Jeonghun Park

Cooperative Base Station Coloring for Pair-Wise Multi-Cell Coordination

This paper proposes a method for designing base station (BS) clusters and cluster patterns for pair-wise BS coordination. The key idea is that each BS cluster is formed by using the second-order Voronoi region, and the BS clusters are assigned to a specific cluster pattern by using edge-coloring for a graph drawn by Delaunay triangulation. The main advantage of the proposed method is that the BS selection conflict problem is prevented, while users are guaranteed to communicate with their two closest BSs in any irregular BS topology. With the proposed coordination method, analytical expressions for the rate distribution and the ergodic spectral efficiency are derived as a function of relevant system parameters in a fixed irregular network model. In a random network model with a homogeneous Poisson point process, a lower bound on the ergodic spectral efficiency is characterized. Through system level simulations, the performance of the proposed method is compared with that of conventional coordination methods: dynamic clustering and static clustering. Our major finding is that, when users are dense enough in a network, the proposed method provides the same level of coordination benefit with dynamic clustering to edge users.

Exploiting Spatial Channel Covariance for Hybrid Precoding in Massive MIMO Systems

We propose a new hybrid precoding technique for massive multi-input multi-output (MIMO) systems using spatial channel covariance matrices in the analog precoder design. Applying a regularized zero-forcing precoder for the baseband precoding matrix, we find an unconstrained analog precoder that maximizes signal-to-leakage-plus-noise ratio (SLNR) while ignoring analog phase shifter constraints. Subsequently, we develop a technique to design a constrained analog precoder that mimics the obtained unconstrained analog precoder under phase shifter constraints. The main idea is to adopt an additional baseband precoding matrix, which we call a compensation matrix. We analyze the SLNR loss due to the proposed hybrid precoding compared to fully digital precoding, and determine which factors have a significant impact on this loss. In the simulations, we show that if the channel is spatially correlated and the number of users is smaller than the number of RF chains, the SLNR loss becomes negligible compared to fully digital precoding. The main benefit of our method stems from the use of spatial channel matrices in such a way that not only is each users desired signal considered, but also the inter-user interference is incorporated in the analog precoder design.

On the Optimal Feedback Rate in Interference-Limited Multi-Antenna Cellular Systems

We consider a downlink cellular network where multi-antenna base stations (BSs) transmit data to single-antenna users by using one of two linear precoding methods with limited feedback: 1) maximum ratio transmission (MRT) for serving a single user or 2) zero forcing (ZF) for serving multiple users. The BS and user locations are drawn from a Poisson point process, allowing expressions for the signal-to-interference coverage probability and the ergodic spectral efficiency to be derived as a function of system parameters, such as the number of BS antennas and feedback bits, and the pathloss exponent. We find a tight lower bound on the optimum number of feedback bits to maximize the net spectral efficiency, which captures the overall system gain by considering both of downlink and uplink spectral efficiency using limited feedback. Our main finding is that, when using MRT, the optimum number of feedback bits scales linearly with the number of antennas, and logarithmically with the channel coherence time. When using ZF, the feedback scales in the same ways as MRT, but also linearly with the pathloss exponent. The derived results provide system-level insights into the preferred channel codebook size by averaging the effects of short-term fading and long-term pathloss.

Multiple-Antenna Transmission With Limited Feedback in Device-to-Device Networks

We consider an overlay device-to-device network, where each multiple-antenna transmitter sends data to a designated single-antenna receiver by using maximum ratio transmission based on limited feedback, and their locations are modeled by a homogeneous Poisson point process. We obtain a lower bound on the optimum number of feedback bits that maximizes net spectral efficiency, capturing the overall system gain of using limited feedback. Our main finding is that the optimum number of feedback bits scales linearly with the number of transmit antennas and logarithmically with the channel coherence time and the inverse of the node density.

Performance analysis of pair-wise dynamic multi-user joint transmission

This paper characterizes the performance of multiuser joint transmission (MU-JT) with pair-wise dynamic base station (BS) clustering. For analyzing the performance of such BS cooperation method, a tractable model is presented by means of stochastic geometry. Using tools of stochastic geometry, a tight lower bound of the instantaneous signal-to-interference ratio (SIR) distribution is derived in a closed form in terms of relevant system parameters: the path-loss exponent and the topologies of users in the BS cooperative region. Our key finding is that the pair-wise dynamic BS cooperation through MU-JT provides a better rate coverage performance than that of single-user joint transmission (SU-JT) over the entire range of the rate threshold when each user is close enough to the associated BS. Through simulations, the exactness of the derived analytical expression is verified.

