Centralized Antenna Systems Research Articles

Precoding Design and Optimization for Multi-Antenna Systems With Limited Feedback

We consider a multi-antenna wireless system consisting of a source transmitting to its destination with limited feedback, where the channel state information (CSI) is considered to be quantized and fed back from the destination to the source in the face of channel quantization errors (CQE). We propose a multi-antenna precoding (MAP) scheme to mitigate an adverse effect of the CQE, which is called the CQE oriented MAP and denoted CQE-MAP for short. Typically, using more channel quantization bits enhances the accuracy of quantized CSI acquired at the source and improves the data rate of the source-destination transmission, which, however, results in an increase of the CSI feedback overhead. We define an effective throughput as the difference of the data transmission rate and the CSI feedback rate that is used for characterizing the system overhead of sending the quantized CSI. An optimization analysis of the CQE-MAP scheme is carried out in terms of maximizing the effective throughput with regard to the number of quantization bits per channel. It is proved that the conventional maximal ratio transmission (MRT) based MAP method denoted by MRT-MAP could be a special case of the proposed CQE-MAP scheme for centralized antenna systems. Simulation results demonstrate that the CQE-MAP generally achieves a higher effective throughput than the MRT-MAP for distributed antenna systems, especially in the high signal-to-noise ratio (SNR) region. It is also illustrated that the effective throughput of CQE-MAP scheme can be further maximized through an optimization of the number of quantization bits per channel. Moreover, with an increase of the SNR or a decrease of the terminal moving speed, an increased number of quantization bits per channel is needed for the sake of maximizing the effective throughput.

Capacity Analysis of Distributed Antenna Systems with Beamforming

Distributed antenna systems (DASs) can reduce the access distance, increase system capacity and improve system performance compared with conventional centralized multiple antenna systems. In this paper, the system capacity of downlink DAS with beamforming is investigated. The mathematical expression of system capacity is derived over the composite channel including Rayleigh fading, shadowing and path loss. At the same time, the interference from other APs is also considered for the studied AP. Numerical methods including Gauss-Hermite integral and composite Simpson’s rule are used to compute the system capacity of DAS under different MS distributions. Numerical results verify the precision of the mathematical analysis of system capacity by comparing Monte-Carlo simulation results. The results also show interference among APs shouldn’t be ignored when APs are close to each other or there are many APs.

Channel-Aware Resource Allocation for Energy-Efficient Cloud Radio Access Networks Under Outage Specifications

This paper investigates a cloud radio access network (C-RAN) architecture for future wireless network, which focuses on centralized processing for spatially distributed remote radio heads (RRHs). Our main goal is to promote an energy efficient C-RAN under the consideration of multiple access interference (MAI) and imperfect channel state information at the transmitter (CSIT) via outage-aware resource allocation. The closed-form expression of fading-induced outage probability for each transceiver pair will be established by analyzing the statistical property of signal-to-interference-plus-noise. In the course of finding feasible solutions to enhance energy efficiency, the optimization procedures named C-RAN-based energy efficient power allocation (CEEPA) and low-complexity CEEPA are proposed. As the number of RRHs grows to infinity, the deterministic equivalents of performance metrics can be derived by applying recent results from random matrix theorem, which lead to an efficient way to obtain the asymptotic-optimal resource allocation policy. Also, the regularized zero-forcing precoding technique is adopted to mitigate MAI and can tackle the impact of imperfect CSIT thanks to the derivation of deterministic equivalents. Numerical simulations show that the proposed resource allocation schemes can provide better energy efficiency and the accuracy of asymptotic expressions is also verified. Furthermore, the merit of a distributed antenna system is demonstrated through the comparison with centralized antenna system.

Energy Efficient Transmission for Multicast Services in MISO Distributed Antenna Systems

This letter aims to solve the energy efficiency (EE) maximization problem for multicast services in a multiple-input single-output (MISO) distributed antenna system (DAS). A novel iterative algorithm is proposed, which consists of solving two subproblems iteratively: the power allocation problem and the beam direction updating problem. The former subproblem can be equivalently transformed into a one-dimension (1-D) quasi-concave problem that is solved by the golden search method. The latter problem can be efficiently solved by the existing method. Simulation results show that the proposed algorithm achieves significant EE performance gains over the existing rate maximization method. In addition, when the backhaul power consumption is low, the EE performance of the DAS is better than that of the centralized antenna system (CAS).

