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Air ground integrated mobile cloud computing (MCC) provides unmanned aerial vehicles (UAVs) the capability to act as an aerial relay with more flexibility and resilience. In the cloud computing architecture, the data generated by ground users (GUs) can be offloaded to the remote server for fast processing. However, the heterogeneity of mobile tasks makes the data size distributed among GUs unbalanced. Besides, the energy efficiency of UAVs movement should be carefully considered for sustainable flight and obstacle avoidance. In general, such a joint trajectory issue can hardly be formulated as a convex optimization in unpredictable and dynamic environments. This paper proposes a potential game combined multi-agent deep deterministic policy gradient (MADDPG) approach to optimize UAVs trajectory with the consideration of offloading delay, energy efficiency as well as obstacle avoidance in the multi-UAV assisted offloading system. In specific, we first model the issue as a mixed integer non-linear problem (MINP), in which the service assignment between multi-user and multi-UAV is solved by potential game. The convergence to a Nash Equilibrium (NE) can be achieved by unilateral service assignment update with infinite iteration. Then, we optimize the trajectory with obstacle avoidance at each UAV by MADDPG approach, which has a great advantage of centralized-training and decentralized-execution to reduce the global synchronized communication overhead. UAVs movement can be optimized in continuity rather than other deep reinforcement learning (DRL) approaches generating discrete simple actions. Experiments demonstrate the proposed game-combined learning algorithm can minimize the offloading delay, enhance UAVs' energy efficiency and avoid the obstacles at the same time.

The effective combination of physical layer security communication and intelligent reflecting surface (IRS) technology has recently attracted extensive attention to improve the system security. Unlike existing works that mostly focus on single-carrier systems, we consider an IRS-assisted multi-carrier MIMO wireless physical layer security communication system, which consists of a legitimate transmitter, a legitimate receiver, an IRS node and an eavesdropper. With the aim of maximizing the sum secrecy rate, the multi-carrier transmit beamforming and IRS reflecting coefficient matrix are jointly optimized under the constraints on the transmit power budget and unit modulus of IRS reflecting coefficients. Since the formulated problem is non-convex, an alternate optimization (AO) algorithm is proposed to deal with it. Specifically, for the given IRS reflecting coefficient matrix, the minorization-maximization (MM) scheme is adopted to approximate the objective function of the subproblem and following that, the closed-form solution of the multi-carrier transmit beamforming is obtained through the Lagrange multiplier method. Then, for the given multi-carrier beamforming, the IRS reflecting coefficient matrix is also optimized through the MM scheme and a suboptimal closed-form solution that satisfies the KKT conditions is derived. Moreover, the discussion is extended to the discrete IRS phase shift case and a heuristic projection method (HPM) is then proposed. Finally, the simulation results validate the effectiveness of the proposed AO based algorithm.

Non-orthogonal multiple access (NOMA) is considered as a potential multiple access technique for supporting massive number of devices with high spectral efficiency for the 5 G and beyond networks. Simultaneous wireless information and power transfer (SWIPT) is considered as an efficient solution for fully autonomous and sustainable communication networks. In this paper, application of SWIPT in heterogeneous networks (HetNets) employing cooperative NOMA (CNOMA) is investigated. The HetNets consists of macro-base stations (MBS) under-laid by small-base stations (SBS). The distribution of MBSs follow Poisson point process model, while the SBS tier supports NOMA and carrier sensing for its transmission. Carrier sensing helps in reducing interference by blocking the base stations within a specific range from transmitting. In this work, the analysis at SBS tier is carried out in two phases. In the direct phase transmission, the SBS transmits the superimposed signal to a user pair, comprising of a cell-centre user (CCU) and a cell-edge user (CEU), using NOMA principle. The CCU acts as a cooperative relay to forward information to the CEU in the cooperative phase. Unlike existing literature, instead of using only the superimposed signal for energy harvesting (EH), in this work, the cooperating user (i.e. CCU) employs EH using interference from the adjacent base stations along with the EH from the superimposed signal. Expressions for outage probability and throughput are derived at the user pair served using NOMA. Comparison of the proposed EH system with existing solutions are highlighted and useful insights are drawn. Monte Carlo simulations are carried out to validate the analytical results.

This paper investigates a novel joint content caching and energy harvesting scheme to enhance unmanned aerial vehicle (UAV) communications in Internet of Things non-orthogonal multiple access (NOMA) network, where a UAV is acted as an aerial relay to serve users on demand. In particular, a neoteric gravitational search-based multi-constraint optimization algorithm is designed to maximize the throughput of the served users by jointly optimizing power allocation, energy harvesting, and time scheduling schemes. The algorithm engages two main characteristics: 1) classification of multiple constraints, which is for reducing the complexity of the problem solving process due to multiple iterations, and 2) design of force sets, which aims to enhance the global search for the optimal solution at the feasible region boundary. Finally, simulations are provided to show the effective convergence and superiority of the proposed algorithm.

