This article investigates the integration of movable antennas (MAs) into multicast communication systems. By discretizing the motion of the MAs, a novel MA-assisted multicast transmission architecture is formulated. An alternating optimization (AO) algorithm based on successive convex approximation is proposed to optimize the transmit beamforming and antenna positions. To gain further insights, the two-user case is examined, and a closed-form expression for the optimal beamformer is derived. On this basis, a low-complexity greedy search algorithm is developed to optimize the placement of the MAs. Furthermore, under the assumption of a line-of-sight propagation environment, a branch-and-bound algorithm is designed to determine the globally optimal antenna configuration with reduced complexity compared to exhaustive search. Numerical simulations confirm that the proposed methods effectively enhance the achievable multicast rate.

Multi-modal Large Language Models (MLLMs) have exhibited impressive capability. However, recently many deficiencies of MLLMs have been found compared to human intelligence, $\textit{e.g.}$, hallucination. To drive the MLLMs study, the community dedicated efforts to building larger benchmarks with complex tasks. In this paper, we propose benchmarking an essential but usually overlooked intelligence: $\textbf{association}$, a human's basic capability to link observation and prior practice memory. To comprehensively investigate MLLM's performance on the association, we formulate the association task and devise a standard benchmark based on adjective and verb semantic concepts. Instead of costly data annotation and curation, we propose a convenient $\textbf{annotation-free}$ construction method transforming the general dataset for our association tasks. Simultaneously, we devise a rigorous data refinement process to eliminate confusion in the raw dataset. Building on this database, we establish three levels of association tasks: single-step, synchronous, and asynchronous associations. Moreover, we conduct a comprehensive investigation into the MLLMs' zero-shot association capabilities, addressing multiple dimensions, including three distinct memory strategies, both open-source and closed-source MLLMs, cutting-edge Mixture-of-Experts (MoE) models, and the involvement of human experts. Our systematic investigation shows that current open-source MLLMs consistently exhibit poor capability in our association tasks, even the currently state-of-the-art GPT-4V(vision) also has a significant gap compared to humans. We believe our benchmark would pave the way for future MLLM studies. $\textit{Our data and code are available at:} https://mvig-rhos.com/llm_inception.

The integrated sensing and communication (ISAC) attracts growing interest in 6G due to the extension of radar capability. This paper devotes to the resource allocation problem in ISAC system design. Both static and dynamic resource allocation schemes are comprehensively investigated, with the primary distinction lying in the allocation of resources to either communication or sensing services, or their shared utilization. We construct two-class multi-server queues of each scheme, respectively, where communication and sensing services are modeled as Poisson processes with distinct arrival and service rates. To derive the steady-state probabilities of the two models, we present proofs of reversibility. Additionally, key matrices are derived for performance measurement purposes. Numerical results demonstrate the superiority of the dynamic allocation scheme while also providing insights into resource investment and division.

Passive Internet-of-Things (IoT), as a new communication technology, is expected to enable massive device connectivity in a cost-effective and energy-efficient manner. One of the most important topics on Passive IoT is to find a suitable waveform that can be affordable for a wide range of IoT devices. In this paper, we propose a novel waveform design that leverages a low-power on-off keying (OOK) wave. This design cleverly reuses the orthogonal frequency division multiplexing (OFDM) signal generated by the legacy 5G base station, i.e., gNodeB (gNB). Additionally, we enhance the OOK waveform using a central part repetition scheme to acquire frequency diversity gains, thereby improving its resilience against fading channels in realistic environments. Thanks to the repetition being applied only to the central part, no additional bandwidth resources are needed to support the low-power waveform. Simulation results are provided to validate the effectiveness of our approach, demonstrating significant potential for low-power transmissions in passive IoT.

A pioneering secure transmission scheme is proposed, which harnesses movable antennas (MAs) to optimize antenna positions for augmenting the physical layer security. Particularly, an MA-enabled secure wireless system is considered, where a multi-antenna transmitter communicates with a single-antenna receiver in the presence of an eavesdropper. The beamformer and antenna positions at the transmitter are jointly optimized under two criteria: power consumption minimization and secrecy rate maximization. For each scenario, a novel suboptimal algorithm was proposed to tackle the resulting nonconvex optimization problem, capitalizing on the approaches of alternating optimization and gradient descent. Numerical results demonstrate that the proposed MA systems significantly improve physical layer security compared to various benchmark schemes relying on conventional fixed-position antennas (FPAs).

