Illsoo Sohn

Comparison of Fractional Frequency Reuse Approaches in the OFDMA Cellular Downlink

Fractional frequency reuse (FFR) is an interference coordination technique well-suited to OFDMA based wireless networks wherein cells are partitioned into spatial regions with different frequency reuse factors. This work focuses on evaluating the two main types of FFR deployments: Strict FFR and Soft Frequency Reuse (SFR). Relevant metrics are discussed, including outage probability, network throughput, spectral efficiency, and average cell- edge user SINR. In addition to analytical expressions for outage probability, system simulations are used to compare Strict FFR and SFR with universal frequency reuse based on a typical OFDMA deployment and uniformly distributed users. Based on the analysis and numerical results, system design guidelines and a detailed picture of the tradeoffs associated with the FFR systems are presented, showing that Strict FFR provides the greatest overall network throughput and highest cell-edge user SINR, while SFR balances the requirements of interference reduction and resource efficiency.

Belief Propagation for Distributed Downlink Beamforming in Cooperative MIMO Cellular Networks

We propose a new graphical model approach to cooperative multiple-input multiple-output (MIMO) cellular networks. The objective is to optimize downlink transmit beamforming at each BS in order to maximize the sum throughput over the entire network. While ideal centralized beamforming requires full channel state information (CSI) sharing among all BSs in the network and huge computational complexity for combinatorial optimization, the proposed graphical model enables distributed beamforming which requires only local CSI sharing between neighboring BSs and efficiently solves the optimization problem in a distributed manner. As distributed solvers for this problem, we derive message-passing algorithms which can be implemented with polynomial-time computational complexity. Furthermore, we make a slight approximation on the objective function to derive a simpler graphical model, providing further complexity saving. Simulation results indicate that the proposed distributed downlink beamforming achieves average cell throughput typically within just 2% of ideal centralized beamforming.

A Graphical Model Approach to Downlink Cooperative MIMO Systems

We propose a new method for computing transmit beamforming vectors for downlink multicell multiple- input multiple-output (MIMO) networks. The key novelty of the work is the application of graphical model theory to the intercell interference problem. An index restriction beamforming strategy is adopted to mitigate intercell interference, and is implemented with the well-known message-passing algorithm known as belief-propagation. The advantage of combining restricted index beamforming with belief propagation is only local channel information is required, and the computational complexity is lower than competing methods for base station cooperation. Numerical results show that the network throughput of downlink multicell MIMO systems is increased by the proposed cooperative method especially for cell edge users while the computational complexity is kept similar to non- cooperative methods.

MIMO Broadcast Channels with Spatial Heterogeneity

We develop a realistic model for multiple-input multiple-output (MIMO) broadcast channels, where each randomly located users average SNR depends on its distance from the transmitter. With perfect channel state information at the transmitter (CSIT), the average sum capacity is proven to scale for many users like αM/2 log K instead of M log log K, where α, M, and K denote the path loss exponent, the number of transmit antennas, and the number of users in a cell. With only partial CSIT, the sum capacity at high SNR eventually saturates due to interference, and the saturation value scales for large B like MB/M-1, where B denotes the quantization resolution for channel feedback.

Low-Energy Adaptive Clustering Hierarchy Using Affinity Propagation for Wireless Sensor Networks

This letter proposes a new low-energy adaptive clustering hierarchy (LEACH) protocol for wireless sensor networks that use a distributed cluster formation based on affinity propagation (AP). The proposed LEACH protocol (LEACH-AP) enables a fully distributed control and resolves practical limitations of conventional LEACH-based protocols by simplifying network functionalities and reducing sensor hardware costs. Simulation results show that the proposed protocol outperforms existing LEACH-based protocols considerably in terms of network lifetime, energy dissipation rate, and total number of transferred bits.

