Fronthaul Links Research Articles

Cell-Free Massive MIMO Power Consumption with Serial Front-Hauls

Massive MIMO deployments have been traditionally based on dedicated links in the front-haul, i.e., between the central processing units and the Access Points (APs). Recently, cell-free massive Multiple-Input Multiple-Output (MIMO) systems based on serial front-haul links have been discussed to simplify the deployments, among other reasons. However, the power consumption models currently used for cell-free massive MIMO deployments typically assume dedicated front-haul links. This paper highlights the inaccuracy of these models when applied to serial front-hauls and proposes simple adaptations to achieve more realistic results. The results obtained for an exemplary scenario show that the front-haul power would represent 61.73% of the total consumed power with the original models. In contrast, with the proposed adaptations, it could be as low as 1.59% of the total consumed power for some serial front-haul configurations. Additionally, the impact of considering APs with lower power consumption is studied, in which case, the percentages above would become 93.15% and 11.96%, respectively. Hence the importance of having power models that fit the front-haul topology.

On the impact of Open RAN Fronthaul Control in scenarios with XR Traffic

Through extensive research and standardization efforts, mobile networks have rapidly evolved, offering improved services and allowing the establishment of new use cases, such as autonomous vehicles, smart cities, Industry 4.0, among others. While 5G networks have brought advancements that can support a broad spectrum of such new use cases, the requirements imposed by time-critical services as the eXtended Reality (XR) and Cloud Gaming (CG) applications still remain a challenge. Next generation networks are envisioned to adopt technologies that will allow them to surpass such barriers. Open Radio Access Network (RAN), utilizing the disaggregation paradigm, stands out as a pivotal technology thanks to its potential to endow the network with flexibility, automation, and intelligence. In fact, Open RAN is considered as one of the key enabling technologies for XR and CG applications. However, disaggregation of the RAN may result in bottlenecks in the links connecting the various parts of the network, like the Open Fronthaul link, especially when considering time-critical traffic. In this paper, we perform an analysis of the impact that the Open Fronthaul capacity limitations can have in the XR and CG traffic under 3GPP defined scenarios. Moreover, to address these limitations, we implement and extend a Fronthaul Control mechanism combined with modulation compression, using the open-source ns-3 based 5G-LENA network simulator. Results showcase that the Open Fronthaul capacity limitation can drastically reduce the performance of the XR and CG applications, and demonstrate the necessity for such mechanisms to be employed in order to meet their requirements in terms of latency and throughput.

Analog Predistorter for RoF Fronthaul Link Excited by a 400MHz Bandwidth 5G NR Signal

Analog Predistorter for RoF Fronthaul Link Excited by a 400MHz Bandwidth 5G NR Signal

Bit weight-aware high-fidelity digital RoF fronthaul based on dual-drive MZM and chirp-dispersion Interaction.

Digital radio-over-fiber (D-RoF) quantizes the wireless waveform to improve the noise tolerance in fronthaul links. Unlike conventional data transmission, the quantization bits exhibit different weights, offering a new strategy to protect the high-weight bits. By introducing a dual-drive MZM (DD-MZM)-based optical transmitter, the interaction between frequency chirp and chromatic dispersion (CD) results in eye closure/open. Such an effect would potentially achieve higher fidelity of the reconstructed wireless signal in the SNR-limited region. In this paper, we first establish a theoretical model for chirp and dispersion-induced eye closure/open, characterizing the influence of fiber length and baud rate on the constellation interval. We then propose two protection schemes for 4-level pulse amplitude modulation (PAM-4), in which the high- and low-weight bits are interleaved and loaded onto the upper or lower arms of DD-MZM to leverage the negative or positive chirp. Furthermore, we extend to high-order modulation formats and propose two uneven optical PAM-8 schemes with different frequency chirps, which protect one or two of the three bits in each PAM-8 symbol, respectively. The chirp-dispersion interaction-enabled uneven optical PAM-4/8 schemes are experimentally demonstrated in intensity modulation and direct detection (IM-DD) D-RoF system. We compare the performance with chirp-free PAM-4/8 schemes generated by differential-driven mode. Then, we adopt the µ-law quantizer to verify the effectiveness of the proposed schemes and the Lloyd-Max algorithm-based quantizer to maximize the signal-to-noise ratio (SNR). With the help of chirp-dispersion interaction-enabled uneven optical PAM-4/8 schemes, the experimental results show 11.3-dB and 4.8-dB SNR gains of the wireless signal after 5-km fiber transmission, respectively.

