References of "Ha, Vu Nguyen 50043938"
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See detailGEO Payload Power Minimization: Joint Precoding and Beam Hopping Design
Ha, Vu Nguyen UL; Nguyen, Ti Ti; Lagunas, Eva UL et al

in Proceedings of IEEE Global Communications Conference GLOBECOM 2022 (2022, December 05)

This paper aims to determine linear precoding (LP) vectors, beam hopping (BH), and discrete DVB-S2X transmission rates jointly for the GEO satellite communication systems to minimize the payload power ... [more ▼]

This paper aims to determine linear precoding (LP) vectors, beam hopping (BH), and discrete DVB-S2X transmission rates jointly for the GEO satellite communication systems to minimize the payload power consumption and satisfy ground users’ demands within a time window. Regarding constraint on the maximum number of illuminated beams per time slot, the technical requirement is formulated as a sparse optimization problem in which the hardware-related beam illumination energy is modeled in a sparsity form of the LP vectors. To cope with this problem, the compressed sensing method is employed to transform the sparsity parts into the quadratic form of precoders. Then, an iterative window-based algorithm is developed to update the LP vectors sequentially to an efficient solution. Additionally, two other two-phase frameworks are also proposed for comparison purposes. In the first phase, these methods aim to determine the MODCOD transmission schemes for users to meet their demands by using a heuristic approach or DNN tool. In the second phase, the LP vectors of each time slot will be optimized separately based on the determined MODCOD schemes. [less ▲]

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See detailJoint Linear Precoding and DFT Beamforming Design for Massive MIMO Satellite Communication
Ha, Vu Nguyen UL; Abdullah, Zaid UL; Eappen, Geoffrey UL et al

in IEEE Global Communications Conference GLOBECOM 2022 (2022, December)

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See detailNon-Coherent Massive MIMO Integration in Satellite Communication
Monzon Baeza, Victor UL; Ha, Vu Nguyen UL; Querol, Jorge UL et al

Scientific Conference (2022, October)

Massive Multiple Input-Multiple Output (mMIMO) technique has been considered an efficient standard to improve the transmission rate significantly for the following wireless communication systems, such as ... [more ▼]

Massive Multiple Input-Multiple Output (mMIMO) technique has been considered an efficient standard to improve the transmission rate significantly for the following wireless communication systems, such as 5G and beyond. However, implementing this technology has been facing a critical issue of acquiring much channel state information. Primarily, this problem becomes more criticising in the integrated satellite and terrestrial networks (3GPP-Release 15) due to the countable high transmission delay. To deal with this challenging problem, the mMIMO-empowered non-coherent technique can be a promising solution. To our best knowledge, this paper is the first work considering employing the non-coherent mMIMO in satellite communication systems. This work aims to analyse the challenges and opportunities emerging with this integration. Moreover, we identified the issues in this conjunction. The preliminary results presented in this work show that the performance measured in bit error rate (BER) and the number of antennas are not far from that required for terrestrial links. Furthermore, thanks to mMIMO in conjunction with the non-coherent approach, we can work in a low signal-to-noise ratio (SNR) regime, which is an excellent advantage for satellite links. [less ▲]

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See detailNovel Reinforcement Learning based Power Control and Subchannel Selection Mechanism for Grant-Free NOMA URLLC-Enabled Systems
Tran, Duc Dung UL; Ha, Vu Nguyen UL; Chatzinotas, Symeon UL

in Proceedings of 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) (2022, August)

Reducing waiting time due to scheduling process and exploiting multi-access transmission, grant-free non-orthogonal multiple access (GF-NOMA) has been considered as a promising access technology for URLLC ... [more ▼]

