Browse ORBi

- What it is and what it isn't
- Green Road / Gold Road?
- Ready to Publish. Now What?
- How can I support the OA movement?
- Where can I learn more?

ORBi

Joint Radio Resource Management and Link Adaptation for Multicasting 802.11ax-based WLAN Systems Ha, Vu Nguyen ; ; 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 ▲] Detailed reference viewed: 32 (4 UL)A survey of multi-access edge computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art ; ; Ha, Vu Nguyen 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 ▲] Detailed reference viewed: 26 (0 UL)RSSI-Based Hybrid Beamforming Design with Deep Learning ; Ha, Vu Nguyen ; 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 ▲] Detailed reference viewed: 25 (0 UL)System Energy-Efficient Hybrid Beamforming for mmWave Multi-user Systems Ha, Vu Nguyen ; ; 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 ▲] Detailed reference viewed: 23 (0 UL)Admission Control and Network Slicing for Multi-Numerology 5G Wireless Networks Ha, Vu Nguyen ; ; 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 ▲] Detailed reference viewed: 27 (4 UL)Wireless Scheduling for Heterogeneous Services With Mixed Numerology in 5G Wireless Networks ; Ha, Vu Nguyen ; 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 ▲] Detailed reference viewed: 26 (0 UL)Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems ; Ha, Vu Nguyen ; 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 ▲] Detailed reference viewed: 28 (0 UL)Computation offloading and resource allocation for backhaul limited cooperative MEC systems ; Ha, Vu Nguyen ; 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 ▲] Detailed reference viewed: 25 (0 UL)Subchannel Allocation and Hybrid Precoding in Millimeter-Wave OFDMA Systems Ha, Vu Nguyen ; ; 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 ▲] Detailed reference viewed: 50 (0 UL)Energy-Efficient Hybrid Precoding for mmWave Multi-User Systems Ha, Vu Nguyen ; ; 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 ▲] Detailed reference viewed: 44 (0 UL)Joint subchannel allocation and hybrid precoding design for mmWave multi-user OFDMA systems Ha, Vu Nguyen ; ; 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 ▲] Detailed reference viewed: 22 (0 UL)End-to-End Network Slicing in Virtualized OFDMA-Based Cloud Radio Access Networks Ha, Vu Nguyen ; 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 ▲] Detailed reference viewed: 26 (0 UL)Uplink/downlink matching based resource allocation for full-duplex OFDMA wireless cellular networks ; Ha, Vu Nguyen ; et al in 2017 IEEE Wireless Communications and Networking Conference (WCNC) proceedings (2017, March 19) In this paper, we study the resource allocation problem for a full-duplex (FD) multiuser wireless system consisting of one FD base-station (BS) and multiple FD mobile nodes. Our main focus is to jointly ... [more ▼] In this paper, we study the resource allocation problem for a full-duplex (FD) multiuser wireless system consisting of one FD base-station (BS) and multiple FD mobile nodes. Our main focus is to jointly optimize the power allocation (PA) and subcarrier assignment (SA) for both uplink (UL) and downlink (DL) transmissions of all users to maximize the system sum-rate. Our design captures the self-interference of FD transceivers and allows the utilization of each subcarrier for multiple concurrent UP and DL transmissions. Since the joint optimization problem is a nonconvex mixed integer program, which is difficult to tackle, we propose to employ the bipartite matching method to address the SA. Toward this end, a fast greedy allocation algorithm is developed to perform initial assignment of UL/DL links to each subcarrier that offers the best sum rate. Then from the obtained SA solution, we adopt the successive convex approximation approach to solve the PA problem whose results are used to calculate the SA weights for re-optimizing the SA by using the bipartite matching method. We then present the numerical results to demonstrate the improvement of our proposed algorithm in comparison with the greedy FD and half-duplex (HD) resource allocation algorithms. [less ▲] Detailed reference viewed: 26 (0 UL)Dynamic Resource Allocation for Full-Duplex OFDMA Wireless Cellular Networks ; Ha, Vu Nguyen ; et al in 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall) proceedings (2016, September 18) This paper focuses on the resource allocation in a full-duplex (FD) multiuser single cell system consisting of one FD base-station (BS) and multiple FD mobile nodes. In particular, we are interested in ... [more ▼] This paper focuses on the resource allocation in a full-duplex (FD) multiuser single cell system consisting of one FD base-station (BS) and multiple FD mobile nodes. In particular, we are interested in jointly optimizing the power allocation (PA) and subcarrier assignment (SA) for uplink (UL) and downlink (DL) transmission of all users to maximize the system sum-rate. First, the joint optimization problem is formulated as nonconvex mixed integer program, a difficult nonconvex problem. We then propose an iterative algorithm to solve this problem. In the proposed algorithm, the PA is obtained by employing the SCALE algorithm, whereas the SA is updated by a gradient method. Finally, we present numerical results to demonstrate the significant gains