Non-orthogonal multiple access (NOMA) is an important technology for the forthcoming 5 G and beyond. However, its privacy often suffers from adversarial eavesdropping, especially for the users with higher transmit power. In this paper, we propose a jamming-aided secure transmission scheme for cooperative NOMA networks with a full-duplex (FD) relay. In this scheme, two pairs of users perform secure transmission with the help of a decode-forward (DF) relay, which forwards information and generates artificial jamming to counteract eavesdropping. The precoding vectors are designed to zero-force the artificial jamming at legal receivers. Then, the channel statistics are calculated, based on which the secrecy rates of legitimate signals are analyzed and the expressions of secrecy outage probability (SOP) are derived. Furthermore, three benchmark schemes are also presented with their performance analyzed to verify the advantage of the proposed scheme. Simulation results show the accuracy of our analysis, and demonstrate that the proposed scheme can effectively reduce the SOP and improve the effective secrecy throughput via artificial jamming and FD relaying.

In cognitive radio networks (CRNs), secondary users (SUs) can opportunistically access the licensed bands through spectrum sensing while primary users (PUs) are inactive. While collaborative spectrum sensing (CSS) exploits the benefits of cooperation, the spectrum sensing data falsification (SSDF) attackers are subsequently introduced, which aim at undermining the performance of CRNs. We thus are motivated to maximize the throughput of the CRNs under SSDF attacks. First, to better discern SSDF attackers, we designate a double reputation based algorithm to differentiate the SSDF attackers and honest secondary users (HSUs). Then the trade off problem is formulated to find the optimal network parameters including, sensing duration, global decision threshold, energy detection threshold, reputation thresholds for maximizing the throughput of the secondary users network (SUN) subject to limited interference with the PUs. To address the non-convexity of the optimization problem, dummy variables are introduced and the alternative optimization method is adopted. Numerical results show that the proposed algorithm is effective in increasing throughput of the SUN with sufficient protection to the PUs.

An onboard energy storage system (OESS) withfast-energy-exchange capability is needed to enable future grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. To facilitate the fast energy exchange, the OESS normally interfaces between a high voltage (HV) bus on the grid side and a low voltage (LV) bus on the vehicle side. The HV bus can be up to 1200 V, while the LV bus is as low as 48 V, which means a high conversion ratio is needed. Resonant converters are commonly used for OESS and designed with large characteristic impedance for wide-range output power and zero-voltage-switching operation. However, such a design leads to large volume and complex customization of the magnetic components. In this paper, a high-conversion-ratio OESS is proposed, which is based on interleaved quasi-resonant converters with small characteristic impedance. The resonant converters are of modular design with a small characteristic impedance to achieve small volume, low cost, and simple customization. Interleaved in LV-side-parallel and HV-side-series configuration, a high conversion ratio can be achieved. Moreover, modulated in quasi-resonant operations, all the switches can realize soft switching even at light load conditions for high efficiency. Burst control for output regulation is implemented in each modular quasi-resonant converter, while interleaved control is implemented between them to minimize the current ripple. The proposed design and control are validated by a compact 5 kW prototype with a charging/discharging current of 100 A, high power efficiency of 98.91%, a high power density of 3.86 W/cm $^3$ , and high-conversion-ratio (up to 20: 1) power flow between a 1000 VDC HV bus and a 48 VDC LV bus.

Conventionally, it was thought that outage probability error floors are unavoidable in uplink non-orthogonal multiple access (NOMA) regardless which type of successive interference cancellation (SIC) is adopted. However, a recent work breaks such myths by introducing the concept of hybrid SIC, which dynamically chooses the decoding order according to the users’ channel conditions and quality of service requirements. However, there is still a limitation in this existing work that the error floors can only be avoidable under a stringent condition on users’ target rates. To this end, this letter proposes a new hybrid SIC scheme by exploiting the benefit of power control, which guarantees no error floor without any conditions on users’ target rates.

In downlink non-orthogonal multiple access (NOMA) with multiuser clustering, a single beamforming (BF) vector precodes the superimposed signal of strong and weak users. This precoding results in an imperfect inter-cluster interference (ICI) cancellation at weak users since the BF vectors are designed based on strong users’ channels. Also, the ICI is highly increased with the number of transmit antennas, causing severe degradation in the performance of weak users and total system. In this paper, first , a new cooperative NOMA is introduced, in which weak user beam-matching (WBM) equalizer is proposed to remove the ICI at weak users by matching weak users’ channels with the designed BF vectors. The process of WBM is accomplished with the aid of device-to-device (D2D) channel state information (CSI) sharing between the nearby strong and weak users. Second , based on WBM principle, strong user beam-matching (SBM) equalizer is proposed to eliminate the generated ICI at strong users in the case of limited feedback. Third , a new power allocation strategy is proposed to improve weak users’ performance by considering the gained throughput from interference cancellation. Finally , besides the sum-rate, which is adopted as the performance metric by most of the existing NOMA works, the bit error rate (BER) of NOMA users in cooperative NOMA is calculated and compared with those in other schemes. Simulation results show that our cooperative NOMA with the proposed equalizers achieves significant sum-rate and BER improvements over other non-cooperative schemes with both perfect and limited feedback scenarios.

