REINFORCEMENT-DRIVEN OPTIMIZATION OF MIMO-OFDM ARCHITECTURES FOR SCALABLE 6G NETWORKS

Virtual multiple-input-multiple-output (MIMO) systems using multiple antennas at the transmitter and a single antenna at each of the receivers have recently emerged as an alternative to point-to-point MIMO systems. This paper investigates the relationship between energy efficiency (EE) and spectral efficiency (SE) for a virtual-MIMO system that has one destination and one relay using compress-and-forward (CF) cooperation. To capture the cost of cooperation, the power allocation (between the transmitter and the relay) and the bandwidth allocation (between the data and cooperation channels) are studied. This paper derives a tight upper bound for the overall system EE as a function of SE, which exhibits good accuracy for a wide range of SE values. The EE upper bound is used to formulate an EE optimization problem. Given a target SE, the optimal power and bandwidth allocation can be derived such that the overall EE is maximized. Results indicate that the EE performance of virtual-MIMO is sensitive to many factors, including resource-allocation schemes and channel characteristics. When an out-of-band cooperation channel is considered, the performance of virtual-MIMO is close to that of the MIMO case in terms of EE. Considering a shared-band cooperation channel, virtual-MIMO with optimal power and bandwidth allocation is more energy efficient than the noncooperation case under most SE values.

Recent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions.

Non-terrestrial network (NTN) communication is included in the 3GPP standard because of its excellent features such as resistance to ground physical attacks and wide coverage. Since the available power and storage resources are limited on high throughput satellites (HTSs), optimizing the resource allocation can greatly improve the performance of the HTS based communication system. Notably, weather and other factors make the channel status between the satellite and the terrestrial base stations constantly change. In this paper, we first exploit the model-free feature of reinforcement learning to formulate the aforementioned dynamic and unpredictable channel conditions into the power allocation problem in HTS systems. Due to the complexity of environment state and power allocation, a deep neural network is introduced to replace the Q table, and a deep reinforcement learning framework is built. Furthermore, a power allocation algorithm based on a deep reinforcement learning framework is presented. Finally, the simulation results show that the proposed algorithm is superior to existing algorithms in terms of the long-term system throughput performance, and has better adaptability to dynamic channel environment, thereby improving network performance.

Not only high spectral efficiency (SE) but also high energy efficiency (EE) are required for future wireless communication systems. Radio orbital angular momentum (OAM) provides a new perspective of mode multiplexing to improve SE. However, there are few studies on the EE performance of OAM mode multiplexing. In this paper, we investigate the SE and EE of a misaligned uniform concentric circle array (UCCA)-based multi-carrier multimode OAM and multiple-input multiple-output (MCMM-OAM-MIMO) system in the line-of-sight (LoS) channel, in which two transceiver architectures implemented by radio frequency (RF) analog synthesis and baseband digital synthesis are considered. The distance and angle of arrival (AoA) estimation are utilized for channel estimation and signal detection, whose training overhead is much less than that of traditional MIMO systems. Simulation results validate that the UCCA-based MCMM-OAM-MI-MO system is superior to conventional MIMO-OFDM system in the EE and SE performances.

Mechanical performance of interpenetrating phase composites with multi-sheet lattice structures

Large-scale Internet of Things (IoT) deployments require long-range wireless communication, particularly in urban and metropolitan environments. Long-Range Wide-Area Network (LoRaWAN), based on LoRa technology, emerges as a promising solution due to its simplicity, flexibility, and low power consumption. However, deploying LoRa networks in dense IoT scenarios presents challenges, such as ensuring efficient communication among numerous devices and maintaining resilience against dynamic channel conditions. This work evaluates Adaptive Data Rate (ADR) and power control mechanisms to optimize LoRa communication parameters using FLoRa, an open-source framework for end-to-end LoRa simulations in OMNeT++. To this end, this paper proposes a novel ADR approach, ADR-I-MODE, which estimates the Signal-to-Noise Ratio (SNR) using the most frequent value in recent transmissions. This technique is validated against its floating-point counterpart, ADR-F-MODE, to demonstrate its efficiency. The proposed method enhances packet delivery rates and energy efficiency across varying channel conditions. Additionally, this work proposes a node-level power control technique, ADR-NPC, to improve connectivity in challenging environments. This technique successfully reconnects up to 86% of dropped nodes with a minimal primary power setting of 2 dBm, demonstrating its potential for future integration into LoRa Alliance standards. These findings contribute to the development of more resilient and scalable LoRaWAN deployments for diverse IoT applications.