Location refinement and power coverage analysis based on distributed antenna

To establish wireless channel suitable for the cabin environment, the power coverage was investigated with distributed antenna system and centralized antenna system based on the actual measurement of channel impulse response. The results indicated that the distributed antenna system has more uniform power coverage than the centralized antenna system. The average relative errors of receiving power of both antennas were calculated. The optimal position of the centralized antenna was obtained by Gaussian function refinement, making the system achieve a better transmission power with the same coverage effect, and providing a reference for antenna location in the future real communication in the cabin.

Energy Efficient and High Capacity Tradeoff in Distributed Antenna System for a Green Cellular Network

Two main concerns for designing a wireless system are more network capacity and less energy consumption. Recently, distributed antenna system (DAS) has received considerable attention due to its potential to provide higher spectral efficiency (SE) and uniform coverage for cellular networks. In this regard, this paper compares the performance of DAS with centralized antenna system (CAS) in LTE-A system in terms of energy efficiency (EE), where practical restrictions such as out-of-cell interference, path loss, and small scale fading are taken into account. Furthermore, the EE and system power consumption are investigated under three different cell-load scenarios (high, moderate, and low load) where different numbers of antennas are activated and remaining of antennas are under sleep mode. Finally, based on the tradeoff between power-saving and EE, two optimal DAS antenna deployments are proposed for low and moderate cell-load scenarios. The results reveal that DAS considerably outperforms CAS in terms of EE and by optimal deploying antennas of DAS significant power-saving and EE are achievable. The proposed methodology achieved savings of up to 27.63% in terms of energy savings in a macrocell with guarantee of a high capacity of data.

다중 안테나 포트를 장착한 분산 안테나 시스템에서의 안테나 설계 방법

본 논문은 포트 당 일정 파워 제약을 전제한 상황에서, 다중 안테나를 장착한 분산 안테나 (distributed antenna: DA) 포트를 갖는 분산 안테나 시스템 (distributed antenna system: DAS)의 안테나 위치 설계 방법을 분석한다. 안테나 위치의 설계를 위해 복잡하게 셀 당 평균 ergodic sum rate를 최대화하는 대신, 본 논문에서는 단일 셀 상황에서는 signal-to-noise ratio (SNR) 기댓값의 lower bound에, 그리고 이중 셀 상황에서는 signal-to-leakage ratio (SLR) 기댓값의 lower bound에 각각 초점을 맞춘다. 단일 셀 상황의 경우, 기존의 반복적 알고리즘에 비해 SNR criterion의 최적화 문제는 닫힌 형태 (closed-form)의 솔루션을 제공한다. 또한, 이중 셀 상황에선 gradient ascent 방법을 이용한 알고리즘을 제안하여 SLR criterion의 최적화 솔루션을 도출한다. In this paper, we optimize antenna locations for a distributed antenna system (DAS) with distributed antenna (DA) ports equipped with multiple antennas under per-DA port power constraint. Maximum ratio transmission and scaled zero-forcing beamforming are employed for single-user and multi-user DAS, respectively. Instead of maximizing the cell average ergodic sum rate, we focus on a lower bound of the expected signal-to-noise ratio (SNR) for the single-cell scenario and the expected signal-to-leakage ratio (SLR) for the two-cell scenario to determine antenna locations. For the single-cell case, optimization of the SNR criterion generates a closed form solution in comparison to conventional iterative algorithms. Also, a gradient ascent algorithm is proposed to solve the SLR criterion for the two-cell scenario. Simulation results show that DAS with antenna locations obtained from the proposed algorithms achieve capacity gains over traditional centralized antenna systems.

Antenna Placement Optimization for Distributed Antenna Systems

In this paper, we propose new algorithms to determine the antenna location for downlink distributed antenna systems (DASs) in single-cell and two-cell environments. We consider the composite fading channel which includes small and large scale fadings. First, for the single-cell DAS, we formulate the optimization problem of distributed antenna (DA) port locations by maximizing the lower bound of the expected signal to noise ratio (SNR). In comparison to the conventional algorithm based on the squared distance criterion which requires an iterative method, our problem generates a closed form solution. Next, for the two-cell DAS, we propose a gradient ascent algorithm which determines the optimum DA locations by maximizing the lower bound of the expected signal to leakage ratio (SLR). In our work, we consider selection transmission, maximal ratio transmission and zero-forcing beamforming (ZFBF) under sum power constraint and study equal gain transmission and scaled ZFBF under per-antenna power constraint. Simulation results show that our proposed algorithms based on both the SNR and the SLR criteria offer a capacity gain over the conventional centralized antenna systems.