In this paper, we aim to unlock the potential of intelligent reflecting surfaces (IRSs) in cognitive internet of things (loT). Considering that the secondary IoT devices send messages to the secondary access point (SAP) by sharing the spectrum with the primary network, the interference is introduced by the IoT devices to the primary access point (PAP) which profits from the IoT devices by pricing the interference power charged by them. A practical path loss model is adopted such that the IRSs deployed between the IoT devices and SAP serve as diffuse scatterers, but each reflected signal can be aligned with its own desired direction. Moreover, two transmission policies of the secondary network are investigated without/with a successive interference cancellation (SIC) technique. The signal-to-interference plus noise ratio (SINR) balancing is considered to overcome the near-far effect of the IoT devices so as to allocate the resource fairly among them. We propose a Stackelberg game strategy to characterize the interaction between primary and secondary networks. For the proposed game, the Stackelberg equilibrium is analytically derived to optimally obtain the closed-form solution of the power allocation and interference pricing. Numerical results are demonstrated to validate the performance of the theoretical derivations.

In this paper, we co-design the transmission power and the offloading strategy for job offloading to a mobile edge computing (MEC) server at Terahertz (THz) frequencies. The goal is to minimize the communication energy consumption while providing ultra-reliable low end-to-end latency (URLLC) services. To that end, we first establish a novel reliability framework, where the end-to-end (E2E) delay equals a weighted sum of the local computing delay, the communication delay and the edge computing delay, and the reliability is defined as the probability that the E2E delay remains below a certain pre-defined threshold. This reliability gives a full view of the statistics of the E2E delay, thus constituting advancement over prior works that have considered only average delays. Based on this framework, we establish the communication energy consumption minimization problem under URLLC constraints. This optimization problem is non-convex. To handle that issue, we first consider the special single-user case, where we derive the optimal solution by analyzing the structure of the optimization problem. Further, based on the analytical result for the single-user case, we decouple the optimization problem for multi-user scenarios into several sub-optimization problems and propose a sub-optimal algorithm to solve it. Numerical results verify the performance of the proposed algorithm.

This paper advocates the use of the distributed compressed sensing (DCS) paradigm to deploy energy harvesting (EH) Internet of Thing (IoT) devices for energy self-sustainability. We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation. We provide theoretical analysis on the performance of both the classical compressive sensing (CS) approach and the proposed distributed CS (DCS)-based approach to data acquisition for EH IoT. Moreover, we perform an in-depth comparison of the proposed DCS-based approach against the distributed source coding (DSC) system. These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation, EH correlation, network size, and energy availability level. Our results unveil that, the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach, and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.

Ambient backscatter communications (AmBC) is a new ultra-low-power communication paradigm, which holds great promise for enabling energy self-sustainability (ESS) to massive data-intensive Internet of Everything (IoE) devices in 6G. Recent advances improve throughput and reliability by adopting multiple-antenna techniques in conventional backscatter communications (CoBC), but they cannot be directly applied to AmBC devices for high spectral and energy efficiency due to the unknown RF source and minimalist design in backscatter tag. To fill this gap, we propose SM-backscatter, an AmBC-compatible system that greatly improves spectral efficiency while maintaining ultra-low-power consumption. Specifically, the SM-backscatter consists of two novel components: i) a multiple-antenna backscatter tag that adopts spatial modulation (SM), and ii) a joint detection algorithm that detects both backscatter and source signals. To this end, we theoretically obtain an optimal detector and propose two suboptimal detectors with low complexity. Subsequently, we derive the BERs of both the backscatter and source signals to analyze the communication performance by introducing a two-step algorithm. Our simulation results verify the correctness of the theoretical analysis and indicate that our system can significantly outperform existing solutions.

