Existing AI-based meeting summarization tools have enabled rapid generation of meeting notes, yet their reliability and user controllability remain limited. This paper explores human-AI collaboration for mobile meeting summarization and presents MeetSumAid, a multifunctional system that integrates summarization algorithms with an interactive user interface. The system is designed to support users in understanding, validating, and refining AI-generated summaries through natural interactions and flexible control mechanisms. By enabling real-time inspection, editing, and feedback, MeetSumAid facilitates reliable collaboration between humans and AI in dynamic meeting scenarios. A user study with 20 participants shows that MeetSumAid significantly improves summary quality, generation efficiency, and user-perceived reliability compared with baseline AI summarizers, while reducing cognitive load. Further analysis reveals how different interface components enhance users' engagement and confidence during collaboration. This work provides a practical step toward reliable and user-centered human-AI collaboration in mobile meeting summarization and offers actionable design implications for future intelligent collaborative systems.

Supporting a massive amount of Internet of Things applications requires a large pool of spectrum. DSM is a promising ecosystem to improve the spectrum efficiency. In the era of LoRaWAN, the physical hardware constraints, along with the bandwidth-hungry applications pose new challenges. In this article, we investigate a novel deep-reinforcement-learning-based spectrum-sharing paradigm, termed Intelligent Overlapping, that explores partially overlapping channels for concurrent spectrum access in LoRaWAN. Our key insight is to leverage the coding redundancy to expand the available spectrum without complicated data processing algorithms. In particular, we learn the extra coding redundancy from the data on the non-overlapping spectrum via a deep-Q-learning network, and we apply such redundancy to recover the data on the overlapping spectrum. In the Media Access Control layer, we predict the channel condition and strategically learn and assign the appropriate overlapping portion to the concurrent access end devices. In the Physical layer, we harness interleaving to randomize the mutual interference to ensure that all the data remains decodable. Simulation results demonstrate that Intelligent Overlapping greatly improves the spectrum efficiency with a fast convergence rate compared to the conventional DSM mechanisms.

Recent years have witnessed a steep grow in the multimedia-oriented Internet of Things (IoT) over vehicular networks. Huge volume of multimedia traffic generated from the in-built IoT devices should be delivered among vehicles and immediate surroundings in real time. However, as network nodes with higher mobility, vehicles often experience more unpredictable wireless channels. Such time-frequency diversity poses substantial challenges to achieve pervasive and real-time multimedia connectivity. The hurdle lies in the inability of automatically approaching the subcarrier level channel variations in the existing video codecs. With coarse-grained traffic delivery rate, video decoding fails, and intermittent connection occurs. To break this stalemate, we propose a fine-grained wireless video streaming strategy, namely iCast, that intelligently achieves the most appropriate data rate and frame protection for multimedia traffic in highly mobile vehicular environments. The insight of iCast is a simple joint source-channel rateless code. It reaps the benefits of the frequency diversity to provide fine-grained data rate for the channel in conjunction with suitable protection for the source. Our experiments show that, by harnessing frequency diversity in mobile environments, iCast outperforms the existing competitive wireless video delivery schemes by up to 5 dB peak signal-to-noise ratio.

Many methods have been proposed for modeling head-related transfer functions (HRTFs) and yield a good performance level in terms of log-spectral distortion (LSD). However, most of them utilize linear weighting to reconstruct or interpolate HRTFs, but not consider the inherent nonlinearity relationship between the basis function and HRTFs. Motivated by this, a domain knowledge-assisted nonlinear modeling method is proposed based on bottleneck features. Domain knowledge is used in two aspects. One is to generate the input features derived from the solution to sound wave propagation equation at the physical level, and the other is to design the loss function for model training based on the knowledge of objective evaluation criterion, i.e., LSD. Furthermore, with utilizing the strong representation ability of the bottleneck features, the nonlinear model has the potential to achieve a more accurate mapping. The objective and subjective experimental results show that the proposed method gains less LSD when compared with linear model, and the interpolated HRTFs can generate a similar perception to those of the database.

