This paper presents a new transparent amplifying intelligent surface (TAIS) architecture for improving multi-user uplink performance in indoor-to-outdoor communications. Unlike passive reconfigurable intelligent surfaces (RIS) that primarily manipulate phase shifts, TAIS operates as an amplifier-based transmissive intelligent surface. It possesses the unique ability to refract and amplify signals from all users. Utilizing indium tin oxide coating and cutting-edge printing techniques, TAIS can be fabricated on windows without causing any visible alterations. This paper focuses on leveraging the TAIS to enhance the uplink spectral efficiency (SE) of multiple users in indoor-to-outdoor communications. Our analysis builds upon key assumptions, including the availability of perfect channel state information (CSI) and the use of a third-order memoryless polynomial model for tractable nonlinear PA characterization. The Bussgang decomposition is applied for nonlinear performance analysis, which remains approximately accurate for practical non-Gaussian signals. By collaboratively optimizing the refraction coefficient matrix of the TAIS and the combiner of the base station, the multi-user uplink SE maximization is achieved with consideration of the nonlinearity in the amplification process of TAIS. An efficient alternating optimization framework is developed to solve the non-convex problem approximately. Another important aspect is that we develop a zero-forcing-based successive convex approximation (ZF-SCA) algorithm to solve the problem with lower computational complexity. By employing the zero-forcing combiner at the BS, the problem is reduced to the refraction coefficient optimization at TAIS, using SCA techniques. Simulations demonstrate that the proposed TAIS system can significantly enhance SE by up to 39.1%, as compared to its alternative methods.

Recent studies have explored the synergy between reconfigurable intelligent surfaces (RIS) and multiple-input multiple-output (MIMO) systems to overcome limitations in spectral efficiency and coverage. While RIS-aided MIMO systems promise transformative gains, their real-world deployment faces critical challenges rooted in hardware imperfections, in particular, the nonlinearity of power amplifiers (PAs) in the MIMO transmitters. To bridge this gap, this paper investigates a holistic design that jointly optimizes RIS phase shifts and base station transmit precoder for the RIS-aided MIMO systems with a practical PA model in real life. We propose a hardware-aware joint design framework that co-optimizes the base station precoder and RIS phase shifts to balance beamforming gain with PA distortion suppression. A closed-form solution for the optimal RIS phase-shifting matrix is derived as a function of the precoder, reducing the joint optimization to an equivalent precoder optimization problem. A low-complexity successive convex approximation-based algorithm is developed for efficient precoder design. Simulations demonstrate that the proposed method achieves up to 30.6% spectral efficiency improvement over benchmarks under PA nonlinearity, highlighting its practical significance in RIS-aided systems.

In this paper, we focus on the nonlinear distortion in a massive MIMO system with nonlinear power amplifiers. It is known that the distortion generated by nonlinear power amplifiers is beamformed in the line-of-sight (LoS) channel. In particular, spurious emissions are caused by beamformed intermodulation (IM) distortion and form as IM beams. We propose a precoder for IM beam suppression in the MIMO system, which aims to suppress the intermodulation beams while maintaining beamforming for the intended receivers. First, we derive the direction of the IM beams under the free-space LoS channel condition. Then, the intermodulation suppression (IMS) precoder is proposed by maximizing the signal-to-leakage ratio. Moreover, we propose a generalized IMS precoder to achieve the trade-off between the intended signal coherent combination and IM beam suppression. Adjusting the weights of the precoder can suppress the IM beams while ensuring a coherent combination of signals at intended receivers. Simulation results show that the proposed IMS precoder can suppress the IM beams and reduce the power leakage in spurious directions. The power gain obtained by the proposed IMS precoder can approach to the maximum ratio transmission while achieving substantial suppression gain for the IM beams.

Drones are evolving into highly capable and adaptable devices, prompting the development of advanced control frameworks. This paper introduces a novel online control framework tailored for a multi-task drone, explicitly addressing the simultaneous execution of in-situ sensing and goods delivery. To tackle this complex scenario, a finite-horizon Markov decision process (FH-MDP) is formulated to ensure not only the prompt delivery of goods but also the minimization of energy consumption and the maximization of the drone's reward for in-situ sensing. A significant contribution lies in establishing the monotonicity and subadditivity of the FH-MDP. This mathematical foundation provides evidence for the existence of an optimal, monotone, deterministic Markovian policy. The crux of the optimal policy revolves around flight distance- and time-related thresholds, determining the precise points at which the drone should switch its optimal action. This unique feature empowers the multi-task drone to make real-time decisions, such as adjusting flight speed or engaging in in-situ sensing, by comparing its current state with these predefined thresholds. This process can be accomplished with a linear complexity, ensuring efficiency in decision-making. The optimality of our approach is rigorously demonstrated through numerical validation, where it is compared against a computationally expensive, dynamic programming-based alternative. Under the considered simulation settings, our approach reduces drone energy consumption by a substantial 19.8% compared to existing benchmarks. This not only highlights the practical effectiveness of the proposed framework but also underscores its potential for significant advancements in the field of drone operations and energy efficiency.

