Haewook Park

Distributed Beamforming Techniques for Weighted Sum-Rate Maximization in MISO Interference Channels

This letter proposes a beamforming technique for weighted sum-rate (WSR) maximization in multiple-input single-output (MISO) interference channels. In order to solve the WSR maximization problem in a distributed manner, we obtain a decoupled problem by applying high signal-to-interference-plus-noise-ratio (SINR) approximation. When there are more than two users, further approximation is employed to fully decouple the problem. Then, the decoupled problems are solved by using a zero-gradient (ZG) based algorithm which converges to a local optimal point with only a few iterations. Unlike the conventional distributed schemes where additional information should be exchanged at each iteration, each transmitter of the proposed scheme utilizes only local channel information to compute its beamformer.

Regularized Interference Alignment Based on Weighted Sum-MSE Criterion for MIMO Interference Channels

The original interference alignment (IA) scheme provides poor sum-rate performance compared to simple orthogonal access schemes such as time-division multiple access (TDMA) in low-to-medium SNR under total power constraint. In this paper, we address this problem by proposing a method of regularizing the IA scheme with a criterion of minimizing the weighted sum of the mean square error (WMSE) function. To perform the regularization process efficiently, the weight terms in the WMSE metric should be computed from the optimal zero-forcing (ZF) schemes. Thus, we first prove the optimality of the enhanced IA algorithm introduced in our previous work in the ZF sense. From simulation results, it is shown that the proposed scheme outperforms the TDMA in overall SNR regime. We can further improve the performance by repeating the proposed regularization process iteratively. Moreover, we propose a modified design that provides robustness in the presence of channel uncertainty.

New Beamforming Techniques Based on Virtual SINR Maximization for Coordinated Multi-Cell Transmission

In this paper, we propose new beamforming techniques based on virtual signal-to-interference-plus-noise ratio (VSINR) for weighted sum-rate (WSR) maximization in coordinated multi-cell transmission. In earlier works based on the VSINR maximization, the parameters which control the interference power and the noise variance were set to fixed values regardless of channel realizations and the signal-to-noise ratio level. In order to obtain an improved WSR performance, we propose a method which adaptively adjusts the parameters after establishing a connection between the WSR and VSINR. Our proposed method can be applied to the cases of various coordination levels among base stations. To address practical implementation issues, a decentralized implementation of the beamforming techniques is also proposed based on local channel state information. Numerical results confirm that the proposed centralized schemes provide near-optimal WSR performance and the proposed decentralized methods show a negligible performance loss compared to the centralized algorithms with reduced system complexity.

Weighted Sum MSE Minimization under Per-BS Power Constraint for Network MIMO Systems

We study joint processing (JP) for network MIMO systems where base stations exchange the users message and channel state information under per-BS power constraint. In this letter, we propose a weighted sum mean square error (WS-MSE) minimization algorithm for the JP systems by considering the channel gain as the weight factor in the MSE metric. To efficiently solve the formulated WS-MSE problem, an alternating optimization method which iteratively finds a local optimal solution is employed in our algorithm. The simulation results confirm that the proposed algorithm provides the sum rate performance close to the near-optimal gradient ascent approach and outperforms conventional schemes. In addition, we also propose a modified WS-MSE design which is robust to channel mismatch caused by channel estimation and feedback errors.

SINR Balancing Techniques in Coordinated Multi-Cell Downlink Systems

In this paper, we study coordinated multi-cell downlink systems where multiple base stations jointly design a transmission strategy by sharing channel state information. Particularly, we tackle the signal-to-interference-plus-noise ratio (SINR) balancing problem to maximize the worst-user rate. We consider single-input single-output (SISO) interference channels (IFC) where all nodes are equipped with a single antenna, and there is one active user in each cell. First, achievable rate regions with symmetric complex (SC) and asymmetric complex (AC) signaling techniques are investigated. Then, we present the optimal and near-optimal SINR balancing algorithms with the SC signaling for two and three user SISO IFC. Due to residual interference, the worst-user rate of the SC signaling is saturated at high signal-to-noise-ratio region. To alleviate this problem, we also propose efficient balancing schemes based on the AC signaling for both two and three-user cases. Simulation results confirm effectiveness of the proposed SINR balancing algorithms and show that a substantial gain of the AC signaling is achieved over the SC signaling in terms of the maximum worst-user rate.