Recent advances in rateless codes facilitate the utilization of dense constellation. By exploiting more constellation points with controlled symbol distance, they ensure higher transmission date rate and better diversity gain against fading. In this paper, we observe that there exists certain redundancy in the existing dense constellation diagram. With proper design, such redundancy can be can be optimized, and exploited to enable a new orthogonal dimension for multiple access. We termed it as hash division multiple access (HDMA). HDMA incorporates a orthogonal, linear and random encoder in PHY layer to construct orthogonal hash space for user separation. An AP-driven MAC protocol is then proposed to fully utilize this transmission concurrency for uplink and downlink multiple access. We verify the feasibility of HDMA via a GNU radio testbed, and further conduct trace-driven simulations to evaluate the multiplexing gain of HDMA. The results reveal that HDMA provides a maximum number of 3 to 5 concurrent transmissions, with performance gain over 131% and 39% compared to 802.11 and AutoMAC.

Emerging Wi-Fi Standards have incorporated growing channel widths to meet the proliferation of wireless services. Wider channel, unfortunately, increases the probability of partial channel overlap, and thus introduces more adjacent channel interference (ACI) than ever. Prior work either tries to avoid ACI, or neglects it in a brute-force way. In this paper, we carefully investigate the interference pattern at physical layer (PHY), and propose interference randomization (InterRandom) to strategically harness ACI for simultaneous transmission. The key insight behind InterRandom is to randomize the interference according to the overlapped ratio, and utilize coding redundancy to recover the corrupted data. To demonstrate the effectiveness of InterRandom, we further propose an InterRandom-enabled Media Access Control layer (MAC) protocol to facilitate Wi-Fi infrastructure mode transmission. We verify the feasibility of InterRandom on GNU radio testbed. Furthermore, our trace-driven simulations show that, InterRandom-aware MAC achieves 190% throughput gain compared to the legacy 802.11ac.

Several functional models for head-related transfer function (HRTF) have been proposed based on spherical harmonic (SH) orthogonal functions, which yield an encouraging performance level in terms of log-spectral distortion (LSD). However, since the properties of subbands are quite different and highly subject-dependent, the degree of SH expansion should be adapted to the subband and the subject, which is quite challenging. In this paper, a sparse spherical harmonic-based model termed SSHM is proposed in order to achieve an intelligent frequency truncation. Different from SH-based model (SHM) which assigns the degree for each subband, SSHM constrains the number of SH coefficients by using an l_1 penalty, and automatically preserves the significant coefficients in each subband. As a result, SSHM requires less coefficients at the same SD level than other truncation methods to reconstruct HRTFs. Furthermore, when used for interpolation, SSHM gives a better fitting precision since it naturally reduces the influence of the fluctuation caused by the movement of the subject and the processing error. The experiments show that even using about 40% less coefficients, SSHM has a slightly lower LSD than SHM. Therefore, SSHM can achieve a better tradeoff between efficiency and accuracy.

Rate adaptation is an essential component in today's wireless standards, which help approach the channel capacity and maximize the throughput. However, how to estimate the optimal data rate in a fluctuated channel remains of great concern. Previous wisdoms leverage PHY layer information for rate estimation, including confidence information like SoftPHY hints, and channel state information (CSI) measurements. However, when experiencing rapid time varying and frequency selective fading channel, the above metrics can be inaccurate. The reason roots from the fact that there are not enough cost-efficient pilots, which are pre-known symbols inserted in a packet for channel estimation. In this paper, we observe that by digging into both PHY layer decoder and upper layer protocol headers, more reliable data bits with high confidence level can be exploited. These data bits, termed smart pilot, can be used to calibrate the channel estimation measurements cost-efficiently. Based on the calibrated estimation, we further propose a novel greedy rate selection algorithm to track the optimal data rate, which successfully avoids the impact of deep fading subcarriers in both legacy 802.11a/g and 802.11n MIMO systems. Our experiments on GNU radio testbed show that SmartPilot quickly tracks the link variance, and improve the channel estimation accuracy by 87%. Furthermore, the trace driven simulation reveals that greedy rate selection algorithm predicts the data rate as good as the optimal rate adaptation algorithms for 802.11 standards.

