Remote Radio Heads Research Articles

Dual-band hybrid fiber-wireless delivery system for high-speed data communications in CRAN based on polarization multiplexing with switching capabilities

This work presents a Radio-over-Fiber architecture for Cloud Radio Access Networks (CRAN) based on polarization division multiplexing for delivering up-converted high-speed data signals using a single laser-photodetector pair and free of polarization tracking devices. This system enables the allocation of data signals in two different bands defined as low-frequency (5–25 GHz) and high-frequency (55–65 GHz) millimeter waves (mmWave). Polarization multiplexing ensures minimal crosstalk when data signals are photonically up-converted while providing all-optical inter-band switching capabilities. Proof-of-concept experiments have been performed by simultaneously transmitting NRZ and multi-carrier modulated (Discrete Multitone - DMT) data signals over a radio-over-fiber link consisting of 1 km of standard optical fiber and radio transmission in the 7.5 GHz (wired) and 60 GHz (wireless) bands, respectively, with an estimated potential total throughput of at least 7.5 Gbps when considering the transmission of NRZ signals in these mmWave carriers. Results exhibit a BER below the FEC limit of 3.8 × 10−3 in all cases analyzed when the received optical power at the remote radio head (RRH) is above −4 dBm. The reduced number of components and versatility of this implementation provide a cost-effective solution for delivering and switching high-speed data signals in RF fronthaul for future 5G/6G communications.

Formal dependability analysis of fault tolerant Virtual Machine allocation strategies in Cloud Radio Access Network

Cloud Radio Access Network (C-RAN) has been proposed as a fifth generation (5G) cellular network that is designed to physically separate the Baseband Units (BBUs) from the Remote Radio Heads (RRHs). The BBUs are placed in the BBU pool/hotel, while the RRHs are located in the cells. This separation enables sharing baseband processing resources among RRHs by generating Virtual Machines (VMs) in the BBU pool, leading to radio coordination and energy saving opportunities. Nevertheless, the failure of a BBU can affect several RRHs, causing outages in the radio network. Therefore, designing a reliable and resilient C-RAN is essential to provide high availability and service continuity in case of BBU failure. This paper deals with using formal verification techniques to analyze the performance and the dependability of C-RAN architecture. We propose fault tolerant VM allocation strategies that can handle BBU failures, thus improving the system’s overall reliability. Based on redundancy techniques, these strategies ensure call recovery through migration to functional BBUs in the case of a BBU failure. In order to evaluate the mutual impact of performance measures on the system’s dependability, we develop performability (combination of performance and dependability) Markov Reward Models (MRMs) for the proposed strategies. We use Probabilistic Model Checking (PMC) to check the performability requirements written with Continuous Stochastic Reward Logic (CSRL). The proposed models are implemented and checked using the PRISM model checker, which supports solving MRMs and checking CSRL properties.

Centralized full-duplex seamless photonic mmW fronthaul link based on phase modulation

We experimentally demonstrate a photonic full-duplex seamless millimeter wave fronthaul link, based on phase modulation, to address the challenges arising from the increased demands on the mobile fronthaul capacity and network densification toward 5G and beyond. The proposed system relies on phase modulation techniques, implemented at both the central office (CO) and remote radio head (RRH), to achieve optical frequency up-conversion of the downlink (DL) signal and optical modulation of the down-converted uplink (UL) signal. Furthermore, our approach includes the frequency down-conversion of the 40 GHz UL signal through an optically generated local oscillator (LO) signal in the RRH, while the laser employed for UL data transmission is situated at the CO, simplifying the remote site’s equipment. An optical waveshaper serves here as a programmable optical filter to provide signals for DL frequency up-conversion, LO generation, and also the optical carrier for UL transmission. In our experimental validation, we have tested our proposed system using 64-quadrature amplitude modulation (64-QAM) for the DL at a frequency of 41 GHz and quadrature phase shift keying (QPSK) for the UL at a frequency of 40 GHz. Notably, our results demonstrate the successful transmission of up to 200 MHz bandwidth for both digital modulation schemes, all while maintaining the error vector magnitude (EVM) well below the specified threshold. Additionally, when employing 5G new radio (NR) orthogonal frequency-division multiplexing (OFDM) signals with the same modulation formats for both links in full-duplex communication, we achieved EVM values as low as 5.2% for the DL and 6.9% for the UL, further highlighting the efficacy and robustness of our proposed solution.