Reducing waiting time due to scheduling process and exploiting multi-access transmission, grant-free non-orthogonal multiple access (GF-NOMA) has been considered as a promising access technology for URLLC-enabled 5G system with strict requirements on reliability and latency. However, GF-NOMAbased systems can suffer from severe interference caused by the grant-free (GF) access manner which may degrade the system performance and violate the URLLC-related requirements. To overcome this issue, the paper proposes a novel reinforcementlearning (RL)-based random access (RA) protocol based on which each device can learn from the previous decision and its corresponding performance to select the best subchannels and transmit power level for data transmission to avoid strong cross-interference. The learning-based framework is developed to maximize the system access efficiency which is defined as the ratio between the number of successful transmissions and the number of subchannels. Simulation results show that our proposed framework can improve the system access efficiency significantly in overloaded scenarios. [less ▲]

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See detailQoE-Oriented Resource Allocation Design Coping with Time-Varying Demands in Wireless Communication Networks
Kebedew, Teweldebrhan Mezgebo UL; Ha, Vu Nguyen UL; Lagunas, Eva UL et al

in IEEE 96st Vehicular Technology Conference, London-Beijing, Sept. 2022 (2022)

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See detailCombining Time-Flexible Satellite Payload with Precoding: The Cluster Hopping Approach
Gupta, Vaibhav Kumar UL; Ha, Vu Nguyen UL; Lagunas, Eva UL et al

in IEEE Transactions on Vehicular Technology (2022)

High throughput geostationary (GEO) satellite systems are characterized by a multi-beam wide coverage. However, developing efficient resource management mechanisms to meet the heterogeneous user traffic ... [more ▼]

High throughput geostationary (GEO) satellite systems are characterized by a multi-beam wide coverage. However, developing efficient resource management mechanisms to meet the heterogeneous user traffic demands remains an open challenge for satellite operators. Furthermore, the spectrum shortage and the ever increasing demands claim for more aggressive frequency reuse. In this paper, we combine the time-flexible payload capabilities known as beam hopping (BH) with precoding techniques in order to satisfy user traffic requests in areas of high demand (i.e. hot-spot areas). The proposed framework considers a flexible beam-cluster hopping where adjacent beams can be activated if needed, forming clusters with various shapes and sizes. In this context, we present three strategies to design the beam illumination patterns. First, a max-min user demand fairness satisfaction problem; second, a penalty-based optimization is considered to penalize the occurrence of adjacent beams in an attempt to avoid precoding whenever possible. Third, seeking a low-complexity design, we propose a queuing-based approach to solve the problem in a time-slot by time-slot basis trying to provide service to users based on the requested demands. The three methods are discussed in detailed and evaluated via numerical simulations, confirming their effectiveness versus benchmark schemes and identifying the pros and cons of each proposed design. [less ▲]

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See detailMulticast MMSE-based Precoded Satellite Systems: User Scheduling and Equivalent Channel Impact
Lagunas, Eva UL; Ha, Vu Nguyen UL; Chien, Trinh-Van et al

in IEEE 96st Vehicular Technology Conference, London-Beijing, Sept. 2022 (2022)

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See detailThe Next Generation of Beam Hopping Satellite Systems: Dynamic Beam Illumination with Selective Precoding
Chen, Lin UL; Ha, Vu Nguyen UL; Lagunas, Eva UL et al

in IEEE Transactions on Wireless Communications (2022)

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See detailJoint Radio Resource Management and Link Adaptation for Multicasting 802.11ax-based WLAN Systems
Ha, Vu Nguyen UL; Kaddoum, Georges; Poitau, Gwenael

in IEEE Transactions on Wireless Communications (2021)

Adopting OFDMA and MU-MIMO techniques for both downlink and uplink IEEE 802.11ax will help next-generation WLANs efficiently cope with large numbers of devices but will also raise some research challenges ... [more ▼]

Adopting OFDMA and MU-MIMO techniques for both downlink and uplink IEEE 802.11ax will help next-generation WLANs efficiently cope with large numbers of devices but will also raise some research challenges. One of these is how to optimize the channelization, resource allocation, beamforming design, and MCS selection jointly for IEEE 802.11ax-based WLANs. In this paper, this technical requirement is formulated as a mixed-integer non-linear programming problem maximizing the total system throughput for the WLANs consisting of unicast users with multicast groups. A novel two-stage solution approach is proposed to solve this challenging problem. The first stage aims to determine the precoding vectors under unit-power constraints. These temporary precoders help re-form the main problem into a joint power and radio resource allocation one. Then, two low-complexity algorithms are proposed to cope with the new problem in stage two. The first is developed based on the well-known compressed sensing method while the second seeks to optimize each of the optimizing variables alternatively until reaching converged outcomes. The outcomes corresponding to the two stages are then integrated to achieve the complete solution. Numerical results are provided to confirm the superior performance of the proposed algorithms over benchmarks. [less ▲]

