This paper investigates the optimal placement of a millimeter-wave base station (BS) within a realistic U-shaped environment with non-convex topology and proposes two deep reinforcement learning (DRL) techniques that achieves better solution than DQN with lower complexity.
Abstract
This paper investigates the optimal placement of a millimeter-wave (mmWave) base station (BS) within a realistic U-shaped environment with non-convex topology. The problem is challenging and NP-hard due to the non-convex topology and the non-convex objective functions which are the sum-rate maximization and max-min fairness, the latter being additionally non-smooth. To address this challenge, the BS placement is formulated as a Markov Decision Process (MDP). Then, we propose two deep reinforcement learning (DRL) techniques: First, the deployment area is discretized into a grid and optimized using a Deep Q-Network (DQN). Second, the U-shaped region is partitioned into continuous subspaces, where a Deep Deterministic Policy Gradient (DDPG) agent is dedicated to each subspace then the best BS placement is selected among partitions. Results demonstrate that optimal placement achieves full coverage and yields a Jain index of 0.99. Furthermore, the proposed partitioned multi-space DDPG achieves better solution than DQN with lower complexity.
Numerical evaluations reveal that the multi-agent DDPG approach substantially outperforms single-agent in dense scenarios, and the multi-agent demonstrates highly efficient computational convergence of dense scenarios with $400$ users.
Omar Rady, Mohamed Ayman, Ali Arafa et al.· 0 citations
In this paper, we investigate downlink scheduling for urban air mobility (UAM) in a cooperative space-air-ground integrated network. Multiple ground stations (GSs) employ narrow three-dimensional beams and share spectrum across multiple subbands, while a satellite provides an orthogonal-band service option. Rapidly time-varying geometry and directional interference require joint decisions on base station association, GS subband assignment, and transmit powers. We formulate a finite-horizon mixed discrete-continuous problem that maximizes sum rate while penalizing handovers and GS overload, using only UAM positions and velocities. To address the combinatorial scheduling problem, we propose GeoSetPPO, a geometry-aware set-attention proximal policy optimization (PPO) method that outputs per-UAM discrete association and subband decisions with permutation-invariant representations. Conditioned on each schedule, GS powers are computed by a per-slot successive convex approximation (SCA) module under per-GS power budgets and minimum signal-to-interference-plus-noise ratio (SINR) constraints. To reduce training cost and improve stability, we adopt a two-stage training strategy that transitions reward evaluation from uniform power to SCA-based power allocation. Simulations demonstrate stable convergence, higher returns than multi-layer perceptron (MLP)- and Transformer-based PPO under the considered training setting, and favorable reward and schedule-feasibility performance relative to algorithm-based and distance-based schedulers. In the larger evaluated network, GeoSetPPO also reduces the scheduling latency from 40.84 ms to 2.90 ms relative to the previous algorithm-based method.
Hyung-Joo Moon, Sangha Park, Chan-Byoung Chae et al.· 0 citations
IRS has received much attention recently and is envisioned as a revolutionary technology for 6G communication networks. In this paper, we consider a multicast traffic pattern in an IRS-aided single-cell MISO communication system, where an IRS is deployed to assist a multi-antenna AP in transmitting independent data streams to each group of multiple single-antenna users. We formulate and solve a new MMF problem by jointly optimizing the beamforming matrix at the AP and the phase shift matrix at the IRS, subject to both the power constraint and the unit-modulus constraints on the phase shifts. To tackle this highly nonconvex fractional problem, efficient algorithms are proposed based on GFP and AO. In each alternating step, a closed-form suboptimal solution for the transmit beamforming matrix is derived using SCA and LDD. For the phase shift optimization of the IRS, a penalty mechanism is introduced to efficiently handle the nonconvex unit-modulus constraints. Simulation results demonstrate the guaranteed convergence and the superiority of the proposed scheme in improving the minimum weighted SINR at the users, as well as the contribution of the IRS in reducing the transmit power consumption and the number of active transmit antennas at the AP.
We study radio node (RN) placement for indoor enterprise networks. Using stochastic geometry (SG), we derive the meta-distribution (MD) of the SINR for a test user equipment (UE), with and without cooperation from outdoor macro base stations (MBSs), and compare these results with an integer linear programming (ILP) approach. SG provides an estimate of the required number of RNs but not their locations, while ILP can yield inaccurate local optima and requires high computational power. To address this, we investigate clustering-based algorithms for initializing RN locations using UE location distributions. Along with standard methods, we propose a weighted $k$-harmonic means (WKHM) clustering strategy tailored to maximize SINR. We then introduce a constrained sequential minimum cut algorithm, \texttt{SeqMinCut}, to merge multiple RNs into larger cells and further improve SINR. This is the first work that integrates SG-based statistical analysis, optimization, and clustering to obtain system design insights, dimensioning rules, and planning strategies for enterprise 5G.
Gourab Ghatak· IEEE Transactions on Network...· 0 citations
The fifth-generation (5G) networks will provide high capacities with less transmission delays. To meet these
features, it is depicted that these networks must be densified that is with many base stations (BSs) dominated by small BSs.
The BS density to be deployed in a network is a vital design issue since an inappropriate BS deployment could give rise to
displeasing consequences, such as excess interferences, void cells that is cells without users, excess power consumption and
operating costs since over 50% of the total cellular network energy consumption is by BSs. In this paper, the simplex method
of linear programming is used to determine the optimal BS density that can consume the optimal power based on maximizing
the service area. Using the cell edge conditions, we generate radii of the two cells and hence respective areas are determined.
Results show that although all the minimum powers and their respective BSs cover the entire service area, there is an optimal
BS combination with an optimal power consumed.
Fredrick William Mukalazi, E. Mugume, J. Serugunda· International Journal of Inn...· 0 citations
This paper investigates the sum-rate maximization problem for downlink rate-splitting multiple access (RSMA) systems equipped with pattern-reconfigurable fluid antennas (PRFA). Two PRFA models are developed: a deployable discrete-selection PRFA (DS-PRFA) model with finite predefined radiation modes, and an idealized continuous-optimization PRFA (CO-PRFA) model based on spherical harmonic expansion that serves as a performance upper bound. The sum-rate maximization problem is formulated by jointly optimizing digital, analog, and antenna-domain precoders along with RSMA power allocation. To solve this non-convex problem, we propose alternating optimization algorithms based on the weighted minimum mean square error (WMMSE) transformation and block coordinate descent with per-antenna decoupling. For DS-PRFA optimization, closed-form solutions are derived, while for CO-PRFA optimization, a preconditioned Riemannian conjugate gradient method is developed on the spherical manifold. Simulation results under the considered settings show that the proposed tri-hybrid RSMA framework with PRFA improves sum-rate performance compared with conventional hybrid precoding, where the CO-PRFA provides an idealized upper benchmark compared with practical DS-PRFA due to the more flexible reconfigurability.
Yijin Pan, Yifeng Ji, Anzheng Tang et al.· IEEE Open Journal of the Com...· 0 citations
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