The development of communication networks is currently undergoing a period of transformation. This paper illustrates this transformation from the growth rate of communication users, network bandwidth, and service revenue. We also analyze the shift in the focus of network technology development from aspects such as information sources, mobile terminals, wireless channels, core networks, edge clouds, data perception, and artificial intelligence. Finally, we briefly outline the new paradigm for network research and development (R&D) in the intelligent era.
To address the contradiction between the explosive growth of wireless data and the limited spectrum resources, semantic communication has been emerging as a promising communication paradigm. In this paper, we thus design a speech semantic coded communication system, referred to as Deep-STS (i.e., Deep-learning based Speech To Speech), for the low-bandwidth speech communication. Specifically, we first deeply compress the speech data through extracting the textual information from the speech based on the conformer encoder and connectionist temporal classification decoder at the transmitter side of Deep-STS system. In order to facilitate the final speech timbre recovery, we also extract the short-term timbre feature of speech signals only for the starting 2s duration by the long short-term memory network. Then, the Reed-Solomon coding and hybrid automatic repeat request protocol are applied to improve the reliability of transmitting the extracted text and timbre feature over the wireless channel. Third, we reconstruct the speech signal by the mel spectrogram prediction network and vocoder, when the extracted text is received along with the timbre feature at the receiver of Deep-STS system. Finally, we develop the demo system based on the USRP and GNU radio for the performance evaluation of Deep-STS. Numerical results show that the accuracy of text extraction approaches 95%, and the mel cepstral distortion between the recovered speech signal and the original one in the spectrum domain is less than 10. Furthermore, the experimental results show that the proposed Deep-STS system can reduce the total delay of speech communication by 85% on average compared to the G.723 coding at the transmission rate of 5.4 kbps. More importantly, the coding rate of the proposed Deep-STS system is extremely low, only 0.2 kbps for continuous speech communication. It is worth noting that the Deep-STS with lower coding rate can support the low-zero-power speech communication, unveiling a new era in ultra-efficient coded communications.
To meet the requirements of electromagnetic (EM) theory and applied physics, this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods. Firstly, based on the survey of three mainstream approaches for acquiring EM properties of media, we identify the difficulties when implementing them in realistic environments. With a focus on addressing these problems and challenges, we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments. Particularly, within this paradigm, we describe the implementation approach of the key technology, namely "multipath extraction using heterogeneous wave propagation data in multi-spectrum cases". Finally, the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.
Constituted by BCH component codes and its ordered statistics decoding (OSD), the successive cancellation list (SCL) decoding of U-UV structural codes can provide competent error-correction performance in the short-to-medium length regime. However, this list decoding complexity becomes formidable as the decoding output list size increases. This is primarily incurred by the OSD. Addressing this challenge, this paper proposes the low complexity SCL decoding through reducing the complexity of component code decoding, and pruning the redundant SCL decoding paths. For the former, an efficient skipping rule is introduced for the OSD so that the higher order decoding can be skipped when they are not possible to provide a more likely codeword candidate. It is further extended to the OSD variant, the box-and-match algorithm (BMA), in facilitating the component code decoding. Moreover, through estimating the correlation distance lower bounds (CDLBs) of the component code decoding outputs, a path pruning (PP)-SCL decoding is proposed to further facilitate the decoding of U-UV codes. In particular, its integration with the improved OSD and BMA is discussed. Simulation results show that significant complexity reduction can be achieved. Consequently, the U-UV codes can outperform the cyclic redundancy check (CRC)-polar codes with a similar decoding complexity.
This paper introduces a simple yet effective approach for developing fuzzy logic controllers (FLCs) to identify the maximum power point (MPP) and optimize the photovoltaic (PV) system to extract the maximum power in different environmental conditions. We propose a robust FLC with low computational complexity by reducing the number of membership functions and rules. To optimize the performance of the FLC, metaheuristic algorithms are employed to determine the parameters of the FLC. We evaluate the proposed FLC in various panel configurations under different environmental conditions. The results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability, responsiveness, and power transfer under various scenarios.
In this paper, an interference cancellation based neural receiver for superimposed pilot (SIP) in multi-layer transmission is proposed, where the data and pilot are non-orthogonally superimposed in the same time-frequency resource. Specifically, to deal with the intra-layer and inter-layer interference of SIP under multi-layer transmission, the interference cancellation with superimposed symbol aided channel estimation is leveraged in the neural receiver, accompanied by the pre-design of pilot code-division orthogonal mechanism at transmitter. In addition, to address the complexity issue for inter-vendor collaboration and the generalization problem in practical deployments, respectively, this paper also provides a fixed SIP (F-SIP) design based on constant pilot power ratio and scalable mechanisms for different modulation and coding schemes (MCSs) and transmission layers. Simulation results demonstrate the superiority of the proposed schemes on the performance of block error rate and throughput compared with existing counterparts.