A full-duplex 5G mobile fronthaul based on analog RoF-WDM-PON supporting flexible frequency-band switching with colorless and simple RRH

In this paper, a full-duplex analog radio over fiber scheme based on wavelength division multiplexing passive optical network (A-RoF-WDM-PON) is proposed for the 5G mobile fronthaul (MFH). For the downlink, a flexible switching of the frequency-band covering sub-6G and millimeter wave (MMW) is enabled by the combination of a polarization controller and a polarizer. The colorless and simple remote radio head (RRH) is realized by the wavelength reuse and phase modulation of the upstream (US) signal. For the uplink, the high-frequency US signal can be photonic down-converted to an immediate frequency (IF) with the assistance of the 2nd order harmonic of the electrical local oscillator (ELO) and the coherent detection is employed to improve the receiver sensitivity. The proposed scheme is experimentally demonstrated by the transmission of a 100-Msym/s 16-QAM downstream (DS) signal over 25 km single-mode fiber (SMF) and a 100-Msym/s 16-QAM US signal over 10 km SMF. The experimental results show that the error vector amplitudes (EVMs) for downlink at frequency bands of 3.5 GHz, 12.5 GHz and 21.5 GHz are 6.30%, 5.90% and 7.15% respectively. The EVM of US signal at 19 GHz is 7.64%. The EVM performance versus the received optical power for both up- and down-link are also measured. Additionally, a dual-wavelength multiplexing transmission is carried out, the measured EVMs in one wavelength channel are 7.65%, 7.63%, 11.33% and 8.47%, and the EVMs for the other wavelength channel are 8.09%, 7.74%, 11.45% and 8.42%, which meets the standard of the Third Generation Partnership Program (3GPP).

A Task Offloading Method Based on User Satisfaction in C-RAN With Mobile Edge Computing

With the continuous development of the communication service industry, users pay more attention to the quality of network service. Previous studies on offloading problems, especially in the Cloud Radio Access Network (C-RAN) architecture with Mobile Edge Computing (MEC), are primarily focused on the economic perspective, with little consideration given to user-oriented satisfaction problems. To fill this gap, this paper proposes a mathematical model for maximizing user satisfaction in the C-RAN architecture with multi-layer MEC. The problem is divided into two stages for solution. The first stage addresses the optimal connection problem between users and Remote Radio Heads (RRHs). The second stage then schedules user tasks reasonably based on the solution obtained in the first stage. The two-stage problems are all proved to be NP-Hard. Two efficient approximation algorithms, namely User-to-RRH Association Algorithm (URAA) and Maximum Satisfaction Algorithm (MSA), are proposed to solve the problems in different stages. This paper proves and analyzes the theoretical performance of the two algorithms. Finally, the performance of the proposed algorithms is verified by simulation experiments. The experimental results demonstrate that the two proposed algorithms can achieve reasonable solutions to the problems, and the user satisfaction level can be maintained at a high level.

An accelerated Benders decomposition approach for virtual base station formation in stochastic Cloud-RANs

In Cloud-RANs, the baseband unit (BBU) is separated from the remote radio head (RRH) at the cell site and migrated to a server cloud to decrease deployment and maintenance costs. A base station (BS) can be formed as a network slice in which the virtual network functions (VNFs) of its BBU can be placed on any server by network virtualization (NFV) technology and logically connected by software-defined networking (SDN). However, the uncertain demands of users make it very difficult to determine a network slice with appropriate VNF placement and flow routing between VNFs. We aim to minimize the cost of providing BS services while meeting their time-varying demands. We consider the network slice scheme and fronthaul establishment jointly and formulate this as an integer linear programming (ILP) problem. We propose scenario classification Benders decomposition (CBD) based on Benders decomposition (BD) but with fewer Benders cuts, accelerating the solution convergence. Moreover, we propose an efficient heuristic approach to obtain good solutions for the ILP problem based on the solutions of its relaxed problems obtained by BD or CBD. The numerical experiments show that when combining our heuristic approach with CBD, the results are close to the optimal solutions of the ILP problem, while the computation time is significantly shorter than those of previous approaches.

