This paper addresses the joint optimization of per-user equipment (UE) bandwidth allocation and UE-access point (AP) association to maximize weighted sum-rate while satisfying heterogeneous quality-of-service (QoS) requirements across enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) slices in the uplink of a network slicing-enabled user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) system. The formulated problem is NP-hard, rendering global optimality computationally intractable. To address this challenge, it is decomposed into two sub-problems, each solved by a computationally efficient heuristic scheme, and jointly optimized through an alternating optimization framework. We then propose (i) a bandwidth allocation scheme that balances UE priority, spectral efficiency, and minimum bandwidth demand under limited resources to ensure fair QoS distribution, and (ii) a priority-based UE-AP association assignment approach that balances UE service quality with system capacity constraints. Together, these approaches provide a practical and computationally efficient solution for resource-constrained network slicing scenarios, where QoS feasibility is often violated under dense deployments and limited bandwidth, necessitating graceful degradation and fair QoS preservation rather than solely maximizing the aggregate sum-rate. Simulation results demonstrate that the proposed scheme achieves up to 52% higher weighted sum-rate, 140% and 58% higher QoS success rates for eMBB and URLLC slices, respectively, while reducing runtime by up to 97% compared to the considered benchmarks.

This paper addresses joint admission control and per-user equipment (UE) bandwidth allocation to maximize weighted sum-rate in network slicing-enabled user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems when aggregate quality-of-service (QoS) demand may exceed available bandwidth. Specifically, we optimize bandwidth allocation while satisfying heterogeneous QoS requirements across enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) slices in the uplink. The formulated problem is NP-hard, rendering global optimality computationally intractable. We decompose it into two sub-problems and solve them via computationally efficient heuristics within a sequential framework. We propose (i) a hierarchical admission control scheme that selectively admits UEs under bandwidth scarcity, prioritizing URLLC to ensure latency-sensitive QoS compliance, and (ii) an iterative gradient-based bandwidth allocation scheme that transfers bandwidth across slices guided by marginal utility and reallocates resources within slices. Simulation results demonstrate that the proposed scheme achieves near-optimal performance, deviating from an interior point solver-based benchmark by at most 2.2% in weighted sum-rate while reducing runtime by 99.7%, thereby enabling practical real-time deployment. Compared to a baseline round-robin scheme without admission control, the proposed approach achieves up to 1085% and 7% higher success rates for eMBB and URLLC slices, respectively, by intentionally sacrificing sum-rate to guarantee QoS. Sensitivity analysis further reveals that the proposed solution adapts effectively to diverse eMBB/URLLC traffic compositions, maintaining 47-51% eMBB and 93-94% URLLC success rates across varying load scenarios, confirming its robustness for resource-constrained large-scale deployments.

In this paper, we investigate the uplink (UL) radio resource management for 5G aerial corridors with an open-radio access network (O-RAN)-enabled cell-free (CF) massive multiple-input multiple-output (mMIMO) system. Our objective is to maximize the minimum spectral efficiency (SE) by jointly optimizing unmanned aerial vehicle (UAV)-open radio unit (O-RU) association and UL transmit power under quality-of-service (QoS) constraints. Owing to its NP-hard nature, the formulated problem is decomposed into two tractable sub-problems solved via alternating optimization (AO) using two computationally efficient algorithms. We then propose (i) a QoS-driven and multi-connectivity-enabled association algorithm incorporating UAV-centric and O-RU-centric criteria with targeted refinement for weak UAVs, and (ii) a bisection-guided fixed-point power control algorithm achieving global optimality with significantly reduced complexity, hosted as xApp at the near-real-time (near-RT) RAN intelligent controller (RIC) of O-RAN. Solving the resource-allocation problem requires global channel state information (CSI), which incurs substantial measurement and signaling overhead. To mitigate this, we leverage a channel knowledge map (CKM) within the O-RAN non-RT RIC to enable efficient environment-aware CSI inference. Simulation results show that the proposed framework achieves up to 440% improvement in minimum SE, 100% QoS satisfaction and fairness, while reducing runtime by up to 99.7% compared to an interior point solver-based power allocation solution, thereby enabling O-RAN compliant real-time deployment.

