Abstracts:A high-altitude platform station (HAPS) located in the stratosphere can provide connectivity over a large area. However, a HAPS can create only a limited number of uncorrelated beams. Therefore, we cannot dedicate a beam to each user, and we need to perform user scheduling to be able to serve a large number of users with a HAPS. To this end, in this paper, we group users into clusters using the K-means algorithm and allocate the users in each cluster to orthogonal frequency resource blocks. The frequencies are reused across the clusters. Since HAPS has limited power resources, we also propose a novel codebook-based hybrid beamforming scheme. The results of our simulations conclusively show that our proposed beamforming scheme outperforms the traditional steering vector-based beamforming scheme in high-rise urban areas. Furthermore, we propose a deep Q-network (DQN)-based power allocation method that considerably outperforms the baseline equal power allocation scheme for large coverage areas. Finally, we compare the interference management efficiency of our proposed orthogonal frequency-division multiplexing (OFDM)-based hybrid beamforming-enabled hemispherical array antenna and baseline rectangular and cylindrical array antennas.

Abstracts:Reconfigurable holographic surfaces (RHSs) have been suggested as an energy-efficient solution for extremely large-scale arrays. By controlling the amplitude of RHS elements, high-gain directional holographic patterns can be achieved. However, the complexity of acquiring real-time channel state information (CSI) for beamforming is exceedingly high, particularly in large-scale RHS-assisted communications, where users may be distributed in the near-field region of RHS. This paper proposes a one-shot multi-user beam training scheme in large-scale RHS-assisted systems applicable to both near and far fields. The proposed beam training scheme comprises two phases: angle search and distance search, both conducted simultaneously for all users. For the angle search, an RHS angular codebook is designed based on holographic principles so that each codeword covers multiple angles in both near-field and far-field regions, enabling simultaneous angular search for all users. For the distance search, we construct the distance-adaptive codewords covering all candidate angles of users in a real-time way by leveraging the additivity of holographic patterns, which is different from the traditional phase array case. Simulation results demonstrate that the proposed scheme achieves higher system throughput compared to traditional beam training schemes. The beam training accuracy approaches the upper bound of exhaustive search at a significantly reduced overhead.

Abstracts:Radio map is a promising technology that connects the user equipment (UE) location and its channel state information (CSI). By applying a radio map, the beamforming vector can be generated based solely on the location of the UE, thereby significantly saving pilot effort. However, the effectiveness of radio map-based beamforming is influenced by several factors, such as positioning errors and channel dynamics. To improve the adaptability and performance of radio map-based beamforming, in this paper we consider integrating location information with reduced pilots and examine the trade-off between pilot overhead and beamforming performance. In particular, an end-to-end method for joint extrapolation, denoising and performing beamforming with reduced pilots is proposed. Subsequently, the beamforming vector obtained from reduced pilots is integrated with that generated by the radio map. Considering the overhead caused by pilots, the support vector machine (SVM) is applied to determine whether it is worth introducing pilots for integration. According to the numerical simulations, the proposed end-to-end method with reduced pilots is superior to that with full pilots in terms of spectral efficiency (SE) and can be improved by integrating with the radio map. In addition, the application of SVM can effectively identify the integration needs, further reducing unnecessary pilot effort and thereby increasing SE.

Abstracts:In the context of 5G and beyond 5G, radio access network (RAN) slicing emerges to enable differentiated services via the instantiation of virtualized logical networks. Despite its promising potential, the resource optimization of RAN slicing confronts significant challenges stemming from the scarcity of spectrum resources, the intricacies of tradeoff between slice service quality and slice security, the mobility of users, and the complexity of wireless interference in multi-cell environments. To address these challenges, we propose a heuristic-assisted multi-agent deep reinforcement learning-based resource allocation scheme for RAN slicing. This scheme aims to augment inter-slice resource isolation for security (quantified as isolation rate) while efficiently accommodating diverse requirements across slices (quantified as satisfaction rate). Through extensive numerical results, we exhibit that our proposed scheme adeptly adapts to multiple user mobility patterns, achieving superior performances in terms of satisfaction rate and isolation rate.

Abstracts:In the realm of semantic communication, the significance of encoded features can vary, while wireless channels are known to exhibit fluctuations across multiple subchannels in different domains. Consequently, critical features may traverse subchannels with poor states, resulting in performance degradation. To tackle this challenge, we introduce a framework called Feature Allocation for Semantic Transmission (FAST), which offers adaptability to channel fluctuations across both spatial and temporal domains. In particular, an importance evaluator is first developed to assess the importance of various features. In the temporal domain, channel prediction is utilized to estimate future channel state information (CSI). Subsequently, feature allocation is implemented by assigning suitable transmission time slots to different features. Furthermore, we extend FAST to the space-time domain, considering two common scenarios: precoding-free and precoding-based multiple-input multiple-output (MIMO) systems. An important attribute of FAST is its versatility, requiring no intricate fine-tuning. Simulation results demonstrate that this approach significantly enhances the performance of semantic communication systems in image transmission. It retains its superiority even when faced with substantial changes in system configuration.

