Baseband Processing Unit Research Articles

Intelligent Reflecting Surface Aided Cloud Access Networks with Federated Learning

Compared with traditional cellular network architecture, cloud access network has some significant advantages in spectrum utilization, energy consumption and network construction cost. However, a high-quality forward link is required between the baseband processing unit pool and remote radio head (RRH) in the cloud access network, which results in limited RRH deployment and affects user access link transmission and coverage. To solve this issue, this paper introduces the intelligent reflector technology into the cloud access network as a solution with low energy consumption, low cost and easy deployment to deal with the existing bottlenecks. Firstly, an efficient channel information acquisition strategy based on federated learning is designed for the smart reflector to enhance the user access link, so as to achieve a compromise between the channel estimation accuracy and cost. On this basis, a robust beamforming design and optimization method of the compression mechanism of the forward link are proposed for the smart reflector to enhance the user access link and the wireless forward link, so as to improve the system transmission performance. Finally, we explore the joint resource allocation method of intelligent reflector assisted cloud access network, and improve the system energy efficiency through the collaborative configuration of intelligent reflector and cloud access network communication resources. The research of this paper will provide an important theoretical basis for the application of intelligent reflectors in cloud access networks, especially for the federated learning.

Cooperative evolution of support vector machine empowered knowledge-based radio resource management for 5G C-RAN

Cloud-radio access network (C-RAN) is an innovative approach in 5th Generation communication networks, where baseband processing units (BBUs) are dissociated from the remote radio heads (RRHs), and a remote cloud-based centralized pool of BBUs is formed. This paper provides an efficient multi-class classification radio resources management (RRM) scheme based on the cooperative evolution of support vector machine (SVM) to assign the spectrum resources of macrocellular users (MUE) i.e. sub-channel of variable bandwidth, to remote-head users (RUEs) and D2D pairs, such that sub-channels can be reused without compromising the quality of service (QoS). First, the C-RAN resource allocation problem is modeled as a NILP problem, with CUs and D2D nodes sharing the same channel. Following that, the proposed strategy is used to allocate resources. A realistic dataset is built for assessing machine learning-based techniques using 5G experimental prototype based on Open Air Interface (OAI). Finally, experimental results demonstrate the effectiveness of the proposed cooperative evolution of SVM-based, multi-class classification RRM over alternative schemes in terms of network throughput, prediction performance, and system utilization.

Multi-Pair Computation for Two-Way Intra Cloud Radio-Access Network Communications

Cloud radio-access networks (C-RAN) have been proposed as an enabling technology for keeping up with the requirements of next-generation wireless networks. Most existing works on C-RAN study the uplink or the downlink separately. However, designing the uplink and the downlink jointly may bring additional advantages, especially if message source-destination information is taken into account. In this paper, this idea is demonstrated by considering pairwise message exchange between users in a C-RAN. A multi-pair two-way transmission scheme is proposed which targets maximizing the end-to-end sum rate. In the proposed scheme, a lattice-based computation strategy is used, where the baseband processing unit (BBU) pool decodes integer linear combinations of paired users’ codewords instead of decoding linear combinations of individual codewords. The BBU pool then precodes the computed signals, compresses, and forwards them to the remote radio heads (RRHs), which in turn decompress the signals and send them to the users. Finally, each user decodes its desired message using its own message as side information. The achievable rate of this scheme is derived, optimized, and evaluated numerically. Simulation results reveal that significant end-to-end rate improvement can be achieved using the proposed scheme compared to existing schemes.

