This paper addresses uplink long-term joint power control, beamformer design, and time-sharing in overloaded cell-free massive MIMO networks, aiming to achieve max-min fairness among users. Resource allocation leverages slowly-varying channel statistics rather than instantaneous channel state information, reducing information-sharing overhead and enabling optimization over longer timescales. We propose a block coordinate ascent algorithm in which each subproblem is optimally solved using a combination of bisection search, fixed point iterations, and linear programming, guaranteeing convergence to a local optimum. Numerical simulations corroborate the gains of the proposed joint approach in terms of spectral efficiency and power savings over more conventional disjoint time-sharing schemes adopting baseline scheduling policies.

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We propose a beam codebook design for integrated sensing and communication (ISAC) that reduces self-interference (SI) to alleviate analog distortion. Our optimization framework, which considers either tapered beamforming or phased arrays for both analog and hybrid schemes, modifies given reference codebooks such that a certain SI power level is achieved. In contrast to other low-SI codebooks, which often rely on hardly interpretable optimization parameters, we provide design guidelines to obtain sensing performance guarantees by deriving analytical bounds on saturation and analog-to-digital quantization in relation to the multipath SI level. By selecting standard reference codebooks in our simulations, we show how our method substantially improves the signal-to-noise ratio for sensing with little impact on 5G-NR communication.

In this paper, we formalize an optimization frame-work for analog beamforming in the context of monostatic integrated sensing and communication (ISAC), where we also address the problem of self-interference in the analog domain. As a result, we derive semidefinite programs to approach detection-optimal transmit and receive beamformers, and we devise a superiorized iterative projection algorithm to approximate them. Our simulations show that this approach outperforms the detection performance of well-known design techniques for ISAC beamforming, while it achieves satisfactory self-interference sup-pression.

In this paper, we propose deep-unfolded versions of the recently proposed superiorized adaptive projected subgradient method for MIMO detection. The proposed methods require a single matrix inverse for initialization, and they have a quadratic per-iteration complexity. Extensive simulations with realistic channel models show that the proposed deep-unfolded detectors outperform not only their untrained counterpart, but also existing methods with similar complexity.

In this paper, we propose a set-theoretic framework for MIMO detection. Various low-complexity MIMO detection algorithms achieve excellent performance on i.i.d. Gaussian channels, but they typically incur high performance loss if realistic channel models are considered. Compared to existing low-complexity iterative detectors such as approximate message passing (AMP), the proposed algorithms do not impose any structure the channel matrix. Simulations with a realistic channel model show that the proposed methods are competitive with detectors based on orthogonal AMP (OAMP), which compute matrix inverses in each iteration. At the same time, the proposed methods do not require matrix inverses, and their complexity is similar to AMP.

In the future, mobility use cases will depend on precise predictions, with Quality of Service (QoS) prediction being a prominent example. This paper presents realistic measurements from today’s vehicles to support robust QoS prediction in the future. Based on a dedicated and controlled measurement campaign, we highlight aspects of the wireless environment and the device characteristics, like the sampling rates, that influence the collected datasets. If not properly handled, such characteristics might hinder the performance of Artificial Intelligence-based algorithms for QoS prediction. Therefore, we also provide insights on dataset characteristics that should be further used to enable easier adoption of AI-based algorithms. New AI-based algorithms should be able to operate in very diverse radio environments with data captured from different devices. We provide several examples that highlight the importance of thoroughly understanding the datasets and their dynamics.

Hybrid analog-/digital beamforming architectures are a promising means of reducing power consumption and hardware costs in large multi-antenna transceivers. However, channel estimation becomes more complicated compared with conventional (fully-digital) architectures because multiple measurements (pilots in subsequent time slots) are required to reconstruct the channel matrix. In this paper, we use variants of the adaptive projected subgradient method to devise online estimation algorithms that exploit temporal correlations of channel samples. Simulations show that these methods are competitive with conventional batch methods in terms of estimation error at a significantly reduced computational cost. We further improve the performance of the proposed methods by exploiting their potential to adapt the analog combiner at runtime.

Semidefinite relaxation followed by randomization is a well-known approach for approximating a solution to the NP-hard max-min fair multicast beamforming problem. While providing a good approximation to the optimal solution, this approach commonly involves the use of computationally demanding interior point methods. In this study, we propose a solution based on superiorization of bounded perturbation resilient iterative operators that scales to systems with a large number of antennas. We show that this method outperforms the randomization techniques in many cases, while using only computationally simple operations.