In indoor environments, various battery-powered Internet of Things (IoT) devices, such as remote controllers and electronic tags on high-level shelves, require efficient energy management. However, manually monitoring remaining energy levels and battery replacement is both inadequate and costly. This paper introduces an energy management system for indoor IoT, which includes a mobile energy station (ES) for enabling on-demand wireless energy transfer (WET) in radio frequency (RF), some energy receivers (ERs), and a cloud server. By implementing a two-stage positioning system and embedding energy receivers into traditional IoT devices, we robustly manage their energy storage. The experimental results demonstrate that the energy receiver can harvest a minimum power of $58$ mW.
Multilevel coding (MLC) is a commonly used polar coded modulation scheme, but challenging to implement in engineering due to its high complexity and long decoding delay for high-order modulations. To address these limitations, a novel two-level serially concatenated MLC scheme, in which the bit-levels with similar reliability are bundled and transmitted together, is proposed. The proposed scheme hierarchically protects the two bit-level sets: the bit-level sets at the higher level are sufficiently reliable and do not require excessive resources for protection, whereas only the bit-level sets at the lower level are encoded by polar codes. The proposed scheme has the advantages of low power consumption, low delay and high reliability. Moreover, an optimized constellation signal labeling rule that can enhance the performance is proposed. Finally, the superiority of the proposed scheme is validated through the theoretical analysis and simulation results. Compared with the bit interleaving coding modulation (BICM) scheme, under 256-quadrature amplitude modulation (QAM), the proposed scheme attains a performance gain of 1.0 dB while reducing the decoding complexity by 54.55%.
This paper studies the sensing base station (SBS) that has great potential to improve the safety of vehicles and pedestrians on roads. SBS can detect the targets on the road with communication signals using the integrated sensing and communication (ISAC) technique. Compared with vehicle-mounted radar, SBS has a better sensing field due to its higher deployment position, which can help solve the problem of sensing blind areas. In this paper, key technologies of SBS are studied, including the beamforming algorithm, beam scanning scheme, and interference cancellation algorithm. To transmit and receive ISAC signals simultaneously, a double-coupling antenna array is applied. The free detection beam and directional communication beam are proposed for joint communication and sensing to meet the requirements of beamwidth and pointing directions. The joint time-space-frequency domain division multiple access algorithm is proposed to cancel the interference of SBS, including multiuser interference and duplex interference between sensing and communication. Finally, the sensing and communication performance of SBS under the industrial scientific medical power limitation is analyzed and simulated. Simulation results show that the communication rate of SBS can reach over 100 Mbps and the range of sensing and communication can reach about 500 m.
The utilization of millimeter-wave frequencies and cognitive radio (CR) are promising ways to increase the spectral efficiency of wireless communication systems. However, conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate, and they are not viable for wideband signals due to their high cost. This paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this limitation. To this end, we propose a multi-task learning (MTL) framework using convolutional neural networks for the joint inference of the underlying narrowband signal number, their modulation scheme, and their location in a wideband spectrum. We demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices, exhibiting a $91.7 \%$ accuracy in the joint inference task when considering up to two narrowband signals over a wideband spectrum. Ultimately, the proposed data-driven approach enables on-the-fly wideband spectrum sensing, combining accuracy, and computational efficiency, which are indispensable for CR and opportunistic networking.
Reconfigurable intelligent surface (RIS) is a novel meta-material which can form a smart radio environment by dynamically altering reflection directions of the impinging electromagnetic waves. In the prior literature, the inter-RIS links which also contribute to the performance of the whole system are usually neglected when multiple RISs are deployed. In this paper we investigate a general double-RIS assisted multiple-input multiple-output (MIMO) wireless communication system under spatially correlated non line-of-sight propagation channels, where the cooperation of the double RISs is also considered. The design objective is to maximize the achievable ergodic rate based on full statistical channel state information (CSI). Specifically, we firstly present a closed-form asymptotic expression for the achievable ergodic rate by utilizing replica method from statistical physics. Then a full statistical CSI-enabled optimal design is proposed which avoids high pilot training overhead compared to instantaneous CSI-enabled design. To further reduce the signal processing overhead and lower the complexity for practical realization, a common-phase scheme is proposed to design the double RISs. Simulation results show that the derived asymptotic ergodic rate is quite accurate even for small-sized antenna arrays. And the proposed optimization algorithm can achieve substantial gain at the expense of a low overhead and complexity. Furthermore, the cooperative double-RIS assisted MIMO framework is proven to achieve superior ergodic rate performance and high communication reliability under harsh propagation environment.
