Nonorthogonal multiple access (NOMA) can facilitate simultaneous data transmissions towards multiple users by using the superposition coding and successive interference cancelation techniques, which can greatly improve the spectrum efficiency. Cooperative relaying and space-time coding can promisingly improve the communication robustness of poor-quality links by achieving the space-diversity gain. Energy harvesting (EH) can prolong the lifetime of energy-limited terminals and make them work continuously. In order to enhance the spectrum efficiency as well as the communication quality, we enable a cluster of EH relays to assist the data transmissions from a base station (BS) to two far-users by using the Alamouti coding based cooperative NOMA strategy. The relays are capable of harvesting wireless energy from a power beacon as well as BS by using time-switching or power-splitting method. According to the energy status and the data decoding status, one relay is selected in a distributed manner according to either Max-min, Max-sum, or Random criterion. We analyze the transmission success probability and the system throughput performance. Extensive simulations are performed to compare the performance of different EH-based space-time coded cooperative NOMA with various relay selection schemes as well as the counterpart orthogonal transmission schemes.

Motivated by the development of single image super-resolution (SR) reconstruction in computer version, classic SR networks have been widely applied to the channel estimation of wireless communication system. To capture the spatial correlations in the reflection element-domain of reconfigurable intelligent surface (RIS), we propose a multi-scale supervised learning-based Laplacian pyramid wide residual network (LapWRes) to achieve the progressive reconstruction of cascaded channel in a coarse-to-fine fashion. The LapWRes can be divided vertically into feature extraction branch (FEB) and channel reconstruction branch (CRB), while it can also be viewed horizontally as multiple channel reconstruction modules (RMs) at different scales. In the FEB, the wide activation residual blocks are stacked to extract the high-frequency information of cascaded channel. In the CRB, the high-frequency and low-frequency information of cascaded channel is fused by utilizing the residual learning. Simulation results show that the LapWRes can achieve better estimation accuracy than other channel estimation schemes and faster convergence than existing SR network-based channel estimation models.

We propose a partial cooperative zero-forcing (PCZF) decoding scheme for the uplink cell-free massive MIMO system, wherein the neighboring access points (APs) around each user equipment (UE) share the channel state information (CSI) and jointly suppress the interference using the zero-forcing technique. Using asymptotic analysis, we derive a closed-form asymptotic expression for a lower bound on the achievable rates. Considering the unique and complex form of the achievable rates, we propose power control schemes according to two criteria. The first criterion is to maximize the minimum achievable rate. For this criterion, we propose a target-SINR-tracking (TST)-based bisection algorithm. Since the power control update functions are standard interference functions, the TST-based bisection method always converges to the optimal solution. The second criterion is to maximize the sum rate, for which we propose two power control algorithms: 1) randomization and scaling algorithm (RSA) and 2) fractional programming algorithm (FPA). In each iteration of the RAS algorithm, we first exploit the randomization technique to transform the sum-rate maximization problem into a series of power minimization problems, and then improve the sum rate by scaling. In the FP algorithm, we derive a lower bound on the sum rate, and then propose an iterative approach based on the Lagrangian dual transform and fractional programming to maximize the sum-rate lower bound. Numerical results validate the theoretical analysis and verify the efficiency of the proposed power control algorithms.

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.

In this article, we propose an uplink cell-free Industrial Internet of Things (IIoT) framework to support a large number of devices with random data arrivals. By adopting nonorthogonal random pilots and the large-scale fading decoding technique, we derive the closed-form expression of the transmission capacity for each terminal. Considering different statistics of random data arrivals, we formulate a long-term stochastic optimization problem to maximize the minimum time average transmission success ratio (TATSR) through jointly determining the power control coefficients and the combining coefficients for each time period. We reformulate the long-term max–min problem into a sequence of subproblems to minimize the Lyapunov drift plus penalty in each time period. We approximate each mixed integer subproblem as a sigmoid optimization problem, and propose an iterative algorithm by the aid of quadratic transform-based fractional programming and the sequential convex programming to solve it. Simulation results show that our proposed scheme can boost the TATSR.

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.

We consider a wirelessly powered Internet of Things (IoT) based on cell-free massive MIMO with energy harvesting. 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. We assume that single antenna sensors send uplink pilots in order to allow APs to detect active users and estimate their channel coefficients. We assume that each AP is equipped with N ≥ 1 antennas and uses the linear minimum mean square error (LMMSE) channel estimation. Through an asymptotic analysis, we derive closedform approximations for the variance of the LMMSE channel coefficient estimates, the amount of harvested energy, and the achievable rates for the uplink data transmission. We next use these expressions to jointly optimize the charging duration and uplink and downlink power control coefficients to minimize the total transmit energy consumptions of APs and sensors, which is crucially important for IoT sensors. Simulation results verify the accuracy of the obtained expressions, and shows that significant gains in energy efficiency can be achieved by the proposed optimization algorithms.

