Optimal resource allocation in urban Internet of Things using 3D beamforming.

Optimization of Resource Allocation in 5G MIMO Networks Using Linear Assignment Algorithms

Massive Multiple Input Multiple Output (MIMO) is an essential technology that can significantly improve the performance of 5G wireless networks by using multiple antennas in base stations, improving coverage, reducing interference, and increasing data throughput. In this comprehensive study, we propose and analyze advanced optimization techniques for resource allocation in 5G MIMO networks, focusing on three distinct approaches: simple sorting, Hungarian Algorithm, and Minimum Cost Flow Algorithm. Simulations are performed using the publicly available DeepMIMO dataset, where we evaluate each method under both static and dynamic scenarios, aiming to optimize bandwidth distribution and minimize power consumption. A key contribution of this work is the formulation and comparative evaluation of the resource allocation problem as an assignment-based model, allowing the examined methods to be compared under common DeepMIMO-based static and dynamic scenarios. The technical contribution of this work lies in the common assignment-based formulation and comparative evaluation of simple sorting, Hungarian, and Minimum Cost Flow allocation methods under the same DeepMIMO-based static and dynamic 5G MIMO scenarios. Our comparative analysis shows that, under the evaluated DeepMIMO-based scenarios, the examined assignment-based methods exhibit different trade-offs in throughput, energy-consumption-related performance, bandwidth utilization, and adaptability to varying user demands, offering useful insights for 5G MIMO resource allocation studies.

QoS-Aware Joint Subcarrier and Power Allocation for OFDM-ISAC V2X Systems

This paper investigates joint subcarrier and power allocation for a multi-user Orthogonal Frequency Division Multiplexing (OFDM)-based Integrated Sensing and Communication (ISAC) system in Vehicle-to-Everything (V2X) environments. The goal is to maximize a weighted sum of the capped effective radar Signal-to-Noise Ratio (SNR) and aggregate communication rate, under per-user constraints on minimum effective radar SNR, communication rate, and range resolution. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) model. To address its non-convexity, we develop a Block Coordinate Descent–based Joint Resource Allocation (BCD-JRA) algorithm that alternates between nonlinear power allocation and mixed-integer subcarrier assignment and is used as a benchmark in our study. To support real-time V2X operation, we further propose a low-complexity two-stage heuristic, termed Phased Constraint Satisfaction and Greedy Allocation (PSGA). PSGA first allocates the minimum resources needed to satisfy the Quality of Service (QoS) constraints, and then greedily assigns remaining resources based on marginal utility gains while accounting for effective radar SNR capping. The simulation results show that PSGA attains utility close to the BCD-JRA benchmark with millisecond-level latency and satisfies all QoS constraints in the reported experiments.

Green Cell-Free Massive MIMO for ISAC: Joint Cloud, Fronthaul and Radio Resource Allocation

Cell-free massive MIMO (CF-mMIMO) combined with integrated sensing and communication (ISAC) is a promising architecture for future 6G networks, enabling new sensing-based applications. However, integrating sensing functionality increases power consumption across the radio, fronthaul, and cloud domains, which is not captured by conventional transmit power optimization approaches. In this paper, we develop a cross-layer end-to-end (E2E) optimization framework for green CF-mMIMO ISAC systems with distributed multi-target detection. We propose a distributed sensing approach in which receive access points (RX-APs) compute local test statistics and forward them to the cloud for aggregation via a weighted combination strategy. We derive maximum a posteriori ratio test (MAPRT) detectors under fully informed (FIS) and partially informed (PIS) scenarios, capturing different levels of side information available at the RX-APs. We formulate a joint optimization problem that minimizes total network power consumption by jointly optimizing transmit power allocation, AP operation modes, communication user and sensing associations, RX-AP assignments, and cloud/fronthaul resources, subject to communication and sensing constraints. The resulting mixed-integer non-convex problem is solved via a two-stage iterative algorithm based on successive convex approximation and penalty-based relaxation. Numerical results demonstrate that the proposed E2E framework significantly reduces total power consumption compared to benchmark schemes, achieving more than 50% savings over transmit-power-only optimization and approximately 13-15% over radio optimization, while maintaining competitive detection performance.

Empowering Rural Areas With Energy-Efficient 5G IAB-Based Fixed Wireless Access Network

Fixed Wireless Access (FWA) has recently emerged as a cost-effective alternative to optical fiber in rural areas, particularly where fiber deployment is economically infeasible. To extend coverage and increase capacity, FWA networks have begun to integrate Integrated Access and Backhaul (IAB) with mid- and high-band spectrum. However, the energy consumption of multi-hop IAB networks scales significantly with the number of hops, a challenge that prior research has not adequately addressed. This paper proposes an energy-efficient framework that minimizes network energy consumption by maximizing Resource Block (RB) utilization while avoiding both over- and under-allocation in multi-hop IAB-based FWA deployments. The proposed method jointly allocates RBs and selects modulation and coding schemes across a mixed set of 5G numerologies to satisfy data rate requirements while minimizing energy consumption. The inherent dynamic interactions among IAB stations render the problem highly complex and non-convex; therefore, we design a disciplined multi-convex programming supported by dynamic programming algorithms to obtain tractable solutions. Furthermore, we introduce a transformer-based prediction to forecast RB distribution, thereby mitigating the need for frequent short-timescale coordination among IAB stations. Our simulation results demonstrate that the proposed approach achieves the required data rates while reducing energy consumption by 14%.

Matching Theory-Based Resource Allocation and Spectrum Sharing for UAV-RIS Assisted Cellular-IoT Systems

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are two emerging technologies envisioned for sixth-generation (6G) wireless systems. These technologies enhance conventional cellular networks by extending coverage and enabling ubiquitous connectivity. However, spectrum scarcity and interoperability challenges create a strong need for efficient spectrum sharing among cellular users supported by these technologies. In this context, this paper considers a dynamic spectrum-sharing method in which data rate-aware spectrum sharing plays a critical role in managing base station power consumption and mitigating interference. We investigate a UAV-RIS-assisted cellular system where legacy cellular users share the spectrum with cellular Internet-of-Things (IoT) devices. To enhance the overall system sum data rate, we propose a joint user pairing, spectrum, power allocation, and RIS phase shift optimization approach based on matching theory. Simulation results demonstrate the effectiveness of the proposed method in improving resource allocation efficiency and significantly enhancing the sum data rate performance of wireless communication systems.

Next-gen D2D communication: efficient resource allocation with clustered combinatorial matching