This paper investigates uplink radio resource optimization of a user-centric (UC) cell-free (CF) massive multiple-input multiple-output (mMIMO) system aided by the rate splitting multiple access (RSMA) technique subject to pilot contamination. We formulate problem to maximize the minimum spectral efficiency (SE) problem by jointly addressing decoding order selection, power allocation, and access point (AP) - user equipment (UE) association assignment. The envisioned optimization exhibits two challenges. First, it requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale CF networks. Second, the optimization is intractable due to its NP-hard and discrete non-linear programming nature. To address the CSI acquisition issue, we utilize a digital twin (DT) of the CF mMIMO system, leveraging its context-awareness to acquire global CSI with reduced overhead. To address computational intractiablity of the optimization problem, we decompose it into three sub-problems. The power allocation sub-problem is transformed into a second-order cone programming problem and solved by the bisection method. Additionally, we propose a computationally efficient heuristic approach for power allocation. Next, we propose an analytical method for the decoding order selection by ranking the channels in descending order of strength. Simulation results validate the ability of the proposed approach to attain the near-optimal performance. Subsequently, the AP-UE association assignment problem is solved by a heuristic approach to further improve the SE performance. Finally, we solve the original NP-hard problem in a unified manner via the block-coordinate descent algorithm. Simulation results underscore a substantial 61% improvement in the SE performance when integrating the RSMA technique into a UC CF mMIMO system.

This paper addresses channel estimation error (CEE) mitigation in user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems under pilot contamination (PC). To suppress the PC, we formulate an optimization problem to minimize the normalized CEE through the joint treatment of the pilot power allocation and the pilot assignment. However, the NP-hard nature of the problem makes finding a global optimal solution computationally infeasible. To tackle this challenge, we decompose the problem into two sub-problems: the fractional programming (FP) technique is employed for pilot power allocation, and a multi-agent reinforcement learning (RL) approach is applied for pilot assignment. The RL-based scheme, however, involves iterative action generation and reward computation, leading to significant overhead from repeated information exchanges. To mitigate this issue, we leverage a digital twin (DT) of the CF mMIMO system, utilizing its virtual environment and contextual awareness to optimize CEE reduction with minimal overhead. By employing closed-form expressions, the proposed schemes achieve computational efficiency without reliance on complex numerical solvers. Finally, we solve the original NP-hard problem via the block-coordinate descent and sequential optimization methods. Numerical evaluations demonstrate that the proposed FP-based pilot power allocation scheme improves the 95%-likely spectral efficiency (SE) by up to 15% and reduces the average pilot transmission power by up to 83% compared to existing methods. Furthermore, the pilot assignment scheme enhances the average SE performance by up to 7.4% over state-of-the-art pilot assignment approaches. These results validate the effectiveness of our proposed framework in reducing CEE, leading to enhanced improvement in system performance in the presence of PC.

In this paper, we investigate user equipment (UE)-radio access technology (RAT) association and bandwidth assignment to maximize sum-rates in a multi-RAT network. To this end, we formulate an optimization problem that jointly addresses UE association and bandwidth allocation, adhering to practical constraints. Because of the NP-hard nature of this problem, finding a globally optimal solution is computationally infeasible. To address this challenge, we propose a centralized and computationally efficient heuristic algorithm that aims to maximize sumrates while enhancing quality of service (QoS). Yet, the proposed approach requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale multi-RAT networks. To alleviate this burden, we use a digital twin (DT) of the multi-RAT network, leveraging its context-awareness to acquire global CSI with reduced overhead. Our numerical results reveal that our approach improves sum-rates by up to 43 % over baseline method, with less than a 5 % deviation from the theoretical optimal solution, while achieving up to a 43 % improvement in QoS. Further analysis reveals that our method not only surpasses the optimal solution in terms of QoS enhancement, but also ensures significant computational efficiency.