Statistical analysis of radio frequency interference in microwave links using frequency scan measurements from multi-transmitter environments

Radio frequency interference (RFI) comprises undesirable distortions from other frequency bands overlapping the specified band of operation. RFI contributes to increased detected power and temperature of the baseband processing units, resulting in considerable discrepancies in the received measurements. RFI mitigation techniques have been proposed to tackle this problem. However, the cost of implementing most RFI removal algorithms can be highly prohibitive. Removing such unwanted signals would involve removing a portion of the desired intelligence, leading to a potential loss of valuable information. The requirement for experimental frequency scans to detect, and mitigate RFI becomes important. Field measurements were conducted across three microwave links located in urban environments. The spectrum analyzer and a 0.6-meter antenna dish were used for the frequency scans at 6–8 GHz in horizontal and vertical polarizations. The availability of the desired frequencies, which is key to frequency planning is presented. The cumulative distribution functions, histogram and probability density functions of the measured data, which are crucial to determining the impact of interference on the microwave links, are presented. The statistical characteristics of the measured data are highlighted. Overall, the RFI data would provide insights on RFI detection and mitigation, leading to a high quality of service for the benefit of mobile subscribers.

Cooperative evolution of SVM-based resource allocation for 5G cloud-radio access network system with D2D communication

In fifth generation (5G) communication networks, the cloud-radio access network (C-RAN) is a new technique, where baseband processing units are decoupled from remote radio heads and a remote cloud-based centralised pool of baseband processing units is established. However, as the system's capacity grows, managing interference between cellular users (CUs) and device-to-device (D2D) users becomes a critical issue. This paper proposes a multi-class classification resources allocation scheme based on the cooperative evolution of support vector machine (SVM) to assign macrocellular users (MUs) spectrum resources to remote-head users (RUs) and D2D pairs, allowing sub-channels to be reused without compromising quality of service (QoS). First, the key resource allocation sets are determined, and a C-RAN resource allocation model is created. Because CUs and D2D nodes are allowed to access the same sub-channel, the ultimate challenge is described as a many-to-one matching sub-game. The 5G C-RAN system allocates resources via a cooperative evolution of an SVM-based multi-class classification algorithm based on user position estimates, with intercell and intracell interference utilised to build the training dataset. The training dataset is prepared based on intercell and intracell interference. Finally, the results show that the proposed cooperative evolution of SVM-based resource allocation approach outperforms standard resource allocation methods.

Towards Cell-Free Massive MIMO: A Measurement-Based Analysis

Cell-free widely distributed massive multiple-input multiple-output (MIMO) systems utilize radio units spread out over a large geographical area. The radio signal of a user equipment (UE) is coherently detected by a subset of radio units (RUs) in the vicinity of the UE and processed jointly at the nearest baseband processing unit (BPU). This architecture promises two orders of magnitude less transmit power, spatial focusing at the UE position for high reliability, and consistent throughput over the coverage area. All these properties have been investigated so far from a theoretical point of view. To the best of our knowledge, this work presents the first empirical radio wave propagation measurements in the form of time-variant channel transfer functions for a linear, widely distributed antenna array with 32 single antenna RUs spread out over a range of 46.5m. The large aperture allows for valuable insights into the propagation characteristics of cell-free systems. Three different co-located and widely distributed RU configurations and their properties in an urban environment are analyzed in terms of time-variant delay-spread, Doppler spread, path-loss, and the correlation of the local scattering function over space. For the development of 6G cell-free massive MIMO transceiver algorithms, we analyze properties such as channel hardening, channel aging as well as the signal to interference and noise ratio (SINR). Our empirical evidence supports the promising claims for widely distributed cell-free systems.

Architecture design of wireless access system in power grid application scenario based on 5th Generation Mobile Communication Technology small base station

The use of digital radio station and GPRS public network is affected by false data, which leads to the small coverage radius of access network and the throughput is inconsistent with the ideal situation. In order to solve these problems, the architecture design of wireless access system in the application scenario of power grid based on 5G small base station is proposed. FPGA channel encoding and decoding is adopted to design base band processing unit, which is helpful to deploy the platform with other network virtualization functions. Using switch, we can get useful information through monitoring module, scheduling module and information description module. The abnormal data are detected by using RF processing unit. In view of the situation of the data produced by the switch having state mutation, the reliability factor is introduced to monitor the false data in real time. The operation status of open architecture wireless access network is analyzed, and the application scenario of power grid is determined. Thus, the non-interference wireless access network is deployed. The experimental results show that the coverage radius of the architecture is 15cm, the static scene throughput range is 26-33KB, and the dynamic scene throughput range is 45-59KB, which has good access effect.

