Uplink Linear Receivers for Multi-cell Multiuser MIMO with Pilot Contamination: Large System Analysis

Base stations with a large number of transmit antennas have the potential to serve a large number of users at high rates. However, the receiver processing in the uplink relies on channel estimates which are known to suffer from pilot interference. In this work, making use of the similarity of the uplink received signal in CDMA with that of a multi-cell multi-antenna system, we perform a large system analysis when the receiver employs an MMSE filter with a pilot contaminated estimate. We assume a Rayleigh fading channel with different received powers from users. We find the asymptotic Signal to Interference plus Noise Ratio (SINR) as the number of antennas and number of users per base station grow large while maintaining a fixed ratio. Through the SINR expression we explore the scenario where the number of users being served are comparable to the number of antennas at the base station. The SINR explicitly captures the effect of pilot contamination and is found to be the same as that employing a matched filter with a pilot contaminated estimate. We also find the exact expression for the interference suppression obtained using an MMSE filter which is an important factor when there are significant number of users in the system as compared to the number of antennas. In a typical set up, in terms of the five percentile SINR, the MMSE filter is shown to provide significant gains over matched filtering and is within 5 dB of MMSE filter with perfect channel estimate. Simulation results for achievable rates are close to large system limits for even a 10-antenna base station with 3 or more users per cell.Comment: Accepted for publication in IEEE Transactions on Wireless Communication

Cellular Systems with Many Antennas: Large System Analysis under Pilot Contamination

Base stations with a large number of transmit antennas have the potential to serve a large number of users simultaneously at higher rates. They also promise a lower power consumption due to coherent combining at the receiver. However, the receiver processing in the uplink relies on the channel estimates which are known to suffer from pilot interference. In this work, we perform an uplink large system analysis of multi-cell multi-antenna system when the receiver employs a matched filtering with a pilot contaminated estimate. We find the asymptotic Signal to Interference plus Noise Ratio (SINR) as the number of antennas and number of users per base station grow large while maintaining a fixed ratio. To do this, we make use of the similarity of the uplink received signal in a multi-antenna system to the representation of the received signal in CDMA systems. The asymptotic SINR expression explicitly captures the effect of pilot contamination and that of interference averaging. This also explains the SINR performance of receiver processing schemes at different regimes such as instances when the number of antennas are comparable to number of users as well as when antennas exceed greatly the number of users. Finally, we also propose that the adaptive MMSE symbol detection scheme, which does not require the explicit channel knowledge, can be employed for cellular systems with large number of antennas.Comment: 5 pages, 4 figure

Reproducing and Explaining Entity and Relation Embeddings for Link Prediction in Knowledge Graphs

Embedding knowledge graphs is a common method used to encode information from the graph at hand projected in a low dimensional space. There are two shortcomings in the field of knowledge graph embeddings for link prediction. The first shortcoming is that, as far as we know, current software libraries to compute knowledge graph embeddings differ from the original papers proposing these embeddings. Certain implementations are faithful to the original papers, while others range from minute differences to significant variations. Due to these implementation variations, it is difficult to compare the same algorithm from multiple libraries and also affects our ability to reproduce results. In this report, we describe a new framework, AugmentedKGE (aKGE), to embed knowledge graphs. The library features multiple knowledge graph embedding algorithms, a rank-based evaluator, and is developed completely using Python and PyTorch. The second shortcoming is that, during the evaluation process of link prediction, the goal is to rank based on scores a positive triple over a (typically large) number of negative triples. Accuracy metrics used in the evaluation of link prediction are aggregations of the ranks of the positive triples under evaluation and do not typically provide enough details as to why a number of negative triples are ranked higher than their positive counterparts. Providing explanations to these triples aids in understanding the results of the link predictions based on knowledge graph embeddings. Current approaches mainly focus on explaining embeddings rather than predictions and single predictions rather than all the link predictions made by the embeddings of a certain knowledge graph. In this report, we present an approach to explain all these predictions by providing two metrics that serve to quantify and compare the explainability of different embeddings. From the results of evaluating aKGE, we observe that the accuracy metrics are better than the accuracy metrics obtained from the standard implementation of OpenKE. From the results of explainability, we observe that the horn rules obtained explain more than 50% of all the negative triples generated

Compressor Startup Flaring Avoidance Design Methodology

Case StudyThis case study outlines the methodology used to avoid the flaring of refrigerant inventory during the startup of a 3-section refrigeration compressor used in LPG chilling service. S&B Engineers and Constructors (S&B) and Energy Control Technologies (ECT) conducted a joint analysis of potential problems in the field caused by high settle out pressure in the compressor casing following shutdown. This analysis involved dynamic simulation of the refrigeration system to develop and test various system improvements for preventing refrigerant loss

How can weather affect crop area diversity? Panel data evidence from Andhra Pradesh, a rice growing state of India

This study analyses the temporal as well as the spatial shift in cropping pattern in Andhra Pradesh during the period from 1971 to 2009. The temporal associations between crop diversity, weather and economic variables have been examined to understand adaptation dynamics by means of cropping pattern shift. We find a significant impact of rabi (winter) season temperature and kharif (summer) season rainfall on cropping diversity. Along with mean weather, annual rainfall distribution has a significant, positive influence on crop diversity. The intra-seasonal distribution of dry days during rabi and kharif has a heterogeneous impact on crop diversity in districts of Andhra Pradesh. Within the state, geographical redistribution of rice area over the years can be considered as adaptation to climatic risk; however, sustainability of the emerging cropping pattern is under question due to a declining share of dry land crops during the study period. Drawing from the results, improving cropping intensity, increasing use of technology inputs and employing a season-wise incentive policy can be useful measures for sustainable diversification of the crop sector in the state

Did technological intervention help to spare land from agriculture: evidence from post liberalisation India

India has witnessed fairly high economic growth since economic liberalisation started in 1991. However, agriculture has remained excluded from the growth experienced in other economic sectors. This growth paradox has serious implications for the agricultural land use pattern. This study uses the Environmental Kuznets Curve hypothesis to examine the impact of agricultural technology and economic development on agricultural land expansion in India. Panel data regression is performed on an unbalanced sample covering information from 25 Indian states for the period 1990 to 2008. Our results suggest a nonlinear (N shaped) relationship between agricultural land expansion and Net State Domestic Product (NSDP) per capita. Two incomes turning points, showing the level of NSDP per capita at which inflection between agricultural land expansion and NSDP per capita takes place, occur at INR 20986.14 and INR 42855.10 respectively. We find mixed results as far as the impact of technological variables on agricultural land expansion is concerned. The study concludes that rapid economic growth in the post liberalisation period has failed to reverse agricultural land expansion in India

Report on industrial user engagement

From the inception of the project, West-Life has kept focus on engaging with Industrial partners. Through partner interactions with Industrial collaborators and establishing new interactions by representations at Industrial participant events, the project has been endeavouring to spread the achievements of West-Life enabled tools and services. West-Life has also been interacting with industrial scientists and solutions providers to understand their needs from the structural biology solutions and services and in training early career/established structural biologists. During the remainder of the project, West-Life will enable users from Industrial research to understand and utilise tools and services provided through the project through concerted efforts on engagement including website, social media, conferences and networking