This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for multi-user uplink enhancement in indoor-to-outdoor communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the multi-user uplink spectral efficiency (SE) of multiple users in indoor-to-outdoor communications. By jointly optimizing the TAIS's refraction coefficient matrix and combiner of the base station, The multi-user uplink SE can be maximized by exploiting the nonlinearity in the TAIS's amplification process. An efficient alternating optimization framework is proposed to solve the non-convex problem approximately. Simulations show that our proposed TAIS can increase the SE by up to 39.1% as compared to its alternative methods.

This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for uplink enhancement in indoor-to-outdoor mmWave communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the uplink spectral efficiency (SE) in indoor-to-outdoor mmWave communications. By jointly optimizing the TAIS’s phase shift matrix and transmit power of the user equipment, the uplink SE can be maximized by exploiting the nonlinearity in the TAIS’s amplification process. The key enabler is that we drive the optimal phase shift matrix that maximizes the SE and deduces its closed-form representation. The SE maximization is then proved to be transferred to the transmit power optimization problem. Another important enabler is that we design a low-complexity algorithm to solve the optimization problem using the difference of convex programming. Moreover, the asymptotic spectral efficiency under nonlinear amplification and power scaling law with infinitely large elements under both the sparse and rich scattering channel models are analyzed. Simulation results show that our proposed TAIS can increase the SE by up to 24.7% as compared to its alternative methods.

Cooperation can help unmanned aerial vehicles (UAVs) improve their plans to visit charging stations and avoid congestion, but can be hindered by privacy concerns. We propose a new, privacy preserving, joint routing, and charging scheduling framework which allows multiple cellular-connected UAVs to jointly optimize their routes and charging schedules in a decentralized fashion. The framework allows each UAV to minimize its energy usage and connectivity outage, maximize its recharged energy, ensure its timely arrival, and preserve its privacy concerning its trajectory and destination. The key idea is that we obfuscate probabilistically the destination of each UAV, and design a new noncooperative Bayesian game among the UAVs to find their best routes and charging schedules toward the obfuscated destinations. Another important aspect is that we prove the game is a potential Bayesian game with a pure-strategy Bayesian Nash equilibrium and the best response yielded with the Bellman–Ford algorithm. This new framework preserves the UAVs’ privacy in the sense that an UAV only shares the probability of its visit to a charging station at different times, and its best response is based on an obfuscated destination. Simulations demonstrate that the framework ensures timely arrivals with near-optimal routes and substantially lower complexity than a centralized routing scheme based on brute force.

This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for uplink enhancement in indoor-to-outdoor mmWave communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the uplink spectral efficiency (SE) in indoor-to-outdoor mmWave communications. By jointly optimizing the TAIS's phase shift matrix and transmit power of the user equipment, the uplink SE can be maximized by exploiting the nonlinearity in the TAIS's amplification process. The key point is that we drive the optimal phase shift matrix that maximizes the SE and deduces its closed-form representation. The SE maximization is then proved to be transferred to the transmit power optimization problem. Another important aspect is that we design a low-complexity algorithm to solve the problem using the difference of convex programming. Simulations show that our proposed TAIS can increase the SE by up to 32.6% as compared to its alternative methods.

This paper puts forth a new application of an unmanned aerial vehicle (UAV) to joint goods delivery and in-situ sensing, and proposes a new algorithm that jointly optimizes the route and sensing task selection to minimize the UAV’s energy consumption, maximize its sensing reward, and ensure timely goods delivery. This problem is new and non-trivial due to its nature of mixed integer programming. The key idea behind the new algorithm is that we interpret the possible waypoints of the UAV as location-dependent tasks to incorporate routing and sensing in one task selection process. Another critical aspect is that we construct a new task-time graph to describe the process, where each vertex corresponds to a task associated with its location, time and reward, and each edge indicates the propulsion energy required for the UAV to travel between two tasks. By redistributing the weight of a vertex to its incoming edges, the new UAV routing and sensing task selection problem can be converted to a weighted routing problem in the new task-time graph and solved optimally using the Bellman-Ford algorithm. Validated by a real-world case study, our approach can outperform its alternatives by over 18% in task reward.