Novel Wake-up Scheme for Energy-Efficient Low-Latency Mobile Devices in 5G Networks

Improved mobile device battery lifetime and latency minimization are critical requirements for enhancing the mobile broadband services and user experience. Long-term evolution (LTE) networks have adopted discontinuous reception (DRX) as the baseline solution for prolonged battery lifetime. However, in every DRX cycle, the mobile device baseband processing unit monitors and decodes the control signaling, and thus, all instances without any actual data allocation leads to unnecessary energy consumption. This fact together with the long start-up and power-down times can prevent adopting frequent wake-up instants, which, in turn, leads to considerable latency. In this work, a novel wake-up scheme is described and studied, to tackle the trade-off between latency and battery lifetime in future 5G networks, seeking thus to facilitate an always-available experience, rather than always-on. Analytical and simulation-based results show that the proposed scheme is a promising approach to control the user plane latency and energy consumption, when the device is operating in the power saving mode. The aim of this article is to describe the overall wake-up system operating principle and the associated signaling methods, receiver processing solutions and essential implementation aspects. Additionally, the advantages compared to DRX-based systems are shown and demonstrated, through the analysis of the system energy-efficiency and latency characteristics, with special emphasis on future 5G-grade mobile devices.

Cost-effective joint optimisation of BBU placement and fronthaul deployment in brown-field scenarios

In this work, one proposes a model to evaluate the optimal deployment of Centralised Radio Access Network (C-RAN) architecture elements, i.e. Base Band processing Units (BBUs) and fronthaul links, in a brown-field scenario, in which traditional base stations are already deployed and a physical network is present. The proposed optimisation framework jointly optimises BBU placement and accesses network infrastructures deployment. It clusterises the Remote Radio Heads in the scenario through a Multicommodity Flow approach and solves the minimum cost fronthaul network deployment through a Rooted Delay-Constrained Minimum Spanning Tree approach. Optical fibre and microwave links are considered as fronthaul infrastructures. The proposed optimisation framework is validated through a comparison with a theoretical output for a canonical scenario, being afterwards applied to a real scenario. A cost analysis for different scenario configurations is presented, and trade-offs and guidelines for a cost optimal deployment of C-RAN are provided. The analysis of results for the real scenario of the city of Lisbon and its surrounding areas shows that the delay budget in the fronthaul network highly impacts on capital expenditures as well as on operational ones. It is shown that a larger delay budget enables an annual cost reduction up to 72% in urban areas and 54% in rural ones.

Waveform Design for Collocated MIMO Radar With High-Mix-Low-Resolution ADCs

Adopting low-resolution analog-to-digital converters (ADCs) for receive antennas of a multiple-input multiple-output (MIMO) system can remarkably reduce the hardware cost, circuit power consumption as well as amount of data to be transferred from RF components and the baseband-processing unit. However, an obvious performance loss is also expected. Towards this end, in this work we introduce a new architecture which contains both high- and low-resolution ADCs (named as high-mix-low-resolution ADCs) for collocated MIMO radar, and the associated problem of waveform design is discussed in detail. Specifically, the problem jointly optimizes the waveform, ADC switch vector and receive filter, so as to maximize the signal-to-interference-plus-quantization-error-and-noise ratio (SIQENR). To tackle the resultant nonconvex problem, an alternating optimization method (AOM) is devised. To be more specific, the ADC switch vector and waveform matrix are optimized with the block successive upper-bound minimization (BSUM) framework based on the Dinkelback method. Furthermore, a continuous and concave function is introduced to approximate the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm to achieve a binary solution of the ADC switch vector. It is shown that the optimal solutions to the ADCs switch vector and waveform matrix can be efficiently attained by using the alternating direction method of multipliers (ADMM) approach and Karush-Kuhn-Tucher (KKT) conditions, respectively. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.