Abstracts:It was shown that by operating over the integer linear combinations (ILCs) of K users’ messages, lattice-code based multiple-access (LCMA) offers increased system load and improved error-rate performance over non-lattice based schemes. This paper advances the existing LCMA system in two aspects. 1) We formulate the spreading sequences optimization problem based on the achievable symmetric rate of LCMA. To solve this problem, we develop three new methods, namely target-switching steepest descent (TS-SD), particle swarm (PS) optimization, and Hadamard concatenation (HC). The TS-SD method always targets on the ILC with the lowest computation rate in the SD process. The PS method treats the spreading matrix as a particle and iteratively updates a swamp of particles’ positions and velocities, based on the relative distance to the best position that are currently known. To further reduce the complexity, we first obtain a solution in a lower dimension, and then apply Hadamard concatenation (HC) which yields a solution for the required dimension. The PS and HC methods are shown to approach the capacity of the MA channel. 2) We put forth a new multi-stage LCMA receiver. In each stage, the receiver attempts to compute as many ILCs as possible. Then, from these ILCs, generalized matrix inversion (GMI) is introduced to recover a subset of $ K$ users’ messages. These recovered messages are cancelled from the original received signal, yielding an equivalent system with less users for the next stage. Such operation continues successively until all K users’ messages are recovered. System loads of up to 400% and near capacity performance are demonstrated for various MA models.

Abstracts:Space-air-ground integrated networks (SAGINs) are emerging as a pivotal element in the evolution of future wireless networks. Despite their potential, the joint design of communication and computation within SAGINs remains a formidable challenge. In this paper, the problem of energy efficiency in SAGIN-enabled probabilistic semantic communication (PSCom) system is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an uncrewed aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSCom technique to compress the transmitting data, while the GTs can automatically recover the missing information. The PSCom is underpinned by shared probabilistic graphs that serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Through analysis, the computation overhead function in PSCom is a piecewise function with respect to the semantic compression ratio. Therefore, it is important to make a balance between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, power, computation capacity, bandwidth, semantic compression ratio, and UAV location constraints. To solve this non-convex non-smooth problem, we propose an iterative algorithm where the closed-form solutions for computation capacity allocation and UAV altitude are obtained at each iteration. Numerical results show the effectiveness of the proposed algorithm.

Abstracts:Orbital angular momentum (OAM) in electromagnetic (EM) waves can significantly enhance spectrum efficiency in wireless communications without requiring additional power, time, or frequency resources. Different OAM modes in EM waves create orthogonal channels, thereby improving spectrum efficiency. Additionally, OAM waves can more easily maintain orthogonality in line-of-sight (LOS) transmissions, offering an advantage over multiple-input and multiple-output (MIMO) technology in LOS scenarios. However, challenges such as divergence and crosstalk hinder OAM’s efficiency. Additionally, channel modeling for OAM transmissions is still limited. A reliable channel model with balanced accuracy and complexity is essential for further system analysis. In this paper, we present a quasi-deterministic channel model for OAM channels in the 5.8 GHz and 28 GHz bands based on measurement data. Accurate measurement, especially at high frequencies like millimeter bands, requires synchronized RF channels to maintain phase coherence and purity, which is a major challenge for OAM channel measurement. To address this, we developed an 8-channel OAM generation device at 28 GHz to ensure beam integrity. By measuring and modeling OAM channels at 5.8 GHz and 28 GHz with a modified 3D geometric-based stochastic model (GBSM), this study provides insights into OAM channel characteristics, aiding simulation-based analysis and system optimization.

Abstracts:In the increasingly critical field of aerial communication, autonomous aerial vehicles (AAVs) have gained significant attention as prominent representatives, and accurate air-to-air (A2A) channel modeling plays a pivotal role in the design and evaluation of reliable communication systems. This paper presents a A2A channel model for AAV communications. It introduces a quasi-deterministic approach to address limitations in existing modeling frameworks. The proposed model uses a truncated ellipsoid to capture the distribution of scatterers in A2A scenarios and, for the first time, incorporates rooftop specular reflection (RSR). Power correction factors, based on the AAV’s airframe structure, position, and posture, are introduced to provide a comprehensive and realistic depiction of the A2A communication channel. The performance of proposed model is assessed by simulating key statistical channel characteristics and comparing with other alternatives. The simulations illustrate how channel behavior is influenced by factors such as flight level, flight trajectory, and AAV posture. The results show that RSR leads to the channel hardening effect, while airframe occlusion causes the received signal power to vary gradually with changes in AAV’s position and posture. The validity of the model is confirmed through comparison with measurement data and ray-tracing results, proving its accuracy and practical application.