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See detailTowards Optimal Energy Efficiency in Cell-Free Massive MIMO Systems
Papazafeiropoulos, A.; Ngo, H.Q.; Kourtessis, Pandelis et al

in IEEE Transactions on Green Communications and Networking (2021), 5(2), 816-831

Motivated by the ever-growing demand for green wireless communications and the advantages of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems, we focus on the design of their downlink ... [more ▼]

Motivated by the ever-growing demand for green wireless communications and the advantages of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems, we focus on the design of their downlink (DL) for optimal energy efficiency (EE). To address this fundamental topic, we assume that each access point (AP) is deployed with multiple antennas and serves multiple users on the same time-frequency resource while the APs are Poisson point process (PPP) distributed, which approaches realistically their opportunistic spatial randomness. Relied on tools from stochastic geometry, we derive a lower bound on the DL average achievable spectral efficiency (SE). Next, we consider a realistic power consumption model for CF mMIMO systems. These steps enable the formulation of a tractable optimization problem concerning the DL EE, which results in the analytical determination of the optimal pilot reuse factor, the AP density, and the number of AP antennas and users that maximize the EE. Hence, we provide useful design guidelines for CF mMIMO systems relating to fundamental system variables towards optimal EE. Among the results, we observe that an optimal pilot reuse factor and AP density exist, while larger values result in an increase of the interference, and subsequently, lower EE. Overall, it is shown that the CF mMIMO technology is a promising candidate for next-generation networks achieving simultaneously high SE and EE. [less ▲]

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See detailTowards Optimal Energy Efficiency in Cell-Free Massive MIMO Systems
Papazafeiropoulos, Anastasios; Ngo, Hien Quoc; Kourtessis, Pandelis et al

in IEEE Transactions on Green Communications and Networking (2021), 5(2), 816-831

Motivated by the ever-growing demand for green wireless communications and the advantages of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems, we focus on the design of their downlink ... [more ▼]

Motivated by the ever-growing demand for green wireless communications and the advantages of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems, we focus on the design of their downlink (DL) for optimal energy efficiency (EE). To address this fundamental topic, we assume that each access point (AP) is deployed with multiple antennas and serves multiple users on the same time-frequency resource while the APs are Poisson point process (PPP) distributed, which approaches realistically their opportunistic spatial randomness. Relied on tools from stochastic geometry, we derive a lower bound on the DL average achievable spectral efficiency (SE). Next, we consider a realistic power consumption model for CF mMIMO systems. These steps enable the formulation of a tractable optimization problem concerning the DL EE, which results in the analytical determination of the optimal pilot reuse factor, the AP density, and the number of AP antennas and users that maximize the EE. Hence, we provide useful design guidelines for CF mMIMO systems relating to fundamental system variables towards optimal EE. Among the results, we observe that an optimal pilot reuse factor and AP density exist, while larger values result in an increase of the interference, and subsequently, lower EE. Overall, it is shown that the CF mMIMO technology is a promising candidate for next-generation networks achieving simultaneously high SE and EE. [less ▲]

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See detailMachine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; Nguyen, van Dinh UL et al

in IEEE Transactions on Wireless Communications (2021), 20(6), 3710-3722

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial ... [more ▼]

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tries all valid antenna subsets. Although approaching the (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between the key system parameters and the selected antennas. The proposed L-ASPD is robust against the number of users and their locations, BS's transmit power, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves higher effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance. [less ▲]

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See detailCompletion Time Minimization in NOMA Systems:Learning for Combinatorial Optimization
Wang, Anyue UL; Lei, Lei UL; Lagunas, Eva UL et al

in IEEE Networking Letters (2021)

In this letter, we study a completion-time minimization problem by jointly optimizing time slots (TSs) and power allocation for time-critical non-orthogonal multiple access (NOMA) systems. The original ... [more ▼]

In this letter, we study a completion-time minimization problem by jointly optimizing time slots (TSs) and power allocation for time-critical non-orthogonal multiple access (NOMA) systems. The original problem is non-linear/non-convex with discrete variables, leading to high computational complexity in conventional iterative methods. Towards an efficient solution, we train deep neural networks to perform fast and high-accuracy predictions to tackle the difficult combinatorial parts, i.e., determining the minimum consumed TSs and user-TS allocation. Based on the learning-based predictions, we develop a low-complexity post-process procedure to provide feasible power allocation. The numerical results demonstrate promising improvements of the proposed scheme compared to other baseline schemes in terms of computational efficiency, approximating optimum, and feasibility guarantee. [less ▲]