Channel-bonding CMOS transceiver for 100 Gbps wireless point-to-point links

5G systems and networks are expected to provide unprecedented data-rate to final users and services, in combination with increased coverage and density. The traffic generated at the edges of the network should be hauled through high capacity data-conveyors. Extremely high data-rate links able to provide optical-fiber like performance in the order of 100 Gbps are required to reduce the cost and increase the flexibility of the network infrastructure deployment. This paper presents a full transceiver architecture based on a channel-bonding radio-frequency front-end operating at millimeter-wave frequencies and digital baseband processing units able to provide such data-rates with a feasible implementation in low-cost CMOS technologies. The baseband section of the receiver includes digital compensation algorithms that allow to cope with some of the radio front-end impairments. The main functionalities of the proposed transceiver architecture are validated in hardware.

Layered Inter-Cluster Cooperation Scheme for Backhaul-Constrained C-RAN Uplink Systems in the Presence of Inter-Cluster Interference.

Despite the potential benefits of reducing system costs and improving spectral efficiency, it is challenging to implement cloud radio access network (C-RAN) systems due to the performance degradation caused by finite-capacity fronthaul links and inter-cluster interference signals. This work studies inter-cluster cooperative reception for the uplink of a two-cluster C-RAN system, where two nearby clusters interfere with each other on the uplink access channel. The radio units (RUs) of two clusters forward quantized and compressed version of the uplink received signals to the serving baseband processing units (BBUs) via finite-capacity fronthaul links. The BBUs of the clusters exchange the received fronthaul signals via finite-capacity backhaul links with the purpose of mitigating inter-cluster interference signals. Optimization of conventional cooperation scheme, in which each RU produces a single quantized signal, requires an exhaustive discrete search of exponentially increasing search size with respect to the number of RUs. To resolve this issue, we propose an improved inter-BBU, or inter-cluster, cooperation strategy based on layered compression, where each RU produces two descriptions, of which only one description is forwarded to the neighboring BBU on the backhaul links. We discuss the optimization of the proposed inter-cluster cooperation scheme, and validate the performance gains of the proposed scheme via numerical results.

Isolation-Aware 5G RAN Slice Mapping Over WDM Metro-Aggregation Networks

To accommodate the heterogeneous requirements of emerging 5G services, 5G Radio Access Network (RAN) will support the rising paradigm of network slicing, where a single physical RAN infrastructure is divided into multiple logical networks characterized by diverse requirements in terms of performance, cost, latency, and security, etc. The optimality of RAN slicing depends on various factors, such as slice requirements, resource availability and adopted network architecture. In 4G, RAN architecture evolved from a distributed architecture towards a 2-layer architecture that separates remote radio heads (RRHs) from baseband processing units (BBUs). However, this architecture has soon shown scalability limitations. To address this problem, the concept of flexible 5G functional split has been proposed, where RAN functions are distributed on a 3-layer architecture featuring a radio unit (RU), a distributed unit (DU), and a central unit (CU). Besides RAN architecture, isolation among different slices, which is necessary for slicing security, has a significant influence on the RAN slicing. In this work, we consider the isolation-aware RAN slice mapping problem considering advanced functional splits and a 3-layer RAN architecture. We propose a dual-objective heuristic algorithm for RAN slice mapping over a physical infrastructure represented by a wavelength division multiplexing (WDM) metro-aggregation networks. The proposed heuristic targets the minimization of 1) the number of active COs (i.e, hosting DUs and/or CUs) and 2) the number of established wavelength channels under constraints of network capacity and latency requirements. As our algorithm is also designed to map the RAN slices with least amount of physical resources for a given level of isolation, we also investigate the impact of slice isolation on resource utilization. Results show how higher isolation results in higher network cost.