A novel orthogonal frequency division multiplexing (OFDM)-based private-public communication framework is proposed, which harnesses reconfigurable intelligent surface (RIS) to improve the sum private and public rates. A two-user RIS-enabled OFDM system is considered, where the total subcarriers are partitioned into three segments, with two exclusively assigned to users for private/secure communications, while the third segment is reserved for public messages. The active beamformer at the transmitter and the passive reflecting beamformer at the RIS are jointly optimized to maximize the summation of the private/secure rate and the public rate. A novel joint design algorithm is proposed by capitalizing on the mathematical structure of the optimal active beamformer, eliminating the need for iterative updates between the transmit beamformer and the RIS-reflecting beamformer. Numerical results underscore the superior computational efficiency of our proposed algorithms compared to state-of-the-art methods based on alternating optimization, all while achieving nearly identical sum-rate performance.

Sub-band full-duplex (SBFD) technology holds significant potential for 6G base stations, owing to its advantageous features such as reduced latency, increased spectrum efficiency and flexibilities in spectrum managements. Nevertheless, an out-of-band regrowth of the downlink (DL) signal in strong distortion scenarios will require large guard band between two adjacent sub-bands thus worsen the spectrum efficiency of the SBFD. A self-interference cancellation (SIC) scheme is proposed for the SBFD under severe distortion conditions. In this scheme, a pre-distortion algorithm is designed to effectively suppress the out-of-band regrowth, coupled with a novel digital SIC approach for reconstructing a high-accuracy digital SIC signal. The simulation results shown that the proposed scheme achieves a performance gain of roughly 10 dB compared with conventional SIC schemes in the presence of strong distortion. Moreover, the scheme can suppress SI signals to be within a range of 1 dB above the noise floor, eliminating the requirement for setting the guard band between the uplink (UL) and DL sub-bands. This ensures a high spectrum efficiency of the SBFD even in challenging conditions.

Ultra Massive MIMO is one of the potential key technologies in 6G, which attracts widespread attention from academia and industry recently. Generally, the system-level simulation is essential to evaluate the performance of Ultra Massive MIMO. However, due to ultra-large-scale antennas and extremely high concurrency, the computation complexity is pretty high, which makes the simulation workload unbearable. To deal with this issue, we propose a two-level parallel optimization via OpenMP and Eigen to drastically promote the simulation efficiency in this paper. Moreover, we provide a comprehensive insight on 6G system-level evaluation platform with modular architecture and parallel computing. It is validated that our simulation platform has the capability of evaluating 6G Ultra Massive MIMO technology and the simulation efficiency can be improved by more than 20 times than that of without parallelization. Based on the simulation results, it is shown that the UE experienced data rate can be increased up to 10 times than that of 5G, which can meet the requirement of 6G.

The orthogonal time frequency space (OTFS) modulation as a promising signal representation attracts growing attention for integrated sensing and communication. This paper devotes to MIMO-OTFS based channel parameter estimation. Inspired by the insight that superimposed signals can be composed iteratively and processed separately, we develop the Space-Alternating Generalized Expectation-Maximization (SAGE) algorithm to support the resolution of multiple paths with modest complexity. Besides, we derive the Cramer-Rao lower bound (CRLB) of the variance of channel parameters as the fundamental limit of sensing in the OTFS modulation scheme as well as the baseline of the performance evaluation. Simulations in various cases demonstrate the feasibility.

In millimeter wave (mmWave) systems, the low-overhead and high-accuracy estimation of channel state information (CSI), especially the angle of departure (AoD) and the angle of arrival (AoA), is essential for hybrid beamforming. The existing adaptive compressed sensing (ACS) based channel estimation algorithm decides the angles by comparing the received powers of different beams. The estimated angular resolution of this power based method is limited by the resolution of beams. In this paper, a correlation based adaptive compressed sensing (CB-ACS) method is proposed. It decides the angles by comparing the correlations between the received vector and the quantized sensing vectors. For the proposed method, two criteria for the beam pattern design are proposed and the cosine beam pattern is proved to be suitable. Then the corresponding hierarchical codebook is designed considering the hardware constraints. Simulation results show that with the proposed scheme, more precise estimation can be realized without increasing the training overhead. Thus, the achievable spectral efficiency of the hybrid precoding system increases.