Partially overlapped channels are barely used for concurrent transmission in WiFi networks, since they lead to collisions where the collided packets cannot be decoded successfully. In this paper, we observe that the actual corrupted symbols by partial-channel interference in OFDM-based WiFi networks are not as severe as we expected. There remains extra coding redundancy that can be exploited from the corrupted symbols, and utilized for packet recovery. Accordingly, we present a novel paradigm termed Piros, in order to Push the lImits of partially concurrent transmission in WiFi networks. Piros strategically leverages the coding redundancy according to the overlap portion in a distributed manner, and extracts useful decoding information from the corrupted symbols to decode the packet with partial-channel interference.

Recently, there is a massive growth in the amount of wireless video traffics. To increase the network efficiency and cater to the needs of Quality of Experience (QoE), researchers propose hybrid MAC schemes, with multiple MAC protocols anchored in a single MAC layer. Different MAC protocols are selected for diverse users' requirements. However, the existing hybrid MAC mainly combines various MAC protocols in time domain. Only one type of traffic can be satisfied at a time, and others have to endure poor QoE. Meanwhile, the channel resources are not fully utilized due to coarse usage in time domain. Motivated by this, we propose FC-MAC, a Fine-grained Cognitive MAC for video streaming in wireless networks. Instead of dividing channel time into slots, FC-MAC splits the channel frequency into fine-grained subchannels, and hybrid different MAC protocols in frequency domain. By dynamically adjusting the bandwidth and the MAC protocols according to the users' needs and channel condition, FC-MAC ensures the QoE at low cost and achieves high multiplexing gain. We conduct extensive simulations to verify the effectiveness of FC-MAC. Numerous results show that compared with time domain hybrid MAC, FC-MAC achieves 190% performance gain.

Rate adaptation is an essential component in today's wireless standards, for its ability to adaptively approach the channel capacity, and maximize the system throughput. The difficulty in rate adaptation stems from estimating the optimal data rate in a fluctuated channel. Previous wisdoms leverage PHY layer information for rate estimation, such as Soft PHY hints or Channel State Information. These information solely comes from one same layer, which are insufficient to track the optimal data rate. We observe that by investigating the information in both PHY layer decoder and upper layer protocol headers, more pilots can be exploited to estimate the optimal data rate across both time and frequency domain. These smart pilots help remove the residual channel effect and calibrate the CSI with minimum overhead. Based on the calibrated CSI, we propose a novel greedy rate selection algorithm to harness frequency diversity, which obtains the optimal data rate over all the subcarriers. Our experiments on GNU radio test bed show that Smart Pilot quickly tracks the link variance, and reduces the residual channel effect by 87%. Further, the trace driven simulation reveals that greedy rate selection algorithm predicts the data rate as good as the optimal rate adaptation algorithms for 802.11 standards.

The underwater acoustic channel is considered as one of the most challenging channels for reliable communications. Long delay spread and Doppler effect bring a great challenge to directly apply the algorithms in radio communications to underwater communications, which calls for modification and enhancement. In this study, a pilot‐assisted linear turbo equalisation algorithm termed P‐TE is proposed. Unlike the existing pilot methods that insert pilot into the modulated symbols to facilitate the channel estimation, the authors insert the known bits into the uncoded binary sequence to assist equalisation as well as decoding with the careful design of the bit positions. A second‐order least mean square adaptive algorithm as well as adaptive step size method is introduced for weights update. Furthermore, a modified low‐density parity‐check decoding algorithm is presented to describe the pilot‐assisted decoding algorithm, which does not increase the complexity. Moreover, pilot extraction from error‐free blocks is proposed for enhanced performance. Experimental results have indicated that P‐TE can significantly improve the performance and reduce the required number of the iterations, leading to lower complexity. Furthermore, experiments show that the effective throughput of P‐TE can be higher than the method without pilot at a signal‐to‐noise ratio of 10 dB.