Massive MU-MIMO Downlink TDD Systems with Linear Precoding and Downlink Pilots

We consider a massive MU-MIMO downlink time-division duplex system where a base station (BS) equipped with many antennas serves several single-antenna users in the same time-frequency resource. We assume that the BS uses linear precoding for the transmission. To reliably decode the signals transmitted from the BS, each user should have an estimate of its channel. In this work, we consider an efficient channel estimation scheme to acquire CSI at each user, called beamforming training scheme. With the beamforming training scheme, the BS precodes the pilot sequences and forwards to all users. Then, based on the received pilots, each user uses minimum mean-square error channel estimation to estimate the effective channel gains. The channel estimation overhead of this scheme does not depend on the number of BS antennas, and is only proportional to the number of users. We then derive a lower bound on the capacity for maximum-ratio transmission and zero-forcing precoding techniques which enables us to evaluate the spectral efficiency taking into account the spectral efficiency loss associated with the transmission of the downlink pilots. Comparing with previous work where each user uses only the statistical channel properties to decode the transmitted signals, we see that the proposed beamforming training scheme is preferable for moderate and low-mobility environments.Comment: Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, Illinois, Oct. 201

Blind Estimation of Effective Downlink Channel Gains in Massive MIMO

We consider the massive MIMO downlink with time-division duplex (TDD) operation and conjugate beamforming transmission. To reliably decode the desired signals, the users need to know the effective channel gain. In this paper, we propose a blind channel estimation method which can be applied at the users and which does not require any downlink pilots. We show that our proposed scheme can substantially outperform the case where each user has only statistical channel knowledge, and that the difference in performance is particularly large in certain types of channel, most notably keyhole channels. Compared to schemes that rely on downlink pilots, our proposed scheme yields more accurate channel estimates for a wide range of signal-to-noise ratios and avoid spending time-frequency resources on pilots.Comment: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 201

How Much Do Downlink Pilots Improve Cell-Free Massive MIMO?

In this paper, we analyze the benefits of including downlink pilots in a cell-free massive MIMO system. We derive an approximate per-user achievable downlink rate for conjugate beamforming processing, which takes into account both uplink and downlink channel estimation errors, and power control. A performance comparison is carried out, in terms of per-user net throughput, considering cell-free massive MIMO operation with and without downlink training, for different network densities. We take also into account the performance improvement provided by max-min fairness power control in the downlink. Numerical results show that, exploiting downlink pilots, the performance can be considerably improved in low density networks over the conventional scheme where the users rely on statistical channel knowledge only. In high density networks, performance improvements are moderate.Comment: 7 pages, 5 figures. IEEE Global Communications Conference 2016 (GLOBECOM). Accepte

Efficient Optimal Joint Channel Estimation and Data Detection for Massive MIMO Systems

In this paper, we propose an efficient optimal joint channel estimation and data detection algorithm for massive MIMO wireless systems. Our algorithm is optimal in terms of the generalized likelihood ratio test (GLRT). For massive MIMO systems, we show that the expected complexity of our algorithm grows polynomially in the channel coherence time. Simulation results demonstrate significant performance gains of our algorithm compared with suboptimal non-coherent detection algorithms. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.Comment: 5 pages, 4 figures, Conferenc

Multi-cell massive MIMO network optimization towards power consumption in suburban scenarios

In this paper, we propose a simulation-based method to design low power multi-cell multi-user massive MIMO network by optimizing the positions of the base stations. Two realistic outdoor suburban areas have been considered in Ghent, Belgium (Europe) and Kinshasa, the Democratic Republic of Congo (Africa), in which the power consumption, the energy efficiency, the network capacity and the multiplexing gain are investigated and compared with LTE networks. The results of the simulations demonstrated that massive MIMO networks provide better performance in the crowded scenario where user's mobility is relatively low. A massive MIMO BS consumes 5-8 times less power than the LTE networks, with a pilot reuse pattern of 3 that helps obtaining a good tradeoff between the higher bit rate requested and the low power requirements in cellular environment