Optimal User Loading in Massive MIMO Systems With Regularized Zero Forcing Precoding

We consider a downlink massive multiple-input multiple output system employing regularized zero-forcing precoding. We derive the asymptotic signal-to-leakage-plus-noise ratio (SLNR) as both the number of antennas and the number of users go to infinity at a fixed ratio. Focusing on spatially uncorrelated channels with homogeneous large scale fading gains, we show that the SLNR is asymptotically equal to signal-to-interference-plus-noise ratio, which allows us to optimize the user loading for spectral efficiency. The results show that the optimal user loading varies depending on the channel signal-to-noise ratio (SNR). As the SNR increases, the optimal user loading decreases at low SNR, but increases at high SNR.

Dynamic bit selection in mixed-ADC cloud-RAN systems

We propose a new mixed-analog-to-digital convertor (mixed-ADC) architecture for cloud-RAN (C-RAN) systems. The RRH is equipped with a mixed-ADC pool that includes multiple ADC units with various resolutions. In this pool, the RRH selects the appropriate ADCs and connects the selected ADCs to each antenna to quantize the received signals, thereby each antenna can have a different resolution ADC. The quantized signals are sent to a centralized baseband unit (BBU) via a capacity limited fronthaul pipe. To maximize the spectral efficiency of the considered system in a single-user uplink phase, we formulate an optimization problem for ADC selection by exploiting an approximation of the generalized mutual information (GMI). Subsequently we propose a solution. The simulations show the improvement in the GMI by using the proposed ADC selection. Our major findings are: i) In a C-RAN with limited fronthaul capacity, the proposed mixed-ADC makes more efficient use of the fronthaul capacity. ii) In selecting ADCs, assigning a high resolution ADC to a strong channel is beneficial.

Optimization of Mixed-ADC Multi-Antenna Systems for Cloud-RAN Deployments

We propose a mixed analog-to-digital converter ADC (mixed-ADC) structure for a cloud-RAN system, where a single-antenna user terminal communicates with a multi-antenna remote radio head (RRH). In the proposed structure, the RRH is equipped with a mixed-ADC pool that includes multiple ADC units with various resolutions. In this pool, the RRH selects the appropriate ADCs and connects the selected ADCs to each antenna to quantize the received signals; thereby each antenna can have a different resolution ADC. The fronthaul capacity is limited, so that the sum of the bits produced in the selected ADCs is also limited. To maximize the spectral efficiency or the energy efficiency of such a system, we propose algorithms for ADC resolution selection based on an approximation of the generalized mutual information in the low signal-to-noise regime. In the proposed algorithms, we show that for spectral efficiency, using high-resolution ADC on the strong channels is beneficial. The results for energy efficiency maximization are similar, though the largest resolutions are reduced to save power. The simulations show that the proposed method provides significant performance improvement.

Adaptive Feedback Partitions in Dynamic Zero-Forcing Beamforming Based on Stochastic Geometry

In this paper, we analyze the performance of dynamic zero-forcing beamforming with limited feedback and propose adaptive feedback partition strategies. Assuming a cellular network model based on stochastic geometry, we derive analytical expressions for the complementary cumulative distribution function (CCDF) of the instantaneous signal-to-interference ratio (SIR) and the ergodic spectral efficiency as functions of the relevant system parameters: the cluster size, the number of feedback bits, the intra-cluster geometry, and the path-loss exponent. By leveraging these expressions, we propose two feedback partition strategies, each of which is applied for known intra-cluster geometry and unknown intra- cluster geometry, respectively. In the simulations, we verify the accurateness of our analysis and demonstrate the proposed strategies by comparing to the equal feedback partition. Our results show that by adaptively partitioning the feedback bits, the intra- cluster interference is efficiently managed whether the intra-cluster geometry is known or not.

Analysis of interference mitigation in mmWave communications

Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates due to the large bandwidth available at mmWave frequencies. Thanks to the small wavelength corresponding to the mmWave frequencies, mmWave systems can benefit from exploiting large antenna arrays at both transmitter and receiver. Although highly directional beamforming has been envisioned to play a key role to realize sufficient link margin, it is also possible to use these large arrays in other ways. For example, a hybrid array architecture can be exploited to either cancel or null an interferer. In this paper we analyse the effect of interference cancellation in downlink mmWave communications. We exploit partial zero forcing (PZF) at the user in order to cancel the interference from a set of interfering base stations (BSs) and derive closed form expression for the probability of coverage. Simulation results show that as the density of base stations increases, interference mitigation through partial zero forcing enhances the probability of coverage implying the necessity of interference mitigation in dense mmWave networks.