Miaomiao Dong

High-precision and low-latency wireless localization is a key objective for future networks. The majority of existing localization methods rely on multi-antenna arrays (MAAs) to estimate directions of arrival (DoAs), but the size and cost of MAAs limit their application in portable electronic devices. Virtual antenna arrays (VAAs), constructed from signals received at different positions by a moving single-antenna receiver, offer a promising alternative. However, VAA-based localization is challenged by the local oscillator frequency offset (LOFO) in transceivers, the irregular array geometry caused by the receiver’s movement, and the non-line-of-sight (NLoS) environments due to physical blockage. To address the above challenges, this paper proposes a VAA-based online localization approach. Specifically, we employ a manifold separation technique based on the Jacobi-Anger expansion to maintain a uniform linear array-like channel representation for the irregular VAA geometry. This enables us to transform the joint estimation of multi-path DoAs, times of arrival (ToAs), and LOFO into a modified two-dimensional atomic norm minimization problem, which is solved using an alternating convex search algorithm. Additionally, we introduce an unscented Kalman filter-based simultaneous localization and mapping algorithm for real-time position tracking in NLoS environments. Extensive simulations validate the effectiveness of our approaches, showing a 70.15% reduction in DoA estimation error and a 54.02% reduction in localization error compared to benchmark methods.

Wireless simultaneous localization and mapping (SLAM) has attracted much attention as a promising technique to empower location based services. However, the accuracy of traditional wireless SLAM systems is limited as the wireless signals are easily disturbed by the uncontrollable radio environments. To mitigate this issue, in this paper, we propose a MetaSLAM system where multiple reconfigurable intelligent surfaces (RISs) are deployed to customize the wireless environments. To be specific, through adjusting the phase shifts of these RISs, the strength of reflected signals can be enhanced in order to resist the variance of radio environments. However, it is challenging to coordinate multiple RISs and optimize their phase shifts especially when their locations are unknown to the agent. In order to address these challenges, we formulate a MetaSLAM optimization problem, and design a two-stage optimization algorithm based on the genetic and particle filter algorithms to solve the formulated problem. Analysis of the complexity and the positioning error bound of the proposed SLAM system are provided. Simulation results show that compared with the benchmark schemes, the positioning error obtained by the MetaSLAM system is reduced by at least 31%.

Today’s growth in the volume of wireless devices coupled with the demand for data-intensive use cases has motivated the deployment of millimeter-wave (mmWave) small-cell networks. Although it is true that mmWave networks can carry a large volume of traffic, highly intermittent connectivity and the challenges related to installing many small-cell base stations (BSs) in urban geometry have impeded its progression into practical networks. To cope with these challenges, we present, in this paper, an approach to the mmWave BS deployment (site planning) problem, based on the minimum-deployment-cost criterion that is subject to user equipment (UE) outage constraints. Unlike the prior works, the proposed model captures the randomness of link blockage and signal-to-interference-plus-noise-ratio (SINR) statistics in mmWave networks. We formulate the minimum-cost deployment problem as large-scale integer nonlinear programming (INP). To deal with the coupled and combinatorial of the problem, the large-scale INP has approached to devise a suboptimal but efficient algorithm by decomposing it into two subproblems: (i) cell coverage optimization and (ii) minimum subset selection. We provide the solutions to each subproblem as well as theoretical justifications of them. Simulation results that illustrate UE outage guarantees of the proposed BS deployment method are presented. The results reveal that the proposed method uniquely distributes the macro-diversity orders that are distinct from other benchmarks.

Recently, the intelligent omni-surface (IOS) has been proposed as a novel instance of metasurface to achieve full-dimensional communications by jointly engineering its reflective and refractive properties. However, optimal beamforming scheme for the IOS is hard to obtain due to the difficulty in acquiring perfect channel state information (CSI). To address this issue, in this paper, we consider an IOS aided system where the beamforming scheme is designed via beam training with codebooks at the base station (BS), the IOS, and users. Given that the refractive/reflective signals are closely related to both incident signals from the BS and phase shifts of IOS elements, the codebooks at the BS and the IOS are designed jointly. Based on the joint BS-IOS codebook, a multi-lobe beam training mechanism is proposed to perform beam training for multiple users simultaneously, thereby reducing the training overhead. The training/feedback overhead of the proposed beam training and the impact of the codebook size are then analyzed theoretically. Simulation results indicate that the proposed scheme achieves a higher sum rate than the state-of-the-art beam training schemes and performs close to the perfect CSI case.

