HUMIES 2026 ENTRY 1. Complete title of the paper(s) Primary paper: Network-Assisted Full-Duplex Cell-Free Massive MIMO Systems Under Infeasible Circumstances Supporting earlier conference version: Differential Evolution for Infeasible Circumstances in Network-Assisted Full-Duplex Cell-Free Massive MIMO 2. Author information Author 1: Trinh Van Chien School of Information and Communications Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam E-mail: chientv@soict.hust.edu.vn Phone: +84 854690917 Author 2: Bui Trong Duc School of Information and Communications Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam E-mail: ducbt@soict.hust.edu.vn Phone: +84 356910806 Author 3: Mohammadali Mohammadi Centre for Wireless Innovation (CWI), Queen’s University Belfast, BT7 1NN Belfast, U.K. E-mail: m.mohammadi@qub.ac.uk Phone: +44 7436278008 Author 4: Hien Quoc Ngo Centre for Wireless Innovation (CWI), Queen’s University Belfast, BT7 1NN Belfast, U.K. E-mail: hien.ngo@qub.ac.uk Phone: +44 7449740849 Author 5: Michail Matthaiou Centre for Wireless Innovation (CWI), Queen’s University Belfast, BT7 1NN Belfast, U.K. E-mail: m.matthaiou@qub.ac.uk Phone: + 44 7476050770 3. Corresponding author Bui Trong Duc School of Information and Communications Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam E-mail: ducbt@soict.hust.edu.vn Phone: +84 356910806 4. Abstract of the paper(s) Cell-free massive multiple-input multiple-output is a potential candidate for future networks with pervasive connectivity by utilizing coherent joint transmission and distributed antenna arrays. This paper studies the exploitation of full-duplex communication for a distributed antenna array. Specifically, we derive a closed-form expression for the uplink and downlink ergodic spectral efficiency (SE) for a network where the APs can flexibly operate in either the full-duplex or half-duplex mode with linear processing and Rayleigh fading channels. A long-term total SE maximization problem is formulated subject to a network operation model and individual SE requirements with limited power budget. Due to the intrinsic nonconvexity and infeasible circumstances where some UEs might not be able to achieve the rate requirements, we adapt differential evolution to design a low computational complexity algorithm that can attain good power allocation and network operation mode in polynomial time. Numerical results demonstrate the effectiveness of our system design and proposed algorithm over state-of-the-art benchmarks with satisfactory service to the majority of UEs, although several ones may be unscheduled under harsh conditions. 5. Criteria claimed B 6. Statement of why the result satisfies the claimed criteria Criterion B: The result is published in a peer-reviewed scientific journal as a new scientific result independent of the fact that the result was created through evolutionary computation. The primary result is published in IEEE Transactions on Wireless Communications as a new contribution to network-assisted full-duplex cell-free massive MIMO systems. The work is not merely an application of an evolutionary algorithm to a toy problem. It contributes a new communication-system design and analysis for a practical 6G-relevant wireless architecture in which access points can flexibly operate in full-duplex or half-duplex modes. The paper derives closed-form uplink and downlink ergodic spectral-efficiency expressions, formulates a quality-of-service-constrained total spectral-efficiency maximization problem under limited power budgets, and addresses infeasible operating circumstances in which not all users can be simultaneously served. The evolutionary-computation component is central to making the result practically useful. The proposed constraint-handling differential evolution algorithm jointly optimizes access-point operation modes, downlink power control, uplink power control, and large-scale fading decoding weights. Unlike conventional optimization approaches that may become computationally expensive, rely on restrictive assumptions, or fail when the quality-of-service constraints make the feasible region empty, the proposed method detects users that cannot be supported under the current channel and power conditions and reallocates resources to serve the remaining users effectively. The result therefore satisfies Criterion B because it is a peer-reviewed journal result in wireless communications, contributing new analytical, algorithmic, and system-level insights independently of the fact that the result is obtained through evolutionary computation. The evolutionary method is not incidental: it is the enabling mechanism that makes it possible to search a mixed, highly nonconvex design space and to handle infeasible circumstances in a scalable manner. 