SCHEDULING FOR MASSIVE MIMO USING CHANNEL AIGING UNDER QOS CONSTRAINTS

Massive multiple-input multiple-output (MIMO) networks support QoS (Quality of Service) by adding a new sublayer Service Data Adaption Protocol on the top of Packet Data Convergence Protocol layer to map between QoS flows and data radio bearers. In downlink for Guaranteed Bit Rate (GBR) flows, the gNB guarantees the Guaranteed Flow Bit Rate (GFBR) that defines the minimum bit rate the QoS flow can provide. So, one of the most important requirements is the minimum rate. The channel aiging helps to improve the sum-rate of Massive MIMO systems by serving more users to increase the spatial multiplexing gain without incurring additional pilot overhead. In this paper, a novel scheduler, termed QoS-Aware scheduling, is designed and proposed for Massive MIMO to use the channel aiging to increase the sum-rate but guarantee the minimum bit rate per user to support QoS. We investigate how many users are enough to serve to maximize the sum-rate while keeping the data rate per user meeting a given threshold. Through the numerical analysis we confirmed that QoS-Aware scheduling can guarantee a minimum rate per user and get a higher useful through-put (goodput) than conventional channel aiging schedulers

Formación de haz híbrido en un sistema MIMO masivo

El presente artículo detalla el diseño y simulación de un sistema de formación de haz híbrido en un sistema MIMO masivo(multiple-input-multipleoutput)) esta tecnología tiene como objetivo principal generar una alta eficiencia espectral y evitar la pérdida de propagación en altas frecuencias, la cual se refleja en la capa física 5G. Al contrario de los sistemas MIMO convencionales, los flujos de datos se dispersan en diferentes direcciones provocando interferencia en el sistema y reduciendo la velocidad en la transmisión de información, donde el procesamiento de señales se lleva a cabo mediante una arquitectura digital o analógico que son diseñados mediante el algoritmo URA (Uniform Rectangular Array). El procesamiento de señales híbridas analógico-digital o conocidas como Beamforming Híbrida tiene como característica principal disminuir el costo, el consumo de energía y reducir la interferencia. Se utiliza para la formación de haz, fundamentado en el algoritmo OMP (Orthogonal Matching Pursuit) en un sistema los dos usuarios tienen gran cantidad de antenas que son operadas por un número limitado de cadenas de radiofrecuencia tanto en el transmisor y receptor. Los resultados muestran la comparación del patrón de radiación de dos sistemas tanto convencional e híbrido tomando como referencia la eficiencia espectral como indicador de desempeño.This paper details the design and simulation of a hybrid beamforming system in a massive MIMO (multiple input-multipleoutput) system. The main objective of this technology is to generate high spectral efficiency and avoid propagation loss at high frequencies, which is reflected in the 5G physical layer. Unlike conventional MIMO systems, data flows are dispersed in different directions causing interference in the system and reducing the speed of information transmission, where signal processing is carried out through a digital or analog architecture that are designed using the URA (Uniform Rectangular Array) algorithm. The hybrid analog-digital signal processing or known as Hybrid Beamforming has as main characteristic to decrease the cost, power consumption and reduce interference. It is used for beamforming, based on the OMP (Orthogonal Matching Pursuit) algorithm in a system where the two users have a large number of antennas that are operated by a limited number of radio frequency chains in both the transmitter and receiver. The results show the comparison of the radiation pattern of two systems both conventional and hybrid taking as reference the spectral efficiency as a performance indicator

Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays

Massive multiple-input multiple-output (MIMO) systems, which utilize a large number of antennas at the base station, are expected to enhance network throughput by enabling improved multiuser MIMO techniques. To deploy many antennas in reasonable form factors, base stations are expected to employ antenna arrays in both horizontal and vertical dimensions, which is known as full-dimension (FD) MIMO. The most popular two-dimensional array is the uniform planar array (UPA), where antennas are placed in a grid pattern. To exploit the full benefit of massive MIMO in frequency division duplexing (FDD), the downlink channel state information (CSI) should be estimated, quantized, and fed back from the receiver to the transmitter. However, it is difficult to accurately quantize the channel in a computationally efficient manner due to the high dimensionality of the massive MIMO channel. In this paper, we develop both narrowband and wideband CSI quantizers for FD-MIMO taking the properties of realistic channels and the UPA into consideration. To improve quantization quality, we focus on not only quantizing dominant radio paths in the channel, but also combining the quantized beams. We also develop a hierarchical beam search approach, which scans both vertical and horizontal domains jointly with moderate computational complexity. Numerical simulations verify that the performance of the proposed quantizers is better than that of previous CSI quantization techniques.Comment: 15 pages, 6 figure