In this article, we propose a wirelessly powered Internet-of-Things (IoT) system based on the cell-free massive MIMO technology. In such a system, during the downlink phase, the sensors harvest radio-frequency (RF) energy emitted by the distributed access points (APs). During the uplink phase, sensors transmit data to the APs using the harvested energy. Collocated massive MIMO and small-cell IoT can be treated as special cases of cell-free IoT. We derive the tight closed-form lower bound on the amount of harvested energy, and the closed-form expression of SINR as the metrics of power transfer and data transmission, respectively. To improve energy efficiency, we jointly optimize the uplink and downlink power control coefficients to minimize the total transmit energy consumption while meeting the target SINRs. Extended simulation results show that cell-free IoT outperforms collocated massive MIMO and small-cell IoT in terms of both downlink and uplink 95% likely performances. Moreover, significant gains can be achieved by the proposed joint power control in terms of both per user throughput and energy consumption.

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.

We consider the underlay spectrum sharing with wireless energy transfer in a large-scale cognitive radio network, where each primary user (PU) can harvest radio frequency (RF) energy from its associated access point (AP). An energy cooperation zone is applied around each PU, wherein the secondary user (SU) with the best channel quality towards PU is selected to cooperatively transfer wireless energy. With SUs’ assistance, each PU can harvest a predefined amount of energy in a shorter time, so there will be more concurrent primary links in the network. We analyze the transmission probability of PUs by properly modeling their energy statuses using Markov chain. Furthermore, a guard zone is applied around each active AP to prohibit the nearby SU transmissions to avoid strong interference, and SUs outside the guard zones of all the active APs can access the spectrum opportunistically. Under the constraint that the area throughput of primary system should be improved by at least a certain degree, the area throughput of secondary system is maximized by jointly determining the SUs’ density and the guard zone radius. Numerical results show that our scheme can well accommodate SUs’ transmissions while guaranteeing PUs’ performance requirement.

For a downlink communication system, we propose a nonorthogonal multiple access scheme with two energy harvesting relays. The base station (BS) transmits the data of both near and far users simultaneously using the superposition coding technique. Two relays alternately amplify and forward the data to the far-user. Each relay can harvest wireless energy from the signals of both the BS and the other transmitting relay, so each relay can achieve more opportunities to accumulate more energy for a higher-quality cooperative relaying. The near-user performs adaptive interference cancelation to retrieve its desired data. Numerical results show that the nonorthogonal scheme greatly outperforms the orthogonal scheme in terms of throughput.

We investigate the long-term scheduling and power control scheme for a wirelessly powered cell-free Internet-of-Things (IoT) network which consists of distributed access points (APs) and a large number of sensors. In each time slot, a subset of sensors is scheduled for uplink data transmission or downlink power transfer. Through asymptotic analysis, we obtain closed-form expressions for the harvested energy and the achievable rates that are independent of random pilots. Then, using these expressions, we formulate a long-term scheduling and power control problem to maximize the minimum time-average achievable rate among all sensors while maintaining the battery state of each sensor higher than a predefined minimum level. Using Lyapunov optimization, the transmission mode, the active sensor set, and the power control coefficients for each time slot are jointly determined. Finally, simulation results validate the accuracy of our derived closed-form expressions and reveal that the minimum time-average achievable rate is boosted significantly by the proposed scheme compared with the simple greedy transmission scheme.