The fact that the millimeter-wave (mmWave) multiple-input multiple-output (MIMO) channel has sparse support in the spatial domain has motivated recent compressed sensing (CS)-based mmWave channel estimation methods, where the angles of arrivals (AoAs) and angles of departures (AoDs) are quantized using angle dictionary matrices. However, the existing CS-based methods usually obtain the estimation result through one-stage channel sounding that have two limitations: (i) the requirement of large-dimensional dictionary and (ii) unresolvable quantization error. These two drawbacks are irreconcilable; improvement of the one implies deterioration of the other. To address these challenges, we propose, in this paper, a two-stage method to estimate the AoAs and AoDs of mmWave channels. In the proposed method, the channel estimation task is divided into two stages, Stage I and Stage II. Specifically, in Stage I, the AoAs are estimated by solving a multiple measurement vectors (MMV) problem. In Stage II, based on the estimated AoAs, the receive sounders are designed to estimate AoDs. The dimension of the angle dictionary in each stage can be reduced, which in turn reduces the computational complexity substantially. We then analyze the successful recovery probability (SRP) of the proposed method, revealing the superiority of the proposed framework over the existing one-stage CS-based methods. We further enhance the reconstruction performance by performing resource allocation between the two stages. We also overcome the unresolvable quantization error issue present in the prior techniques by applying the atomic norm minimization method to each stage of the proposed two-stage approach. The simulation results illustrate the substantially improved performance with low complexity of the proposed two-stage method.

The accelerated convergence of digital and real-world lifestyles has imposed unprecedented demands on today’s wireless network architectures, as it is highly desirable for such architectures to support wireless devices everywhere with high capacity and minimal signaling overhead. Conventional architectures, such as cellular architectures, are not able to satisfy these requirements simultaneously, and are thus no longer suitable for the future era. In this paper, we propose a capacity-centric (C2) architecture for future wireless networking. It is designed based on the principles of maximizing the number of non-overlapping clusters with the average cluster capacity guaranteed to be higher than a certain threshold, and thus provides a flexible way to balance the capacity requirement against the signaling overhead. Our analytical results reveal that C2 has superior generality, wherein both the cellular and the fully coordinated architectures can be viewed as its extreme cases. Simulation results show that the average capacity of C2 is at least three times higher compared to that of the cellular architecture. More importantly, different from the widely adopted conventional wisdom that base-station distributions dominate architecture designs, we find that the C2 architecture is not over-reliant on base-station distributions, and instead the user-side information plays a vital role and cannot be ignored.

While the Poisson point process (PPP) has been widely employed to model the user distribution in many network design problems, an existing challenge is that it often reveals inaccuracy in small-cell networks. In this paper, instead of employing PPP, we capture the randomness of user equipment (UE) by collecting many their realizations. Specifically, we focus on the millimeter-wave (mmWave) base station (BS) deployment problem in an urban geometry, based on the application of a scenario sampling approach, previously introduced for largescale optimization, to quantitatively sample a portion of the UE realizations. Motivated by the scenario sampling, a reduced-scale mmWave BS deployment problem is formulated, whose optimal solution is attained by the proposed low-complexity iterative search algorithm. A required number of samples that guarantee a specified majority of the link quality constraints is analyzed. Simulation results verify the scenario sampling theory and the effectiveness of the proposed algorithm.

Recent experimental studies reveal that urban millimeter wave (mmWave) channels are often dominated by both the line-of-sight (LoS) and geometry-dependent first-order reflection paths. In such environments, it is possible that any LoS and/or reflection components from surrounding interferers can critically deteriorate the link quality. Analyzing such impacts is, however, challenging in the absence of appropriate mmWave network interference models. In this paper, we address this challenge by providing an analytical stochastic geometry model that quantifies the total amount of network interference. Our study reveals that dense mmWave networks are often interference-limited. The geometry-dependent first-order reflection interference is a dispensable component and must be encountered when analyzing and designing mmWave networks.

Directional narrow beams are used in the millimeter wave systems to provide sufficient array gains to overcome the severe pathloss as well as boost the system throughput. However, the directional link can be easily blocked by obstacles and incurs outage when the system is surrounded by many random obstacles. The blockage impediment of the millimeter wave system could be very critical and must be clearly analyzed before the system can be deployed and used to its full potential. In this work, we analyze and quantify the outage probability of a point-to-point millimeter wave link in the presence of first- order reflection paths. In our model, the reflection paths can be formed by the random obstacles. These obstacles can either reflect or block beams from the transmitter to the receiver. When the line-of-sight (LoS) path is blocked, the transmitter and receiver can form directional beams along with one of the reflection paths to recover the communication. We characterize the distribution of the number of obstacles that can potentially create the first-order reflection paths and derive the outage probability. Simulation results demonstrate the accuracy of our analysis. The results also show that the presence of the first-order reflections can mitigate the outage by a great amount.