This paper presents a novel importance-aware quantization, subcarrier mapping, and power allocation (IA-QSMPA) framework for semantic communication in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, empowered by a pretrained Vision Transformer (ViT). The proposed framework exploits attention-based importance extracted from a pretrained ViT to jointly optimize quantization levels, subcarrier mapping, and power allocation. Specifically, IA-QSMPA maps semantically important features to high-quality subchannels and allocates resources in accordance with their contribution to task performance and communication latency. To efficiently solve the resulting nonconvex optimization problem, a block coordinate descent algorithm is employed. The framework is further extended to operate under finite blocklength transmission, where communication errors may occur. In this setting, a segment-wise linear approximation of the channel dispersion penalty is introduced to enable efficient joint optimization under practical constraints. Simulation results on multi-view image classification and single-object detection tasks demonstrate that IA-QSMPA significantly outperforms conventional methods in both ideal and finite blocklength transmission scenarios, achieving superior task performance and communication efficiency.

A novel multibeam satellite system design is proposed in this paper based on jointly optimizing power and carrier allocation in order to best match the asymmetric traffic requests. This design introduces higher and asymmetric interference levels throughout the coverage. However, both power and bandwidth will be used more efficiently. Even though the problem of power and bandwidth allocation has been addressed in terrestrial wireless communications, it is new in satellite systems and since architecture and channel are different, available results and algorithms are not applicable to satellite payload systems. In this paper we formulate the resource allocation problem as max-min SINR balancing based on the recently introduced axiomatic-based interference model, but in addition, we also optimize the carrier allocation when performing the SINR balancing problem. An analytical solution for the optimal carrier allocation is proposed and we iteratively find the optimal power allocation for each beam. The Shannon (upper bound) and current state-of-the art PHY layer technology: DVB-S2 are proposed to be implemented in order to obtain the gap between them. Simulation results show significant improvements in terms of power gain, spectral efficiency and traffic matching ratio comparing with conventional system, which is designed based on uniform bandwidth and power allocation.

In this paper, we first give an overview of the coexistence of cellular with IEEE 802.11 technologies in the unlicensed bands. We then present a coexistence mechanism for Fifth‐Generation (5G) New Radio on Unlicensed (NR‐U) small cells located within buildings to coexist with the IEEE 802.11ad/ay, also termed as Wireless Gigabit (WiGig). Small cells are dual‐band enabled operating in the 60 GHz unlicensed and 28 GHz licensed millimeter‐wave (mmW) bands. We develop an interference avoidance scheme in the time domain to avoid cochannel interference (CCI) between in‐building NR‐U small cells and WiGig access points (APs). We then derive average capacity, spectral efficiency (SE), and energy efficiency (EE) performance metrics of in‐building small cells. Extensive system‐level numerical and simulation results and analyses are carried out for a number of variants of NR‐U, including NR standalone, NR‐U standalone, and NR‐U anchored. We also analyze the impact of the spatial reuse of both mmW spectra of multiple NR‐U anchored operators with a WiGig operator. It is shown that NR‐U anchored provides the best average capacity and EE performances, whereas NR‐U standalone provides the best SE performance. Moreover, both vertical spatial reuse intrabuilding level and horizontal spatial reuse interbuilding level of mmW spectra in small cells of an NR‐U anchored can improve its SE and EE performances. Finally, we show that by choosing appropriate values of vertical and horizontal spatial reuse factors, the proposed coexistence mechanism can achieve the expected SE and EE requirements for the future Sixth‐Generation (6G) mobile networks.

This paper presents the symbol error rate (SER) vs. signal-to-noise (SNR) ratio performance of OFDM system with QAM modulation. It has been observed that the optimal power allocation between pilot and information symbols can improve the performance gain by 2.3 dB relative to the equal power allocation between pilot and information symbols, for a system with N=3D512 sub-carriers, and a channel with L=3D40 taps. The SER performance is then compared with Rayleigh-faded as well as Rician-faded channels. It has been further concluded that the SER vs. SNR performance graph in Rayleigh-faded channel environment is better than that in Rician-faded channel. This idea implies that in OFDM case, the SER vs. SNR performance is better in scattered rich channel environments. The SER vs. SNR performance graph is degraded by equal magnitude for both equal-power and optimal-power cases, which implies that the difference between the equal-power and optimal power case is independent of the channel fading model being used. It is also observed that by varying the standard-deviation of the channel, the SER vs. SNR performance graph is not going to be affected if the channel is considered Rayleigh-faded.