Widely linear processing (WLP) can provide attractive performance improvement in wireless communication systems. However, performance improvement brought by WLP is mainly achieved by exploiting signal's non-circularity, which leads to the fact that existing works on WLP are mainly confined to the cases related to non circular signals or improper signals with imbalanced I/Q components. In this paper, WLP for circular signals, such as PSK signals, is investigated by exploiting the hidden properties of PSK signals. The hidden properties of PSK signals are firstly studied, and a unified mathematical model is derived to describe the hidden properties of PSK signals. Furthermore, a widely linear sphere decoding (WLSD) algorithm exploiting PSK signals' hidden properties is proposed for MIMO systems. Compared to traditional sphere decoding (SD), WLSD has little performance loss, and it transforms the traditional SD searching for the true transmitted vector into the shrunk searching for the corresponding rotation vector, the candidate rotation vectors of WLSD are only (1/2)NTof SD candidate vectors. Simulation results show that the proposed algorithm can achieve quasi-optimal BER performance, while the computational complexity is significantly reduced by more than a half compared with Schnorr-Euchner sphere decoder.

This paper investigates a typical optimization problem for 60 GHz multiuser hybrid beamforming, which is to maximize the minimal SINR over all of the considered subcarriers under a total power constraint. In order to reduce the complexity, two methods of using the fixed analog beamforming (ABF) weights are presented rather than traditional iterative methods, which are codebook-based method and singular value decomposition (SVD) based method. The performance of hybrid beamforming with the fixed ABF weights are evaluated by computer simulations and compared with that of using the existing iterative method. Channel model defined in IEEE 802.11ad is utilized in the simulation. Simulation results show that the average max-min SINR achieved by the presented methods of using the fixed ABF weights are close to or even higher than that of the iterative method.

Power consumption and costs of analogue front‐end (AFE) chains are often not negligible for large‐scale antenna array systems. A low‐complexity hardware architecture is to use a number of AFE chains that are less than the number of antennas. Beamforming for this low‐complexity hardware architecture involves both digital and analogue beamforming, which is termed hybrid analogue/digital beamforming. In this study, minimising the transmit power subject to signal‐to‐interference‐plus‐noise ratio (SINR) constraints and maximising the minimal SINR under the constraint of the transmit power, are investigated, respectively, for hybrid analogue/digital beamforming. Properties on the feasibility and optimality of the two problems are derived. Numerical algorithms based on semi‐definite positive relaxation are proposed in an attempt to solve the two optimisation problems. The performance of the proposed algorithms is evaluated under the Gaussian and 60 GHz channel models. It is shown that the performance of hybrid analogue/digital beamforming is highly related to the correlation of the channel coefficients of subcarriers. Additionally, for different extents of the users’ spatial separability, some heuristics ways of designing analogue beamforming are shown to be able to approach to at least the local optimums under the 60 GHz channel model.

Under the consideration of the cost and power consumption, analog or radio frequency (RF) beamforming is usually adopted in the 60 GHz wireless systems. Hence, obtaining the multiple- input and multiple-output (MIMO) channel information is costly and of high overhead for the concerned system. In this work, an efficient multi-user scheme is proposed for the single carrier block transmission (SCBT) based 60 GHz system. It schedules users that are unlikely to cause strong multi-user interference (MUI) without knowing the MIMO channel information. Additionally, the proposed scheme enables to further mitigate the residual MUI after scheduling on the effective channels while the system can still employ low cost arrays that have very limited capability of nulling the interference. Evaluation of the proposed scheme is performed by computer simulation based on the channel model defined in 802.11ad.

A Bayesian-based probabilistic model is presented for unconstrained handwritten offline Chinese text line recognition. After pre-segmentation of a text line, plenty of invalid characters are produced which heavily interfere in the process of text line recognition. The proposed probabilistic model can incorporate isolated character recognition, character sample verification, and n-gram language model in a simple way, leading to more reliable recognition of a text line. When testing on HIT-MW database, experiments show that the proposed method can achieve character-level recognition accuracies of 63.19% without language model and 73.97% with bi-gram language model, respectively, outperforming the most recent results testing on the same dataset.