Fluid antenna system (FAS) revolutionizes wireless communications via utilizing position-flexible antennas that dynamically optimize channel conditions and mitigate multipath fading. This innovation is particularly valuable in indoor environments, in which signal propagation is severely degraded due to structural obstructions and complex multipath reflections. In this paper, we investigate the channel modeling and the joint optimization of antenna positioning, beamforming, and power allocation for indoor FAS. In particular, we propose a layout-specific channel model, and employ the novel group relative policy optimization (GRPO) algorithm for tackling the optimization problem. Compared to the state-of-the-art Sionna model, our model achieves an 83.3% reduction in computation time with an approximately 3 dB increase in root-mean-square error (RMSE). When simplified to a two-ray model, our model allows for a closed-form antenna position solution with near-optimal performance. For the joint optimization problem, our GRPO algorithm outperforms proximal policy optimization (PPO) and other baselines in sum-rate, while requiring only 50.8% computational resources of PPO, thanks to its group advantage estimation. Simulation results show that increasing either the group size or trajectory length in GRPO does not yield significant improvements in sum-rate, suggesting that these parameters can be selected conservatively without sacrificing performance.

Accurate radio map estimation is essential for the planning and optimization of wireless communication networks. However, most existing deep learning-based methods rely on an unrealistic assumption that Receive Signal Strength (RSS) measurements are uniformly distributed across the target area. To bridge this gap, we propose a trajectory-based data sampling method for practical simulation and introduce a novel representation that integrates measured RSS with wireless signal propagation models using two-channel grayscale images. With this representation, we develop the Radio Residual Denoising Diffusion Model (RRDDM) to reconstruct radio maps from sparse and trajectory-based measurements. Experimental evaluations demonstrate that RRDDM significantly outperforms existing approaches, including Deep Completion Autoencoders (Deep AE) and Radio Map Estimation via Conditional Generative Adversarial Network (RME-GAN), achieving a 20 ∼ 30% reduction in Root-Mean-Square Error under trajectory-based measurement conditions. Moreover, RRDDM also achieves superior performance in uniformly sampled scenarios, demonstrating its robustness and effectiveness in diverse measurement environments.

The multiple-input multiple-output (MIMO) wiretap channel (WTC) serves as a fundamental model for exploring information-theoretic secrecy in wireless communication systems, involving a transmitter, a legitimate user, and an eavesdropper. This paper investigates the optimality of proper complex signals in complex WTCs. Our primary contribution lies in the derivation of a determinant inequality, which establishes that the secrecy rate of degraded complex MIMO WTCs is maximized when the signal is proper, meaning that its pseudo-covariance matrix is a zero matrix. Remarkably, we extend this result beyond the degraded scenario to the general complex WTC by leveraging a min-max reformulation of the secrecy capacity. Thus, we demonstrate that focusing on proper signals is sufficient when examining the secrecy capacity of the complex WTC. Overall, this work highlights the significance of the determinant inequality we derive and its implications for optimizing secrecy rates in the complex WTC.

In this paper, we characterize the generalized degrees-of-freedom (GDoF) region of the two-user$(M,N_{1},N_{2})$multiple-input multiple-output (MIMO) broadcast channel with delayed channel state information at the transmitter (CSIT), where there are one transmitter with$M$antennas and two receivers with$N_{1}$and$N_{2}$antennas, respectively. Under delayed CSIT, different from the existing converse approaches in the multiple-input single-output (MISO) GDoF and MIMO degrees-of-freedom (DoF) models, we incorporate new components into traditional approaches for this MIMO GDoF converse. For the achievability, we generalize the existing MISO achievable scheme. Our result reveals how the channel strength and antenna configuration impact the GDoF region of the two-user MIMO broadcast channel with delayed CSIT. Furthermore, the extension of our converse to a GDoF outer region of the$K$-user MIMO broadcast channel with delayed CSIT is also provided.

