Dual-functional radar-communication systems are extensively employed for the detection and control of unmanned aerial vehicle groups and play crucial roles in scenario monitoring. In this study, we address the downlin...
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Dual-functional radar-communication systems are extensively employed for the detection and control of unmanned aerial vehicle groups and play crucial roles in scenario monitoring. In this study, we address the downlink precoding problem in large-scale multi-user multiple-inputmultiple-output dual-function radar-communication systems equipped with low-resolution quantized digital-to-analog converters. To tackle this issue, we develop a weighted optimization framework that minimizes the mean squared error between the transmitted symbols and their estimates while satisfying specific radar performance requirements. Due to the complexity introduced by discrete constraints, we decompose the original problem into three sub-problems to reduce computational burden. Furthermore, we propose a dynamic projection refinement algorithm within the alternating direction method of multiplier framework to efficiently solve these sub-problems. Numerical experiments demonstrate that our proposed method outperforms existing state-of-the-art techniques, particularly in terms of bit error rate in low signal-to-noise ratio scenarios.
A base-station (BS) equipped with multiple antennas can use its spatial dimensions in three different ways: 1) to serve multiple users, thereby achieving a multiplexing gain;2) to provide spatial diversity in order to...
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A base-station (BS) equipped with multiple antennas can use its spatial dimensions in three different ways: 1) to serve multiple users, thereby achieving a multiplexing gain;2) to provide spatial diversity in order to improve user rates;and 3) to null interference in neighboring cells. This paper answers the following question: What is the optimal balance between these three competing benefits? We answer this question in the context of the downlink of a cellular network, where multi-antenna BSs serve multiple single-antenna users using zero-forcing beamforming with equal power assignment, while nulling interference at a subset of out-of-cell users. Any remaining spatial dimensions provide transmit diversity for the scheduled users. Utilizing tools from stochastic geometry, we show that, surprisingly, to maximize the per-BS ergodic sum rate, with an optimal allocation of spatial resources, interference nulling does not provide a tangible benefit. The strategy of avoiding inter-cell interference nulling, reserving some fraction of spatial resources for multiplexing, and using the rest to provide diversity, is already close-to-optimal in terms of the sum-rate. However, interference nulling does bring significant benefit to cell-edge users, particularly when adopting a range-adaptive nulling strategy where the size of the cooperating BS cluster is increased for cell-edge users.
Pilot contamination (PC) is a major impediment of large-scale multi-cell multiple-inputmultiple-output systems. Hence, we propose an optimal pilot design for time-domain channel estimation, which is capable of comple...
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Pilot contamination (PC) is a major impediment of large-scale multi-cell multiple-inputmultiple-output systems. Hence, we propose an optimal pilot design for time-domain channel estimation, which is capable of completely eliminating PC. More specifically, a sophisticated combination of downlink training and "scheduled" uplink training is designed with the aid of the optimal pilot set. Given the optimal pilot set, every user acquires its unique downlink time-domain channel state information (CSI) through downlink training. The estimated downlink CSIs are then embedded in the uplink training. As a result, PC can be completely eliminated, at the cost of a slight increase in training computational complexity. Our simulation results demonstrate the power of the proposed scheme. Most significantly, our scheme imposes a modest training overhead of (L + 3), training-phase durations corresponding to the number of orthogonal frequency division multiplexing symbols, where L is the number of cells, which is substantially lower than that imposed by some of the existing PC elimination schemes. Therefore, it imposes a less stringent requirement on the channel's coherence time. Finally, our scheme does not need any information exchange between base stations.
This paper considers the problem of pilot contamination (PC) in large-scale multi-cell multiple-inputmultiple-output-aided orthogonal frequency division multiplexing systems. We propose an efficient scheme relying on...
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This paper considers the problem of pilot contamination (PC) in large-scale multi-cell multiple-inputmultiple-output-aided orthogonal frequency division multiplexing systems. We propose an efficient scheme relying on an optimal pilot design conceived for time-domain channel estimation, which can either completely eliminate PC or significantly reduce it, depending on the channel's coherence time. This is achieved by designing an optimal pilot set allowing us to beneficially group the users in all the cells and to assign a time-shifted pilot transmission to the different groups. Unlike the existing PC elimination schemes, which require an excessively long channel coherence time, our proposed scheme is capable of completely eliminating PC under a much shorter coherence time. Moreover, the existing PC elimination schemes can no longer be used if the channel coherent time is insufficiently large. By contrast, even for extremely short channel coherent time, our scheme can still be implemented to significantly reduce PC. This is particularly beneficial for high velocity scenarios. Our simulation results demonstrate the efficiency of the proposed scheme.
large-scale MIMO （multiple-inputmultiple-output） systems with numerous low-power antennas can provide better performance in terms of spectrum efficiency, power saving and link reliability than conventional MIMO. Fo...
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large-scale MIMO （multiple-inputmultiple-output） systems with numerous low-power antennas can provide better performance in terms of spectrum efficiency, power saving and link reliability than conventional MIMO. For large-scale MIMO, there are several technical issues that need to be practically addressed （e.g., pilot pattern design and low-power transmission design） and theoretically addressed （e.g., capacity bound, channel estimation, and power allocation strategies）. In this paper, we analyze the sum rate upper bound of large-scale MIMO, investigate its key technologies including channel estimation, downlink precoding, and uplink detection. We also present some perspectives concerning new channel modeling approaches, advanced user scheduling algorithms, etc.
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