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See detailFeasible Point Pursuit and Successive Convex Approximation for Transmit Power Minimization in SWIPT-Multigroup Multicasting Systems
Gautam, Sumit UL; Lagunas, Eva UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Green Communications and Networking (2021)

We consider three wireless multi-group (MG) multicasting (MC) systems capable of handling heterogeneous user types viz., information decoding (ID) specific users with conventional receiver architectures ... [more ▼]

We consider three wireless multi-group (MG) multicasting (MC) systems capable of handling heterogeneous user types viz., information decoding (ID) specific users with conventional receiver architectures, energy harvesting (EH) only users with non-linear EH module, and users with joint ID and EH capabilities having separate units for the two operations, respectively. Each user is categorized under unique group(s), which can be of MC type specifically meant for ID users, and/or an energy group consisting of EH explicit users. The joint ID and EH users are a part of both EH group and single MC group. We formulate an optimization problem to minimize the total transmit power with optimal precoder designs for the three aforementioned scenarios, under certain quality-of-service constraints. The problem may be adapted to the well-known semidefinite program and solved via relaxation of rank-1 constraint. However, this process leads to performance degradation in some cases, which increases with the rank of solution obtained from the relaxed problem. Hence, we develop a novel technique motivated by the feasible-point pursuit successive convex approximation method in order to address the rank-related issue. The benefits of proposed method are illustrated under various operating conditions and parameter values, with comparison between the three above-mentioned scenarios. [less ▲]

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See detailNOMA-Enabled Multi-Beam Satellite Systems: Joint Optimization to Overcome Offered-Requested Data Mismatches
Wang, Anyue UL; Lei, Lei UL; Lagunas, Eva UL et al

in IEEE Transactions on Vehicular Technology (2021), 70(1), 900-913

Non-orthogonal multiple access (NOMA) has potentials to improve the performance of multi-beam satellite systems. The performance optimization in satellite-NOMA systems could be different from that in ... [more ▼]

Non-orthogonal multiple access (NOMA) has potentials to improve the performance of multi-beam satellite systems. The performance optimization in satellite-NOMA systems could be different from that in terrestrial-NOMA systems, e.g., considering distinctive channel models, performance metrics, power constraints, and limited flexibility in resource management. In this paper, we adopt a metric, offered capacity to requested traffic ratio (OCTR), to measure the requested-offered data rate mismatch in multi-beam satellite systems. In the considered system, NOMA is applied to mitigate intra-beam interference while precoding is implemented to reduce inter-beam interference. We jointly optimize power, decoding orders, and terminal-timeslot assignment to improve the max-min fairness of OCTR. The problem is inherently difficult due to the presence of combinatorial and non-convex aspects. We first fix the terminal-timeslot assignment, and develop an optimal fast-convergence algorithmic framework based on Perron-Frobenius theory (PF) for the remaining joint power-allocation and decoding-order optimization problem. Under this framework, we propose a heuristic algorithm for the original problem, which iteratively updates the terminal-timeslot assignment and improves the overall OCTR performance. Numerical results show that the proposed algorithm improves the max-min OCTR by 40.2% over orthogonal multiple access (OMA) in average. [less ▲]

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See detailCoverage Probability and Ergodic Capacity of Intelligent Reflecting Surface-Enhanced Communication Systems
Trinh, van Chien UL; Tu, Lam Thanh; Chatzinotas, Symeon UL et al

in IEEE Communications Letters (2021), 25(1), 69-73

This paper studies the performance of a single-input single-output (SISO) system enhanced by the assistance of an intelligent reflecting surface (IRS), which is equipped with a finite number of elements ... [more ▼]

This paper studies the performance of a single-input single-output (SISO) system enhanced by the assistance of an intelligent reflecting surface (IRS), which is equipped with a finite number of elements under Rayleigh fading channels. From the instantaneous channel capacity, we compute a closed-form expression of the coverage probability as a function of statistical channel information only. A scaling law of the coverage probability and the number of phase shifts is further obtained. The ergodic capacity is derived, then a simple upper bound to simplify matters of utilizing the symbolic functions and can be applied for a long period of time. Numerical results manifest the tightness and effectiveness of our closed-form expressions compared with Monte-Carlo simulations. [less ▲]