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See detailA survey of multi-access edge computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art
Pham, Quoc Viet; Fang, Fang; Ha, Vu Nguyen UL et al

in IEEE Access (2020)

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic ... [more ▼]

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research. [less ▲]

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See detailRSSI-Based Hybrid Beamforming Design with Deep Learning
Hojatian, Hamed; Ha, Vu Nguyen UL; Nadal, Jérémy et al

in 2020 IEEE International Conference on Communications Proceedings (2020, June 07)

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex ... [more ▼]

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required. [less ▲]

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See detailSystem Energy-Efficient Hybrid Beamforming for mmWave Multi-user Systems
Ha, Vu Nguyen UL; Nguyen, Duy H. N.; Frigon, Jean-Francois

in IEEE Transactions on Green Communications and Networking (2020), 4(4), 2473-2400

This paper develops energy-efficient hybrid beamforming designs for mmWave multi-user systems where analog precoding is realized by switches and phase shifters such that radio frequency (RF) chain to ... [more ▼]

This paper develops energy-efficient hybrid beamforming designs for mmWave multi-user systems where analog precoding is realized by switches and phase shifters such that radio frequency (RF) chain to transmit antenna connections can be switched off for energy saving. By explicitly considering the effect of each connection on the required power for baseband and RF signal processing, we describe the total power consumption in a sparsity form of the analog precoding matrix. However, these sparsity terms and sparsity-modulus constraints of the analog precoding make the system energy-efficiency maximization problem non-convex and challenging to solve. To tackle this problem, we first transform it into a subtractive-form weighted sum rate and power problem. A compressed sensing-based re-weighted quadratic-form relaxation method is employed to deal with the sparsity parts and the sparsity-modulus constraints. We then exploit alternating minimization of the mean-squared error to solve the equivalent problem where the digital precoding vectors and the analog precoding matrix are updated sequentially. The energy efficiency upper bound and a heuristic algorithm are also examined for comparison purposes. Numerical results confirm the superior performances of the proposed algorithm over benchmark energy-efficiency hybrid precoding algorithms and heuristic one. [less ▲]

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See detailAdmission Control and Network Slicing for Multi-Numerology 5G Wireless Networks
Ha, Vu Nguyen UL; Nguyen, Ti Ti; Le, Long Bao et al

in IEEE Networking Letters (2020)

This letter studies the admission control and network slicing design for 5G New Radio (5G-NR) systems in which the total bandwidth is sliced to support the enhanced mobile broadband (eMBB) and ultra ... [more ▼]

This letter studies the admission control and network slicing design for 5G New Radio (5G-NR) systems in which the total bandwidth is sliced to support the enhanced mobile broadband (eMBB) and ultra reliable and low latency communication (URLLC) services. We allow traffic from the eMBB bandwidth part to be overflowed to the URLLC bandwidth part in a controlled manner. We develop a mathematical framework to analyze the blocking probabilities of both eMBB and URLLC services based on which the network slicing and admission control is jointly optimized to minimize the blocking probability of the eMBB traffic subject to the blocking probability constraint for the URLLC traffic. An efficient iterative algorithm is proposed to deal with the underlying problem. [less ▲]

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See detailWireless Scheduling for Heterogeneous Services With Mixed Numerology in 5G Wireless Networks
Nguyen, Ti Ti; Ha, Vu Nguyen UL; Le, Long Bao

in IEEE Communications Letters (2019)

This letter studies the scheduling problem which determines how time-frequency resources of different numerologies can be allocated to support heterogeneous services in 5G wireless systems. Particularly ... [more ▼]