In space-air-ground integrated networks (SAGIN), receivers experience diverse interference from both the satellite and terrestrial transmitters. The heterogeneous structure of SAGIN poses challenges for traditional interference management (IM) schemes to effectively mitigate interference. To address this, a novel UAV-RIS-aided IM scheme is proposed for SAGIN, where different types of channel state information (CSI) including no CSI, instantaneous CSI, and delayed CSI, are considered. According to the types of CSI, interference alignment, beamforming, and space-time precoding are designed at the satellite and terrestrial transmitter side, and meanwhile, the UAV-RIS is introduced for the cooperating interference elimination process. Additionally, the degrees of freedom (DoF) obtained by the proposed IM scheme are discussed in depth when the number of antennas on the satellite side is insufficient. Simulation results show that the proposed IM scheme improves the system capacity in different CSI scenarios, and the performance is better than the existing IM benchmarks without UAV-RIS, but the performance improvement is at the cost of the requirement on the elements of UAV-RIS.

Most of the existing research on degrees-of-freedom (DoF) with imperfect channel state information at the transmitter (CSIT) assume the messages are private, which may not reflect reality as the two receivers can request the same content. To overcome this limitation, we therefore consider the hybrid unicast and multicast messages. In particular, we characterize the optimal DoF region for the two-user multiple-input multiple-output (MIMO) broadcast channel (BC) with imperfect CSIT and hybrid messages. For the converse, we establish a three-step procedure to exploit the utmost possible relaxation. For the achievability, since the DoF region is with specific three-dimensional structure regarding antenna configurations and CSIT qualities, we verify the existence or non-existence of corner point candidates via the feature of antenna configurations and CSIT qualities categorization, and provide a hybrid message-aware rate-splitting scheme. Besides, we show that to achieve the strictly positive corner points, it is unnecessary to split the unicast messages into private and common parts. This implies adding a multicast message may mitigate the rate-splitting complexity.

Terrestrial robots, i.e., unmanned ground vehicles (UGVs), and aerial robots, i.e., unmanned aerial vehicles (UAVs), operate in separate spaces. To exploit their complementary features (e.g., fields of views, communication links, computing capabilities), a promising paradigm termed integrated robotics network therefore emerges, which provides communications for cooperative UAVs-UGVs applications. However, how to efficiently deploy UAVs and schedule the UAVs-UGVs connections according to different UGV tasks become challenging. In this paper, we consider the sum-rate maximization problem, where UGVs plan their trajectories autonomously and are dynamically associated with UAVs according to their planned trajectories. Although this problem is a NP-hard mixed integer program, a fast polynomial time algorithm using alternating gradient descent and penalty-based binary relaxation, is devised. Simulation results demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose a rate-splitting design and characterize the sum-degrees-of-freedom (DoF) for the K-user multiple-input-single-output (MISO) broadcast channel with mixed channel state information at the transmitter (CSIT) and order-(K − 1) messages, where mixed CSIT refers to the delayed and imperfect-current CSIT, and order-(K − 1) message refers to the message desired by K − 1 users simultaneously. In particular, for the sum-DoF lower bound, we propose a rate-splitting scheme embedding with retrospective interference alignment. In addition, we propose a matching sum-DoF upper bound via genie signalings and extremal inequality. Opposed to existing works for K = 2, our results show that the sum-DoF is saturated with CSIT quality when CSIT quality thresholds are satisfied for K > 2.

In this paper, we study the optimality of proper signals for wiretap channels (WTC). We characterize the secrecy capacity of WTC in both separate and augmented forms, and show that proper Gaussian signals can achieve the secrecy capacity for both single-input multiple-output (SIMO) and single-input single-output (SISO) WTC. We also use an accelerated difference of convex functions algorithm (ADCA) to verify our results.

This letter investigates joint beamforming and power optimization in a vehicle-to-infrastructure (V2I) system for integrated passive sensing and communications techniques. Specifically, the base station (BS) with multiple antennas delivers downlink data to multiple vehicles, respectively, and a sensing receiver simultaneously utilizes the downlink data signal to sense the motion of one vehicle. The sensing receiver is deployed separately. It collects the line-of-sight (LoS) signals from the BS and the scattered signals via the target vehicle, such that the distance and velocity of the target vehicle can be detected in a passive manner. As a result, the joint downlink beamforming and power optimization at the BS would be able to maximize the weighted summation of downlink data rates, subject to constraints on the signal-to-interference-plus-noise ratios (SINRs) of the two signals for passive sensing. In order to solve the above non-convex optimization problem, we first derive the optimal receiving beams for the data and sensing receivers given arbitrary transmission beam design, and then propose a low-complexity iterative algorithm to find a sub-optimal design of transmission beams based on the successive convex approximation (SCA) method. Simulations demonstrate the good performance, fast convergence and useful design insights.

