Low Earth orbit satellite (LEOS) communications are vital for advancing global connectivity. However, these systems are vulnerable to threats from malicious jamming and the conflict between safety and transmission rates. Covert communication is a promising technology that can reduce the detectability of wireless transmissions at high rates. Therefore, in this paper, we introduced atwo-layer holographicapproach in the LEOS covert communication system based on holographic jamming-parasitic modulation (HJPM) and holographic multiple-input multiple-output (HMIMO). In HJPM, the transmitter superim-poses information signals onto jamming signals, allowing the legitimate receiver to reconstruct useful information from the jamming, effectively hiding the information within the jamming. The HMIMO surface utilizes holographic beamforming to achieve dynamic high-directional gains, thereby enhancing communication for legitimate users while meeting covertness constraints. Additionally, we proposed an optimization algorithm, i.e., Tri-CoHo, that combines elastic parasitic modulation depth with hybrid beamforming to maximize covert transmission rate in LEOS communications. Results showed that our scheme achieves a higher communication performance while maintaining a high level of undetectability compared to benchmarks.

Large language models (LLMs) have shown remarkable abilities by significantly scaling up model size, but this has also greatly increased their computing overhead. Traditional solutions to reduce the overhead are offloading inference tasks to cloud servers. With the evolution of computing power networks, computing resources are increasingly distributed at the network edge, allowing inference tasks to be handled locally. This edge inference can reduce traffic stress on backbone networks from cloud offloading and improve the utilization of heterogeneous edge computing power. However, the challenge is how to balance the gigantic computing workloads of LLMs with the limited computing power at the edge. To overcome this, a mixture-of-depths (MoD) Transformer based tri-parallel distributed approach was introduced. By dynamically allocating computing power to specific positions in the Transformer and parallel computing, this approach maximizes the capabilities of heterogeneous edge nodes to achieve resource-efficient inference. Simulation results showed that the proposal performs best in various edge environments, reducing inference delay by up to 24.3 % and energy consumption by up to 37.5%, respectively.

The increasing demand for integrated sensing and communication (ISAC) in sixth generation (6 G) mobile networks calls for advancements in sensing parameter estimation technologies. Orthogonal frequency division multiplexing (OFDM) is the core technology of the fifth generation (5G) system with excellent resilience to multi-path fading and is gaining popularity as an ISAC waveform. However, the performance of traditional range/velocity (r/v) estimation algorithms, e.g., multiple signal classification (MUSIC), is restricted by the resolution defined by the bandwidth and symbol duration of OFDM, especially in multi-target scenarios. To overcome this issue, we proposed a REsidual network based SPEctra Calibration (RESPEC) algorithm. It improved the multi-target sensing accuracy by residual network that resists gradient vanishment to calibrate the spectra generated with the two-dimensional MUSIC (2D-MUSIC). Simulation results demonstrated the superiority in r/v estimation accuracy of RESPEC in multi-target scenarios, compared to the traditional 2D-MUSIC algorithm and other benchmarks. The results also exposed a trade-off between neural network depth and communication bandwidth for range estimation.

The growing demand for edge computing is driving the proliferation of wireless computing power infrastructures and poses significant challenges to network energy efficiency (EE). Traditional offloading schemes rely solely on multi-access edge computing (MEC) servers. High-quality communication links and adequate distributed resources are expected to improve EE. Inspired by reconfigurable intelligent surface (RIS) and device-to-device communication technologies, this paper first proposed an edge-end collaborative computing system in RIS-empowered heterogeneous wireless computing power networks. Through resource virtualization, heterogeneous computing powers on MEC servers and nearby devices are unified into resource pools for efficient utilization. In this wireless system, task offloading is closely coupled with channel allocation, power coordination, RIS phase shift design, and base station receive beamforming. To tackle it, this paper suggested a block coordinate descent (BCD)-based framework that decouples the problem into three sub-problems. For each sub-problem, specialized solutions are applied: the Rayleigh quotient maximization and concave-convex procedure for the beamforming and power allocation co-design sub-problem, dimensionality reduction for 3-dimensional task offloading and channel allocation co-pairing sub-problem, and convex optimization for the RIS phase shift control sub-problem. Numerical results showed that the proposed method can outperform benchmark approaches in terms of EE and delay by up to 28.5% and 22.9%, respectively.

The forthcoming sixth generation (6G) mobile communication system aims to advance technologies that span and integrate computation and communications. Computing power networks (CPNs) and virtualized radio access networks (vRANs) are regarded as two fundamental techniques to achieve this integration. Network functions of virtualized next-generation Node Bs (vgNBs) are implemented on general-purpose servers to process protocol stacks. The energy consumption of vgNBs accounts for a significant portion of energy consumption. However, the proliferation of computing power nodes results in increased energy consumption in CPNs. Power usage effectiveness (PUE) reflects the efficiency of computing nodes while efficiency of computing power (ECP) is adopted to indicate data rates per computing power unit. In this work, a joint load adjustment and sleep management scheme was designed to maximize ECP while minimizing PUE. The optimization problem was formulated as a mixed integer non-linear programming (MINLP) problem, which is NP-hard. A quantum genetic algorithm (QGA) with non-equal size quantum register was suggested to solve this problem. Simulation results demonstrated that the proposed algorithm could outperform benchmark approaches in terms of convergence speed, ECP, PUE, and computing power consumption. When compared to other methods, the proposed approach could improve ECP and computation energy consumption by up to 19.5% and 21.7%, respectively.

The active reconfigurable intelligent surface (RIS) consists of multiple independently controllable reflective elements, each equipped with a power amplifier (PA). Deploying it in a wireless communication environment allows the amplified signal to be reflected to users, thereby enhancing the performance. However, a large number of independent PAs brings challenges in energy efficiency (EE). To address this issue, we introduced a sub-connected hybrid RIS (SC-HRIS). The SC-HRIS consists of multiple passive and active elements, with active elements organized into groups, each equipped with a dedicated PA. For RIS-assisted downlink multi-user multiple-input single-output systems, we considered joint transmit beamforming and hybrid RIS coefficient design. The EE maximization problem was formulated and solved by fractional programming in conjunction with the block coordinate descent (BCD) method. Simulation results proved that the proposed SC-HRIS could outperform the other RIS approaches in terms of EE.

Ultradense networks (UDNs) are considered to be a key technology that can meet the growing rate requirements caused by the explosion of user equipments (UEs) in the Internet of Things (IoT) applications. The dense deployment of small-cell base stations (SBSs) facilitates the reuse of spectrum resources but also leads to significant interference among adjacent SBSs. Joint transmission (JT) technology can alleviate intercell interference and improve throughput. However, signal processing and backhaul during BS cooperation require additional power consumption, which reduces the energy efficiency (EE) of UDNs. Additionally, the arrival time of received signals from different cooperative BSs at UEs results in asynchronous interference, which poses a significant challenge for JT. To improve EE, we need to determine the clustering strategy and address the interference issue of asynchronous JT. Specifically, the EE-centric (EEC) clustering scheme was proposed based on the maximal independent set of graph theory to determine the SBS clusters. In each cluster, asynchronous gap generation and gap compensation operations were employed to eliminate tail interference of asynchronous JT and compensate for the gap between adjacent received blocks, respectively. This approach effectively mitigated the asynchronous interference at UEs. Simulation results demonstrated that the proposed asynchronous interference cancelations in EEC clusters can significantly improve sum rate and EE compared to other schemes.