This letter studies the scheduling problem which determines how time-frequency resources of different numerologies can be allocated to support heterogeneous services in 5G wireless systems. Particularly, this problem aims at scheduling as many users as possible while meeting their required service delay and transmission data. To solve the underlying integer programming (IP) scheduling problem, we first transform it into an equivalent integer linear program (ILP) and then develop two algorithms, namely Resource Partitioning-based Algorithm (RPA) and Iterative Greedy Algorithm (IGA) to acquire efficient resource scheduling solutions. Numerical results show the desirable performance of the proposed algorithms with respect to the optimal solution and their complexity-performance tradeoffs. [less ▲]

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See detailJoint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems
Nguyen, Ti Ti; Ha, Vu Nguyen UL; Le, Long Bao et al

in IEEE Transactions on Wireless Communications (2019)

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the ... [more ▼]

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This optimization problem is studied in this paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. Numerical results show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65% compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. The proposed algorithms with additional DC at the fog server lead to a further reduction of the WEDC. [less ▲]

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See detailComputation offloading and resource allocation for backhaul limited cooperative MEC systems
Nguyen, Duy Phuong; Ha, Vu Nguyen UL; Le, Long Bao

in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) proceedings (2019, September 22)

In this paper, we jointly optimize computation offloading and resource allocation to minimize the weighted sum of energy consumption of all mobile users in a backhaul limited cooperative MEC system with ... [more ▼]

In this paper, we jointly optimize computation offloading and resource allocation to minimize the weighted sum of energy consumption of all mobile users in a backhaul limited cooperative MEC system with multiple fog servers. Considering the partial offloading strategy and TDMA transmission at each base station, the underlying optimization problem with constraints on maximum task latency and limited computation resource at mobile users and fog servers is non-convex. We propose to convexify the problem exploiting the relationship among some optimization variables from which an optimal algorithm is proposed to solve the resulting problem. We then present numerical results to demonstrate the significant gains of our proposed design compared to conventional designs without exploiting cooperation among fog servers and a greedy algorithm. [less ▲]

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See detailSubchannel Allocation and Hybrid Precoding in Millimeter-Wave OFDMA Systems
Ha, Vu Nguyen UL; Nguyen, Duy H. N.; Frigon, Jean-François

in IEEE Transactions on Wireless Communications (2018)

Constrained by the number of transmitted data streams, this paper proposes sub-carrier allocation (SA) and hybrid precoding (HP) designs for sum-rate maximization in mm-wave OFDMA systems. The ... [more ▼]

Constrained by the number of transmitted data streams, this paper proposes sub-carrier allocation (SA) and hybrid precoding (HP) designs for sum-rate maximization in mm-wave OFDMA systems. The optimization is first formulated as a computation sparsity-constrained HP design problem, which is non-convex and challenging to solve. Two two-stage solution approaches are proposed. In the first approach, a fully digital precoder (FDP) is optimized considering the computation sparsity constraint in the first stage. In the second approach, the sparsity constraint is only imposed in the second stage. To find the FDP, we employ the minimization of the weighted mean-squared error and the ℓ 1 -reweighted methods to tackle the non-convex objective function and sparsity constraints, respectively. In the second stage of each approach, we exploit an alternating weighted mean-squared error minimization algorithm to reconstruct HP's based on the FDP found in the first stage. Two novel analog precoding designs, namely semi-definite-relaxation-based and projected-gradient-descent-based, are then proposed to optimize the analog part of the obtained HP's. We also study the impacts of various system parameters on the system sum-rate and provide resource provisioning insights for HP systems. Numerical results show the superior performances of the proposed designs over joint SA and HP benchmark algorithms. [less ▲]

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See detailEnergy-Efficient Hybrid Precoding for mmWave Multi-User Systems
Ha, Vu Nguyen UL; Nguyen, Duy H. N.; Frigon, Jean-Francois

in 2018 IEEE International Conference on Communications (ICC) Proceedings (2018, May 20)

