Jeffrey G. Andrews

Future wireless networks must deliver high spectral efficiency in dense environments where many users require simultaneous high-rate communication. Higher carrier frequencies offer more bandwidth but suffer from shorter range and lower channel rank, making new capacity enhancing strategies essential. This paper studies joint antenna array design at the base station (BS) and user devices in line-of-sight (LoS) uplink multiuser multiple-input multiple-output (MU-MIMO) systems with the goal of exploiting radiative near-field effects to unlock full multiplexing gains. In particular, we establish a scaling law under which there exist uniform sparse array configurations whose sum rate gap to an interference-free upper bound decays with the number of BS antennas. For dense user deployments beyond this scaling regime, we propose a joint array design algorithm with provable performance guarantees, in which each user array is configured to maximize its single-user achievable rate conditioned on the BS array, followed by non-uniform BS array optimization using a submodular surrogate. Our simulation results verify that uniform sparse arrays achieve the interference-free regime predicted by the scaling law. Outside this regime, the proposed joint design closely tracks the interference-free upper bound, demonstrating the benefit of non-uniform BS array optimization.

We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-average interference and absolute interference inflicted upon each primary ground user never exceeds specified thresholds. We solve this NP-hard constrained, combinatorial satellite selection problem through Lagrangian relaxation to decompose it into simpler problems which can then be solved through subgradient methods. A high-fidelity simulation is developed based on public FCC filings and technical specifications of the Starlink and Kuiper systems. We use this case study to illustrate the effectiveness of our approach and that explicit protection is indeed necessary for healthy coexistence. We further demonstrate that deep learning models can be used to predict the primary satellite system associations, which helps the secondary system avoid inflicting excessive interference and maximize its own capacity.

This paper introduces a novel deep learning-based user-side feedback reduction framework, termed self-nomination. The goal of self-nomination is to reduce the number of users (UEs) feeding back channel state information (CSI) to the base station (BS), by letting each UE decide whether to feed back based on its estimated likelihood of being scheduled and its potential contribution to precoding in a multiuser MIMO (MU-MIMO) downlink. Unlike SNR- or SINR-based thresholding methods, the proposed approach uses rich spatial channel statistics and learns nontrivial correlation effects that affect eventual MU-MIMO scheduling decisions. To train the self-nomination network under an average feedback constraint, we propose two different strategies: one based on direct optimization with gradient approximations, and another using policy gradient-based optimization with a stochastic Bernoulli policy to handle non-differentiable scheduling. The framework also supports proportional-fair scheduling by incorporating dynamic user weights. Numerical results confirm that the proposed self-nomination method significantly reduces CSI feedback overhead. Compared to baseline feedback methods, self-nomination can reduce feedback by as much as 65%, saving not only bandwidth but also allowing many UEs to avoid feedback altogether (and thus, potentially enter a sleep mode). Self-nomination achieves this significant savings with negligible reduction in sum-rate or fairness.

Tightly integrated low earth orbit (LEO) satellite communications and terrestrial integrated sensing and communication (ISAC) are expected to be key novel aspects of the 6G era. Spectrum sharing between satellite and terrestrial cellular networks may, however, cause severe interference. This paper introduces a cooperation framework for integrated coexistence between satellite and terrestrial networks where the terrestrial network also deploys multistatic ISAC. Unlike prior works that assume ideal channel state information (CSI) acquisition, the proposed approach develops a practical structure consisting of pre-optimization and refinement stages that leverages the predictability of satellite CSI. In addition, a co-design of terrestrial beamforming and satellite power allocation utilizing a weighted minimum mean-squared error algorithm is proposed, and a target-radar association method designed for multistatic ISAC is presented. Simulation results show that the proposed approach significantly enhances the performance of these integrated networks. Furthermore, it is confirmed that the overall performance approaches the interference-free benchmark as the number of spot beams and radar receivers increases, demonstrating the feasibility of spectral coexistence between the two networks.

This paper introduces a novel framework for high-accuracy outdoor user equipment (UE) positioning that applies a conditional generative diffusion model directly to high-dimensional massive MIMO channel state information (CSI). Traditional fingerprinting methods struggle to scale to large, dynamic outdoor environments and require dense, impractical data surveys. To overcome these limitations, our approach learns a direct mapping from raw uplink Sounding Reference Signal (SRS) fingerprints to continuous geographic coordinates. We demonstrate that our DiffLoc framework achieves unprecedented sub-centimeter precision, with our best model (DiffLoc-CT) delivering 0.5 cm fusion accuracy and 1-2 cm single base station (BS) accuracy in a realistic, ray-traced Tokyo urban macro-cell environment. This represents an order-of-magnitude improvement over existing methods, including supervised regression approaches (over 10 m error) and grid-based fusion (3 m error). Our consistency training approach reduces inference time from 200 steps to just 2 steps while maintaining exceptional accuracy even for high-speed users (15-25 m/s) and unseen user trajectories, demonstrating the practical feasibility of our framework for real-time 6G applications.