Sum rate maximization in UAV-assisted data harvesting network supported by CF-mMIMO system exploiting statistical CSI

Unmanned Aerial Vehicles (UAVs) have been considered to have great potential in supporting reliable and timely data harvesting for Sensor Nodes (SNs) from an Internet of Things (IoT) perspective. However, due to physical limitations, UAVs are unable to further process the harvested data and have to rely on terrestrial servers, thus extra spectrum resource is needed to convey the harvested data. To avoid the cost of extra servers and spectrum resources, in this paper, we consider a UAV-based data harvesting network supported by a Cell-Free massive Multiple-Input-Multiple-Output (CF-mMIMO) system, where a UAV is used to collect and transmit data from SNs to the central processing unit of CF-mMIMO system for processing. In order to avoid using additional spectrum resources, the entire bandwidth is shared among radio access networks and wireless fronthaul links. Moreover, considering the limited capacity of the fronthaul links, the compress-and-forward scheme is adopted. In this work, in order to maximize the ergodically achievable sum rate of SNs, the power allocation of ground access points, the compression of fronthaul links, and also the bandwidth fraction between radio access networks and wireless fronthaul links are jointly optimized. To avoid the high overhead introduced by computing ergodically achievable rates, we introduce an approximate problem, using the large-dimensional random matrix theory, which relies only on statistical channel state information. We solve the nontrivial problem in three steps and propose an algorithm based on weighted minimum mean square error and Dinkelbach's methods to find solutions. Finally, simulation results show that the proposed algorithm converges quickly and outperforms the baseline algorithms.

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.

Ubiquitous learning models for 5G communication network utility maximization through utility-based service function chain deployment

The main problem of deploying service function chains in virtualized 5G networks is dealt with in wireless 5G communications and effective ubiquitous learning models. The aim is to ensure differentiated network performance for a wide range of services while maximizing the collaborative revenue of infrastructure operators and wireless virtual operators. To achieve this, a utility-based service function chain deployment strategy is introduced, tailored to the specific characteristics of Ubiquitous Learning based 5G-RAN-Architecture. Consideration is given to the virtual operator's maximum tolerable end-to-end latency, minimum service rate requirements, and the infrastructure operator's constraints on computing and link resources. It also looks at how different deployment scenarios for service function chains affect network performance and creates a utility model using a business framework. The ultimate objective is to optimize the collective revenue of infrastructure operators and virtual operators. The approach leverages genetic algorithms and Matlab's Linprog function for iterative problem-solving. The graph clearly shows that the SFC deployment algorithm in this study uses less infrastructure resources for Front Haul links. This implies that the algorithm successfully reduces the load on Front Haul lines, which in turn lowers the cost of SFC deployment and makes it easier to deploy more SFCs across the infrastructure. This work contributes to the evolution of 5G wireless communications and its seamless integration with ubiquitous learning models.

Federated learning-based trajectory prediction for dynamic resource allocation in moving small cell networks

With the evolution of fifth generation (5G) of mobile communication, vehicular edge computing (VEC) and moving small cell (MSC) network are gaining attention because of their capability to provide improved quality-of-service (QoS) to vehicular users. The ultimate goal of VEC-enabled MSC network is to diminish the vehicular penetration effect and path loss resulting in improved network performance for vehicular users. In this paper, we explore distributed resource block (RB) allocation for fronthaul links of MSCs with probabilistic mobility in VEC-enabled MSC network. The proposed work exploits the computational power of road side units (RSUs) deployed with VEC servers present along the road sides to allocate the resources to MSCs in a distributed manner by exchanging limited information, with the objective of maximizing the data rate achieved by MSC network. Moreover, we propose federated learning (FL)-based position prediction of MSCs to predict the trajectory of MSCs in advance for efficient prediction of resource allocation in MSC network. Simulations results are presented to compare the position prediction dependent distributed and centralized time interval dependent interference graph-based resource allocation to MSCs in terms of RB utilization and average achievable data rate of MSC network. For comparison, we further investigated centralized as well as distributed threshold time dependent interference graph-based allocation of resources to MSC network.

