LAMBO: Large Language Model Empowered Edge Intelligence

Next-generation edge intelligence is anticipated to bring huge benefits to various applications, e.g., offloading systems. However, traditional deep offloading architectures face several issues, including heterogeneous constraints, partial perception, uncertain generalization, and lack of tractability. In this context, the integration of offloading with large language models (LLMs) presents numerous advantages. Therefore, we propose an LLM-Based Offloading (LAMBO) framework for mobile edge computing (MEC), which comprises four components: (i) Input embedding (IE), which is used to represent the information of the offloading system with constraints and prompts through learnable vectors with high quality; (ii) Asymmetric encoderdecoder (AED) model, which is a decision-making module with a deep encoder and a shallow decoder. It can achieve high performance based on multi-head self-attention schemes; (iii) Actor-critic reinforcement learning (ACRL) module, which is employed to pre-train the whole AED for different optimization tasks under corresponding prompts; and (iv) Active learning from expert feedback (ALEF), which can be used to finetune the decoder part of the AED while adapting to dynamic environmental changes. Our simulation results corroborate the advantages of the proposed LAMBO framework.

Large AI Model-Based Semantic Communications

Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed-reality, and the Internet of everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation, frequent knowledge updates, and insecure knowledge sharing. Fortunately, the development of the large AI model provides new solutions to overcome above issues. Here, we propose a large AI model-based SC framework (LAM-SC) specifically designed for image data, where we first design the segment anything model (SAM)-based KB (SKB) that can split the original image into different semantic segments by universal semantic knowledge. Then, we present an attention-based semantic integration (ASI) to weigh the semantic segments generated by SKB without human participation and integrate them as the semantic-aware image. Additionally, we propose an adaptive semantic compression (ASC) encoding to remove redundant information in semantic features, thereby reducing communication overhead. Finally, through simulations, we demonstrate the effectiveness of the LAM-SC framework and the significance of the large AI model-based KB development in future SC paradigms.

Energy Minimization for Active RIS-Aided UAV-Enabled SWIPT Systems

In this paper, we consider an active reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle(UAV)-enabled simultaneous wireless information and power transfer(SWIPT) system with multiple ground users. Compared with the conventional passive RIS, the active RIS deploying the internally integrated amplifiers can offset part of the multiplicative fading. In this system, we deal with an optimization problem of minimizing the total energy cost of the UAV. Specifically, we alternately optimize the trajectories, the hovering time, and the reflection vectors at the active RIS by using the successive convex approximation (SCA) method. Simulation results show that the active RIS performs better in energy saving than the conventional passive RIS.

Over-the-Air Federated Averaging with Limited Power and Privacy Budgets

To jointly overcome the communication bottleneck and privacy leakage of wireless federated learning (FL), this paper studies a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system with a limited sum power budget. With DP-OTA-FedAvg, the gradients are aligned by an alignment coefficient and aggregated over the air, and channel noise is employed to protect privacy. We aim to improve the learning performance by jointly designing the device scheduling, alignment coefficient, and the number of aggregation rounds of federated averaging (FedAvg) subject to sum power and privacy constraints. We first present the privacy analysis based on differential privacy (DP) to quantify the impact of the alignment coefficient on privacy preservation in each communication round. Furthermore, to study how the device scheduling, alignment coefficient, and the number of the global aggregation affect the learning process, we conduct the convergence analysis of DP-OTA-FedAvg in the cases of convex and non-convex loss functions. Based on these analytical results, we formulate an optimization problem to minimize the optimality gap of the DP-OTA-FedAvg subject to limited sum power and privacy budgets. The problem is solved by decoupling it into two sub-problems. Given the number of communication rounds, we conclude the relationship between the number of scheduled devices and the alignment coefficient, which offers a set of potential optimal solution pairs of device scheduling and the alignment coefficient. Thanks to the reduced search space, the optimal solution can be efficiently obtained. The effectiveness of the proposed policy is validated through simulations.