Millimeter-wave (mmWave) communication provides ultra-high data rates and low-latency performance for vehicular networks. However, its reliability decreases under high mobility due to beam misalignment, rapid channel variation, and frequent transitions between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. To address these challenges, we propose an enhanced genetic algorithm (EGA) for real-time joint optimization of beam steering angles, power allocation, and beamforming weights. We integrate EGA with a hybrid beamforming structure that employs a uniform planar array (UPA) based on orthogonal uniform linear arrays (ULAs) to enable independent control of azimuth and elevation angles. This configuration improves spatial resolution and ensures rapid beam adaptation under dynamic vehicular conditions. To suppress sidelobes and reduce inter-beam interference, a Hamming window is applied to the antenna weights. Furthermore, we employ LOS/NLOS awareness, speed and angle-aware beam switching, and density-aware power control to enhance connectivity and dynamically adjust power allocation. Simulation results show that the proposed EGA-based beamforming outperforms the baseline GA, the traditional method, and other benchmark methods in terms of SINR, spectral efficiency, and throughput, while reducing path loss, transmission delay, beam switching delay, energy consumption, and outage probability.

The Cognitive Radio (CR) technique has mainly dealt with how spectrum can be sensed, the co-existence of primary and secondary users, and the channel access aspect. A key aspect of these radios is the `cognition' gained through a spectrum scanning process. The benefit of this cognition is apparent and well-studied in terms of achieving better communication performance on selected spectrum. The benefits in terms of reduced energy consumption, however, due to easier channel access and less contention have been rarely quantified in prior work. This work defines and studies the impact of important parameters on the energy consumption of a CR node under dynamic channel conditions. Results obtained include the range of parameter values under which a CR node is more energy-efficient than a conventional non-CR node, and a comparison of the effectiveness of different spectrum scanning algorithms.

This paper investigates a multi-pair two-way massive MIMO amplify-and-forward full-duplex relay (FDR) system over Ricean fading channels, where multiple pairs of full-duplex users exchange information within pair through a full-duplex relay with a very large number of antennas (M for transmission and M for reception). First, the zero-forcing reception/zero-forcing transmission and maximum-ratio combining/maximum-ratio transmission processing matrices with imperfect channel state information at the relay are presented. Then, the corresponding asymptotic expressions (in M) of the end-to-end signal-to-interference-plus-noise ratio are derived. Finally, the asymptotic spectral efficiency (SE) and energy efficiency (EE) at the general power scaling schemes when the number of the relay antennas tends to infinity is analyzed. Theoretical analyses and simulation results show that, when M → ∞, the effects of channel estimation error, the self-loop of each user as well as the relay, inter-user interferences, the noise at users and the relay can be eliminated, respectively, if the power scaling scheme is properly selected. Besides, the considered multi-pair two-way FDR outperforms the multi-pair two-way half-duplex relaying on SE and EE performances when M is large. Moreover, a low complexity power control scheme is proposed to optimize the SE and we analyze the impact of the number of user pairs on the SE.

Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth deterministic strategy gradient (H-DDPG) framework is proposed to jointly optimize the access point (AP) clustering and power allocation. The framework uses two-layer control networks operating on different timescales to enhance EE of downlinks in CF-mMIMO systems by cooperatively optimizing AP clustering and power allocation. In this framework, the high-level processing of system-level problems, namely AP clustering, enhances the wireless network configuration by utilizing DDPG on the large timescale while meeting the minimum spectral efficiency (SE) constraints for each user. The low layer solves the link-level sub-problem, that is, power allocation, and reduces interference between APs and improves transmission performance by utilizing DDPG on a small timescale while meeting the maximum transmit power constraint of each AP. Two corresponding DDPG agents are trained separately, allowing them to learn from the environment and gradually improve their policies to maximize the system EE. Numerical results validate the effectiveness of the proposed algorithm in term of its convergence speed, SE, and EE.

In this paper, we develop a closed-form lower bound for the achievable uplink rate of users in a single-cell massive multiple-input multiple-output (MIMO) system using zero-forcing (ZF) receiver. The base station (BS) is equipped with a large number of uniformly and linearly spaced antennas and serves single-antenna users. It estimates channel state information (CSI) using uplink pilot symbols. The channel model is also supposed to have a finite dimension. We then define the spectral efficiency as the sum of derived uplink rates. Through power allocation between data and pilot symbols, we further optimize this approximate expression of spectral efficiency. Simulation results are presented to evaluate the approximation of spectral efficiency and to show the advantage of our power allocation scheme in comparison to equal power allocation. The results also demonstrate that in low signal-to-noise ratios, more power should be allocated to the pilot symbols.