This paper proposes a pilot power allocation scheme that simultaneously reduces both the pilot contamination effect and per-user pilot transmission power for the user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems during the uplink training phase. The proposed pilot power allocation scheme comprises a simple algorithm that tries to minimize the channel estimation error iteratively for improving the estimation quality. It is found from the simulated results that the average convergence time of the proposed scheme demonstrates a scalable attribute. Moreover, numerical results verify the effectiveness of the proposed scheme in improving the 95%-likely spectral efficiency performance by up to 16% and reducing the average pilot transmission power by up to 78 % compared to a scheme that transmits full pilot power (i.e., no control) and a recent pilot power allocation scheme that uses successive approximation technique with first-order Taylor approximation.

This paper tackles the problem of maximizing the minimum spectral efficiency (SE) via allocation of the uplink optimal transmission power in a rate splitting multiple access (RSMA)-aided user-centric (UC) cell-free (CF) massive MIMO (mMIMO) system. It is shown that the optimization problem is non-convex. The problem is then transformed into a second-order cone programming problem which is solved iteratively by leveraging the bisection method. Numerical results demonstrate the effectiveness of the RSMA technique to improve the 95%-likely SE gain by up to 332% compared to a UC CF mMIMO system with no power control.

In this paper, an effective pilot assignment algorithm is proposed to counter the pilot contamination (PC) issue caused by limited pilot sequences during massive access in a user-centric cell-free massive multiple-input multiple-output system. The problem of pilot assignment is formulated as max-min spectral efficiency (SE) optimization problem which is NP-hard. Hence, the paper proposes a heuristic-based algorithm to assign pilots to users (UEs) in such a way that the repetitive assignment among the associated UEs for all the access points can be avoided. Numerical results demonstrate that the proposed algorithm achieves up to 37% average SE gain while reducing the interference power by up to 52% relative to the random and two state-of-the-art pilot assignment schemes. Moreover, the proposed pilot assignment scheme exhibits the best balance in the trade-off between the performance criteria and the computational complexity relative to its comparatives.

This paper proposes a power allocation scheme that improves the overall spectral efficiency (SE) and fairness performance, and reduces a significant amount of per user transmission power for the user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems during uplink transmission. The proposed power allocation scheme comprises of a new power allocation model based on some adjustable parameters and the large-scale fading coefficients, and an algorithm to adapt these parameters for improving the minimum SE performance among all UEs. One important aspect of the proposed scheme is its simplicity from a design perspective which avoids complex optimization methods. Moreover, the proposed scheme provides fairness performance close to that of the max-min-SE based power control strategy and offers a SE performance comparable to that of the power control scheme that maximizes the sum-SE (max-sum-SE) while reducing the average transmission power simultaneously. Numerical results show that, compared to the max-min-SE scheme, max-sum-SE scheme, and a recent fractional power allocation policy, the proposed power allocation scheme improves the SE performance by up to 64.3% while reducing the average transmission power by up to 89%.

In this paper, an adaptive pilot assignment scheme is proposed to solve pilot contamination (PC) caused by the reuse of limited pilot sequences, while allowing massive access in the scalable Cell-free massive multiple-input multiple-output systems. The proposed allocation scheme consists of algorithms for determining initial serving relationships between access points (APs) and user equipments (UEs), and an adaptive group forming strategy where groups are formed with UEs that contain the least common associated APs. The proposed scheme guarantees the association of all UEs and better spectral efficiency (SE) by reducing PC efficiently with limited pilot sequences. Numerical results prove that the proposed allocation scheme outperforms the random assignment scheme and a recent pilot assignment proposal that allows massive access in respect to 95%-likely SE and average SE performance.