In quantum state discrimination, typically the design of measurement operators or probe state is formulated assuming the set of possible states is perfectly known, but this may yield designs which are sensitive to deviations in the realized set of states. For example, the channel through which a transmitted state is sent may not be deterministic, but instead characterized by a classical distribution over quantum channels. In this paper, we consider the design of measurement schemes and probe states for quantum detection over an uncertain quantum channel. We present stochastic gradient-based algorithms to maximize the expected performance over the channel distribution for various design objectives, including the detection probability and mutual information. Furthermore, we introduce a scheme that leverages the isometric extension of a quantum channel to measure the channel output in an enlarged Hilbert space such that the channels are more distinguishable, while simultaneously reducing the effective dimension and thereby reducing the optimization complexity. Finally, we apply the proposed algorithms to multicopy channel discrimination.

Next-generation wireless technologies such as rate-splitting multiple access (RSMA) and massive MIMO are characterized by optimization problems too complex to solve in real-time, hence suboptimal heuristics are adopted in practice. As we explore in this paper, machine learning techniques have the potential to upend this paradigm, offering new algorithms customized for a particular distribution of problems. We consider MISO downlink beamforming optimization for NOMA, SDMA, and RSMA with sum rate and min rate criteria. We apply the framework of learning to optimize to learn an RNN optimizer that produces beamformers with much less computation than existing optimization algorithms such as weighted-MMSE. The RNN inference complexity scales linearly with the size of the antenna array and therefore is suitable for massive MIMO. We show that the learned optimizer is also compatible with a distributed beamforming scenario such as cell-free massive MIMO with information exchange facilitated by a central processor. Our simulation results show that the learned optimizer is competitive with state-of-the-art optimization methods, but requires a fraction of the computational cost.

In this work, we consider a backscatter communication system wherein multiple asynchronous sources (tags) exploit the reverberation generated by a nearby radar transmitter as an ambient carrier to deliver a message to a common destination (reader) through a number of available subchannels. We propose a new encoding strategy wherein each tag transmits both pilot and data symbols on each subchannel and repeats some of the data symbols on multiple subchannels. We then exploit this signal structure to derive two semi-blind iterative algorithms for joint estimation of the data symbols and the subchannel responses that are also able to handle some missing measurements. The proposed encoding/decoding strategies are scalable with the number of tags and their payload and can achieve different tradeoffs in terms of transmission and error rates. Some numerical examples are provided to illustrate the merits of the proposed solutions.

In this work, we exploit the radar clutter (i.e., the ensemble of echoes generated by the terrain and/or the surrounding objects in response to the signal emitted by a radar transmitter) as a carrier signal to enable an ambient backscatter communication from a source (tag) to a destination (reader). The proposed idea relies on the fact that, since the radar excitation is periodic, the radar clutter is itself periodic over time scales shorter than the coherence time of the environment. Upon deriving a convenient signal model, we propose two encoding/decoding schemes that do not require any coordination with the radar transmitter or knowledge of the radar waveform. Different tradeoffs in terms of transmission rate and error probability can be obtained upon changing the control signal driving the tag switch or the adopted encoding rule; also, multiple tags can be accommodated with either a sourced or an unsourced multiple access strategy. Some illustrative examples are provided.

In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, which senses multiple spatial directions and serves multiple users. Upon resorting to an orthogonal frequency division multiplexing (OFDM) transmission format and a differential phase shift keying (DPSK) modulation, we study the design of the radiated waveforms and of the receive filters employed by the radar and the users. The approach is communication-centric, in the sense that a radar-oriented objective is optimized under constraints on the average transmit power, the power leakage towards specific directions, and the error rate of each user, thus safeguarding the communication quality of service (QoS). We adopt a unified design approach allowing a broad family of radar objectives, including both estimation- and detection-oriented merit functions. We devise a suboptimal solution based on alternating optimization of the involved variables, a convex restriction of the feasible search set, and minorization-maximization, offering a single algorithm for all of the radar merit functions in the considered family. Finally, the performance is inspected through numerical examples.

Freeze/thaw (FT) processes at the earth’s surface can have a considerable effect on global carbon, energy, and hydrologic cycles. Therefore, an accurate representation of FT is valuable to adequately monitor and model these processes. In this study, we assess the relationship between satellite-based FT products and modeled surface and soil temperatures over North America. In addition, hourly land surface temperature (LST) from the Geostationary Operational Environmental Satellite (GOES) system is also compared to FT classifications. Utilizing the higher spatial resolution temperatures (~5 km), we assess subgrid-scale variability and its relationship to coarser microwave FT classifications (>25 km). We also examine product agreement and subpixel characteristics across the land cover, climate, and topography. FT classifications are shown to vary widely depending on these variables, leading to an ambiguous definition of frozen and thawed states. Our results suggest that current products can characterize FT transitions with consistent subfreezing surface characteristics in far northern regions (>50 °N). However, uncertainty associated with FT classifications is shown to increase considerably as latitude decreases. Our results also suggest that fractional FT products, utilizing data inputs, such as LST, would provide a considerable improvement in mountainous regions with high intergrid cell heterogeneity, in regions characterized by ephemeral FT events (i.e., regions < 40 °N), as well as during freeze and thaw onset periods. This study also provides insight to improving the representation of surface FT state by providing a clearer definition of the subpixel scale temperature characteristics that govern existing frozen classifications.

We present two model-based neural network architectures purposed for sporadic user detection and channel estimation in massive machine-type communications. In the scenario under consideration, a base station assigns the users a set of pilot sequences that is linearly dependent, but because user activity is sporadic the detection/estimation problem is amenable to sparse recovery algorithms. Further, we consider a millimeter-wave wireless channel, so that the channel vectors are sparse in a known dictionary. We apply the deep unfolding framework to design custom neural network layers by unrolling two iterative optimization algorithms: (1) linearized alternating direction method of multipliers, which we apply to a constrained convex problem, and (2) vector approximate message passing featuring a novel denoiser based on the iterative shrinkage thresholding algorithm. The networks thus inherit domain knowledge as encapsulated by the signal model, and suitable operations as informed by the algorithms—in the same spirit as convolutional networks that exploit structure inherent in images and audio, except grounded in optimization and statistics. The networks, trained on synthetic data generated from the block-fading millimeter-wave multiple access channel model, offer improved complexity and accuracy relative to their iterative counterparts, and are potentially a boon to cell-free MIMO systems.

We present two model-based neural network architectures purposed for sporadic user activity detection and channel estimation in the massive connectivity regime. In the considered scenario, the set of pilot sequences assigned to users is linearly dependent; but assuming user activity is sporadic, the detection/estimation problem is amenable to sparse recovery algorithms. We apply the deep unfolding framework to unroll two such algorithms, (1) linearized alternating direction method of multipliers and (2) vector approximate message passing, into a set of custom neural network layers. The networks thus inherit domain knowledge encapsulated in the signal model, plus suitable layer operations informed by the algorithms. The networks, trained on randomly generated data, offer improved complexity and accuracy relative to their iterative counterparts, and are a potential boon to cell-free massive MIMO systems.