Incoming Traffic Control of Fronthaul in 5G Mobile Network for Massive Multimedia Services

The cloud radio access network (C-RAN) is composed of optical networks and is known to a fronthaul network. In the fronthaul network, remote radio heads (RRHs) connect to a baseband processing unit (BBU) and BBUs connect to the BBU pool in the 5G core network. Multimedia traffic in radio is transmitted to the core network through the fronthaul network. Although the fronthaul is an optical network, bandwidth of the fronthaul is insufficient for mobile multimedia services because mobile multimedia services are based on large amounts of data. Therefore, it is necessary to control the bandwidth usage in the fronthaul. In 5G mobile networks, RRHs can use a mobile edge computing (MEC) server as an edge cloud and can perform complicated operations in the MEC using knowledge of fronthaul. The proposed method controls incoming traffic to the fronthaul network using knowledge according to the network condition in the fronthaul. When the bandwidth of the fronthaul becomes full due to a large amount of traffic, incoming traffic to the fronthaul network is controlled. The MEC server acts as a buffer for incoming multimedia traffic. Through the proposed method, transmission efficiency for massive multimedia traffic in the fronthaul can be improved. The performance is validated through computer simulation.

Cellular Fronthaul Offloading Using Device Fogs, Caching, and Network Coding

This paper considers a device-based Fog Radio Access Network (F-RAN), where users’ smart devices (denoted by F-UEs) can be exploited to cache popular files so as to communicate them when requested by other F-UEs among them. This architecture restricts the involvement of the central baseband processing unit (BBU) to serving those F-UEs not immediately served by their peers, thus offloading the BBU's fronthaul spectrum and increasing the overall capacity of the network. This paper aims to further maximize fronthaul offloading in this architecture using network coding (NC). The latter exploits both the cached and previously received files by different F-UEs as side information to serve more requesting F-UEs in each transmission from their peers or the BBU so as to maximize fronthaul offloading. Being half-duplex devices (they can only either send or receive in any given time), the problem of BBU fronthaul offloading in this setting is formulated over two transmission phases on an NC graph. After showing that this problem is NP-hard, the paper proposes two novel heuristic algorithms to solve it in real-time. In addition, a lower-bound on the offloading gain of these algorithms is derived for a special file placement case, their asymptotic optimality is proven, and their complexities are analyzed. Simulation results both show that these proposed algorithms perform closely to the optimal solution and quantify the significant offloading gains achieved by them.

Novel Wake-up Scheme for Energy-Efficient Low-Latency Mobile Devices in 5G Networks

Improved mobile device battery lifetime and latency mini-mization are critical requirements for enhancing the mobile broadband services and user experience. Long-term evolution (LTE) networks have adopted discontinuous reception (DRX) as the baseline solution for prolonged battery lifetime. However, in every DRX cycle, the mobile device baseband processing unit monitors and decodes the control signaling, and thus all instances without any actual data allocation leads to unnecessary energy consumption. This fact together with the long start-up and power-down times can prevent adopting frequent wake-up instants, which in turn leads to considerable latency. In this work,a novel wake-up scheme is described and studied, to tackle the trade-off between latency and battery lifetime in future 5G networks, seeking thus to facilitate an always-available experience, rather than always-on. Analytical and simulation-based results show that the proposed scheme is a promising approach to control the user plane latency and energy consumption, when the device is operating in the power saving mode. The aim of this article is to describe the overall wake-up system operating principle and the associated signaling methods,receiver processing solutions and essential implementation aspects. Additionally, the advantages compared to DRX-based systems are shown and demonstrated, through the analysis of the system energy-efficiency and latency characteristics, with special emphasis on future 5G-grade mobile devices

Rateless Coded Multi-User Uplink Transmission With Distributed Fronthaul Compression in Cloud RAN

In this paper, we design the rateless coded uplink transmission in cloud radio access network (C-RAN) with two users, two remote radio heads (RRHs), and one baseband processing unit (BBU) pool under block fading channel. Each user sends the Raptor coded signals continuously until receiving ACK from the BBU pool. A distributed fronthaul compression scheme with LDPC code is proposed for the RRHs which can reduce the compression loss without increasing the fronthaul traffic, through leveraging the correlation between the received signals at both RRHs. To guarantee the compression performance, the LDPC code profile is optimized based on extrinsic information transfer (EXIT) analysis. Furthermore, the Raptor code profiles applied at the two users are also jointly optimized to improve the average system throughput. Simulation results show that the proposed transmission scheme with the optimized LDPC compression code and Raptor code can achieve good BER and throughput performance.