7. Full citation of the paper(s) Primary paper: T. V. Chien, B. T. Duc, M. Mohammadi, H. Q. Ngo, and M. Matthaiou, “Network-Assisted Full-Duplex Cell-Free Massive MIMO Systems Under Infeasible Circumstances”, IEEE Transactions on Wireless Communications, vol. 25, pp. 15993–16008, 2026, doi: 10.1109/TWC.2026.3685665 Supporting earlier conference version: T. V. Chien, B. T. Duc, M. Mohammadi, H. Q. Ngo, and M. Matthaiou, “Differential Evolution for Infeasible Circumstances in Network-Assisted Full-Duplex Cell-Free Massive MIMO”, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’25), pp. 1497–1505, 2025, doi: 10.1145/3712256.3726476. 8. Prize money division Any prize money, if any, is to be divided equally among the co-authors. 9. Statement of why this entry should be considered the “best” This entry should be considered among the strongest Humies submissions because it demonstrates evolutionary computation producing a human-competitive scientific result in a demanding real-world engineering domain: 6G-oriented network-assisted full-duplex cell-free massive MIMO. First, the problem is both practically important and technically difficult. Full-duplex cell-free massive MIMO is a promising architecture for future wireless networks, but its practical deployment is hindered by residual self-interference, cross-link interference, mixed uplink/downlink traffic demands, quality-of-service constraints, power limitations, and the combinatorial choice of access-point operation modes. Under harsh propagation or limited power, the natural formulation can become infeasible if even one user cannot meet the required spectral-efficiency threshold. This creates a difficult system-design problem: the network must decide not only how to allocate power and choose full-/half-duplex operation, but also which users can be supported without wasting resources. Second, the proposed evolutionary method directly addresses this infeasibility rather than avoiding it. The constraint-handling differential evolution algorithm searches over a coupled design space including access-point mode assignment, downlink power control, uplink power control, and large-scale fading decoding weights. The algorithm also incorporates a repair and constraint-handling mechanism that identifies unsupported users and reallocates resources to maintain service quality for the remaining users. This is a meaningful human-competitive capability because the algorithm produces a network operation policy that is difficult to obtain by manual design or by direct application of standard convex-optimization tools. Third, the result is externally validated by publication in a leading peer-reviewed IEEE journal and by an earlier peer-reviewed GECCO 2025 version. The GECCO 2025 version was also nominated for the Best Paper Award in the Real-World Applications track, providing additional community recognition that the evolutionary-computation contribution is relevant to real-world problem solving. Fourth, the performance improvements are substantial. The proposed method consistently outperforms benchmark schemes based on constraint-handling genetic algorithms, constraint-handling particle swarm optimization, and traditional NAFD assignment. Across the tested configurations, the proposed algorithm achieves performance gains of up to 28% over CHPSO, 29% over CHGA, and 82% over traditional NAFD, with further improvements of approximately 10%–30% over robust benchmarks. These gains demonstrate that the evolutionary design is not only theoretically interesting but also empirically superior in the target communication-system setting. Finally, the entry is a strong Humies candidate because it bridges evolutionary computation and wireless communications in a way that produces a publishable engineering result, not merely an optimization demonstration. It contributes closed-form spectral-efficiency analysis, a new dynamic network-assisted full-duplex cell-free massive MIMO design, an infeasibility-aware differential-evolution algorithm, and extensive benchmark comparisons. The result therefore exemplifies the purpose of the Humies: evolutionary computation generating results competitive with human-created scientific and engineering solutions. 10. General type of genetic or evolutionary computation used DE — Differential Evolution. More specifically, the work uses a constraint-handling differential evolution algorithm designed for mixed and highly nonconvex wireless-network optimization under infeasible quality-of-service constraints. 11. Date of publication of each paper Primary paper: IEEE Transactions on Wireless Communications. Date of publication: April 28, 2026. Current version: April 30, 2026. Supporting earlier conference version: GECCO ’25, July 14–18, 2025, Malaga, Spain.