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See detailA Low-complexity Resource Optimization Technique for High Throughput Satellite
Abdu, Tedros Salih UL; Kisseleff, Steven UL; Lagunas, Eva UL et al

Scientific Conference (2021)

The high throughput satellites with flexible payloads are expected to provide a high data rate to satisfy the increasing traffic demand. Furthermore, the reconfiguration capability of flexible payloads ... [more ▼]

The high throughput satellites with flexible payloads are expected to provide a high data rate to satisfy the increasing traffic demand. Furthermore, the reconfiguration capability of flexible payloads opens the door to more advanced system optimization techniques and a better utilization of satellite resources. Consequently, we can obtain high demand satisfaction at the user side. For this, dynamically adaptive high-performance and low-complexity optimization algorithms are needed. In this paper, we propose a novel low-complexity resource optimization technique for geostationary (GEO) High Throughput Satellites. The proposed method minimizes the transmit power and the overall satellite bandwidth while satisfying the demand per beam. This optimization problem turns out to be non-convex. Hence, we convexify the problem using Dinkelbach method and Successive Convex Approximation (SCA). The simulation result shows that the proposed scheme provides better flexibility in resource allocation and requires less computational time compared to the state-of-art benchmark schemes. [less ▲]

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See detailSymbol-Level Precoding with Constellation Rotation in the Finite Block Length Regime
Kisseleff, Steven UL; Alves Martins, Wallace UL; Chatzinotas, Symeon UL et al

in IEEE Communications Letters (2021)

This paper tackles the problem of optimizing the parameters of a symbol-level precoder for downlink multiantenna multi-user systems in the finite block length regime. Symbol-level precoding (SLP) is a non ... [more ▼]

This paper tackles the problem of optimizing the parameters of a symbol-level precoder for downlink multiantenna multi-user systems in the finite block length regime. Symbol-level precoding (SLP) is a non-linear technique for multiuser wireless networks, which exploits constructive interference among co-channel links. Current SLP designs, however, implicitly assume asymptotically infinite blocks, since they do not take into account that the design rules for finite and especially short blocks might significantly differ. This paper fills this gap by introducing a novel SLP design based on discrete constellation rotations. The rotations are the added degree of freedom that can be optimized for every block to be transmitted, for instance, to save transmit power. Numerical evaluations of the proposed method indicate substantial power savings, which might be over 99% compared to the traditional SLP, at the expense of a single additional pilot symbol per block for constellation de-rotation. [less ▲]

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See detailA Novel Learning-based Hard Decoding Scheme and Symbol-Level Precoding Countermeasures
Mayouche, Abderrahmane UL; Alves Martins, Wallace UL; Tsinos, Christos G. et al

in IEEE Wireless Communications and Networking Conference (WCNC), Najing 29 March to 01 April 2021 (2021)

In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In ... [more ▼]

In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design a hard decoding scheme by using precoded pilot symbols as training data. Within this, we propose an ML framework for a multi-antenna hard decoder that allows an Eve to decode the transmitted message with decent accuracy. We show that MU-MISO systems are vulnerable to such an attack when conventional block-level precoding is used. To counteract this attack, we propose a novel symbol-level precoding scheme that increases the bit-error rate at Eve by obstructing the learning process. Simulation results validate both the ML-based attack as well as the countermeasure, and show that the gain in security is achieved without affecting the performance at the intended users. [less ▲]

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See detailPrecoding-Aided Bandwidth Optimization for High Throughput Satellite Systems
Abdu, Tedros Salih UL; Lei, Lei UL; Kisseleff, Steven UL et al

Scientific Conference (2021)

Linear precoding boosts the spectral efficiency of the satellite system by mitigating the interference signal. Typically, all users are precoded and share the same bandwidth regardless of the user demand ... [more ▼]