Unmanned aerial vehicles (UAVs) have been extensively applied to goods delivery and in-situ sensing. It becomes increasingly probable that multiple UAVs are delivering goods and carrying out sensing tasks at the same time. The destinations of the UAVs are usually required to jointly design their trajectories and sensing selections, leading to privacy concerns for the UAVs. This paper presents a new game-theoretic routing framework for joint goods delivery and sensing of multiple cellular-connected UAVs, where the UAVs minimize their energy consumption and connectivity outage, maximize their sensing reward, and ensure timely goods delivery and trajectory privacy by optimizing their trajectories and sensing task selections in a decentralized manner. The key idea is that we unify routing and sensing in a single task selection process, which is further transformed into routing on a task-time graph. Another important aspect is that we design a non-cooperative potential game for the routing on the task-time graph. A distributed strategy is developed, where each UAV only reports its sensing task selections and withholds its destination information and its best response produced by the Bellman-Ford algorithm. By this means, the destination and trajectory privacy of the UAVs are protected. Simulations show that the new game-theoretic approach can ensure timely delivery and achieve close-to-optimal solutions with significantly lower complexity compared to a centralized brute-force approach.

The development of location-based services (LBS) leads to the rapid growth of location data, potentially increasing the threat to location privacy. Existing location obfuscation techniques focus on two-dimensional (2D) planar areas and overlook the features of road networks. In this paper, we leverage differential privacy and propose a new notion of Road Network-Indistinguishability (RN-Indistinguishability) to measure the indistinguishability of locations in road networks. With the RN-Indistinguishability, we design a Cloaking Region Obfuscation (CRO) mechanism to protect the location privacy of vehicles on roads. With the CRO mechanism, vehicle locations in a cloaking region are obfuscated following the same obfuscation distribution. The proposed CRO mechanism is proved to achieve RN-Indistinguishability and can be generalized with road network features holding the triangle inequality. Comprehensive experiments show that the CRO mechanism outperforms existing 2D obfuscation mechanisms in real-world road networks.

Non-terrestrial networks (NTN) can provide connectivity in unreachable or remote areas. The massive multiple-input multiple-output (MIMO) is a promising architecture for future NTN networks through different platforms, such as earth orbit satellites or airborne vehicles. The long transmission distance and large coverage area challenge the physical layer design in a massive MIMO system. In particular, there is a clear trade-off between power amplifier (PA) efficiency and linearity: PAs are most efficient close to saturation, generating the most nonlinearities and degrading the achievable rate. In this paper, we study the power allocation and array selection in a distributed LoS massive MIMO system with maximum ratio transmission (MRT), by taking PA nonlinearity characteristics into account. With the objective to maximize the sum spectral efficiency (SE) with total power constraints, we first formulate the power allocation problem as nonlinear programming. Then, we propose an iterative power allocation algorithm based on the multiplier punitive method. Simulation results corroborate that the proposed power allocation can maximize spectral efficiency with awareness of the PA nonlinearity and significantly prevents it from degrading performance.

Unmanned aerial vehicle (UAV) has demonstrated its usefulness in goods delivery. However, the delivery distances are often restrained by the battery capacity of UAVs. This paper integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive goods delivery. Dynamic programming (DP) is first applied to minimize the energy consumption of a UAV and ensure its timely arrival at its destination, by optimizing the control policy of the UAV. The control policy involves decisions including flight speed, hitchhiking (on collaborative ground vehicles), or recharging at roadside charging stations. Another key aspect is that we reveal the conditions of the remaining flight distance or the elapsed time, only under which the optimal action of the UAV changes. Accordingly, thresholds are derived, and the optimal control policy can be instantly made by comparing the remaining flight distance and the elapsed time with the thresholds. Simulations show that the proposed algorithms can improve the flight distance by 48%, as compared with existing alternatives. The proposed threshold-based technique can achieve the same performance as the DP-based solution, while significantly reducing the computational complexity.

In this paper, we experimentally analyze the spatial distribution of nonlinear distortion in massive MIMO systems with various array topologies and user locations. With an indoor channel measurement, we reveal the spatial distortion distribution of the in-band (IB) and out-of-band (OOB) power leakage in a real-life scenario. We further investigate the power leakage under different antenna array topologies: including uniform linear array (ULA), uniform rectangular array (URA), distributed linear subarrays (DIS). The impact of user location on the per antenna distortion is also visualized. The results indicate that the DIS array configuration achieves the lowest in-band and out-of-band power leakage, which renders the distributed array a potential to reduce the linearity requirement of PAs when scaling up a practical massive MIMO system.

