. So that, a subgradient method is applied for the computational resource allocation and also successive convex approximation (SCA) and dual decomposition methods are adopted to solve the max-min fairness problem. The simulation results exhibit considerable EE improvement for various traffic models in addition to guaranteeing the fairness requirements. It also proved that the proposed computational partitioning scheme managed to significantly improve the total throughput for mobile computing services.

Threatening the 5G core via PFCP DoS attacks: the case of blocking UAV communications

The modern communications landscape requires reliable, high-speed, high-throughput and secure links and sessions between user equipment instances and the data network. The 5G core implements the newly defined 3GPP network architecture enabling faster connectivity, low latency, higher bit rates and network reliability. The full potential of this set of networks will support a set of critical Internet of things (IoT) and industrial use cases. Nevertheless, several components and interfaces of the next-generation radio access network (NG-RAN) have proven to be vulnerable to attacks that can potentially obstruct the network’s capability to provide reliable end-to-end communication services. Various inherent security flaws and protocol-specific weaknesses have also been identified within the 5G core itself. However, little to no research has gone into testing and exposing said core-related weaknesses, contrary to those concerning the NG-RAN. In this paper, we investigate, describe, develop, implement and finally test a set of attacks on the Packet Forwarding Control Protocol (PFCP) inside the 5G core. We find that, by transmitting unauthorised session control packets, we were able to disrupt established 5G tunnels without disrupting subscribers’ connectivity to the NG-RAN, thus hindering the detection of said attacks. We evaluate the identified PFCP attacks in a drone-based scenario involving 5G tunnelling between two swarms.

Edge-enhanced graph neural network for DU-CU placement and lightpath provision in X-Haul networks

The emerging mobile services have imposed several new challenges on the radio access network (RAN), which stimulates its evolution from cloud-RAN to next-generation RAN (NG-RAN). In NG-RAN, baseband functions are redefined as the radio unit (RU), distributed unit (DU), and central unit (CU), which are, respectively, connected by optical front/mid/backhaul (X-Haul) networks. The DU-CU placement and lightpath provision need an elaborate strategy for allocating resources tailored to user requests. However, most existing methods fail to fully perceive network conditions and then make inappropriate solutions, which may result in over-consumption of processing and transmission resources. Therefore, we propose a reinforcement-learning-based DU-CU placement and lightpath provision strategy using an edge-enhanced graph neural network, i.e, EGNN-RL. The EGNN is leveraged to adequately exploit graph-structured features in X-Haul networks, while proximal-policy-optimization-based RL is introduced to maintain policy stability. In addition, this problem is formulated as an integer linear programming model to find the optimal solution. We validate the proposed strategy under different service types (i.e., enhanced mobile broadband, ultra-reliable low latency communication, and massive machine connections) and different numbers of services, respectively. The results show that our scheme can achieve higher resource efficiency compared with existing methods. Moreover, it also adapts to new networks with the same nodes through fine-tuning the model. This can decrease nearly 15% of the retaining time and obtain similar performance with retaining.

Dynamic Functional Split Adaptation in Next-Generation Radio Access Networks

The architecture of 5G networks relies on partial function centralization to reduce operating costs and allow for improved interference management. Nonetheless, partial centralization results in suboptimal operation if the functional split between centralized and distributed functions is not adapted to the time-varying network conditions. Previous work has tackled the problem of selecting the optimal split for fixed conditions, yet it is unclear whether and when dynamic deployments are superior to static ones. In this work, we provide a comprehensive study on the feasibility and profitability of a 5G network featuring a dynamically-adapted functional split. We formulate the selection of this split as a novel optimization problem that combines performance and operating cost into revenue. Moreover, we base on a dedicated cost model to propose multiple adaptation strategies and evaluate them under a wide range of network designs and conditions. Finally, we show that a dynamic functional split may lead to substantially cost reductions (or revenue increases) with respect to static configurations.