Evaluation of interference between 300 GHz band fronthaul links using measured high gain antenna radiation patterns

Evaluation of interference between 300 GHz band fronthaul links using measured high gain antenna radiation patterns

Leveraging Massive MIMO Over mmWave Fronthaul Link for Throughput Maximization in Cloud Radio Access Networks

Leveraging Massive MIMO Over mmWave Fronthaul Link for Throughput Maximization in Cloud Radio Access Networks

Cell-free ISAC massive MIMO systems with capacity-constrained fronthaul links

Integrated sensing and communication (ISAC), millimeter wave (mmWave) massive multiple-input multiple-output (MIMO), and cell-free networks are emerged as some of the most potential physical layer enablers for the future wireless communication networks. In this paper, we consider a cell-free ISAC mmWave massive MIMO system with hybrid analog and digital precoders while accounting for the impacts of capacity-constrained fronthaul links. In the system, the digital baseband processing is dictated at a geographical scale while the analog precoders are carried out at each transmit access point (AP). Based on the characteristics of the considered model, we formulate the optimization problem as a function of power allocation and fronthaul compression strategies. To address this problem, two block coordinate descend (BCD)-based methods are proposed to alternatively solve the three decomposed subproblems corresponding to the power allocation, the downlink and backward fronthaul compression strategies. Simulation results indicate that the proposed schemes can achieve 90% performance of the optimal full-search (FS) method with much lower computational complexity. In addition, there exists an optimal configuration of APs, UEs, and antennas for a cell-free ISAC system with capacity-constrained fronthaul links.

One-bit quantization delta-sigma modulation-based autoencoder for power-efficient free-space communication.

This Letter proposes a novel, to the best of our knowledge, approach utilizing a delta-sigma modulation (DSM)-based 1-bit autoencoder (AE) for efficient encoding and decoding in various channel conditions. Simulation analysis demonstrates the AE's ability to mitigate noise by reducing a peak-to-average power ratio (PAPR) and enhancing an in-band power of the signals, particularly under low signal-to-noise ratios (SNRs). The AE-DSM achieves theoretical transmission performance even at SNRs below 6 dB. In a 40-m free-space link experiment, the AE-DSM exhibits an 8.4-dB lower bit error rate (BER) compared to 64QAM-DSM, enabling a transmission rate of 1.31 Gbps. Furthermore, the 1-bit AE-DSM significantly reduces power consumption in the receiving analog-to-digital converter (ADC), facilitates transmission at low SNRs, and effectively mitigates nonlinear effects. Consequently, the DSM-based AE holds immense potential for future mobile fronthaul links.

Bayesian Receiver Design via Bilinear Inference for Cell-Free Massive MIMO With Low-Resolution ADCs

We propose a novel joint channel and data estimation (JCDE) scheme to combat the rate limitation in fronthaul links of cell-free massive MIMO (CF-mMIMO) systems introduced by the use of analog-to-digital converters (ADCs) at access points (APs), which makes channel estimation and multi-user detection at the central AP (CAP) challenging. The latter problem is solved here via the new JCDE scheme which differs from state-of-the-art (SotA) alternatives due to two contributions. The first is the design and incorporation of de-quantization (DQ) step which relies only on scalar Gaussian approximation (SGA) assumptions in conformity with mild central limit theorem (CLT), in contrast to the much harder asymptotic conditions required by the classic bilinear generalized approximate message passing (BiGAMP) algorithm. The second is a modification of bilinear Gaussian belief propagation (BiGaBP), whereby quantized outputs are linearized via the Bussgang decomposition enabling tractable signal processing. The resulting DQ-aided JCDE method achieves both low-complexity and high-accuracy by exploiting both the spatial degrees of freedom (DoF) obtained from, and the observations at the CAP to compensate for the low-resolution distortion introduced by, the distributed APs. The efficacy of the proposed method over the SotA is confirmed via computer simulations.