Localization is crucial for nowadays' communication systems, especially for beamforming techniques in massive MIMO systems. Large-scale MIMO systems have exhibited their advantages in communications. In the meantime, they also have the potential to provide accurate localization with their high angular resolution. In this paper, we study indoor localization performance of a Massive MIMO system with a 64-antenna Uniform Linear Array (ULA). Based on the sparse reconstruction method, we propose a Mixed field Sparse Bayesian Learning (MSBL) algorithm to localize devices for both near-field and far-field scenarios. Using the measurement results from our massive MIMO testbed, we show that our proposed MSBL algorithm can improve the localization accuracy by 49% with only a few snapshots. The performance of our algorithm is also robust to low Signal-to-Noise Ratio (SNR) conditions.

In this paper, we study the hybrid beamforming for the wideband mmWave MIMO-OFDM system with observation of beam squint. Firstly, we present the beam squint effect in the wideband mmWave system. We further characterize the mmWave wideband channel from Saleh-Valenzuela model. Secondly, in the full-connected hybrid architecture, we seek for the optimal hybrid precoder in the mmWave MIMO-OFDM system, which aims to maximize the spectral efficiency. The precoder design problem is formulated as the matrix factorization in this paper. To find the optimal precoder, wideband hybrid precoding (WHP) algorithm is proposed by using the manifold optimization. Finally, simulation results show the proposed algorithm could approximate to the optimal digital precoder in term of spectral efficiency for the wideband mmWave MIMO-OFDM system.

We study the WiFi offloading problem in smart communications and adaptively seek for the optimal offloading strategies with the consideration of the mobility management and the dynamical nature of network state. With users mobility management, we formulate the offloading ratio optimization problem based on Markov process. Then, we propose a novel Congestion‐Optimal WiFi Offloading (COWO) algorithm based on subgradient method, which aims to obtain the optimal offloading ratio for each access point (AP) to maximize the throughput and minimize the network congestion. Due to the computational complexity of subgradient method, we further improve the COWO algorithm by the equivalent transformation. By viewing all the APs as one virtual WiFi network, we try to optimize the identical offloading ratio for virtual WiFi network and develop a Virtualized Congestion‐Optimal WiFi Offloading (VCOWO) algorithm with lower complexity. Under the equivalent conditions, the performance of the VCOWO algorithm could well approximate the optimal results obtained by the COWO algorithm. It is found that the VCOWO algorithm could obtain the upper bound of multiple APs WiFi offloading performance. Moreover, we investigate the impacts of user mobility on the WiFi offloading performance. Simulation results show that the proposed algorithm could achieve higher throughput with lower network congestion compared with other current offloading schemes.

The unprecedented data increase has imposed great challenges to cellular networks. Traffic offloading by heterogeneous radio access technologies (RATs) is an effective approach for enhancing network capacity. Without costly and time-consuming infrastructure investments, WiFi offloading is deemed as the prospective evolution for the heterogeneous integration. However, previously proposed schemes mainly focus on alleviating burden by offloading data to WiFi as much as possible, without systematic considerations of the network congestion. In this paper, based on Markov process, we investigate the congestion offloading problem in user random mobility model, and prove it could be solved in subgradient method with equivalent transformation. Moreover, the congestion-aware WiFi offloading algorithm is proposed for multiple WiFi APs network offloading scenario, aiming to balance the capacity increase with congestion characterized by blocking probability. In addition, the upper bound and lower bound of blocking probability is deduced in optimization. Simulation results show that, by optimizing user offloading probability, the algorithm proposed could achieve maximum throughput with lower blocking probability.

In 5G heterogeneous evolution, the unlicensed band has captured much attention. Specified by 3GPP Release 13, LTE WLAN aggregation (LWA) is deemed as an effective approach for spectrum integration of 5G heterogeneous networks (HetNet). However, most of previous works about LWA lie in the architecture design, and rarely investigate LWA algorithm analytically. In this paper, we formulate the network access and aggregation problem for delay- tolerant application in multiple slots, and further develop a delay-aware LTE WLAN aggregation algorithm (DLWA) based on dynamic programming, which is aimed to minimize the user payment with QoS requirement. To reduce the complexity, we prove optimal decision policy and simplify the searching space of scheme sets. Simulation results show that, comparing with the current WLAN interworking solutions, the algorithm could lower the payment and achieve high completion probability under specified transmission deadline. The framework presented can support WLAN offloading scheme as well, which enables the best use of unlicensed resource.