Nonsmooth Optimization Algorithms for Multicast Beamforming in Content-Centric Fog Radio Access Networks

This paper considers a content-centric fog radio access network (F-RAN). Its multi-antenna remote radio heads (RRHs) are capable of caching and executing signal processing for content delivery to its users. The fronthaul traffic is thus saved since its baseband processing unit (BBU) needs to transfer only the cache-missed content items to the RRHs via limited-capacity fronthaul links. The problem of beamforming design maximizing the energy efficiency in content delivery subject to the quality-of-content-service constraints in terms of content throughput and fronthaul limited-capacity is addressed. Unlike the user's throughput in user-centric networks, the content throughput in content-centric networks is no longer a differentiable function of the beamforming vectors. The problem is inherently high-dimensional due to the involvement of many beamforming vectors even in simple cases of three RRHs serving three users. Path-following algorithms, which invoke a simple convex quadratic optimization problem to generate a better feasible point, are proposed for computation of this nonsmooth and high-dimensional optimization problem. We also employ generalized zero-forcing beamforming, which forces the multi-content interference to zero or nearly to zero to reduce the problem dimensionality for computational efficiency. Numerical results are provided to demonstrate their computational effectiveness. They also reveal that when the fronthaul traffic becomes more flexible, hard-transfer fronthauling is more energy efficient than soft-transfer fronthauling.

Resource allocation scheme for 5G C-RAN: a Swarm Intelligence based approach

The recent fifth generation (5G) system enabled a highly promising evolution of Cloud Radio Access Network (C-RAN). Unlike the conventional Radio Access Network (RAN), the C-RAN decouples the baseband processing unit (BBU) from the remote radio head (RRH) by allowing BBUs from multiple Base Stations (BSs) to operate into a centralized BBU pool on a remote cloud-based infrastructure and a scalable deployment of light-weight RRHs. In this paper, we propose an efficient resource allocation scheme for 5G C-RAN called Bee-Ant-CRAN. The challenge addressed is to design a logical joint mapping between User Equipment (UE) and RRHs as well as between RRHs and BBUs. This is done adaptively to network load conditions, in a way to reduce the overall network costs while maintaining the user QoS and QoE. The network load has been formulated as a mixed integer nonlinear problem with a number of constraints. Then, the formulated optimization problem is decomposed into two stage resource allocation problem: UE-RRH association and RRH-BBU mapping. Therefore, a modified Artificial Bee Colony is developed as a swarm intelligence based approach to build the UE-RRH mapping (resource allocation). Moreover, an ameliorated Ant Colony Optimization algorithm is proposed to solve the RRH-BBU mapping (clustering) problem. Computational results demonstrate that our proposed Bee-Ant-CRAN scheme reduces the resource wastage and significantly improves the spectral efficiency as well as the throughput.

A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G.

The rapidly increasing interest from various verticals for the upcoming 5th generation (5G) networks expect the network to support higher data rates and have an improved quality of service. This demand has been met so far by employing sophisticated transmission techniques including massive Multiple Input Multiple Output (MIMO), millimeter wave (mmWave) bands as well as bringing the computational power closer to the users via advanced baseband processing units at the base stations. Future evolution of the networks has also been assumed to open many new business horizons for the operators and the need of not only a resource efficient but also an energy efficient ecosystem has greatly been felt. The deployment of small cells has been envisioned as a promising answer for handling the massive heterogeneous traffic, but the adverse economic and environmental impacts cannot be neglected. Given that 10% of the world’s energy consumption is due to the Information and Communications Technology (ICT) industry, energy-efficiency has thus become one of the key performance indicators (KPI). Various avenues of optimization, game theory and machine learning have been investigated for enhancing power allocation for downlink and uplink channels, as well as other energy consumption/saving approaches. This paper surveys the recent works that address energy efficiency of the radio access as well as the core of wireless networks, and outlines related challenges and open issues.