Performance Analysis and Low-Complexity Design for XL-MIMO with Near-Field Spatial Non-Stationarities

Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of supporting extremely high system capacities with large numbers of users. In this work, we build a framework for the analysis and low-complexity design of XL-MIMO in the near-field with spatial non-stationarities. Specifically, we first analyze the theoretical performance of discrete-aperture XL-MIMO using an electromagnetic (EM) channel model based on the near-field spherical wave-front. We analytically unveil the impact of the discrete aperture and polarization mismatch on the received power. We also review the amplitude-aware Fraunhofer distance based on the considered EM channel model. Our analytical results indicate that a limited part of the XL-array receives the majority of the signal power in the near-field, which leads to a notion of visibility region (VR) of a user. Thus, we propose a VR detection algorithm and exploit the acquired VR information to design a low-complexity symbol detection scheme. Furthermore, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the correctness of the analytical results and the effectiveness of the proposed algorithms. It reveals that our algorithms approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

A Framework for Transmission Design for Active RIS-Aided Communication with Partial CSI

Active reconfigurable intelligent surfaces (RISs) have recently been proposed to compensate for the severe multiplicative fading effect of conventional passive RIS-aided systems. Each reflecting element of active RISs is assisted by an amplifier such that the incident signal can be reflected and amplified instead of only being reflected as in passive RIS-aided systems. This work addresses the practical challenge that, on the one hand, in active RIS-aided systems the perfect individual CSI of the RIS-aided channels cannot be acquired due to the lack of signal processing power at the active RISs, but, on the other hand, this CSI is required to calculate the expected system data rate and RIS transmit power needed for transceiver design. To address this issue, we first derive closed-form expressions for the average achievable rate and the average RIS transmit power based on partial CSI of the RIS-aided channels. Then, we formulate an average achievable rate maximization problem for jointly optimizing the active beamforming at both the base station (BS) and the RIS. This problem is then tackled using the majorization--minimization (MM) algorithm framework, and, for each iteration, semi-closed-form solutions for the BS and RIS beamforming are derived based on the Karush-Kuhn-Tucker (KKT) conditions. To ensure the quality of service (QoS) of each user, we further formulate a rate outage constrained beamforming problem, which is solved using the Bernstein-Type inequality (BTI) and semidefinite relaxation (SDR) techniques. Numerical results show that the proposed algorithms can efficiently overcome the challenges imposed by imperfect CSI in active RIS-aided wireless systems.

Resource Allocation for Cell-free Massive MIMO-enabled URLLC Downlink Systems

Ultra-reliable and low-latency communication (URLLC) is a pivotal technique for enabling the wireless control over industrial Internet-of-Things (IIoT) devices. By deploying distributed access points (APs), cell-free massive multiple-input and multiple-output (CF mMIMO) has great potential to provide URLLC services for IIoT devices. In this paper, we investigate CF mMIMO-enabled URLLC in a smart factory. Lower bounds (LBs) of downlink ergodic data rate under finite channel blocklength (FCBL) with imperfect channel state information (CSI) are derived for maximum-ratio transmission (MRT), full-pilot zero-forcing (FZF), and local zero-forcing (LZF) precoding schemes. Meanwhile, the weighted sum rate is maximized by jointly optimizing the pilot power and transmission power based on the derived LBs. Specifically, we first provide the globally optimal solution of the pilot power, and then introduce some approximations to transform the original problems into a series of subproblems, which can be expressed in a geometric programming (GP) form that can be readily solved. Finally, an iterative algorithm is proposed to optimize the power allocation based on various precoding schemes. Simulation results demonstrate that the proposed algorithm is superior to the existing algorithms, and that the quality of URLLC services will benefit by deploying more APs, except for the FZF precoding scheme.

