Abstract-We propose a novel pilot sequence design to mitigate pilot contamination in multi-cell multiuser massive multiple-input multiple-output networks. Our proposed design generates pilot sequences in the multi-cell network and devises power allocation at base stations (BSs) for downlink transmission. The pilot sequences together with the power allocation ensure that the user capacity of the network is achieved and the pre-defined signalto-interference-plus-noise ratio (SINR) requirements of all users are met. To realize our design, we first derive new closed-form expressions for the user capacity and the user capacity region. Built upon these expressions, we then develop a new algorithm to obtain the required pilot sequences and power allocation. We further determine the minimum number of antennas required at BSs to achieve certain SINR requirements of all users. Numerical results are presented to corroborate our analysis and to examine the impact of key parameters, such as the pilot sequence length and the total number of users, on the network performance. A pivotal conclusion is reached that our design achieves a larger user capacity region than the existing designs and needs less antennas at the BS to fulfill the pre-defined SINR requirements of all users in the network than the existing designs.
The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems’ design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems.
We propose a novel location-aware pilot assignment scheme to mitigate pilot contamination in massive multipleinput multiple-output (MIMO) networks, where the channels are subjected to Rician fading. Our proposed scheme utilizes the location information of users as the input to conduct pilot assignment in the network. Based on the location information, we first determine the line of sight (LOS) interference between the intended signal and the interfering signal. Our analysis reveals that the LOS interference converges to zero as the number of antennas at the base station (BS) goes to infinity, whereas for finite number of antennas at the BS the LOS interference indeed depends on specific pilot allocation strategies. Following this revelation, we assign pilot sequences to all the users in the massive MIMO network such that the LOS interference is minimized for finite number of antennas at the BS. Our proposed scheme outperforms the random pilot assignment in terms of achieving a much higher uplink sum rate for reasonable values of the Rician K-factor. Moreover, we propose a new performance metric, which measures the strength of the LOS interference and demonstrate that it is a good metric to analyze the performance of pilot assignment schemes. Theoretical analysis is provided and verified through numerical simulations to support the proposed scheme.
In this paper, we investigate downlink power control in massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays. The base station (BS) in each cell consists of multiple antenna arrays, which are deployed in arbitrary locations within the cell. Due to the spatial separation between antenna arrays, the large-scale propagation effect is different from a user to different antenna arrays in a cell, which makes power control a challenging problem as compared to conventional massive MIMO. We assume that the BS in each cell obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum ratio transmission for the downlink. We then derive a closed-form spectral efficiency (SE) expression, where the channels are subject to correlated fading. Utilizing the derived expression, we propose a maxmin power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate that, for the network considered in this work, optimizing for max-min SE through the max-min power control improves the sum SE of the network as compared to the equal power allocation.
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