In this paper, with the assumption of only channel statistics information available at the receivers, a novel concept of multiplicative uniquely decomposable constellations is proposed for multi‐user single‐input multiple‐output (MU‐SIMO) FSO systems in weak atmospheric turbulence, which is modeled by spatially dependent lognormal fading channels. The design enables the fast decoding at the users' sides and meanwhile allows flexibility in assigning each user's ordering to meet different priority needs. Simulation results show that our proposed design has a significant performance improvement over the TDMA scheme at the same rates.

A proactive spectrum monitoring and spectrum monitoring data (SMD) transmission coexistence system is investigated in dynamic spectrum sharing network, where a spectrum monitor (SM) aims to monitor the spectrum information from the electromagnetic signal sent by the suspicious transmitter to the suspicious destination by using a portion of its antennas and transmit its SMD to the spectrum data fusion center by using the rest antennas. The SMD transmission of the SM can also be employed to jam the suspicious destination to realise the effective proactive spectrum monitoring. Thus, the precoding and antenna selection scheme are jointly designed at the SM to maximise the sum of the achievable spectrum monitoring rate and the SMD transmission rate. The scenario that the SM can assess the statistical channel state information of the suspicious links based on the spectrum information obtained from the spectrum database as the spectrum management node is then considered. The spectrum monitoring/SMD transmission success probability is also derived, and a trade‐off between them is further revealed. Simulation results show that the proposed schemes can obtain higher sum rate of the spectrum monitoring and the SMD transmission, which also verifies its effectiveness and above “security‐reliability” trade‐off.

Indoor human wireless sensing is crucial for many applications such as smart homes and security monitoring. It needs to understand both the number of people and their activities, which is often difficult, especially in large spaces. This paper proposes a combined people number counting and action recognition method by using the channel state information (CSI) of Wi‐Fi signal, which aims to simultaneously estimate the number of people and their actions in wireless sensing applications. First, a jump region removal algorithm is developed to calibrate the phase difference. Second, a cumulative sliding variance algorithm is designed for the detection of moving targets in the environment. Then a multi‐dimensional feature is formed by the amplitude, frequency domain, and phase difference of CSI, and based on it, a two‐level classifier based on the SVM algorithm is used to estimate the number of people and their actions. Experimental results show that the average accuracy of the system for the people counting is 93.5%, and the activity recognition rate of one‐person and two‐person is 99.6% and 94.6% respectively.

Wireless sensor networks (WSNs) are critically resource‐constrained due to wireless sensor nodes' tiny memory, low processing units, power limitations, and narrow communication bandwidth. The data reduction technique is one of the most widely used techniques to reduce transmitted data over the wireless sensor networks and to minimize the sensor nodes' energy consumption, particularly, the entire network in general. This paper proposes a reliable dual prediction data reduction approach for WSNs. This approach performs data reduction through two phases: the data reduction phase (DRP) and data prediction phase (DPP). The DRP is mainly to decrease the number of transmissions between the sensor node and the sink node, thereby minimizing energy consumption. It also detects faulty data and discards them at the sensor node. The discarded faulty data at the sensor nodes are replaced by estimated values at the sink node to maintain data reliability. DPP runs at the sink node or base station, which works in synchronization with the sensor nodes. This phase is responsible for predicting the non‐transmitted data based on the Kalman filter. The simulation results demonstrate that the proposed approach is efficient and effective in data reduction, data reliability, and energy consumption.

A Vehicle to Anything (V2X) packet data is used for a vehicle safety and automated driving applications. It is broadcasted periodically to provide a warning message or share the vehicular sensor's data among vehicles. This paper proposes IEEE 802.11p based dual channel transmission and packet selection method to improve the Packet Detection Rate (PDR) for V2X broadcasting messages. The PDR gain of the proposed scheme over the single channel transmission is given theoretically by the packet error rate of a single radio channel. And the PDR improvement is evaluated by a computer simulation in a highway or urban multipath fading channel.