of our proposed design compared to that due to two fast greedy algorithms. [less ▲] Detailed reference viewed: 25 (0 UL)Coordinated Multipoint Transmission Design for Cloud-RANs With Limited Fronthaul Capacity Constraints Ha, Vu Nguyen ; ; in IEEE Transactions on Vehicular Technology (2016), 65(9), 7432-7447 In this paper, we consider the coordinated multipoint (CoMP) transmission design for the downlink cloud radio access network (Cloud-RAN). Our design aims to optimize the set of remote radio heads (RRHs ... [more ▼] In this paper, we consider the coordinated multipoint (CoMP) transmission design for the downlink cloud radio access network (Cloud-RAN). Our design aims to optimize the set of remote radio heads (RRHs) serving each user and the precoding and transmission power to minimize the total transmission power while maintaining the fronthaul capacity and users' quality-of-service (QoS) constraints. The fronthaul capacity constraint involves a nonconvex and discontinuous function that renders the optimal exhaustive search method unaffordable for large networks. To address this challenge, we propose two low-complexity algorithms. The first pricing-based algorithm solves the underlying problem through iteratively tackling a related pricing problem while appropriately updating the pricing parameter. In the second iterative linear-relaxed algorithm, we directly address the fronthaul constraint function by iteratively approximating it with a suitable linear form using a conjugate function and solving the corresponding convex problem. For performance evaluation, we also compare our proposed algorithms with two existing algorithms in the literature. Finally, extensive numerical results are presented, which illustrate the convergence of our proposed algorithms and confirm that our algorithms significantly outperform the state-of-the-art existing algorithms. [less ▲] Detailed reference viewed: 24 (0 UL)Resource allocation for uplink OFDMA C-RANs with limited computation and fronthaul capacity Ha, Vu Nguyen ; in 2016 IEEE International Conference on Communications (ICC) proceedings (2016, May 22) This paper considers the joint fronthaul resource and rate allocation for the OFDMA uplink cloud radio access networks (C-RANs). This amounts to determine users' transmission rates and quantization bit ... [more ▼] This paper considers the joint fronthaul resource and rate allocation for the OFDMA uplink cloud radio access networks (C-RANs). This amounts to determine users' transmission rates and quantization bit allocation for I/Q baseband signals, which must be transferred from remote radio heads (RRHs) to the cloud over the capacity-limited fronthaul network. Our design aims at maximizing the system sum rate through optimal allocation of fronthaul capacity and cloud computation resources. Toward this end, we propose a novel two-stage approach to solve the underlying non-linear integer problem. In the first stage, we relax the integer variables to attain a relaxed problem, which is solved by employing a pricing-based method. Interestingly, we show that the pricing-based problem is convex with respect to each optimization variable, which can be, therefore, solved efficiently. In addition, we develop a novel mechanism to iteratively update the pricing parameter which is proved to converge. In the second stage, we propose two different rounding strategies, which are applied to the obtained continuous solution of the relaxed problem to achieve a feasible solution for the original problem. Finally, we present numerical results to demonstrate the significant sum-rate gains of our proposed design with respect to a standard greedy algorithm. [less ▲] Detailed reference viewed: 24 (0 UL)Computation capacity constrained joint transmission design for C-RANs Ha, Vu Nguyen ; in 2016 IEEE Wireless Communications and Networking Conference proceedings (2016, April 03) This paper considers the joint processing design for the cloud radio access network (C-RAN) with limited cloud computation capacity. This amounts to determine the set of remote radio heads (RRHs) serving ... [more ▼] This paper considers the joint processing design for the cloud radio access network (C-RAN) with limited cloud computation capacity. This amounts to determine the set of remote radio heads (RRHs) serving each user and the corresponding precoding vectors whose corresponding computation effort (CE) is a non-linear function of the number of antennas pooled from all serving RRHs and the modulation bits. Toward this end, we propose a novel three-step approach to solve the underlying mixed non-linear integer program. First, we transform this problem into a group association problem (GAP) with additional association constraints where each user must be associated with exactly one particular group of RRHs. Second, we study the relaxed power minimization problem (PMP) where the group association integer variables are relaxed and the computational constraint functions are approximated by weighted linear functions of transmission powers. We prove that this relaxed PMP can be solved optimally and the obtained optimal solution satisfies all association constraints of the original GAP problem. Third, we develop an iterative procedure to update the weight parameters of the approximated computational constraint functions to drive the achieved