Generalized MMSE Beamforming for Downlink MIMO Systems

In this paper, a generalized MMSE beamforming is proposed for downlink multiple input multiple output (MIMO) systems. Unlike the conventional MMSE beamforming, cellular environments are considered where each user is randomly distributed in a cell and has the different temporal correlation of fading channel. We apply the proposed generalized MMSE beamforming to two important downlink MIMO scenarios, which are multiuser MIMO (MU-MIMO) and multicell MIMO (MC-MIMO). We derive the closed-form expressions of the generalized MMSE beamforming for both scenarios using convex optimization technique. The derived beamforming solution captures the impacts of the random geometry and different temporal correlations of users. Numerical results verify that the generalized MMSE beamforming achieves lower BER and higher average sum-rate than the previous beamforming schemes developed for MU-MIMO and MC-MIMO including the conventional MMSE beamforming.

Distributed Load Balancing via Message Passing for Heterogeneous Cellular Networks

This paper presents a distributed load-balancing algorithm that maximizes the network-wide sum rate in heterogeneous cellular networks (HetNets). Unlike previous studies that have considered a logarithmic utility defined with respect to the sum rate, we maximize the sum rate directly to achieve the best user association for the HetNet. To capture realistic communication scenarios, we also consider a minimum rate constraint for individual users. The corresponding problem is formulated as a combinatorial optimization of which finding the solution becomes computationally demanding as the size of the network grows. Another challenge in HetNets is that the information exchange among base stations (BSs) is limited if each tier of BSs is deployed by different network vendors or users, and this brings about the need for distributed control. To resolve these challenges, we introduce a promising approach based on a message-passing framework and derive a distributed load-balancing algorithm. The proposed algorithm developed via message passing provides a very efficient solution for the load-balancing problem with reduced computational complexity. We compare the proposed algorithm with existing load-balancing strategies. Simulation results verify that the proposed algorithm significantly improves resource utilization and mitigates the congestion of macro BSs, thereby resulting in a multifold gain to the sum rate.

Affinity Propagation for Energy-Efficient BS Operations in Green Cellular Networks

This paper develops a distributed strategy to identify an energy-efficient base station (BS) network configuration for green cellular networks. During off-peak periods where traffic demands are only a fraction of the peak-time traffic demands, a subset of BSs is switched off to minimize operational energy consumption without affecting service to any of network users. To this end, we formulate a combinatorial optimization of jointly determining BS switching and user association. This formulation, however, requires a computationally demanding task as the population of the network grows. To resolve these challenges, we introduce a graphical-model approach to the optimization formulation and derive a distributed algorithm based on affinity propagation, which is a message-passing algorithm developed for data clustering in data-mining techniques. The proposed algorithm operates via simple local information exchanges among users and BSs and provides a very efficient solution for energy-saving management with low computational costs. We also present a green protocol that transforms commercial cellular networks into green radio networks using the proposed algorithm. Simulation results verify that the developed solution significantly improves the energy savings and resource utilization in the network.

Message-Passing-Based Dynamic Point Selection for Coordinated Multipoint Transmission

This letter develops a dynamic point selection strategy for coordinated multipoint transmission using a message-passing approach. The dynamic determination of the best transmit point for individual users with the objective of the sum-rate maximization can be cast as a bipartite b-matching problem, the computational cost of which, however, becomes quickly intractable with the increasing number of users. Therefore, this letter develops a message-passing algorithm that solves this computationally demanding challenge. Simulation results show that the proposed algorithm outperforms existing greedy-style approaches and provides a very efficient solution for the maximal sum-rate configuration.

Low-Feedback-Rate and Low-Complexity Downlink Multiuser MIMO Systems

In this paper, we propose a practical downlink multiuser multiple-input-multiple-output (MU-MIMO) system. The proposed MU-MIMO system focuses on improving two limiting factors for practical implementations of MU-MIMO: 1) feedback rate and 2) computational complexity. First, users efficiently feed their channel-state information back with low feedback rate based on the proposed channel-quantization method. Second, the beamforming matrix at the base station is easily computed using singular value decomposition (SVD). Numerical results show that the proposed MU-MIMO system achieves a higher sum rate than conventional MU-MIMO systems, particularly at low feedback rate, while the computational complexity is kept reasonable.