In order to improve the energy efficiency (EE) in the underlay cognitive radio (CR)networks, a power allocation strategy based on an actor-critic reinforcement learning is proposed, where a cluster of cognitive users (CUs) can simultaneously access to the same primary spectrum band under the interference constraints of the primary user (PU), by employing the non-orthogonal multiple access (NOMA) technique. In the proposed scheme, the optimization of the power allocation is formulated as a non-convex optimization problem. Additionally, the power allocation for different CUs is based on the actor-critic reinforcement learning model, in which the weighted data rate is set as the reward function,and the generated action strategy (i.e. the power allocation) is iteratively criticized and updated. Both the CU’s spectral efficiency and the PU’s interference constrains are considered in the training of the actor-critic reinforcement learning. Furthermore, the first order Taylor approximation as well as other manipulations are adopted to solve the power allocation optimization problem for the sake of considering the conventional channel conditions. According to the simulation results, we find that our scheme could achieve a higher spectral efficiency for the CUs compared to a benchmark scheme without learning process as well as the existing Q-learning based method, while the resultant interference affecting the PU transmission can be maintained at a given tolerated limit.

6G IoT networks aim for providing significantly higher data rates and extremely lower latency. However, due to the increasingly scarce spectrum bands and ever-growing massive number IoT devices (IoDs) deployed, 6G IoT networks face two critical challenges, i.e., energy limitation and severe signal attenuation. Simultaneous wireless information and power transfer (SWIPT) and cooperative relaying provide effective ways to address these two challenges. In this paper, we investigate the energy self-sustainability (ESS) of 6G IoT network and propose an OFDM based bidirectional multi-relay SWIPT strategy for 6G IoT networks. In the proposed strategy, the transmission process is equally divided into two phases. Specifically, in phase1 two source nodes transmit their signals to relay nodes which will then use different subcarrier sets to decode information and harvest energy, respectively. In phase2 relay nodes forward signals to corresponding destination nodes with the harvested energy. We maximize the weighted sum transmission rate by optimizing subcarriers and power allocation. Our proposed strategy achieves larger weighted sum transmission rate comparing with the benchmark scheme.

A key challenge to the scalable deployment of the energy self-sustainability (ESS) Internet of Everything (IoE) for sixth-generation (6G) networks is juggling massive connectivity and high spectral efficiency (SE). Cell-free massive multiple-input multiple-output (CF mMIMO) is considered as a promising solution, where many wireless access points perform coherent signal processing to jointly serve the users. However, massive connectivity and high SE are difficult to obtain at the same time because of the limited pilot resource. To solve this problem, we propose a new framework for ESS IoE networks where the user activity detection (UAD) and channel estimation are decoupled. A UAD detector based on deep convolutional neural networks, an initial access scheme, and a scalable power control policy are proposed to enable the practical scalable CF mMIMO implementation. We derive novel and exact closed-form expressions of harvested energy and SE with maximum ratio (MR) processing. Using local partial minimum mean-square error and MR combining, simulation results prove that the proposed framework can serve more users, improve the SE performance, and achieve better user fairness for the considered ESS IoE networks.

Unmanned aerial vehicles (UAVs) can be employed as aerial base stations (BSs) due to their high mobility and flexible deployment. This paper focuses on a UAV-assisted wireless network, where users can be scheduled to get access to either an aerial BS or a terrestrial BS for uplink transmission. In contrast to state-of-the-art designs focusing on the instantaneous cost of the network, this paper aims at minimizing the long-term average transmit power consumed by the users by dynamically optimizing user association and power allocation in each time slot. Such a joint user association scheduling and power allocation problem can be formulated as a Markov decision process (MDP). Unfortunately, solving such an MDP problem with the conventional relative value iteration (RVI) can suffer from the curses of dimensionality, in the presence of a large number of users. As a countermeasure, we propose a distributed RVI algorithm to reduce the dimension of the MDP problem, such that the original problem can be decoupled into multiple solvable small-scale MDP problems. Simulation results reveal that the proposed algorithm can yield lower long-term average transmit power consumption than both the conventional RVI algorithm and a baseline algorithm with myopic policies.