Efficient RRH Activation Management for 5G V2X

Vehicle-to-everything (V2X) communication is one of the key technologies of 5G New Radio to support emerging applications such as autonomous driving. Due to the high density of vehicles, Remote Radio Heads (RRHs) will be deployed as Road Side Units to support V2X. Nevertheless, activation of all RRHs during low-traffic off-peak hours may cause energy wasting. The proper activation of RRH and association between vehicles and RRHs while maintaining the required service quality are the keys to reducing energy consumption. In this work, we first formulate the problem as an Integer Linear Programming optimization problem and prove that the problem is NP-hard. Then, we propose two novel algorithms, referred to as “Least Delete (LD)” and “Largest-First Rounding with Capacity Constraints (LFRCC).” The simulation results show that the proposed algorithms can achieve significantly better performance compared with existing solutions and are competitive with the optimal solution. Specifically, the LD and LFRCC algorithms can reduce the number of activated RRHs by 86 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 89 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in low-density scenarios. In high-density scenarios, the LD algorithm can reduce the number of activated RRHs by 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> . In addition, the solution of LFRCC is larger than that of the optimal solution within 7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on average.

Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs

In this work, we propose novel HARQ prediction schemes for Cloud RANs (C-RANs) that use feedback over a rate-limited feedback channel (2 - 6 bits) from the Remote Radio Heads (RRHs) to predict at the User Equipment (UE) the decoding outcome at the BaseBand Unit (BBU) ahead of actual decoding. In particular, we propose a Dual Autoencoding 2-Stage Gaussian Mixture Model (DA2SGMM) that is trained in an end-to-end fashion over the whole C-RAN setup. Using realistic link-level simulations in the sub-THz band at 100 GHz, we show that the novel DA2SGMM HARQ prediction scheme clearly outperforms all other adapted and state-of-the-art schemes. The DA2SGMM shows a superior performance in terms of blockage detection as well as HARQ prediction in the no-blockage and single-blockage cases. In particular, the DA2SGMM with 4~bit feedback achieves a more than 200 % higher throughput in average compared to its best alternative. Compared to regular HARQ, the DA2SGMM reduces the maximum transmission latency by more than 72.4 %, while maintaining more than 75 % of the throughput in the no-blockage scenario. In the single-blockage scenario, DA2SGMM significantly increases the throughput for most of the evaluated Signal-to-Noise-Ratios (SNRs) compared to regular HARQ.

Evaluating the Performance of 5G NR in Indoor Environments: An Experimental Study

The 5G wireless standard has emerged as a trans-formative technology with the potential to revolutionize various industries by providing enhanced connectivity and communication capabilities. This advanced standard offers a diverse range of applications, including Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communication (mMTC). In this Scientific research paper, we present a comprehensive analysis of the performance and capabilities of a deployed indoor 5G network in a controlled laboratory environment. The experimental setup comprises an Amarisoft Callbox, serving as the 5G core, along with a Remote Radio Head (RRH) and user equipment (UEs). Our primary objective is to assess the network's performance by evaluating the downlink, uplink, and joint downlink/uplink transmissions for TCP and UDP protocols between the gNodeB and user equipment (UE). To achieve this, we carefully examine key performance metrics such as latency, power consumption, CPU utilization, and signal quality. Through various configurations that involve MIMO 2 technology and a 30 kHz sub-carrier spacing, we investigate the impact of different NR Bandwidth settings. By establishing TCP and UDP connections for both uplink and downlink scenarios, we meticulously measure and scrutinize the system's performance, thereby providing valuable insights into its efficiency and suitability for diverse application requirements.

Expected Policy Gradient for Network Aggregative Markov Games in Continuous Space.

In this article, we investigate the Nash-seeking problem of a set of agents, playing an infinite network aggregative Markov game. In particular, we focus on a noncooperative framework where each agent selfishly aims at maximizing its long-term average reward without having explicit information on the model of the environment dynamics and its own reward function. The main contribution of this article is to develop a continuous multiagent reinforcement learning (MARL) algorithm for the Nash-seeking problem in infinite dynamic games with convergence guarantee. To this end, we propose an actor-critic MARL algorithm based on expected policy gradient (EPG) with two general function approximators to estimate the value function and the Nash policy of the agents. We consider continuous state and action spaces and adopt a newly proposed EPG to alleviate the variance of the gradient approximation. Based on such formulation and under some conventional assumptions (e.g., using linear function approximators), we prove that the policies of the agents converge to the unique Nash equilibrium (NE) of the game. Furthermore, an estimation error analysis is conducted to investigate the effects of the error arising from function approximation. As a case study, the framework is applied on a cloud radio access network (C-RAN) by modeling the remote radio heads (RRHs) as the agents and the congestion of baseband units (BBUs) as the dynamics of the environment.