Realizing edge intelligence consists of sensing, communication, training, and inference stages. Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time. This paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC). As such, the sensing and communication stages are merged so as to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. However, ISAC also introduces additional interference between sensing and communication functionalities. To address this challenge, this paper proposes a classification error minimization formulation to design the ISAC beamforming and time allocation. The globally optimal solution is derived via the rank-1 guaranteed semidefinite relaxation, and performance analysis is performed to quantify the ISAC gain over that of conventional edge intelligence. Simulation results are provided to verify the effectiveness of the proposed ISAC-assisted edge intelligence system. Interestingly, we find that ISAC is always beneficial, when the duration of generating a sample is more than the duration of uploading a sample. Otherwise, the ISAC gain can vanish or even be negative. Nevertheless, we still derive a sufficient condition, under which a positive ISAC gain is feasible.

The secrecy capacity of the multiple-input multiple-output (MIMO) Gaussian cognitive interference channel with common, private and confidential messages is characterized in this paper. To show the achievability, we use jointly Gaussian auxiliary random variables to evaluate the existing single-letter description of the capacity region for the discrete memoryless channel. The converse part is established by invoking linear estimation theory and a new version of the extremal inequality. To resolve the Gaussian optimality problem in the extremal inequality, the perturbation framework is employed. Our results explore the connections between the MIMO Gaussian broadcast channel and its distributive antennas counterpart, i.e., the MIMO Gaussian interference channel.

In this paper, we investigate the generalized degrees-of-freedom (GDoF) of the asymmetric interference channel with delayed channel state information at the transmitter (CSIT), where each transmitter has two antennas, each receiver has one antenna, and the strength for each interfering link can vary. The optimal sum-GDoF is characterized by matched converse and achievability proof. Through our results, we also reveal that in our antenna setting, the symmetric GDoF lower bound in [Mohanty et. al, TIT 2019] can be elevated, and the symmetric GDoF upper bound in [Mohanty et. al, TIT 2019] is tight in fact.

This article studies the problem of Sum-secure Degrees of Freedom (SDoF) of the$(M,M,N,N)$multiple-input–multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled Vehicles-to-Vehicles (V2V). For this problem, we propose two novel transmission schemes, i.e., the interference decoding scheme and the interference alignment scheme, and thus establish a sum-SDoF lower bound. In particular, to optimize the phase duration, we analyze the security and decoding constraints and formulate a linear-fractional optimization problem. Furthermore, we show that the derived sum-SDoF lower bound is the sum-SDoF for$M \le N/2$,$N=M$, and$2N \le M$antenna configurations, and reveal that for a fixed$N$, the optimal$M$to maximize the sum-SDoF is not less than$2N$. Through simulations, we examine the secure sum-rate performance of proposed transmission schemes and reveal that using local output feedback can lead to a higher secure sum-rate than that by using delayed channel state information at the transmitter (CSIT).

For the two-user multiple-input multiple-output (MIMO) broadcast channel with delayed channel state information at the transmitter (CSIT) and arbitrary antenna configurations, all the degrees-of-freedom (DoF) regions are obtained. However, for the three-user MIMO broadcast channel with delayed CSIT and arbitrary antenna configurations, the DoF region of order-2 messages is still unclear and only a partial achievable DoF region of order-1 messages is obtained, where the order-2 messages and order-1 messages are desired by two receivers and one receiver, respectively. In this paper, for the three-user MIMO broadcast channel with delayed CSIT and arbitrary antenna configurations, we first design transmission schemes for order-2 messages and order-1 messages. Next, we propose to analyze the achievable DoF region of transmission scheme by transformation approach. In particular, we transform the decoding condition of transmission scheme w.r.t. phase duration into the achievable DoF region w.r.t. achievable DoF, through achievable DoF tuple expression connecting phase duration and achievable DoF. As a result, the DoF region of order-2 messages is characterized and an achievable DoF region of order-1 messages is completely expressed. Besides, for order-1 messages, we derive the sufficient condition, under which the proposed achievable DoF region is the DoF region.

