We consider a cognitive radio-based Internet-of-Things (CR-IoT) network consisting of one primary IoT (PIoT) system and one secondary IoT (SIoT) system. The IoT devices of both the PIoT and the SIoT respectively monitor one physical process and send randomly generated status updates to their associated access points (APs). The timeliness of the status updates is important as the systems are interested in the latest condition (e.g., temperature, speed and position) of the IoT device. In this context, two natural questions arise: (1) How to characterize the timeliness of the status updates in CR-IoT systems? (2) Which scheme, overlay or underlay, is better in terms of the timeliness of the status updates. To answer these two questions, we adopt a new performance metric, named the age of information (AoI). We analyze the average peak AoI of the PIoT and the SIoT for overlay and underlay schemes, respectively. Simple asymptotic expressions of the average peak AoI are also derived when the PIoT operates at high signal-to-noise ratio (SNR). Based on the asymptotic expressions, we characterize a critical generation rate of the PIoT system, which can determine the superiority of overlay and underlay schemes in terms of the average peak AoI of the SIoT. Numerical results validate the theoretical analysis and uncover that the overlay and underlay schemes can outperform each other in terms of the average peak AoI of the SIoT for different system setups.

In this paper, we propose a two-phase non-orthogonal multiple access (NOMA) scheme with cooperative relaying. In the first phase , an access point (AP) linearly combines the signals of near-user $\mathrm U_1$ and far-user $\mathrm U_2$ and then broadcasts the superimposed signal to $\mathrm U_1$ and multiple relays. $\mathrm U_1$ tries to decode its desired data by treating the signal of $\mathrm U_2$ as interference. In a cooperative zone between AP and $\mathrm U_2$ , multiple relays try to decode the signal of $\mathrm U_2$ by decoding and canceling the signal of $\mathrm U_1$ . In the second phase , a decodable relay is selected to forward the far-user's signal and, meanwhile, if $\mathrm U_1$ fails to decode its desired signal in the first phase, it will try to decode and cancel the far-user's signal to further decode its desired signal again. We analyze the system throughput and investigate the impacts of various parameters. Numerical results show that our proposed scheme can greatly outperform the time division multiple access and the non-cooperative NOMA schemes in terms of throughput for most system settings.

We propose a simultaneous wireless information and power transfer scheme based on the power-splitting technique for the downlink of multi-user massive antenna-array systems. The base station (BS) can transmit both the wireless energy and information simultaneously to the user equipments (UEs). Using the harvested energy, each UE can transmit its pilot signal to the BS for the downlink channel estimation by exploiting the channel reciprocity of the time division duplexing system. When the antenna scale is large enough, the ergodic achievable rates of UEs are derived in closed-form. To maximize the minimum achievable rate among all the UEs, an iterative algorithm with low-complexity is proposed to jointly optimize the power allocation coefficients of the BS and the power-splitting ratios of the UEs. In each iteration of the proposed algorithm, the optimal power-splitting ratios can be determined according to the closed-form expressions for a given power allocation coefficient. The convergency, optimality, and complexity of our proposed algorithm are analyzed theoretically. The equal power allocation is shown to be optimal when the transmit power of BS is large enough. Furthermore, the optimal number of antennas is determined to maximize the energy efficiency. Simulation results are provided to validate our closed-form approximations and verify the efficiency of our proposed algorithm.

We investigate the activity detection and channel estimation issues for cell-free Internet of Things (IoT) networks with massive random access. In each time slot, only partial devices are active and communicate with neighboring access points (APs) using non-orthogonal random pilot sequences. Different from the centralized processing in cellular networks, the activity detection and channel estimation in cell-free IoT is more challenging due to the distributed and user-centric architecture. We propose a two-stage approach to detect the random activities of devices and estimate their channel states. In the first stage, the activity of each device is jointly detected by its adjacent APs based on the vector approximate message passing (Vector AMP) algorithm. In the second stage, each AP re-estimates the channel using the linear minimum mean square error (LMMSE) method based on the detected activities to improve the channel estimation accuracy. We derive closed-form expressions for the activity detection error probability and the mean-squared channel estimation errors for a typical device. Finally, we analyze the performance of the entire cell-free IoT network in terms of coverage probability. Simulation results validate the derived closed-form expressions and show that the cell-free IoT significantly outperforms the collocated massive MIMO and small-cell schemes in terms of coverage probability.

Recently, the commercial potentials and practical values of Mobility as a Service (MaaS) have been witnessed for sustainable development of smart cities in the field of intelligent transportation. Driven by the Internet of Vehicles (IoV), vehicles gradually become mobile living spaces to satisfy diverse demands of occupants, where the media functionalities, scenario scopes, and user engagements can be all expended dramatically. In the era of 5G/B5G, the intelligent connected vehicles are considered as super media because of the improved In-Vehicle Infotainment (IVI). In this paper, relying on the advanced communication and intelligent techniques, we investigate the IoV-based vehicle-mounted media scenarios and construct IVI solutions for the MaaS. Firstly, we propose the characteristics of vehicle-mounted media scenarios and present the IVI evolution from the aspects of hearing, vision and multi-sensory media services. Secondly, we point out the main features and limitations of IoV-based IVI in 3G/4G. Finally, exploring the potential of super media, both the linkage display and IVI collaborative modes in vehicle-centric media scenarios are introduced as IVI solutions for the MaaS in 5G-V2X and beyond, which intend to further improve the audio-visual experiences of occupants and observers in urban environments.

Cooperative diversity has recently been proposed as a promising technology to achieve spatial diversity in wireless networks. In this paper, we analyze the performance of SNR-based hybrid decode-amplify-forward (HDAF) relaying cooperative diversity networks over independent non-identical flat Rayleigh fading channels with maximum ratio combining (MRC) technique. Closed-form expressions for the outage and bit error probability of the HDAF relaying scheme are derived. Computer simulations are carried out to illustrate and validate the correctness of analytical results. Besides, the impacts of the SNR threshold and relay location on the performances of outage and bit error probability are investigated.