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An important aspect of 5G networks is the development of Radio Access Network (RAN) slicing, a concept wherein the virtualized infrastructure of wireless networks is subdivided into slices (or enterprises), tailored to fulfill specific use-cases. A key focus in this context is the efficient radio resource allocation to meet various enterprises' service-level agreements (SLAs). In this work, we introduce Helix: a channel-aware and SLA-aware RAN slicing framework for massive multiple input multiple output (MIMO) networks where resource allocation extends to incorporate the spatial dimension available through beamforming. Essentially, the same time-frequency resource block (RB) can be shared across multiple users through multiple antennas. Notably, certain enterprises, particularly those operating critical infrastructure, necessitate dedicated RB allocation, denoted as private networks, to ensure security. Conversely, some enterprises would allow resource sharing with others in the public network to maintain network performance while minimizing capital expenditure. Building upon this understanding, Helix comprises scheduling schemes under both scenarios: where different slices share the same set of RBs, and where they require exclusivity of allocated RBs. We validate the efficacy of our proposed schedulers through simulation by utilizing a channel data set collected from a real-world massive MIMO testbed. Our assessments demonstrate that resource sharing across slices using our approach can lead up to 60.9% reduction in RB usage compared to other approaches. Moreover, our proposed schedulers exhibit significantly enhanced operational efficiency, with significantly faster running time compared to exhaustive greedy approaches while meeting the stringent 5G sub-millisecond-level latency requirement. 

In this paper, we propose a method to repurpose the multi-user MIMO downlink transmission for joint wireless communication and imaging. The key idea is to introduce the concept of virtual users in the communication coverage area and use the existing MUMIMO beamforming methods to jointly beamform towards real and virtual users. The virtual users are placed to complement the locations of actual users, with the objective to illuminate the scene as uniformly as possible. We study a single-parameter tradeoff, introduced by a power split parameter between real and virtual users. We demonstrate via simulated examples that the virtual user concept is effective in providing a scalable imaging and communications performance tradeoff for cases where the real users are clustered in small geographical areas. 

Multiple-input, multiple-output (MIMO) radars can estimate radial velocities of moving objects, but not their tangential velocities. In this paper, we propose to exploit multi-bounce scattering in the environment to form an effective multi-“look” synthetic aperture and enable estimation of a moving object's entire velocity vector - both tangential and radial velocities. The proposed approach enables instantaneous velocity vector estimation with a single MIMO radar, without additional sensors or assumptions about the object size. The only requirement of our approach is the existence of at least one resolvable multi-bounce path to the object from a static landmark in the environment. The approach is validated both in theory and simulation. 

An important aspect of 5G networks is the development of Radio Access Network (RAN) slicing, a concept wherein the virtualized infrastructure of wireless networks is subdivided into slices (or enterprises), tailored to fulfill specific use-cases. A key focus in this context is the efficient radio resource allocation to meet various enterprises’ service-level agreements (SLAs). In this work, we introduce Helix: a channel-aware and SLAaware RAN slicing framework for massive multiple input multiple output (MIMO) networks where resource allocation extends to incorporate the spatial dimension available through beamforming. Essentially, the same time-frequency resource block (RB) can be shared across multiple users through multiple antennas. Notably, certain enterprises, particularly those operating critical infrastructure, necessitate dedicated RB allocation, denoted as private networks, to ensure security. Conversely, some enterprises would allow resource sharing with others in the public network to maintain network performance while minimizing capital expenditure. Building upon this understanding, Helix comprises scheduling schemes under both scenarios: where different slices share the same set of RBs, and where they require exclusivity of allocated RBs. We validate the efficacy of our proposed schedulers through simulation by utilizing a channel data set collected from a real-world massive MIMO testbed. Our assessments demonstrate that resource sharing across slices using our approach can lead up to 60.9% reduction in RB usage compared to other approaches. Moreover, our proposed schedulers exhibit significantly enhanced operational efficiency, with significantly faster running time compared to exhaustive greedy approaches while meeting the stringent 5G sub-millisecond-level latency requirement. 

Multiple-input, multiple-output (MIMO) radars can estimate radial velocities of moving objects, but not their tangential velocities. In this paper, we propose to exploit multi-bounce scattering in the environment to form an effective multi-“look” synthetic aperture and enable estimation of a moving object's entire velocity vector - both tangential and radial velocities. The proposed approach enables instantaneous velocity vector estimation with a single MIMO radar, without additional sensors or assumptions about the object size. The only requirement of our approach is the existence of at least one resolvable multi-bounce path to the object from a static landmark in the environment. The approach is validated both in theory and simulation. 

Abstract—As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce a new adaptive MCS selection framework for massive MIMO systems that operates without any feedback from users by solely relying on instantaneous uplink channel estimates. Our proposed method can effectively operate in multi-user scenarios where user feedback imposes excessive delay and bandwidth overhead. To learn the mapping between the user channel matrices and the optimal MCS level of each user, we develop a Convolutional Neural Network (CNN)-Long Short-Term Memory Network (LSTM)-based model and compare the performance with the state-of-the-art methods. Finally, we validate the effectiveness of our algorithm by evaluating it experimentally using real-world datasets collected from the RENEW massive MIMO platform. Index Terms—Adaptive MCS Selection, Machine Learning, Convolutional Neural Network, Long Short-Term Memory Network, Channel State Information, Feedback Delay.