DC-RAN dynamic coverage methods to improve its performance

Abstract&#x0D; In cellular networks, there are usually times when the density of the network changes at different times of the day, where congested areas attract a large demand for communications and data, which generates peak demand at specific times of the day, and vice versa also happens in the same area but at another time of the day, and in order to meet the needs of this changing demand the network must keep pace with changes in the density of the areas served. This can only be done if the network has a dynamic ability to handle the change in traffic density and demand intensity, which requires turning on/off a certain number of Remote Radio Heads (RRHs) that represent the Radio Frequency (RF) front end of the mobile network. For the purpose of providing a reliable and acceptable telecommunications service to users and financially useful to network operators by rationing energy consumption and meeting the required needs, dynamic 5G networks can do this by relying on a Dynamic Cloud Radio Access Network (DC-RAN) that enables them to respond to demand variables by shifting several types of coverage cells into a single serviced area and stopping them when needed.

Latency Guarantee for Task Computation in Wireless-Powered Cloud Radio Access Networks

With the massive and rapid deployment of Internet of Things (IoT) devices, the number of IoT devices in the cloud radio access network (C-RAN) has increased dramatically. It is intractable to provide deterministic quality of service (QoS) guarantee for task processing due to the task burstiness and the time-varying channels. In order to guarantee latency requirements of energy-limited IoT devices in C-RAN, this paper proposes a statistical latency guarantee scheme for task computation. With the assistance of densely distributed remote radio heads (RRHs) providing wireless power transfer to the IoT devices, a wireless powered edge computing C-RAN model is constructed. Then, the problem of minimizing task latency violation probability is formulated. With the help of effective capacity theory, the problem is decoupled into multiple sub-problems, which are QoS parameter optimization, task offloading optimization, wireless power transfer and energy allocation. Thereafter, low-complexity schemes are designed to jointly optimize the wireless power transfer, energy allocation and task offloading. The effectiveness of the proposed statistical latency guarantee scheme is finally validated by extensive simulations.

Efficient Network Slicing for 5G Services in Cloud Fog-RAN Deployment Over WDM Network

Network slicing in fifth generation (5G) radio access network (RAN) enables serving massive network traffic with diverse and stringent quality of service (QoS) requirements. Multiple logical networks can be built using RAN slicing over a single RAN infrastructure. This paper considers three different types of slices that are standardized by third generation partner project (3GPP): ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine type communication (mMTC). Each slice type has unique requirements for data rate, latency, and reliability. Cloud RAN (C-RAN) was proposed to address the requirements of 5G services, which involves physical separation between the remote radio head (RRH) and baseband unit (BBU) in 2-layers. While C-RAN enables more efficient resource utilization and energy consumption, it limits network scalability. Furthermore, C-RAN is unable to meet the stringent latency demands of URLLC services, as well as the massive fronthaul capacity required for eMBB requests. To address this issue, we propose a novel cloud fog RAN (CF-RAN) over wavelength division multiplexing (WDM) architecture. In CF-RAN over WDM the RAN functions are divided into 3 layers, which include the RRH at layer 1, fog nodes at layer 2, and BBU hotels at layer 3. We employ the emerging fog computing paradigm in optical and wireless networks in this suggested architecture. To facilitate low latency URLLC requests, the fog nodes are located closer to the cell site (CS). We propose an integer linear programming (ILP)-based mathematical model. The model's objective is to decrease the number of active BBU hotels and fog nodes while remaining compliant with practical network restrictions. Furthermore, a low complexity greedy heuristic algorithm is proposed to solve the problem and is compared to the branch & bound (B&B) algorithm, which is assumed to provide an optimal solution with exponential complexity. The proposed 3-layer CF-RAN over WDM architecture achieves a 70% improvement in BBU centralization and a 50% reduction in request blocking when compared to the standard 2-layer architecture.