Active RIS Aided Integrated Sensing and Communication Systems

This paper considers an active reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. We aim to maximize Radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the beamforming matrix at the dual-function Radar-communication (DFRC) base station (BS) and the reflecting coefficient matrix at the active RIS subject to the quality of service (QoS) constraint of communication users (UE) and the transmit power constraints of active RIS and DFRC BS. In the proposed scenario, we mainly focus on the four-hop BS-RIS-target-RIS-BS sensing link, and the direct BS-target-BS link is assumed to be blocked. Due to the coupling of the beamforming matrix and the reflecting coefficient matrix, we use the alternating optimization (AO) method to solve the problem. Given reflecting coefficients, we apply majorization-minimization (MM) and semidefinite programming (SDP) methods to deal with the nonconvex QoS constraints and Radar SINR objective functions. An initialization method is proposed to obtain a high-quality converged solution, and a sufficient condition of the feasibility of the original problem is provided. Since the signal for sensing is reflected twice at the same active RIS panel, the Radar SINR and active RIS transmit power are quartic functions of RIS coefficients after using the MM algorithm. We then transform the problem into a sum of square (SOS) form, and a semidefinite relaxation (SDR)-based algorithm is developed to solve the problem. Finally, simulation results validate the potential of active RIS in enhancing the performance of the ISAC system compared to the passive RIS, and indicate that the transmit power and physical location of the active RIS should be carefully chosen.

RIS-Position and Orientation Estimation in Dispersive mmWave MIMO Scenarios

In this paper, we investigate the problem of estimating the position and the angle of rotation of a mobile station (MS) in a millimeter wave (mmWave) multiple-input-multiple-output (MIMO) system aided by a reconfigurable intelligent surface (RIS). The virtual line-of-sight (VLoS) link created by the RIS and the non-line-of-sight (NLoS) links that originate from scatterers in the considered environment are utilized to facilitate the estimation. A two-step positioning scheme is exploited, where the channel parameters are first acquired, and the position-related parameters are then estimated. The channel parameters are obtained through a coarser and a subsequent finer estimation processes. As for the coarse estimation, the distributed compressed sensing orthogonal simultaneous matching pursuit (DCS-SOMP) algorithm, the maximum likelihood (ML) algorithm, and the discrete Fourier transform (DFT) are utilized to separately estimate the channel parameters. The obtained channel parameters are then jointly refined by using the space-alternating generalized expectation maximization (SAGE) algorithm, which circumvents the high-dimensional optimization issue of ML estimation. Departing from the estimated channel parameters, the positioning-related parameters are estimated. The performance of estimating the channel-related and position-related parameters is theoretically quantified by using the Cramer-Rao lower bound (CRLB). Simulation results demonstrate the superior performance of the proposed positioning algorithms.

Channel Estimation for RIS-aided mmWave Massive MIMO System Using Few-bit ADCs

Millimeter wave (mmWave) massive multiple-input multiple-output (massive MIMO) is one of the most promising technologies for the fifth generation and beyond wireless communication system. However, a large number of antennas incur high power consumption and hardware costs, and high-frequency communications place a heavy burden on the analog-to-digital converters (ADCs) at the base station (BS). Furthermore, it is too costly to equipping each antenna with a high-precision ADC in a large antenna array system. It is promising to adopt low-resolution ADCs to address this problem. In this paper, we investigate the cascaded channel estimation for a mmWave massive MIMO system aided by a reconfigurable intelligent surface (RIS) with the BS equipped with few-bit ADCs. Due to the low-rank property of the cascaded channel, the estimation of the cascaded channel can be formulated as a low-rank matrix completion problem. We introduce a Bayesian optimal estimation framework for estimating the user-RIS-BS cascaded channel to tackle with the information loss caused by quantization. To implement the estimator and achieve the matrix completion, we use efficient bilinear generalized approximate message passing (BiG-AMP) algorithm. Extensive simulation results verify that our proposed method can accurately estimate the cascaded channel for the RIS-aided mmWave massive MIMO system with low-resolution ADCs.