telecommunications infrastructure teams

Abstract: Rate splitting multiple access (RSMA) has emerged as a promising non-orthogonal transmission mechanism to improve spectral- and energy efficiency, and user fairness in multi-antenna broadband channels. To further enhance its performance, in this paper we investigate a novel cooperative RSMA (C-RSMA) transmission framework assisted by reconfigurable intelligent surface (RIS) and user relaying. More specifically, we first formulate the common- and private-rate maximization problem satisfying the transmit power constraint, by jointly optimizing the transmit beamformers at the BS and the reflective beamformer at the RIS. The above formulated problem is an intractable and NP-hard problem as the optimization variables are coupled and the strong residual self-interference (SI) is involved. To conquer the challenge mentioned above, we propose a low-complexity alternative optimization approach to jointly optimize the transmit and reflective beamforming. Simulation results show that our approach is capable of enabling superior sum-rate performance than the RIS-assisted space division multiple access (SDMA), half-duplex RSMA, and SDMA without using RIS schemes.

Abstract: Network slicing over multi-domain optical networks enables resource isolation for high-bandwidth, virtually-dedicated networks. However, guaranteeing end-to-end (E2E) performance remains challenging when domains withhold internal topology and performance metrics from external entities. Under this limited visibility, a control function (slice coordinator) needs to select E2E monitoring paths to detect and localize link failures. This paper formulates two complementary optimization problems, the initial and progressive monitoring path selection problems, which together realize closed-loop autonomous monitoring at the slice coordinator level. The initial problem maximizes failure coverage without prior information, while the progressive problem concentrates additional paths around suspected failure locations identified from prior monitoring. We propose a deep reinforcement learning (DRL) algorithm that adaptively selects an optimal set of monitoring paths for each phase. Experiments using the GNPy optical network simulator demonstrate that our approach outperforms baseline algorithms for both problems and achieves near-optimal localization in the initial selection, suggesting the feasibility of AI-driven closed-loop failure localization in future multi-domain optical network architectures.

Abstract: Motivated by emerging mission-critical applications and an increasingly congested spectrum, we develop a decentralized multi-agent reinforcement learning (MARL) model for dynamic spectrum access. The model enables secondary users to learn effective transmission strategies across shared frequency bands while minimizing collisions with high-priority primary users and among themselves. We design the agent-level learners following a Markov potential game approach, connecting independent local updates to system-level improvement. We instantiate this design using lightweight linear actor-critic learners suitable for resource-constrained edge devices, rather than computationally intensive centralized or deep multi-agent architectures. Across spectrum environments with different incumbent activities, the learned policies adapt their transmission policy and waiting behavior to preserve throughput while greatly reducing transmission collisions relative to random and forecast-aware heuristic baselines. The results establish the value of decentralized MARL and shows up to 96.8% reduction in overall collisions.

Abstract: This paper proposes an advanced realization principle for wideband multiuser multiple-input multiple-output orthogonal frequency-division multiplexing (MU-MIMO OFDM) transmitters, where the conventional transmitter-side baseband chain is physically synthesized in the wave domain. For design and optimization purposes, this fully wave-domain wideband MU-MIMO OFDM transmitter implemented by a cascaded SIM structure is functionally partitioned into two cascaded SIM blocks. The first block, denoted as SIM_1, integrates symbol loading and channel-adaptive MU-MIMO precoding updated at the channel-coherence timescale, mapping the user streams to a virtual port-subcarrier representation. The second block, denoted as SIM_2, acts as an offline-configured sampling-rate modulator that materializes the inverse discrete Fourier transform (IDFT) and cyclic prefix (CP) insertion directly in the wave domain. This baseband-free architecture establishes a virtual-to-physical transition from information bits to radiated CP-extended OFDM waveforms. To account for practical nonidealities, SIM_2 is optimized to fit the ideal multi-port CP-OFDM operator, and its residual response is mapped into an effective coupling matrix. Then, SIM_1 is optimized in a communication-oriented manner by jointly adapting discrete phase shifts and stream-subcarrier power loading to maximize the sum spectral efficiency. Results demonstrate the convergence, architecture trade-off, wave-domain OFDM materialization accuracy, and competitive performance of the proposed baseband-free transmitter.