Improved Next-Generation Radio Access Networks Using a Centralized Opto-Electronic Oscillator

Next-generation 5G and 6G radio access networks (RANs) require millimetre-wave (mm-W) oscillators to generate extremely low phase-noise signals for the frequency up-conversion and down-conversion in radio units (RUs). The opto-electronic oscillator (OEO) is an outstanding candidate for generating a high-purity mm-W signal in a centralized radio access network (C-RAN) where the distributed base-stations are dislocated from the digital units (DUs) and there are only RUs on the remote side. In this paper we propose placing an OEO in the central-office, while distributing its signal from there to multiple RU base-stations through the mobile front-haul network using a radio-over-fibre (RoF) transmission approach. This new approach was used in experiments that proved the smaller degradation of the phase noise compared to a degradation of 6 dB for the well-known frequency-doubling electrical oscillator in the RU. In addition, we present the signal-strength degradation due to fibre dispersion in mm-W signal distribution, as well as the challenges in long-term stability and multimode operation. We propose solutions to overcome these drawbacks and make our new approach useful for a centralized carrier signal distribution in next-generation RANs.

FPGA-accelerated SmartNIC for supporting 5G virtualized Radio Access Network

Disaggregated, virtualized, and open next-generation eNodeB (gNB) could bring several benefits to the Next Generation Radio Access Network (NG-RAN) by enabling more market competition and customer choice, lower equipment costs, and improved network performance. This can be achieved through gNB-central unit (CU)-control plane (CP), gNB-CU-user plane (UP) and gNB-distributed unit (DU) separation, CU and DU function virtualization, and zero touch RAN management and control. However, to achieve the performance required by specific foreseen 5G usage scenarios (e.g., Ultra Reliable Low Latency Communications — URLLC), offloading selected disaggregated gNB functions into an accelerated hardware becomes a necessity.To this aim, this study proposes the implementation of 5G DU Low-PHY layer functions into an FPGA-based SmartNIC exploiting the Open Computing Language (OpenCL) framework to facilitate the integration of accelerated 5G functions within the mobile protocol stack. The proposed implementation is compared against (i) a CPU-based OpenAirInterface implementation, and (ii) a GPU-based implementation of IFFT exploiting clfft and cufft libraries. Experimental results show that the different optimization techniques implemented in the proposed solution reduce the Low-PHY processing time and the use of FPGA resources. Moreover, the GPU-based implementation of the cufft and the proposed FPGA-based implementation have a lower processing time and power consumption compared to a CPU-based implementation for up to two cores. Finally, the implementation in a SmartNIC reduces the delay added by the host-to-device communication through the Peripheral Component Interconnect Express (PCIe) interface, considering both functional split options 2 and 7-1.

A Method for the Design of Bicomplex Orthogonal DSP Algorithms for Applications in Intelligent Radio Access Networks

In this paper, a new method for designing orthogonal bicomplex digital signal processing (DSP) algorithms is developed. In contrast to those previously reported on, the method proposed is universal, since it is not affected by the order or the type of the real digital processing algorithm employed as a prototype. The method is based on a transformation starting with either real or complex orthogonal DSP algorithms represented in the z-domain, and transforming them into orthogonal bicomplex algorithms. The proposed new method is applied in the design of bilinear orthogonal bicomplex DSP systems with a canonical number of elements, the main advantage of which is that the order of the digital system is reduced by a factor of four. As well as being canonical, the orthogonal bicomplex digital systems are also symmetrical structures, as a result of which they offer parallelism and subsequent unification. It is experimentally shown that bicomplex orthogonal DSP algorithms acquire the properties of the initial algorithm prototype, irrespective of whether it is real or complex. Since the new design method is universally applicable, it can be used to develop bicomplex orthogonal digital algorithms of any order and type. Being simple, canonical, and symmetrical—and, thus, leading to lower equipment cost, reduced complexity, and higher energy efficiency—these structures may well be appropriate for the enhancement of the implementation of intelligent algorithms in next-generation radio access networks.

Broadband and High-Capacity Silicon Photonics Single-Sideband Modulator

We propose a novel photonic-integrated single-sideband (SSB) modulator realized in silicon on insulator technology operating either in suppressed-carrier or full-carrier mode with continuously tunable carrier-to-sideband ratio (CSBR). Large rejection levels of the spurious sidebands and residual carrier in excess of 40&#x00A0;and 30&#x00A0;dB, respectively, with a CSBR tunability over a <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>&#x00A0;50&#x00A0;dB range, are demonstrated. By employing a single silicon photonics (SiP) phase modulator and a compact and high-performance filtering element, the proposed architecture exhibits a compact layout and tolerates high driving voltage levels, leading to large OSNR values of the modulated sideband in excess of 50&#x00A0;dB. The device supports broadband RF data signals and its operation is tested with an up to 5&#x00A0;Gbps ASK-modulated carrier at 16.5&#x00A0;GHz, limited by available RF equipment. Furthermore, the technique allows to implement a frequency-multiplication mechanism for the input RF carrier, and up to threefold effective bandwidth enhancement of the SiP modulator is obtained by generating an optical SSB signal with a modulated spectral component spaced by 49.5&#x00A0;GHz from the optical carrier. Such features are promising for implementing cost-effective optical distribution of millimeter-wave wireless data signals supporting the evolution of next-generation radio access networks.