In this paper, we propose a sub-6GHz channel assisted hybrid beamforming (HBF) for mmWave system under both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios without mmWave channel estimation. Meanwhile, we resort to the self-supervised approach to eliminate the need for labels, thus avoiding the accompanied high cost of data collection and annotation. We first construct the dense connection network (DCnet) with three modules: the feature extraction module for extracting channel characteristic from a large amount of channel data, the feature fusion module for combining multidimensional features, and the prediction module for generating the HBF matrices. Next, we establish a lightweight network architecture, named as LDnet, to reduce the number of model parameters and computational complexity. The proposed sub-6GHz assisted approach eliminates mmWave pilot resources compared to the method using mmWave channel information directly. The simulation results indicate that the proposed DCnet and LDnet can achieve the spectral efficiency that is superior to the traditional orthogonal matching pursuit (OMP) algorithm by $13.66\%$ and $10.44\%$ under LOS scenarios and by $32.35\%$ and $27.75\%$ under NLOS scenarios, respectively. Moreover, the LDnet achieves $98.52\%$ reduction in the number of model parameters and $22.93\%$ reduction in computational complexity compared to DCnet.
This paper investigates how to achieve integrated sensing and communication (ISAC) based on a cell-free radio access network (CF-RAN) architecture with a minimum footprint of communication resources. We propose a new passive sensing scheme. The scheme is based on the radio frequency (RF) fingerprint learning of the RF radio unit (RRU) to build an RF fingerprint library of RRUs. The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side. The receiver extracts the channel parameters from the signal and estimates the channel environment, thus locating the reflectors in the environment. The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture. Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.
The deployment of multiple intelligent reflecting surfaces (IRSs) in blockage-prone millimeter wave (mmWave) communication networks have garnered considerable attention lately. Despite the remarkably low circuit power consumption per IRS element, the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs, resulting in lower overall energy efficiency (EE). To tackle this challenge, we propose a flexible and efficient approach that individually controls the status of each IRS element. Specifically, the network EE is maximized by jointly optimizing the associations of base stations (BSs) and user equipments (UEs), transmit beamforming, phase shifts of IRS elements, and the associations of individual IRS elements and UEs. The problem is efficiently addressed in two phases. First, the Gale-Shapley algorithm is applied for BS-UE association, followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beamforming, phase shifts, and element-UE associations. To reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks, we introduce an efficient algorithm to solve the associations between IRS elements and UEs. Numerical results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.
Time Division Multiplexing-Passive Optical Networks (TDM-PONs) play a vital role in Fiber-to-the-Home (FTTH) deployments. To improve the service quality of home networks, FTTH is expanding to the Fiber-to-the-Room (FTTR) scenario, where fibers are deployed to connect individual rooms (i.e., Fiber In-premises Network (FIN) in the ITU-T G.9940 standard). In this scenario, a point-to-multipoint (P2MP) fiber network is deployed as FTTR FIN to offer gigabit access to each room, which forms a two-tier cascaded network together with the FTTH segment. To optimize the capacity utilization of the cascaded network and reduce the overall system cost, a centralized architecture, known as Centralized Fixed Access Network (C-FAN), has been introduced. C-FAN centralizes the medium access control (MAC) modules of both the FTTH and FTTR networks at the FTTH’s Optical Line Terminal (OLT) for unified control and management of the cascaded network. We develop a unified bandwidth scheduling protocol by extending the ITU-T PON standard for both the upstream and downstream directions of C-FAN. We also propose a unified dynamic bandwidth allocation (UDBA) algorithm for efficient bandwidth allocation for multiple traffic flows in the two-tier cascaded network. Simulations are conducted to evaluate the performance of the proposed control protocol and the UDBA algorithm. The results show that, in comparison to the conventional DBA algorithm, the UDBA algorithm can utilize upstream bandwidth more efficiently to reduce packet delay and loss, without adversely impacting downstream transmission performance.