Joint Energy Harvesting and Transmission Optimization for Cell-Free Massive MIMO With Network-Assisted Full Duplexing

This paper investigates the problem of joint energy harvesting (EH) and transmission optimization for cell-free massive MIMO with network-assisted full duplexing (NAFD), where each access point (AP) works in full-duplex mode, half-duplex mode or another flexible mode and is connected to a central processing unit (CPU) via compressed fronthaul links. The high demand on the fronthaul links and series cross-link interference (CLI) are the two main problems in cell-free massive MIMO with NAFD. Therefore, in our scheme, we propose a joint strategy of EH, fronthaul compression, and CLI cancelation to maximize the energy efficiency (EE) of the system under constraints of guaranteed quality-of-service (QoS), EH and compressed fronthaul requirements. A two-stage strategy of EH and transceiver design is proposed to solve the complicated optimization problem. Specifically, in the first stage, we propose an EH/information receiving (IR) antenna selection algorithm to design the operation mode of the antennas for uplink users and receiving APs. In the second stage, with the antennas selected in the first stage, we further attempt to solve the EE maximization problem. Furthermore, to solve the highly nonconvex EE maximization problem, we propose a fractional programming (FP)-based two-tier iterative algorithm in which a series of nonconvex–convex approximation methods, such as FP, iterative convex approximation and equivalent formulation, are used. Simulation results demonstrate that the proposed algorithms yield a higher EE gain than the traditional time-division duplexing (TDD) and co-frequency co-time full duplexing (CCFD) schemes.

Robust Beamforming Design for Cache-Enabled C-RAN With Fronthaul Multicast

In this article, we study a robust beamforming design problem in the cached-enable cloud radio access network, where the imperfect channel state information of the access links are considered. Two transmission schemes, nonsimultaneous transmission (NST) scheme and simultaneous transmission (ST) scheme, are developed. To improve the system performance, the cooperative transmission technique is applied. In NST scheme, the base stations (BSs) obtain the uncached required files from the central processor (CP) and then transmit all required files to users. Based on this, we design an effective transmission model based on cache files and the qualities of the fronthaul link channels, and formulate a transmission delay minimization problem. Then, an alternative optimization algorithm is developed to solve it. In ST scheme, the BSs obtain the uncached files from the CP and transmit the cached files to users simultaneously. We divide two cases according to the access link transmission rate and cached files. For each case, we formulate an optimization problem by jointly designing the fronthaul multicast beamforming and access link transmission beamforming. Next, an iterative algorithm is developed via the L0 norm approximation, successive convex approximation and semidefinite relaxation techniques. Finally, simulation results show the proposed schemes can reduce the transmission delay better than the conventional multicast schemes.

Cognitive RF–FSO Fronthaul Assignment in Cell-Free and User-Centric mMIMO Networks

Cell-free massive MIMO (CF-mMIMO) network and its user-centric (UC) version are considered as promising techniques for the next generations of wireless networks. However, fronthaul and backhaul assignments are challenging issues in these networks. In this paper, energy efficiencies of uplink transmission for the CF- and UC-mMIMO networks are studied, wherein access points (APs) are connected to aggregation nodes (ANs) through radio frequency (RF) and/or free-space optic (FSO) fronthauls, and the ANs are connected to a central processing unit via fiber backhauls. The achievable data rates are derived by taking into account the effects of hardware non-ideality at the APs and ANs, FSO alignment and weather conditions. To have a robust and energy-efficient network, especially in the presence of FSO misalignment and adverse weather conditions, firstly, a cognitive RF--FSO fronthaul assignment algorithm is proposed at the cost of sharing the available RF bandwidth between the access and fronthaul links. Then, optimal power allocations at the users and APs are investigated, and two analytical approaches are proposed to solve the non-convex optimization problem. Through numerical results, we have discussed that the cognitive RF--FSO fronthaul assignment achieves higher energy efficiency compared to that of FSO-only, RF-only, or simultaneously using RF and FSO fronthauls.