In this paper, we consider a multiuser uplink status update system, where a monitor aims to timely collect randomly generated status updates from multiple end nodes through a shared wireless channel. We adopt the recently proposed metric, termed age of information (AoI), to quantify the information timeliness and freshness. Due to the random generation of the status updates at the end node side, the monitor only grasps a partial knowledge of the status update arrivals. Under such a practical scenario, we aim to address a fundamental multiuser scheduling problem: how to schedule the end nodes to minimize the network-wide AoI? To solve this problem, we formulate it as a partially observable Markov decision process (POMDP), and develop a dynamic programming (DP) algorithm to obtain the optimal scheduling policy. By noting that the optimal policy is computationally prohibitive, we further design a low-complexity myopic policy that only minimizes the one-step expected reward. Simulation results show that the performance of the myopic policy approaches that of the optimal policy and is superior to that of the baseline policy.

Secure degrees-of-freedom (SDoF) for multiple-input multiple-output (MIMO) interference channel with confidential messages and delayed channel state information at transmitter (CSIT) remains unclear, even for single-input single-output (SISO) case. In this paper, we propose an achievable SDoF for MIMO interference channel with confidential messages and delayed CSIT by designing a multi-phase achievable scheme. For the proposed scheme, we first establish a multi-phase transmission procedure with undetermined phase durations. In the next, the security and decoding conditions on feasible phase durations are derived. Finally, an achievable SDoF maximization problem with respect to phase durations is solved under security and decoding conditions. Due to effective coordination of interference, the proposed achievable SDoF can be 20% greater than the SDoF of MIMO wiretap channel with delayed CSIT, which removes one transmitter from the MIMO interference channel.

Degrees of freedom (DoF) of the three-user multiple-input multiple-output (MIMO) broadcast channel (BC) with delayed CSIT was derived for most antenna configurations except for the case of , where transmitter has M antennas and each receiver has N antennas. In this paper, for that problem, we propose an effective scheme for acquiring a higher achievable DoF than the value via existing methods. In the initial transmission phase, we transmit more data symbols than the amount that the receivers can instantaneously decode. Then, we generate auxiliary symbols for decoding the data symbols. Specifically, our scheme introduces an integrated design for the generation of auxiliary symbols. As a result, a higher achievable DoF, i.e., [12MN/(7M+2N)], can be achieved for specific antenna configurations, where .2N <; M <; 2.5N.

The achievable degree of freedom (DoF) boosting has been demonstrated on a single-input single-output (SISO) X channel by using outdated and instantaneous channel state information at transmitter (CSIT) synergistically, in contrast to that of using completely outdated CSIT. However, the means by which the DoF gain can be obtained in a multiple-input multiple-output (MIMO) system remains unclear. This paper proposes an interference alignment scheme with synergistic CSIT for MIMO X channel. We show that the achievable DoF is greater than the optimal DoF obtained with outdated CSIT, and that achievable DoF equals to the optimal value with outdated CSIT and transmitter cooperation.

Due to high system capacity requirement, 3GPP Long Term Evolution (LTE) is likely to adopt frequency reuse factor 1 at the cost of suffering severe inter-cell interference (ICI). One of combating ICI strategies is network cooperation of resource allocation (RA). For LTE uplink RA, requiring all the subcarriers to be allocated adjacently complicates the RA problem greatly. This paper investigates the joint multicell RA problem for LTE uplink. We model the uplink RA and ICI mitigation problem using pure binary-integer programming (BIP), with integrative consideration of all users' channel state information (CSI). The advantage of the pure BIP model is that it can be solved by branch- and- bound search (BBS) algorithm or other BIP solving algorithms, rather than resorting to exhaustive search. The system-level simulation results show that it yields 14.83% and 22.13% gains over single-cell optimal RA in average spectrum efficiency and 5th percentile of user throughput, respectively.