A New Beamforming Design for MIMO AF Relaying Systems With Direct Link

In this paper, we propose a new beamforming technique that maximizes the end-to-end signal-to-noise ratio (SNR) for amplify-and-forward multiple-input-multiple-output cooperative relaying systems with direct link between thesource and the destination. Instead of conventional schemes resorting to an iterative method, such as a gradient ascentalgorithm, our scheme provides a simple closed-form solution for source-relay joint beamformer designs. To this end, wefirst derive a new expression of the end-to-end SNR for the cooperative relaying systems and its lower bound, which isgiven as the harmonic mean of two individual SNRs. Then, a new beamforming scheme, which adaptively optimizes one of the two SNRs depending on the channel condition, is proposed. In addition, we perform a diversity order analysis of the proposed scheme and show that our scheme achieves a full diversity order of relaying systems with direct link. It is confirmed by simulation results that the proposed technique obtains almost identical performance to the gradient ascent algorithm with much reduced complexity, and our analytical work accurately predicts the numerical results.

Capacity and error probability analysis of diversity reception schemes over generalized-K fading channels using a mixture gamma distribution

In this paper, we analyze the error probability and ergodic capacity performance for diversity reception schemes over generalized-K fading channels using a mixture gamma (MG) distribution. With high accuracy, the MG distribution can approximate a variety of composite fading channel models and provide mathematically tractable properties. In contrast to previous analysis approaches that require complicated signal-to-noise ratio (SNR) statistics, it is shown that a distribution of the received SNR for diversity reception schemes is composed of a weighted sum of gamma distributions by exploiting the properties of the MG distribution. Then, based on this result, we can derive the exact average symbol error probability and simple closed-form expressions of diversity and array gains for maximal ratio combining and selection combining. In addition, an expression of the ergodic capacity for these schemes is obtained in independent and identically distributed fading channels. Our results lead to meaningful insights for determining the system performance with parameters of the MG distribution. We show that our analysis can be expressed with any number of receiver branches over various fading conditions. Numerical results confirm that the derived error probability and ergodic capacity expressions match well with the empirical results.

Feedback Bit Allocation Schemes for Multi-User Distributed Antenna Systems

In this paper, we propose a feedback bit allocation algorithm for multi-user downlink distributed antenna systems with limited feedback. We consider a composite fading channel with small scale fadings and path loss, and assume the case where each user is served by only one distributed antenna (DA) port while each DA port can support any number of users. In order to efficiently determine bit allocation, we propose an iterative algorithm which minimizes an upper bound of a mean rate loss. Compared to conventional bit allocation methods, the proposed algorithm can be applied to more general system configurations. Simulation results show that our proposed algorithm offers a performance gain of 20% over an equal bit allocation scheme.

Beamforming and Power Allocation Designs for Energy Efficiency Maximization in MISO Distributed Antenna Systems

In this paper, we present a beamforming and power allocation algorithm for a downlink multiple-input single-output distributed antenna system which maximizes energy efficiency (EE). To reduce the computational complexity of conventional joint optimization approaches relying on an iterative method, we propose a near optimal scheme based on a closed-form solution. Employing the decomposition property of the joint optimization problem, the EE problem is solved in two steps. First, we determine the beamforming strategy for the EE maximization exposing the structure of beamforming vectors. Then, the optimal power allocation is presented as a closed-form solution by solving Karush-Kuhn-Tucker conditions. Through numerical simulations, we confirm that the proposed solution shows the performance almost identical to the jointly optimum method with much reduced complexity.

Transceiver Designs for Multipoint-to-Multipoint MIMO Amplify-and-Forward Relaying Systems

In this paper, we consider multipoint-to-multipoint multi-input multi-output relay systems where multiple source-destination pairs simultaneously communicate through a single multiple antenna relay. In this system configuration, we optimize two metrics when designing the source, relay and destination filters. The first one is sum mean-squared-error (MSE) and the second one is maximum pairwise MSE. These two problems are shown to be non-convex, and thus it is quite complex to find an analytical solution. To address this issue, we first introduce the sum MSE minimization scheme based on global channel state information (CSI) at all nodes. Then, a method which reduces complexity and CSI requirements is proposed by utilizing error covariance decomposition and MSE duality between broadcast channel and multiple access channel. Second, the pairwise MSE balancing algorithms are developed to guarantee fairness for all pairs. Similar to the case of the sum MSE minimization problem, we simplify the original pairwise MSE balancing problem to a second-order cone programming problem which can be solved using standard optimization tools. Through numerical simulations, we confirm the effectiveness of our proposed schemes.