In this paper, we present an algorithm for capacity optimization in intelligent reflecting surface (IRS)-based multiple-input multiple-output (MIMO) communication systems. To maximize the capacity of elements in IRS, we use augmented Lagrange method with the equivalent transformations on the covariance matrix and reflection matrix constraints. This results an adjustable phase shift on the incident signal. Furthermore, we reshape the complex-valued covariance matrix and reflection matrix to a vector for the ease of calculating partial derivatives to find the search direction. Then, the quasi-Newton updates and modified Broyden-Fletcher-Goldfarb-Shano (BFGS) method in the complex domain form are used to find the local minimum. Finally, numerical simulation results demonstrate that our proposed IRS-aided system using the algorithm performs better than the state-of-the-art and the conventional communication systems.

In the emerging sixth generation (6G) communication network, energy harvesting (EH) is a promising technology to achieve the unlimited energy supply and hence makes the wireless communication systems self-sustainable in terms of energy. However, in practice, the efficiency of energy harvesting is often low due to the limited device capability. In this paper, we formulate three types of different EH architectures, i.e., the harvest-use architecture, the harvest-store-use architecture, and the harvest-use-store architecture from the perspective of energy storage efficiency. We propose resource allocation schemes to jointly design the sensor power and duty-cycle via an alternating optimization algorithm under the above EH architectures, in both simultaneous and non-simultaneous harvesting and utilization models, aiming at achieving a higher throughput and energy efficiency. Non-ideal circuit power is also considered. Numerical results show that our proposed schemes under EH architectures outperform the existing classic continuous transmission schemes.

Mobile communication standards have been developed into a new era of B5G and 6G. In recent years, low earth orbit (LEO) satellites and space Internet have become hot topics. The integrated satellite and terrestrial systems have been widely discussed by industries and academics, and even are expected to be applied in those huge constellations in construction. This paper points out the trends of two stages towards system integration of the terrestrial mobile communication and the satellite communications: to be compatible with 5G, and to be integrated within 6G. Based on analysis of the challenges of both stages, key technologies are thereafter analyzed in detail, covering both air interface currently discussed in 3GPP for B5G and also novel network architecture and related transmission technologies toward future 6G.

In cognitive radio networks, spectrum sensing under circumstances of dynamically varying noise and lacking prior information is a key challenge to the conventional spectrum sensing algorithms. Since the necessary information is rather difficult to obtain practically, most existing spectrum sensing methods are fettered in applications. Motivated by these, in this paper, a Frequency domain Goodness of Fit Test (FGoF) based spectrum sensing method is proposed. The FGoF makes full use of underlying information in Guard-Bands and the advantages of GoF test works for any distribution. Analytical and simulated results show that the FGoF is a robust spectrum sensing method in cognitive radio with the inherent advantages of invulnerability to dynamically varying noise.

For the energy sharing problem of distributed antenna system (DAS) with energy harvesting (EH), a distributed antenna system model capable of sharing collected energy among the components in system is proposed. Compared with the existing model in literatures, the proposed model connects with smart grid through a unified interface and facilitates energy management and scheduling. Based on the proposed model, three kinds of energy sharing methods including the partial energy sharing method, the complete energy sharing method and the self-sustaining energy sharing method are analyzed. Under various energy sharing methods, the corresponding optimization problems of power allocation among the remote antenna units (RAUs) are described, formed and solved. As a result, the corresponding power allocation algorithm to each method has been concluded. Simulation results show that the proposed model is more efficient in terms of the channel capacity and energy efficiency, compared to the existing model.

In the current multi-carrier communications, Orthogonal Frequency Division Multiplexing (OFDM) is widely considered as a leading technology. For mobile applications, however, the orthogonality between subcarriers is deteriorated by Doppler frequency shift, which will introduce serious subcarrier phase rotation in the received signals and degrade the system performance. Thus, a method of differential grouping weighted symmetry data-conjugate (DWSCC) have been previously presented to obtain a better inter-carrier interference (ICI) suppressing effect and Bit Error Rate (BER) performance with no loss of spectral efficiency. In this paper, a novel scheme applying a completely different method of subcarrier interactive mapping is put forward. By mapping two different symbols which are both conjugated or multiplied by a complex weighting factor onto a pair of symmetric subcarriers, the presented scheme can greatly reduce the influence of subcarriers phase rotation caused by Doppler frequency shift in highly mobile environments. Analysis and simulation results indicate that comparing with the DWSCC method, our formulated scheme can not only maintain the spectrum utilization with no loss, but also have the advantages of an improvement on reduction effect and BER performance as well as a lower computational complexity in highly mobile environments.