Fronthaul-Aware Scheduling Strategies for Dynamic Modulation Compression in Next Generation RANs

Next generation Radio Access Networks (RANs) consider virtualized architectures in which base station functions are distributed in different logical nodes, connected through fronthaul (FH) links. To reduce the FH deployment costs and the required FH capacity, operators may install a single FH link shared among multiple cells and exploit key enabling techniques, such as modulation compression, to reduce FH data. In shared FH capacity scenarios, it is essential to provide efficient methods to control and optimize the FH resources' utilization with limited impact on the air interface performance. In this paper, a multi-cell multi-user scenario with a shared FH link across multiple cells is considered. We focus on optimizing the resource allocation and modulation compression of each user, in a centralized and dynamic manner, aiming to maximize the air interface performance subject to a shared FH capacity constraint. The problem is formulated as a convex optimization problem, which allows deriving the optimal resource allocation and modulation compression per user. Then, we evaluate the proposed FH-aware scheduling methods against baseline holistic strategies over an end-to-end dynamic 5G NR system-level simulator based on ns-3. Under a tight available FH capacity, results show gains that vary from 16% to 567% in different percentile statistics of the user-perceived throughput.

Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms

Device activity detection in the emerging cell-free massive multiple-input multiple-output (MIMO) systems has been recognized as a crucial task in machine-type communications, in which multiple access points (APs) jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a centralized algorithm and a distributed algorithm that satisfy the highly nonconvex constraints in a gentle fashion as the iteration number increases, so that the sequence generated by the proposed algorithms can get around bad stationary points. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms outperform state-of-the-art approaches, and the proposed accelerated distributed algorithm achieves close detection performance to that of the centralized algorithm but with a much smaller number of bits to be transmitted on the fronthaul links.

Design of a RoF fronthaul link based on optical generation of mm-waves for 5G C-RANs

In this paper, a cloud-radio access networks based (C-RANs) radio-over-fiber (RoF) system have been reported with the optical generation of 28 GHz and 57 GHz millimeter-wave using phase modulation and stimulated Brillouin scattering effect in optical fibers. As a result, a cost-effective system with improved performance is achieved by the proposed method. Based on the proposed scheme, a 28 GHz (licensed band) and 57 GHz (Un-licensed band) optical millimeter-waves are generated by simulation only using 14 GHz and 16 GHz radio frequency (RF) respectively. Because the performance of any optical transmission system is limited by the Q factor and the bit error rate (BER) and any input power of the laser into the fiber needs to be less than the Brillouin threshold in order to avoid the sharp degradation of the Q factor. Also, the Brillouin threshold for this study setup is determined which is around 6 dBm in the case of licensed band and 2 dBm in the case of unlicensed band. The simulation results show that the proposed scheme can provide 100 km single-mode fiber for 28 GHz and 25 km for 57 GHz with transmission rates of 10 Gbps for both of them. In addition, a stable millimeter-wave RoF link, high quality carriers, and a reduction in nonlinearity effects are achieved with the proposed scheme.

An integrated photonic-assisted phased array transmitter for direct fiber to mm-wave links

Millimeter-wave (mm-wave) phased arrays can realize multi-Gb/s communication links but face challenges such as signal distribution and higher power consumption hindering their widespread deployment. Hybrid photonic mm-wave solutions combined with fiber-optics can address some of these bottlenecks. Here, we report an integrated photonic-assisted phased array transmitter applicable for low-power, compact radio heads in fiber to mm-wave fronthaul links. The transmitter utilizes optical heterodyning within an electronically controlled photonic network for mm-wave generation, beamforming, and steering. A photonic matrix phase adjustment architecture reduces the number of phase-shift elements from M × N to M + N lowering area and power requirements. A proof-of-concept 2 × 8 phased array transmitter is implemented that can operate from 24–29 GHz, has a steering range of 40°, and achieves 5 dBm EIRP at an optical power of 55 mW without using active mm-wave electronics. Data streams at 2.5 Gb/s are transmitted over 3.6 km of optical fiber and wirelessly transmitted attaining bit-error rates better than 10−11.