Vu Nguyen Ha

Distributed Base Station Association and Power Control for Heterogeneous Cellular Networks

In this paper, we propose a universal joint base station (BS) association (BSA) and power control (PC) algorithm for heterogeneous cellular networks. Specifically, the proposed algorithm iteratively updates the BSA solution and the transmit power of each user. Here, the new transmit power level is expressed as a function of the power in the previous iteration, and this function is called the power update function (puf). We prove the convergence of this algorithm when the puf of the PC strategy satisfies the so-called “two-sided scalable (2.s.s.) function” property. Then, we develop a novel hybrid PC (HPC) scheme by using noncooperative game theory and prove that its corresponding puf is 2.s.s. Therefore, this HPC scheme can be employed in the proposed joint BSA and PC algorithm. We then devise an adaptation mechanism for the HPC algorithm so that it can support the signal-tointerference-plus-noise ratio (SINR) requirements of all users whenever possible while exploiting multiuser diversity to improve the system throughput. We show that the proposed HPC adaptation algorithm outperforms the well-known Foschini-Miljianic PC algorithm in both feasible and infeasible systems. In addition, we present the application of the developed framework to design a hybrid access scheme for two-tier macrocell-femtocell networks. Numerical results are then presented to illustrate the convergence of the proposed algorithms and their superior performance, compared with existing algorithms in the literature.

Fair Resource Allocation for OFDMA Femtocell Networks With Macrocell Protection

We consider the joint subchannel allocation and power control problem for orthogonal frequency-division multiple-access (OFDMA) femtocell networks in this paper. Specifically, we are interested in the fair resource-sharing solution for users in each femtocell that maximizes the total minimum spectral efficiency of all femtocells subject to protection constraints for the prioritized macro users. Toward this end, we present the mathematical formulation for the uplink resource-allocation problem and propose an optimal exhaustive search algorithm. Given the exponential complexity of the optimal algorithm, we develop a distributed and low-complexity algorithm to find an efficient solution for the problem. We prove that the proposed algorithm converges and we analyze its complexity. Then, we extend the proposed algorithm in three different directions, namely, downlink context, resource allocation with rate adaption for femto users, and consideration of a hybrid access strategy where some macro users are allowed to connect with nearby femto base stations (FBSs) to improve the performance of the femto tier. Finally, numerical results are presented to demonstrate the desirable performance of the proposed algorithms.

Cooperative transmission in cloud RAN considering fronthaul capacity and cloud processing constraints

We investigate the cooperative transmission design for the cloud radio access network (C-RAN) considering fronthaul capacity and cloud processing constraints. Specifically, we consider the joint transmission scheme where the baseband signals and precoding vectors are processed and calculated by the cloud, which are delivered over the fronthaul links to the remote radio heads (RRHs) to form the RF signals for being transmitted to the users. We formulate the joint optimization problem for precoding design and allocation of RRHs, fronthaul capacity, and BBU processing resources to minimize the total transmission power subject to QoS constraints of the users. We present both optimal exhaustive search algorithm and two low-complexity algorithms to solve the resource allocation problem where the first one can achieve the Pareto optimality and the second one can determine an efficient solution with pretty low complexity. Numerical results confirm the excellent performance of the proposed low-complexity algorithms.

Energy-efficient coordinated transmission for Cloud-RANs: Algorithm design and trade-off

In this paper, we consider the energy-efficient co-ordinated transmission design for downlink transmission in the cloud radio access network (Cloud-RAN) considering fronthaul capacity and user QoS constraints. Specifically, we assume that baseband signals are processed in the cloud, which are delivered to remote radio heads (RRHs) equipped with multiple antennas over fronthaul links for transmissions to single-antenna users. The design amounts to determine the set of RRHs to serve each user as well as the precoding and power levels for downlink transmission while maintaining the fronthaul capacity and user QoS constraints. Toward this end, we study the two closely-related problems, namely pricing-based total power and fronthaul capacity tradeoff (PFT) and fronthaul-constrained power minimization (FCPM) problems. We employ the concave approximation and gradient search methods to solve the PFT problem for the given pricing coefficients, which capture the power and fronthaul capacity tradeoff. Then, we develop an efficient algorithm to address the FCPM problem by iteratively solving the PFT problem while intelligently updating the pricing coefficients. Numerical results confirm the excellent performance of the our proposed algorithms and illustrate underlying tradeoffs among total transmission power, fronthaul capacity, and cluster size.

Joint coordinated beamforming and admission control for fronthaul constrained cloud-RANs

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.

Resource Allocation Optimization in Multi-User Multi-Cell Massive MIMO Networks Considering Pilot Contamination

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.

Computation capacity constrained joint transmission design for C-RANs

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.

Sparse precoding design for cloud-RANs sum-rate maximization

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.

End-to-End Network Slicing in Virtualized OFDMA-Based Cloud Radio Access Networks

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 lower-level 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.

Dynamic Resource Allocation for Full-Duplex OFDMA Wireless Cellular Networks

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.