Non-orthogonal multiple access (NOMA) is proved to be useful to satisfy the requirements of beyond 5^th generation such as massive multi-user connection. Here we compare the performances of two NOMA schemes: low code rate spreading (LCRS) scheme and interleaver division multiple access (IDMA) scheme. It is found that LCRS is superior to IDMA when number of users is small due to coding gain achieved. While IDMA is preferred when number of users is high because repetition applied in IDMA can suppress multi-user interference effectively. And interleaver is important in IDMA for randomizing the interference. Also, this paper evaluates the impact of channel decoder. It is observed that Log-MAP decoder has much better performance than that of Max-Log-MAP when number of users is large. Thus, it is recommended to use Log-MAP decoder in NOMA in high user overloading case. We also compared the performance of NOMA by using different type of channel codes. We find that NOMA using specific convolutional code has a better performance than that of using specific LDPC code when number of users is high.

In order to reduce the interference, a novel, cluster-based medium access control (MAC) protocol with load aware for VANETs is proposed in this paper. First, all vehicles on roads are grouped into stable clusters in the light of their direction, number of neighbors, link reliability, and traffic load. By utilizing the advantages of centralized control in software defined VANETs (SDVN), cluster stability can be maintained in real-time. Second, a contention-free MAC mechanism composed of inter-cluster multi-channel allocation and intra-cluster dynamic TDMA frame allocation is proposed to prevent co-channel interference and hidden terminal interference. Simulation results show that the proposed protocol outperforms some existing protocols in cluster stability, delivery ratio, throughput and delay performance.

With the ever-growing number of base stations (BSs) and user equipments (UEs) in ultra-dense networks (UDN), reusing the same pilot sequences among the cells is inevitable. With pilot reuse scheme, the channel estimation obtained at a BS contains not only the desired channel-state information (CSI) but also interference from neighboring cells, which can severely degrade CSI estimation performance and adversely affect communication performance. In this paper we consider a pilot contamination avoidance based on pilot pattern design for UDN where the pilot reuse employed and the interfering users from neighboring cells may be not at lower power levels at the BS compared to the in-cell users. We present a novel statistical interference model of sub-carriers to describe the non-deterministic interference from neighboring cells. Then, we provide a pilot pattern design model with non-uniform pilot distribution. Based on this, a pilot contamination avoidance based on pilot pattern design is proposed where pilot reuse scheme and the non-deterministic interference from neighboring cells are taken into consideration. Unlike existing interference mitigation approaches, the proposed method eliminates interference through the method of interference avoidance and can be applied to different kinds of channel estimation algorithms. Simulation results showed that the proposed approach can effectively avoid the interference and ensure the accuracy of channel estimation.

Cards Recognition Systems,(CRSs) are representative computer vision-based applications. They have a broad range of usage scenarios. For example, they can be used to recognize images containing business cards, personal identification cards, and bank cards etc. Even though CRSs have been studied for many years, it is still difficult to recognize cards in camera-based images taken by ordinary devices, e.g., mobile phones. Diversity of viewpoints and complex backgrounds in the images make the recognition task challenging. Existing systems employing traditional image processing schemes are not robust to varied environment, and are inefficient in dealing with natural images, e.g., taken by mobile phones. To tackle the problem, we propose a novel framework for card recognition by employing a Convolutional Neutral Network (CNN) based approach. The system localizes the foreground of the image by utilizing a Fully Convolutional Network (FCN). With the help of the foreground map, the system localizes the corners of the card region and employs perspective transformation to alleviate the effects from distortion. Text lines in the card region are detected and recognized by utilizing CNN and Long Short Term Memory,(LSTM).To evaluate the proposed scheme, we collect a large dataset which contains 4,065 images in a variety of shooting scenarios. Experimental results demonstrate the efficacy of the proposed scheme. Specifically, it is able to achieve an accuracy of 90.62% in the end-to-end test, outperforming the state-of-the-art.