Deep neural network (DNN)-based algorithms are emerging as an important tool for many physical and MAC layer functions in future wireless communication systems, including for large multi-antenna channels. However, training such models typically requires a large dataset of high-dimensional channel measurements, which are very difficult and expensive to obtain. This paper introduces a novel method for generating synthetic wireless channel data using diffusion-based models to produce user-specific channels that accurately reflect real-world wireless environments. Our approach employs a conditional denoising diffusion implicit model (cDDIM) framework, effectively capturing the relationship between user location and multi-antenna channel characteristics. We generate synthetic high fidelity channel samples using user positions as conditional inputs, creating larger augmented datasets to overcome measurement scarcity. The utility of this method is demonstrated through its efficacy in training various downstream tasks such as channel compression and beam alignment. Our diffusion-based augmentation approach achieves over a 1-2 dB gain in NMSE for channel compression, and an 11 dB SNR boost in beamforming compared to prior methods, such as noise addition or the use of generative adversarial networks (GANs).

This paper proposes and analyzes novel deep learning methods for downlink (DL) single-user multiple-input multiple-output (MIMO) and multi-user MIMO (MU-MIMO) systems operating in time division duplex mode. A motivating application is the 6G upper midbands (7-24 GHz), where the base station (BS) antenna arrays are large, user equipment array sizes are moderate, and theoretically optimal approaches are practically infeasible for several reasons. To deal with uplink (UL) pilot overhead and low signal power issues, we introduce the channel-adaptive pilot, as part of the novel analog channel state information feedback mechanism. Deep neural network (DNN)-generated pilots are used to linearly transform the UL channel matrix into lower-dimensional latent vectors. Meanwhile, the BS employs a second DNN that processes the received UL pilots to directly generate near-optimal DL precoders. The training is end-to-end which exploits synergies between the two DNNs. For MU-MIMO precoding, we propose a DNN structure inspired by theoretically optimum linear precoding. The proposed methods are evaluated against genie-aided upper bounds and conventional approaches, using realistic upper midband datasets. Numerical results demonstrate the potential of our approach to achieve significantly increased sum-rate, particularly at moderate to high signal-to-noise ratio and when UL pilot overhead is constrained.

This work provides a rigorous assessment of the feasibility of spectrum sharing between large low-earth orbit (LEO) satellite constellations. For concreteness, we focus on the existing Starlink system and the soon-to-be-launched Kuiper system, the latter of which is prohibited from inflicting excessive interference onto incumbent Starlink ground users. We carefully model and study the potential downlink interference between the two systems at 20 GHz and investigate how strategic satellite selection may be used by Kuiper to serve its own ground users while also protecting Starlink ground users. We then extend this notion of satellite selection to the case where Kuiper has limited knowledge of Starlink’s serving satellite. Throughout our analysis, we examine the distribution of interference and SINR over time as each constellation orbits the globe. Our findings reveal that there is virtually always the potential for very high or extremely low interference, depending on which Starlink and Kuiper satellites are being used to serve their ground users. Consequently, we show that Kuiper can protect Starlink ground users, with high probability, by strategically selecting which of its satellites are used to serve its ground users. Simultaneously, Kuiper is capable of delivering near-maximal downlink SINR to its own ground users. This highlights a potential feasible route to the coexistence of two dense LEO satellite systems, even in scenarios where one system has limited knowledge of the other’s serving satellites.

The contours of 6G -- its key technical components and driving requirements -- are finally coming into focus. Through twenty questions and answers, this article defines the important aspects of 6G across four categories. First, we identify the key themes and forces driving the development of 6G, and what will make 6G unique. We argue that 6G requirements and system design will be driven by (i) the tenacious pursuit of spectral (bits/Hz/area), energy (bits/Joule), and cost (bits/dollar) efficiencies, and (ii) three new service enhancements: sensing/localization/awareness, compute, and global broadband/emergency connectivity. Second, we overview the important role of spectrum in 6G, what new spectrum to expect in 6G, and outline how the different bands will be used to provide 6G services. Third, we focus our attention on the 6G physical layer, including waveforms, MIMO advancements, and the potential use of deep learning. Finally, we explore how global connectivity will be achieved in 6G, through non-terrestrial networks as well as low-cost network expansion via disaggregation and O-RAN. Although 6G standardization activities will not begin until late 2025, meaning this article is by definition speculative, our predictions are informed by several years of intensive research and discussions. Our goal is to provide a grounded perspective that will be helpful to both researchers and engineers as we move into the 6G era.

The contours of 6G—its key technical components and driving requirements—are finally coming into focus. Through twenty questions and answers, this article defines the important aspects of 6G across four categories. First, we identify the key themes and forces driving the development of 6G, and what will make 6G unique. We argue that 6G requirements and system design will be driven by (i) the tenacious pursuit of spectral (bits/Hz/area), energy (bits/Joule), and cost (bits/dollar) efficiencies, and (ii) three new service enhancements: sensing/localization/awareness, compute, and global broadband/emergency connectivity. Second, we overview the important role of spectrum in 6G, what new spectrum to expect in 6G, and outline how the different bands will be used to provide 6G services. Third, we focus our attention on the 6G physical layer, including waveforms, MIMO advancements, and the potential use of deep learning. Finally, we explore how global connectivity will be achieved in 6G, through non-terrestrial networks as well as low-cost network expansion via disaggregation and O-RAN. Although 6G standardization activities will not begin until late 2025, meaning this article is by definition speculative, our predictions are informed by several years of intensive research and discussions. Our goal is to provide a grounded perspective that will be helpful to both researchers and engineers as we move into the 6G era.