Flexible mm-Wave Sigma-Delta-Over-Fiber MIMO Link

Millimeter-wave and multiple-input-multiple-output (MIMO) technologies combine broad bandwidth with spatial diversity to offer a greater data rate. This paper investigates a flexible millimeter-wave sigma-delta-over-fiber based transmitter solution with digital beamforming MISO and MIMO functionality. Those functions are controlled by a central unit connecting a remote radio head with a standardized QSFP28 fiber link. The central unit generates binary encoded intermediate frequency signals using bandpass sigma-delta modulation. The QSFP28 based fiber link transmits the intermediate frequency bitstreams to the remote radio head. The remote radio head consists of a QSFP28 module, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$90^{\circ }$</tex-math></inline-formula> hybrids, and upconverters. The remote radio head feeds four parallel, independent, coherent, and central-unit controlled 28 GHz signals to a linear array transmitting antenna. The transmitter performance is experimentally verified, demonstrating up to 800 Msym/s at an EVM/NMSE of 6.7%/-23.5 dB when tested with a 64 quadrature amplitude modulation (64-QAM) modulation scheme. Digital over-the-air beamforming MISO functionality is demonstrated up to 700 Msym/s across 1 m wireless distance. MIMO communication capabilities is demonstrated by over-the-air transmission of two independent 500 Msym/s to two spatially separated receivers. The results show that the proposed link can be used for realization of scalable, low-cost and flexible transmitter solution for emerging distributed antenna systems.

Fuzzy logic based dynamic wavelength and bandwidth allocation for 5G front haul networks

The Quality of Service (QoS) requirements for 5G are very strict in terms of latency of Fifth Generation (5G) traffic. 5G fronthaul networks between the baseband unit and remote radio heads are planned to use Next Generation - Passive Optical Networks 2 (NG-PON2) to carry their traffic. However, these Time Wavelength Division Multiplexing (TWDM) PON based networks also provide service to other traffic such as residential and corporate users. Therefore, it is crucial to provide QoS guarantees to 5G traffic. The main issue includes allocating transmission over multiple wavelengths while maintaining QoS. Although using all the available wavelengths all the time may seem to be the simple solution, each active wavelength increases the cost of operation. To solve these issues, this paper proposes a technique using fuzzy logic to dynamically allocate wavelength and bandwidth for 5G fronthaul networks. Two types of traffic, i.e., 5G traffic and normal traffic are considered while the problem of Dynamic Wavelength and Bandwidth Allocation (DWBA) is formulated as a queueing theory problem using penalties and a cost function. Penalties are assigned for waiting packets as well as each active wavelength. Then, modeling the number of active wavelengths, the number of packets in the system, the total cost, and the traffic intensity as input fuzzy variables while modeling the change in the number of active wavelengths as the fuzzy output variable. The Mamdani implication is used for the fuzzy inference engine and the height method is used for defuzzification of the output variable. Simulations in MATLAB show that the proposed technique can maintain the latency of 5G traffic lower than the defined threshold while also significantly reducing the number of active wavelengths. A comparison with the existing state-of-the-art techniques shows that the proposed technique results in an improvement of at least 46% and 29% in terms of the average number of active wavelengths and the average latency for 5G traffic, respectively.

OTFS Signaling for SCMA With Coordinated Multi-Point Vehicle Communications

This paper investigates an uplink coordinated multi-point (CoMP) coverage scenario, in which multiple mobile users are grouped for sparse code multiple access (SCMA), and served by the remote radio head (RRH) in front of them and the RRH behind them simultaneously. We apply orthogonal time frequency space (OTFS) modulation for each user to exploit the degrees of freedom arising from both the delay and Doppler domains. As the signals received by the RRHs in front of and behind the users experience respectively positive and negative Doppler frequency shifts, our proposed OTFS-based SCMA (OBSCMA) with CoMP system can effectively harvest extra Doppler and spatial diversity for better performance. Based on maximum likelihood (ML) detector, we analyze the single-user average bit error rate (ABER) bound as the benchmark of the ABER performance for our proposed OBSCMA with CoMP system. We also develop a customized Gaussian approximation with expectation propagation (GAEP) algorithm for multi-user detection and propose efficient algorithm structures for centralized and decentralized detectors. Our proposed OBSCMA with CoMP system leads to stronger performance than the existing solutions. The proposed centralized and decentralized detectors exhibit effective reception and robustness under channel state information uncertainty.

