DGoT: Dynamic Graph of Thoughts for Scientific Abstract Generation

The method of training language models based on domain datasets has obtained significant achievements in the task of generating scientific paper abstracts. However, such models face problems of generalization and expensive training costs. The use of large language models (LLMs) to solve the task of generating paper abstracts saves the cost of model training. However, due to the hallucination problem of LLM, it is often necessary to improve the reliability of the results through multi-round query prompt approach such as Graph of Thoughts (GoT), which also brings additional reasoning costs. In this paper, we propose a Dynamic Graph of Thought (DGoT). It not only inherits the advantages of the existing GoT prompt approach, but also dynamically adjust the graph structure according to data characteristics while reducing model reasoning cost. Experimental results show that our method's cost-effectiveness in abstract generation tasks is only 43.7% to 56.4% of other multi-round query prompt approaches. Our code is available at https://github.com/JayceNing/DGoT.

Sum-Rate Optimization for RIS-Aided Multiuser Communications with Movable Antenna

Reconfigurable intelligent surface (RIS) is known as a promising technology to improve the performance of wireless communication networks, which has been extensively studied. Movable antenna (MA) is a novel technology that fully exploits the antenna position for enhancing the channel capacity. In this paper, we propose a new RIS-aided multiuser communication system with MAs. The sum-rate is maximized by jointly optimizing the beamforming, the reflection coefficient (RC) values of RIS and the positions of MAs. A fractional programming-based iterative algorithm is proposed to solve the formulated non-convex problem, considering three assumptions for the RIS. Numerical results are presented to verify the effectiveness of the proposed algorithm and the superiority of the proposed MA-based system in terms of sum-rate.

Secure Antenna Selection and Beamforming in MIMO Systems

This work proposes a novel joint design for multiuser multiple-input multiple-output wiretap channels. The base station exploits a switching network to connect a subset of its antennas to the available radio frequency chains. The switching network and transmit beamformers are jointly designed to maximize the weighted secrecy sum-rate for this setting. The principal design problem reduces to an NP-hard mixed-integer non-linear programming. We invoke the fractional programming technique and the penalty dual decomposition method to develop a tractable iterative algorithm that effectively approximates the optimal design. Our numerical investigations validate the effectiveness of the proposed algorithm and its superior performance compared with the benchmark.

Revealing the Impact of SIC in NOMA-ISAC

The impact of successive interference cancellation (SIC) in non-orthogonal multiple access integrated sensing and communications (NOMA-ISAC) is analyzed. A two-stage SIC-based framework is proposed to deal with the inter-communication user and inter-functionality interferences. The performance of sensing and communications (S\&C) is analyzed for two SIC orders, i.e., the communications-centric SIC and the sensing-centric SIC. For each design, diversity orders, high signal-to-noise ratio (SNR) slopes, and high-SNR power offsets of the sensing rate (SR) and communication rate (CR) are derived as insights. Analytical results indicate that i) the main influence of SIC order on the SR and CR lies in the high-SNR power offsets; ii) ISAC provides more degrees of freedom than frequency-division S\&C (FDSAC). Numerical results show that the SR-CR region of ISAC entirely covers that of FDSAC.

Joint Receive Antenna Selection and Beamforming in RIS-Aided MIMO Systems

This work studies a low-complexity design for reconfigurable intelligent surface (RIS)-aided multiuser multiple-input multiple-output systems. The base station (BS) applies receive antenna selection to connect a subset of its antennas to the available radio frequency chains. For this setting, the BS switching network, uplink precoders, and RIS phase-shifts are jointly designed, such that the uplink sum-rate is maximized. The principle design problem reduces to an NP-hard mixed-integer optimization. We hence invoke the weighted minimum mean squared error technique and the penalty dual decomposition method to develop a tractable iterative algorithm that approximates the optimal design effectively. Our numerical investigations verify the efficiency of the proposed algorithm and its superior performance as compared with the benchmark.

Statistical-CSI-Based Antenna Selection and Precoding in Uplink MIMO

Classical antenna selection schemes require instantaneous channel state information (CSI). This leads to high signaling overhead in the system. This work proposes a novel joint receive antenna selection and precoding scheme for multiuser multiple-input multiple-output uplink transmission that relies only on the long-term statistics of the CSI. The proposed scheme designs the switching network and the uplink precoders, such that the expected throughput of the system in the long term is maximized. Invoking results from the random matrix theory, we derive a closed-form expression for the expected throughput of the system. We then develop a tractable iterative algorithm to tackle the throughput maximization problem, capitalizing on the alternating optimization and majorization-maximization (MM) techniques. Numerical results substantiate the efficiency of the proposed approach and its superior performance as compared with the baseline.

On the Performance of Downlink MIMO-ISAC

This letter analyzes the performance of sensing and communications (S\&C) achieved by a multiple-input multiple-output downlink integrated S\&C (ISAC) system. Three typical ISAC scenarios are studied, including the sensing-centric design, communications-centric design, and Pareto optimal design. For each scenario, diversity orders and high signal-to-noise ratio slopes of the sensing rate and communication rate are derived to gain further insights. It is found that ISAC can provide more degrees of freedom and a broader rate region than existing frequency-division S\&C (FDSAC) techniques.

Integrated Sensing and Communications: A Mutual Information-Based Framework

Integrated sensing and communications (ISAC) is potentially capable of circumventing the limitations of existing frequency-division sensing and communications (FDSAC) techniques. Hence, it has recently attracted significant attention. This article aims to propose a unified analytical framework for ISAC from a mutual information (MI) perspective. Based on the proposed framework, the sensing performance and the communication performance are evaluated by the sensing MI and the communication MI, respectively. The unity of this framework is originated from the fact that the sensing and communication (S\&C) performance metrics, i.e., the S\&C MI, have the similar physical and mathematical properties as well as the same unit of measurement. Based on this framework, the S\&C performance of downlink and uplink ISAC systems is investigated and compared with that of FDSAC systems. Along each considered system settings, numerical results are provided to demonstrate the superiority of ISAC over conventional FDSAC designs. Finally, promising open research directions are provided in the context of MI-based ISAC.

Capacity Scaling Law in Massive MIMO with Antenna Selection

Antenna selection is capable of handling the cost and complexity issues in massive multiple-input multiple-output (MIMO) channels. The sum-rate capacity of a multiuser massive MIMO uplink channel is characterized under the Nakagami fading. A mathematically tractable sum-rate capacity upper bound is derived for the considered system. Moreover, for a sufficiently large base station (BS) antenna number, a deterministic equivalent (DE) of the sum-rate bound is derived. Based on this DE, the sum-rate capacity is shown to grow double logarithmically with the number of BS antennas. The validity of the analytical result is confirmed by numerical experiments.

Lens Antenna Arrays-Assisted mmWave MU-MIMO Uplink Transmission: Joint Beam Selection and Phase-Only Beamforming Design

This paper considers a lens antenna array-assisted millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) system. The base station's beam selection matrix and user terminals' phase-only beamformers are jointly designed with the aim of maximizing the uplink sum rate. In order to deal with the formulated mixed-integer optimization problem, a penalty dual decomposition (PDD)-based iterative algorithm is developed via capitalizing on the weighted minimum mean square error (WMMSE), block coordinate descent (BCD), and minorization-maximization (MM) techniques. Moreover, a low-complexity sequential optimization (SO)-based algorithm is proposed at the cost of a slight sum rate performance loss. Numerical results demonstrate that the proposed methods can achieve higher sum rates than state-of-the-art methods.