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

The mapping of a virtual network service onto a physical network infrastructure is a challenging task due to the joint allocation of virtual resources across nodes and links, the diverse technical requirements of end-users, the coordination between multiple host domains, and others. This issue is exacerbated further by the extension of virtualization to the next-generation radio access network (NG-RAN) architecture and the provisioning of radio access network (RAN) slicing. To that end, this article focuses on the mapping problem of the virtual network functions (VNFs), as well as their internal and external virtual links (VLs), of a RAN slice subnet onto intelligent points of presence (I-PoPs) and transport networks in the NG-RAN architecture. In this context, in contrast to the majority of the state-of-the-art proposals, which frequently fail to achieve performance objectives and neglect resource allocation constraints, this article introduces automation and intelligence at an architectural level to map VNFs and VLs onto their corresponding physical nodes and links, with the goal of achieving superior efficiency in virtual resource utilization while granting the performance of a RAN slice subnet. Benefiting from a top-down approach, the key contributions of this article are: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</i> to extend the architectural framework of network slicing towards the NG-RAN architecture and provide a comprehensive overview and critical analysis of the components and functionalities of a RAN slice subnet; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ii)</i> to integrate the Experiential Network Intelligence (ENI) framework into a joint architecture of the network functions virtualization–management and orchestration (NFV–MANO), Third Generation Partnership Project-network slicing management system (3GPP-NSMS), and I-PoPs in order to render automation and intelligence to the management and orchestration aspects of a RAN slice subnet in the NG-RAN architecture; and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(iii)</i> to propose a learning-assisted architectural solution for mapping the VNFs, as well as their internal and external VLs, of a RAN slice subnet onto the underlying I-PoPs and transport networks.

PlaceRAN: optimal placement of virtualized network functions in Beyond 5G radio access networks

The fifth-generation mobile evolution introduces Next-Generation Radio Access Networks (NG-RAN), splitting the RAN protocol stack into the eight disaggregated options combined into three network units, i.e., Central, Distributed, and Radio. The disaggregated units reach full interoperability on Open RAN. Further advances allow the RAN software to be virtualized (vNG-RAN) on top of general-purpose hardware, enabling the management of disaggregated units and protocols as radio functions. The placement of these functions is challenging since the best decision must be based on multiple constraints, e.g., the RAN protocol stack split, routing paths in network topologies with restricted bandwidth and latency, asymmetric computational resources, etc. The literature does not deal with general placement problems with high functional split options and protocol stack analysis. This article proposes the first exact model for positioning radio functions for vNG-RAN planning, named PlaceRAN, as a Binary Integer Linear Programming (BILP) problem. The objective is to minimize the computing resources and maximize the aggregation of radio functions. The evaluation considered two realistic network topologies, and the results reveal that PlaceRAN achieves an optimized high-performance aggregation level. It is flexible for RAN deployment overcoming the network restrictions, and up to date with the most advanced vNG-RAN design and development.

Latency and quality-aware task offloading in multi-node next generation RANs

Next-Generation Radio Access Network (NG-RAN) is an emerging paradigm that provides flexible distribution of cloud computing and radio capabilities at the edge of the wireless Radio Access Points (RAPs). Computation at the edge bridges the gap for roaming end users, enabling access to rich services and applications. In this paper, we propose a multi-edge node task offloading system, i.e., QLRan, a novel optimization solution for latency and quality tradeoff task allocation in NG-RANs. Considering constraints on service latency, quality loss, edge capacity, and task assignment, the problem of joint task offloading, latency, and Quality Loss of Result (QLR) is formulated in order to minimize the User Equipment (UEs) task offloading utility, which is measured by a weighted sum of reductions in task completion time and QLR cost. The QLRan optimization problem is proved as a Mixed Integer Nonlinear Program (MINLP) problem, which is a NP-hard problem. To efficiently solve the QLRan optimization problem, we utilize Linear Programming (LP)-based approach that can be later solved by using convex optimization techniques. Additionally, a programmable NG-RAN testbed is presented where the Central Unit (CU), Distributed Unit (DU), and UE are realized by USRP boards and fully container-based virtualization approaches. Specifically, we use OpenAirInterface (OAI) and Docker software platforms to deploy and perform the NG-RAN testbed for different functional split options. Then, we characterize the performance in terms of data input, memory usage, and average processing time with respect to QLR levels. Simulation results show that our algorithm performs significantly improves the network latency over different configurations.