C-RAN Zero-Forcing With Imperfect CSI: Analysis and Precode &amp; Quantize Feedback

Downlink joint transmission by a cluster of remote radio-heads (RRHs) is essential for enhancing throughput in future cellular networks. This method requires global channel state information (CSI) at the processing unit that designs the joint precoder. To this end, extensive CSI must be shared between the RRHs and that unit. This paper proposes two contributions. The first is a new upper bound on the rate loss, which implies a lower bound on the achievable rate for an RRHs-cluster employing joint zero-forcing (ZF) with incomplete CSI with single-user decoding (SUD) at the mobile stations (MSs). The second contribution, which follows insights from the bound, is a new CSI sharing scheme that drastically reduces the significant overhead associated with acquiring global CSI for joint transmission. In a nutshell, each RRH applies a local precoding matrix that creates low-dimensional effective channels that can be quantized more accurately with fewer bits, thereby reducing the overhead of sharing CSI. In addition to the CSI sharing-overhead, this scheme reduces the data rate delivered to each RRH in the cluster.

Minimizing traffic cost of content distribution and storage allocation in cloud radio access networks

In Cloud Radio Access Networks (C-RANs), remote radio heads (RRHs) and baseband units (BBUs) are deployed for storing contents to alleviate the bottleneck of the data cloud, shorten the delay of content request, and reduce the cost of network service. In this paper, we advocate the issue of content distribution in C-RANs to meet the ever-increasing contents of 5G cellular networks. Our goal is to minimize the total traffic cost of content transmission by jointly distributing content and allocating storage in RRHs and BBUs of C-RANs. The problem is formulated as a mix-integer linear programming (MIP). Then, an algorithm with a bounded approximation ratio is proposed to obtain the feasible solution of the MIP. The simulation results validate that the proposed algorithm outperforms two very recent heuristic algorithms in the metric of traffic cost. They also demonstrate that the difference between the solution obtained by the algorithm and the optimal solution of the MIP is very small. In addition, the experimental results show that the proposed algorithm is feasible in C-RANs. Further, another algorithm is proposed for exchanging contents between neighbor RRHs in content provision.

Computation Offloading and Resource Allocation in C-RAN Supporting Wireless Charging

In this paper, we consider a Cloud-Radio Access Network (C-RAN) with multiple enhanced remote radio heads (RRHs), which support for wireless charging, computation offloading and information transmission. The employment of enhanced RRHs requires a redesign for efficient resource allocation. Our goal is to minimize the weighted sum of energy transfer time and computation time by jointly optimizing energy transmission time, energy beamforming vectors, offloading factors, information transmission time, and combining vectors, while satisfying the communication performance requirements of terminal nodes. The problem is formulated as a mixed-integer nonlinear programming, which can be solved by alternating optimization (AO) utilizing semidefinite relaxation (SDR) and nonlinear transformation. To reduce the online computation delay of AO algorithm, we propose an optimization method based on deep learning framework, which can determine optimal solution with trained neural network in real-time application. To improve the performance of learning algorithm, we use two methods to set labels for training deep neural network to obtain the optimal offloading decision: pre-optimization and self-evolution. Simulation results show that with trained neural network, the algorithms based on deep learning can achieve similar performance to AO algorithm with less computation time.

Functional Split-Aware Optimal BBU Placement for 5G Cloud-RAN Over WDM Access/Aggregation Network

Fifth generation (5G) cloud-radio access network (C-RAN) aims at providing better performance and support to various applications with stringent data rate and latency requirements. In C-RAN, the processing units, known as the baseband units (BBUs), are segregated from the individual remote radio heads (RRHs) and moved to a convenient location to serve more than one RRH. This migration leads to an efficient resource allocation and cost-effective solution at the expense of a huge fronthaul traffic between the RRH and BBU hotel. The required fronthaul data rate largely depends on the employed functional split options. In this article, we propose a novel optimal BBU placement with a mixed functional split scheme to combat the fronthaul latency challenge while providing the network with greater flexibility and cost reduction. We introduce an integer linear programming (ILP)-based BBU placement problem with a mixed functional split selection approach to simultaneously minimize the number of BBU hotels and fibers, thereby reducing the network cost. Furthermore, a heuristic algorithm is proposed to solve the proposed model for large network scenarios. The obtained results show that an improvement of 25% and 50% is realized with the proposed scheme over the conventional fixed split option scheme for small and large network scenarios, respectively.