Linear precoding boosts the spectral efficiency of the satellite system by mitigating the interference signal. Typically, all users are precoded and share the same bandwidth regardless of the user demand. This bandwidth utilization is not efficient since the user demand permanently varies. Hence, demand-aware bandwidth allocation with linear precoding is promising. In this paper, we exploited the synergy of linear precoding and flexible bandwidth allocation for geostationary (GEO) high throughput satellite systems. We formulate an optimization problem with the goal to satisfy the demand by taking into account that multiple precoded user groups can share the different bandwidth chunks. Hence, optimal beam groups are selected with minimum bandwidth requirement to match the per beam demand. The simulation results show that the proposed method of combining bandwidth allocation and linear precoding has better bandwidth efficiency and demand satisfaction than benchmark schemes. [less ▲]

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See detailAn SDN Based Testbed for Dynamic Network Slicing in Satellite-Terrestrial Networks
Mendoza Montoya, Jesus Fabien; Minardi, Mario UL; Chatzinotas, Symeon UL et al

in IEEE MeditCom proceeding (2021)

6G networks are expected to meet ambitious perfor- mance parameters of coverage, data rates, latency, etc. To fulfill these objectives, the implementation of non-GEO satellite con- stellations is expected ... [more ▼]

6G networks are expected to meet ambitious perfor- mance parameters of coverage, data rates, latency, etc. To fulfill these objectives, the implementation of non-GEO satellite con- stellations is expected to improve coverage, capacity, resilience, etc. as well as the implementation of new advanced network virtualization algorithms in order to optimize network resources. However, the integration of these technologies represents new challenges, such as the execution of network slicing schemes in highly dynamic environments and network awareness require- ments. In this regard, Software Defined Networking (SDN) is seen as a required 6G technology enabler in order to provide better satellite-terrestrial integration approaches and Virtual Network (VN) implementation solutions. In this paper, we present an experimental testbed for non-GEO satellite constellations integration solution and VNE algorithms implementation adapted to highly variable network conditions that builds upon SDN. A laboratory testbed has been developed and validated, consisting in SDN-based satellite-terrestrial dynamic substrate network emulated in Mininet, a Ryu SDN controller with an End-to-End (E2E) Traffic Engineering (TE) application for the VNs estab- lishment and a Virtual Network Embedding (VNE) algorithm implemented in Matlab. [less ▲]

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See detailData-driven Precoded MIMO Detection Robust to Channel Estimation Errors
Mayouche, Abderrahmane UL; Alves Martins, Wallace UL; Chatzinotas, Symeon UL et al

in IEEE Open Journal of the Communications Society (2021)

We study the problem of symbol detection in downlink coded multiple-input multiple-output (MIMO) systems with precoding and without the explicit knowledge of the channel-state information (CSI) at the ... [more ▼]

We study the problem of symbol detection in downlink coded multiple-input multiple-output (MIMO) systems with precoding and without the explicit knowledge of the channel-state information (CSI) at the receiver. In this context, we investigate the impact of imperfect CSI at the transmitter (CSIT) on the detection performance. We first model the CSIT degradation based on channel estimation errors to investigate its impact on the detection performance at the receiver. To mitigate the effect of CSIT deterioration at the latter, we propose learning based techniques for hard and soft detection that use downlink precoded pilot symbols as training data. We note that these pilots are originally intended for signal-to-interference-plus-noise ratio (SINR) estimation. We validate the approach by proposing a lightweight implementation that is suitable for online training using several state-of-the-art classifiers. We compare the bit error rate (BER) and the runtime complexity of the proposed approaches where we achieve superior detection performance in harsh channel conditions while maintaining low computational requirements. Specifically, numerical results show that severe CSIT degradation impedes the correct detection when a conventional detector is used. However, the proposed learning-based detectors can achieve good detection performance even under severe CSIT deterioration, and can yield 4-8 dB power gain for BER values lower than 10-4 when compared to the classic linear minimum mean square error (MMSE) detector. [less ▲]

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See detailPrecoding for Satellite Communications: Why, How and What next?
Mysore Rama Rao, Bhavani Shankar UL; Lagunas, Eva UL; Chatzinotas, Symeon UL et al

in IEEE Communications Letters (2021)

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See detailDemand-based Scheduling for Precoded Multibeam High-Throughput Satellite Systems
Jubba Honnaiah, Puneeth UL; Lagunas, Eva UL; Spano, Danilo et al

in IEEE Wireless Communications and Networking Conference (WCNC), March 2021 (2021)