Friendship paradox states that individuals are likely to have fewer friends than their friends do, on average. Despite of its wide existence and appealing applications in real social networks, the mathematical understanding of friendship paradox is very limited. Only few works provide theoretical evidence of single-step and multi-step friendship paradoxes, given that the neighbors of interest are one-hop and multi-hop away from the target node. However, they consider non-evolving networks, as opposed to the topology of real social networks that are constantly growing over time. We are thus motivated to present a first look into friendship paradox in evolving networks, where newly added nodes preferentially attach themselves to those with higher degrees. Our analytical verification of both single-step and multi-step friendship paradoxes in evolving networks, along with comparison to the non-evolving counterparts, discloses that "friendship paradox is even more paradoxical in evolving networks", primarily from three aspects: 1) we demonstrate a strengthened effect of single-step friendship paradox in evolving networks, with a larger probability (more than 0.8) of a random node's neighbors having higher average degree than the random node itself; 2) we unravel higher effectiveness of multi-step friendship paradox in seeking for influential nodes in evolving networks, as the rate of reaching the max degree node can be improved by a factor of at least $\widetilde{\Theta}(t^{2/3})$ with $t$ being the network size; 3) we empirically verify our findings through both synthetic and real datasets, which suggest high agreements of results and consolidate the reasonability of evolving model for real social networks.
Semantic communication (SemCom) aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy. Nevertheless, semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism (e.g., hybrid automatic repeat request [HARQ]) becomes indispensable. In that regard, instead of discarding previously transmitted information, the incremental knowledge-based HARQ (IK-HARQ) is deemed as a more effective mechanism that could sufficiently utilize the information semantics. However, considering the possible existence of semantic ambiguity in image transmission, a simple bit-level cyclic redundancy check (CRC) might compromise the performance of IK-HARQ. Therefore, there emerges a strong incentive to revolutionize the CRC mechanism, thus more effectively reaping the benefits of both SemCom and HARQ. In this paper, built on top of swin transformer-based joint source-channel coding (JSCC) and IK-HARQ, we propose a semantic image transmission framework SC-TDA-HARQ. In particular, different from the conventional CRC, we introduce a topological data analysis (TDA)-based error detection method, which capably digs out the inner topological and geometric information of images, to capture semantic information and determine the necessity for re-transmission. Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework, especially under the limited bandwidth condition, and manifest the superiority of TDA-based error detection method in image transmission.
Wireless communications in extreme environments, such as underwater and underground, is an essential technology for interconnecting various devices and enables data transmission and networking. Existing wireless technologies using electromagnetic (EM) waves face many known problems, such as high path loss, unpredictable multi-path fading, and large antenna size in the lossy medium. In this article, the magnetic induction (MI) based physical layer communication is introduced as a promising solution for wireless transmissions in extreme environments. Specifically, the fundamentals of the MI-based communications are reviewed. Then, with the goal of establishing reliable and low-power links between small-size devices, we review several key physical layer technologies for MI-based communications, including the MI-based signal modulations, magnetic beamforming, and relay transmissions, and summarize their state-of-the-art research advances. Finally, the related open issues and challenges in each area are analyzed and presented for future investigations.
Intelligent vehicle applications provide convenience but raise privacy and security concerns. Misuse of sensitive data, including vehicle location, and facial recognition information, poses a threat to user privacy. Hence, traffic classification is vital for promptly overseeing and controlling applications with sensitive information. In this paper, we propose ET-Net, a framework that combines multiple features and leverages self-attention mechanisms to learn deep relationships between packets. ET-Net employs a multi-similarity triplet network to extract features from raw bytes, and exploits self-attention to capture long-range dependencies within packets in a session and contextual information features. Additionally, we utilizing the loss function to more effectively integrate information acquired from both byte sequences and their corresponding lengths. Through simulated evaluations on datasets with similar attributes, ET-Net demonstrates the ability to finely distinguish between nine categories of applications, achieving superior results compared to existing methods.
With the deployment of ultra-dense low earth orbit (LEO) satellite constellations, LEO satellite access network (LEO-SAN) is envisioned to achieve global Internet coverage. Meanwhile, the civil aviation communications have increased dramatically, especially for providing airborne Internet services. However, due to dynamic service demands and on-board LEO resources over time and space, it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks (UDLSN). In this paper, we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service management. Firstly, we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight management. Secondly, considering latency-sensitive and latency-tolerant services, we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service continuity. Finally, we propose a proximal policy optimization-based access and service management algorithm to solve the formulated problem. Simulation results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.