solution to an efficient and feasible solution of the original problem. Finally, we present numerical results to demonstrate the significant gains of our proposed design compared to that due to a fast greedy algorithm. [less ▲] Detailed reference viewed: 23 (0 UL)Resource allocation optimization in multi-user multi-cell massive MIMO networks considering pilot contamination ; Ha, Vu Nguyen ; in IEEE Access (2015), 3 In this paper, we study the joint pilot assignment and resource allocation for system energy efficiency (SEE) maximization in the multi-user and multi-cell massive multi-input multi-output network. We ... [more ▼] In this paper, we study the joint pilot assignment and resource allocation for system energy efficiency (SEE) maximization in the multi-user and multi-cell massive multi-input multi-output network. We explicitly consider the pilot contamination effect during the channel estimation in the SEE maximization problem, which aims to optimize the power allocation, the number of activated antennas, and the pilot assignment. To tackle the SEE maximization problem, we transform it into a subtractive form, which can be solved more efficiently. In particular, we develop an iterative algorithm to solve the transformed problem where optimization of power allocation and number of antennas is performed, and then pilot assignment optimization is conducted sequentially in each iteration. To tackle the first sub-problem, we employ a successive convex approximation (SCA) technique to attain a solvable convex optimization problem. Moreover, we propose a novel iterative low-complexity algorithm based on the Hungarian method to solve the pilot assignment sub-problem. We also describe how the proposed solution approach can be useful to address the sum rate (SR) maximization problem. In addition to the algorithmic developments, we characterize the optimal structure of both SEE and SR maximization problems. The numerical studies are conducted to illustrate the convergence of the proposed algorithms, impacts of different parameters on the SR and SEE, and significant performance gains of the proposed solution compared the conventional design. [less ▲] Detailed reference viewed: 32 (0 UL)Sparse precoding design for cloud-RANs sum-rate maximization Ha, Vu Nguyen ; ; in 2015 IEEE Wireless Communications and Networking Conference (WCNC) proceedings (2015, March 09) This paper considers a sparse precoding design for sum-rate maximization in a cloud radio access network (Cloud-RAN). Constrained by the fronthaul link capacity and transmit power limit at each remote ... [more ▼] This paper considers a sparse precoding design for sum-rate maximization in a cloud radio access network (Cloud-RAN). Constrained by the fronthaul link capacity and transmit power limit at each remote radio head (RRH), the sparse design amounts to determine the precoders at the RRHs as well as the set of serving RRHs for each mobile user. In this work, we first formulate the fronthaul link constraints as non-convex and discontinuous constraints with sparsity terms. These sparsity terms are then iteratively approximated into linear forms by means of reweighted ℓ1-norm with conjugate functions. Finally, to determine the beamforming vectors, the non-convex sum-rate maximization problem with linear constraints is transformed into an equivalent problem of iterative weighted mean-squared error minimization. Convergence of the proposed iterative algorithm is then proved and verified by the presented numerical results. In addition, numerical results demonstrate the superior performance by the proposed algorithm over a previously proposed one in literature. [less ▲] Detailed reference viewed: 26 (0 UL)Joint coordinated beamforming and admission control for fronthaul constrained cloud-RANs Ha, Vu Nguyen ; in 2014 IEEE Global Communications Conference proceedings (2014, December 08) In this paper, we consider the joint coordinated beamforming and admission control design for cloud radio access networks (Cloud-RANs). Specifically, the set of multi-antenna remote radio heads (RRHs ... [more ▼] In this paper, we consider the joint coordinated beamforming and admission control design for cloud radio access networks (Cloud-RANs). Specifically, the set of multi-antenna remote radio heads (RRHs) serving each single-antenna user and the corresponding beamforming vectors are optimized to minimize the total transmission power subject to constraints on the capacity of fronthaul links, maximum powers of RRHs, and the minimum signal to interference plus noise ratios (SINRs) of users. Since the minimum SINR requirements of all users may not be guaranteed, some users may need to be removed so that all constraints can be satisfied. This NP-hard beamforming and admission control problem can be typically solved via a greedy algorithm. We instead propose a novel convex relaxation approach to formulate the underlying problem to a single-stage semi-definite program (SDP) based on which we develop an iterative algorithm to solve it. We then present numerical results to demonstrate the significant gains of the proposed algorithm compared to the greedy counterpart. Also, the impacts of the target SINR and cluster size on the number of supported users and total transmission power are also studied. [less ▲] Detailed reference viewed: 25 (0 UL) |
||