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 consider underlay spectrum sharing with wireless energy transfer in a large-scale cognitive radio network, where each primary user (PU) can harvest radio frequency 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 toward 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 the 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.

In this study, the authors propose an in‐band full‐duplex massive multi‐user multiple‐input multiple‐output system which exploits the separate‐antenna arrays at base station and the shared‐antenna at users. First, the channel state information of the two antenna arrays is estimated using the minimum mean‐square‐error method according to the channel reciprocity, and then the matched‐filter and zero‐forcing linear processing methods are adopted to analyse the system spectral efficiency (SE). The authors derive the lower and upper bounds of the uplink and downlink achievable rates in succinct forms to measure the system performance. The selection of full/half‐duplex mode is also discussed with respect to the number of users. Numerical and simulation results show that the optimal downlink SE can be achieved with the increase of active users, and the full/half‐duplex mode selection can help improve the system SE with the variation of traffic loads.

The authors consider the multiuser cognitive radio network, where a primary link coexists with multiple secondary transmitters (STs) which intend to communicate with an access point (AP). All the STs can harvest the radio frequency energy from the AP, while the primary terminals have the continuous power supply. The primary data transmission and the ST energy harvesting can be simultaneously performed to improve both the spectral and the energy efficiencies. The STs that have correctly decoded the primary data are classified as potential relays. Each potential relay will calculate the channel quality between itself and the primary receiver and the best one is selected to relay the primary data using the harvested energy. Thanks to the cooperation from STs, the primary data can be more reliably transmitted and the throughput requirement of primary system can be more easily satisfied in a shorter time, and as a reward all the STs except the selected one can transmit their own data to the AP in the remaining time. To facilitate the primary data cooperation, the energy transfer and the secondary data transmission, the optimal time allocation can be determined through maximising the STs’ throughput while guaranteeing the throughput of primary system.

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.

In this paper, we study a multi-pair two-way relay network consisting of two groups of user equipments (TIEs), who want to exchange information through a common relay equipped with massive antennas. In the multiple access phase, the TIEs transmit information to the relay using the energy harvested in the last time block, and the relay decodes information using zeroforcing (ZF) or maximal ratio combining (MRC) technique. In the broadcasting phase, the relay performs simultaneous wireless information and power transfer (SWIPT), and each TIE receives energy and information using the power-splitting scheme. Due to the channel hardening effect of the large-scale antenna array, the harvested energy of each TIE in each time block is asymptotically constant. Based on the derived achievable rates of TIEs, a multi-objective optimization problem (MOOP) is formulated to maximize the achievable rates of all the pairs. Through solving the MOOP, the power-splitting ratios of TIEs can be determined in closed-form for the ZF-based relaying. For the MRC-based relaying, a two-stage iterative Pareto improvement algorithm is proposed to achieve the Pareto optimality. Simulation results are presented to validate our theoretical analysis and verify the efficiency of our proposed algorithm in improving the system rate.

In this paper, we propose an energy harvesting (EH) based spectrum leasing protocol in the cognitive radio network, where multiple secondary transmitters (STs) can simultaneously harvest the radio frequency energy from the primary user (PU) transmission. After the EH period, the PU transmits its data with the assistance from the STs. Among all the STs that can correctly decode the primary data and harvest enough energy, the one with the best channel status towards the primary receiver is selected to relay the primary data. Thanks to the STs' cooperation, the primary data can be more easily delivered using less time, and as a reward, all the STs except the selected one can transmit their own data in the TDMA manner. The data success probabilities of both primary and secondary systems are analyzed by considering the EH and information decoding statuses of STs. Numerical results show that our protocol can well accommodate the STs' transmissions while guaranteeing the PU performance.

This study investigates the downlink error‐rate performance for a multicell distributed antenna system (DAS) over Nakagami‐ m fading channels, where arbitrary number of remote antenna units are uniformly deployed within each macrocell. An antenna selection transmission scheme is presented to reduce the power consumption and the number of interfering signals from outside the target macrocell. Unlike most previous studies in which the interference plus noise is assumed to be Gaussian distributed with constant variance, the co‐channel interference here is considered as a random variable. The authors first derive a closed‐form bit error rate (BER) expression by adopting the commonly used Gaussian‐ Q ‐function approximation. Furthermore, consider the desired signal and the interfering signals undergo different fading severities, they derive a precise BER expression in terms of single‐integral by applying the characteristic function. For the special case of Rayleigh fading, an exact closed‐form BER expression is obtained. Finally, these error‐rate expressions which can be evaluated efficiently indicate some significant insights into the characteristics of the antenna selection transmission for DAS. Simulation results are provided to validate their theoretical analysis.