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See detailSatellite Broadband Capacity-on-Demand: Dynamic Beam Illumination with Selective Precoding
Chen, Lin UL; Lagunas, Eva UL; Chatzinotas, Symeon UL et al

in European Signal Processing Conference (EUSIPCO), Dublin, Ireland, Aug. 2021 (2021)

Efficient satellite resource utilization is one of the key challenges in next generation high-throughput satellite communication system. In this context, dynamic coverage scheduling based on traffic ... [more ▼]

Efficient satellite resource utilization is one of the key challenges in next generation high-throughput satellite communication system. In this context, dynamic coverage scheduling based on traffic demand has emerged as a promising solution, focusing system capacity into geographical areas where it is needed. Conventional Beam Hopping (BH) satellite system exploit the time-domain flexibility, which provides all available spectrum to a selected set of beams as long as they are not adjacent to each other. However, large geographical areas involving more than one adjacent beam may require full access to the available spectrum during particular instances of time. In this paper, we address this problem by proposing a dynamic beam illumination scheme combined with selective precoding, where only sub-sets of beams that are subject to strong inter-beam interference are precoded. With selective precoding, complexity at the groundsegment is reduced and only considered when needed. Supporting results based on numerical simulations show that the proposed scheme outperforms the relevant benchmarks in terms of demand matching performance. [less ▲]

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See detailScheduling Design and Performance Analysis of Carrier Aggregation in Satellite Communication Systems
Al-Hraishawi, Hayder UL; Maturo, Nicola UL; Lagunas, Eva UL et al

in IEEE Transactions on Vehicular Technology (2021)

Carrier Aggregation is one of the vital approaches to achieve several orders of magnitude increase in peak data rates. While carrier aggregation benefits have been extensively studied in cellular networks ... [more ▼]

Carrier Aggregation is one of the vital approaches to achieve several orders of magnitude increase in peak data rates. While carrier aggregation benefits have been extensively studied in cellular networks, its application to satellite systems has not been thoroughly explored yet. Carrier aggregation can offer an enhanced and more consistent quality of service for users throughout the satellite coverage via combining multiple carriers, utilizing the unused capacity at other carriers, and enabling effective interference management. Furthermore, carrier aggregation can be a prominent solution to address the issue of the spatially heterogeneous satellite traffic demand. This paper investigates introducing carrier aggregation to satellite systems from a link layer perspective. Deployment of carrier aggregation in satellite systems with the combination of multiple carriers that have different characteristics requires effective scheduling schemes for reliable communications. To this end, a novel load balancing scheduling algorithm has been proposed to distribute data packets across the aggregated carriers based on channel capacities and to utilize spectrum efficiently. Moreover, in order to ensure that the received data packets are delivered without perturbing the original transmission order, a perceptive scheduling algorithm has been developed that takes into consideration channel properties along with the instantaneous available resources at the aggregated carriers. The proposed modifications have been carefully designed to make carrier aggregation transparent above the medium access control (MAC) layer. Additionally, the complexity analysis of the proposed algorithms has been conducted in terms of the computational loads. Simulation results are provided to validate our analysis, demonstrate the design tradeoffs, and to highlight the potentials of carrier aggregation applied to satellite communication systems. [less ▲]

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See detailAnalog Beamforming with Antenna Selection for Large-Scale Antenna Arrays
Arora, Aakash UL; Tsinos, Christos; Mysore Rama Rao, Bhavani Shankar UL et al

in Proc. 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2021)

In large-scale antenna array (LSAA) wireless communication systems employing analog beamforming architectures, the placement or selection of a subset of antennas can significantly reduce the power ... [more ▼]

In large-scale antenna array (LSAA) wireless communication systems employing analog beamforming architectures, the placement or selection of a subset of antennas can significantly reduce the power consumption and hardware complexity. In this work, we propose a joint design of analog beamforming with antenna selection (AS) or antenna placement (AP) for an analog beamforming system. We approach this problem from a beampattern matching perspective and formulate a sparse unit-modulus least-squares (SULS) problem, which is a nonconvex problem due to the unit-modulus and the sparsity constraints. To that end, we propose an efficient and scalable algorithm based on the majorization-minimization (MM) framework for solving the SULS problem. We show that the sequence of iterates generated by the algorithm converges to a stationary point of the problem. Numerical results demonstrate that the proposed joint design of analog beamforming with AS outperforms conventional array architectures with fixed inter-antenna element spacing. [less ▲]

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