This paper aims to study an energy-efficiency (EE) maximization hybrid precoding (HP) design for mmWave multi-user (MU) systems where the analog precoding (AP) matrix is realized by a number of switches ... [more ▼]

This paper aims to study an energy-efficiency (EE) maximization hybrid precoding (HP) design for mmWave multi-user (MU) systems where the analog precoding (AP) matrix is realized by a number of switches and phase shifters so that a connection between an RF chain and a transmit antenna can be switched off for energy saving. By explicitly considering the effect of each connection on the required power of digital precoding (DP) and AP design process, we describe the total power consumption as a sparsity form of the AP matrix. Together with the novel sparsity-modulus constraints of AP matrix, these sparsity terms make our system EE maximization (SEEM) problem be non-convex and challenging to solve. To tackle the SEEM problem, we first transform it into a subtractive-form weighted sum rate and power (WSRP) problem. We then exploit an alternating minimization of the mean-squared error algorithm to solve the WSRP problem where the DP vectors and AP matrix are updated alternatively, and a compressed sensing-based re-weighted quadratic- form relaxation method is employed to deal with the sparsity parts and the sparsity-modulus constraints. [less ▲]

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See detailJoint subchannel allocation and hybrid precoding design for mmWave multi-user OFDMA systems
Ha, Vu Nguyen UL; Nguyen, Duy H. N.; Frigon, Jean-Francois

in 2017 IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) proceedings (2017, October 08)

This paper studies hybrid precoding (HP) for mmWave multi-user OFDMA systems with sub-carrier allocation (SA) consideration. Constrained by a computation limit on the total number of data streams that can ... [more ▼]

This paper studies hybrid precoding (HP) for mmWave multi-user OFDMA systems with sub-carrier allocation (SA) consideration. Constrained by a computation limit on the total number of data streams that can be processed, we aim to jointly optimize the SA and HP design to maximize the system sum-rate. This optimization is first formulated as a computation sparsity-constrained HP design problem, which is non-convex and challenging to solve. We then propose two-stage solution approach to tackle the problem. In stage one, we optimize the fully digital precoding (FDP) considering the computation sparsity constraint. In the second stage, we exploit an alternating MMSE minimization algorithm to reconstruct the HP's based on the achieved FDP. A novel analog precoding design, namely “Projected-Gradient-Descent based”, is then proposed to optimize the analog part of the HP's. [less ▲]

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See detailEnd-to-End Network Slicing in Virtualized OFDMA-Based Cloud Radio Access Networks
Ha, Vu Nguyen UL; Le, Long Bao

in IEEE Access (2017), 5

We consider the resource allocation for the virtualized OFDMA uplink cloud radio access network (C-RAN), where multiple wireless operators (OPs) share the C-RAN infrastructure and resources owned by an ... [more ▼]

We consider the resource allocation for the virtualized OFDMA uplink cloud radio access network (C-RAN), where multiple wireless operators (OPs) share the C-RAN infrastructure and resources owned by an infrastructure provider (InP). The resource allocation is designed through studying tightly coupled problems at two different levels. The upper-level problem aims at slicing the fronthaul capacity and cloud computing resources for all OPs to maximize the weighted profits of OPs and InP considering practical constraints on the fronthaul capacity and cloud computation resources. Moreover, the lower-level problems maximize individual OPs' sum rates by optimizing users' transmission rates and quantization bit allocation for the compressed I/Q baseband signals. We develop a two-stage algorithmic framework to address this two-level resource allocation design. In the first stage, we transform both upper-level and lowerlevel problems into corresponding problems by relaxing underlying discrete variables to the continuous ones. We show that these relaxed problems are convex and we develop fast algorithms to attain their optimal solutions. In the second stage, we propose two methods to round the optimal solution of the relaxed problems and achieve a final feasible solution for the original problem. Numerical studies confirm that the proposed algorithms outperform two greedy resource allocation algorithms and their achieved sum rates are very close to sum rate upper-bound obtained by solving relaxed problems. Moreover, we study the impacts of different parameters on the system sum rate, performance tradeoffs, and illustrate insights on a potential system operating point and resource provisioning issues. [less ▲]

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