Deep reinforcement learning-based radio function deployment for secure and resource-efficient NG-RAN slicing

Network functions virtualization is a prominent technology for next-generation radio access network (NG-RAN) slicing to achieve customization for various vertical services, such as auto-manufacturing and auto-driving. However, when virtualized radio function blocks with different security levels of services share a common server to achieve network resource-saving, a co-resident threat is exposed due to the lack of physical isolation. Therefore, designing a secure and efficient NG-RAN slicing strategy is necessary but difficult, especially for such distributed networks in which different tenants have distinct network bandwidth and security level requirements. To this end, we first formulate this slicing problem as an ILP model called Secure isolation and resource Efficiency-oriented Multi-Objective NG-RAN Slicing (SEMONS). However, the SEMONS is not practical for online execution due to its high computational complexity. Then, we propose a secure and efficient RAN slicing method for fast online execution based on a deep reinforcement learning (DRL) framework. The Wolpertinger policy algorithm is leveraged to train the agent in the DRL framework, which combines the deep deterministic policy gradient with the K-Nearest-Neighbor algorithm to reduce the size of the exploration space. Then the training complexity is reduced, and the learning results are optimized. Extensive simulation results show that the DRL framework can obtain the near-optimal secure and efficient NG-RAN slicing strategies compared to the SEMONS model, with only 6% target deviation on average, and it also outperforms the greedy baseline algorithm by 14.5%.

Universal Hash Based Built-In Secure Transport in FlexE Over WDM Networks

Flexible Ethernet (FlexE) over WDM transport is one of the key technologies for achieving high-speed next generation radio access network (NG-RAN). The security issue, such as optical fiber wiretap, becomes increasingly prominent. However, optical or electrical layer secure strategies cannot support higher rate and lower overhead transport, respectively. To this end, in this paper, we propose a cross-layer secure transport strategy in end-to-end FlexE over WDM networks, which leverages Universal Hashing based FlexE data block permutation and multiple parallel fiber transmission to achieve anti-eavesdropping. Different levels of attack ability are considered for measuring the impact on network security and resources utilization. Furthermore, the trade-off between efficient resources utilization and guarantee of higher level of transport security is also explored through building an integer linear programming (ILP) model. For the computation complexity of ILP model at large scale input conditions, a Security and resource-Efficiency-oriented Ant Colony optimization algorithm (SEAC) is proposed. Simulation results show that the cross-layer defense strategy is effective to struggle against intruders with different levels of attack ability, and the SEAC algorithm can achieve near-optimal performance and outperform the traditional first-fit algorithm.

Wireless Caching: Making Radio Access Networks More than Bit-Pipelines

Caching has attracted much attention recently because it holds the promise of scaling the service capability of radio access networks (RANs). We envision that caching will ultimately make next-generation RANs more than bit-pipelines and emerge as a multi-disciplinary area via the union with communications, pricing, recommendation, compression, and computation units. By summarizing cutting-edge caching policies, we trace a common root of their gains to the prolonged transmission time, which is then traded for higher spectral or energy efficiency. To realize caching, the physical layer and higher layers have to function together, with the aid of prediction and memory units, which substantially broadens the concept of cross-layer design to a multi-unit collaboration methodology. We revisit caching from a generalized cross-layer perspective, with a focus on its emerging opportunities, challenges, and theoretical performance limits. To motivate the application and evolution of caching, we conceive a hierarchical pricing infrastructure that provides incentives to network operators and users. To make RANs even more proactive, we design caching and recommendation jointly, showing a user what it might be interested in and what has been done for it. Furthermore, the user-specific demand prediction motivates edge compression and proactive MEC as new applications. The beyond-bit-pipeline RAN is a paradigm shift that brings with it many cross-disciplinary research opportunities.