The cooperative non-orthogonal multiple access (NOMA) networks with one pair primary user and one pair cognitive user share the same spectrum resource via a common relay is considered in this paper. We propose a dynamic power transmission scheme for both uplink and downlink NOMA transmission in cognitive relay networks, which preserves the quality of service for the primary user. The closed-form expressions of overall outage probability and average sum rate for the proposed dynamic power transmission scheme of cognitive relay NOMA networks are derived. Both developed analytical results and Monte Carlo simulations show that the proposed dynamic power control scheme can dramatically enhance performance gain for the proposed networks, compared to other existing NOMA power allocation schemes.

This paper studies user scheduling and power allocation problem to maximize the energy efficiency (EE) for non-orthogonal multiple access (NOMA) in downlink Coordinated Multi-Point networks. In the proposed framework, a more practical scenario the imperfect channel state information, imperfect successive interference cancellation and data outage are investigated. To address the considered problem, the optimization problem is formulated constrained by the total power and the outage probability requirements. However, the EE objective function is with a non-convex structure. Accordingly, we first convert the optimization problem to make the objective function concave and analytically tractable. Furthermore, we split the joint optimization problem to find a suboptimal solutions to the original problem. As a result, we first propose a suboptimal user-scheduling algorithm to improve the system's EE. Due to the non-convex function of the transmit power, we invoke a sequential successive convex approach to address the non-convex problem by its lower bound concave function. Besides, the fractional objective function is converted to its equivalent subtractive form. Finally, we derive a power control scheme to address the proposed framework. Simulation results endorse the effectiveness of the proposed algorithm and their performance gains in terms of EE compared to both NOMA and OFDMA variants.

This paper studies the performance of unmanned aerial vehicle (UAV)-enabled NOMA networks with selective incremental relaying and imperfect channel state information (CSI). In particular, an access point transmits a superposed signal based on NOMA principle to an UAV terminal (UT) which can act as a relay to assist the information transmission for a ground terminal (GT). Different from the conventional relaying protocol requiring UT to relay the message all the time no matter how the received signal quality of two terminals are, we propose a novel NOMA-based selective incremental relaying protocol that allows relay to forward messages on the condition of the received signal with satisfied quality at UT and the unsatisfied quality signal at GT. The closed-form solutions of outage probabilities for the two terminals are characterized with the elevation angle-based path loss exponent and small-scale fading. Moreover, the benchmark schemes are provided to exhibit the superiority of the proposed protocol. Finally, simulation and theoretical results validate the correctness of the outage analysis, demonstrate the advantage of the proposed relaying protocol over its counterparts, and show that the stronger impact of channel estimation error on GT than UT in terms of outage probability.

In this paper we investigate downlink performance in a non-orthogonal multiple access (NOMA) based vehicular communication network. In contrast to the previous works wherein the vehicles are simply assumed to be located anywhere in the coverage area, we consider practical cases where vehicles are located on a system of roads. We then adopt a system model based on Poisson Line Cox Point (PLC) processes, where the road system is represented through a Poisson Line (PL) Process. Vehicles ordered according to their distances to their corresponding roadside units (RSUs) are then served by using power domain NOMA. Adopting analytical tools from the stochastic geometry, we then analyse the outage performance of the system. Numerical results corroborate our analysis and demonstrate how the system settings influence the considered NOMA based V2X network.

In this paper, we analyze the performance of a non-orthogonal multiple access (NOMA) cognitive relay system, where a multi-antenna full-duplex cognitive transmitter employs NOMA concept to assist the transmission from a wireless-powered primary transmitter to its corresponding receiver, while simultaneously communicating with a cognitive receiver. A practical non-linear energy harvesting (EH) model, which taking into account harvester's sensitivity and saturation effects is considered. We propose an optimum beamforming design at the cognitive transmitter such that the rate of cognitive network is maximized, under a constraint that the rate of the primary network is above a certain threshold. Results show that proposed optimization framework can substantially enlarge the rate region toward both primary and secondary networks. Furthermore, in order to characterize the network delay-constrained throughput, tractable outage probability expressions for primary and secondary networks assuming sub-optimum zero-forcing based beamforming scheme are derived. Our results reveal that due to required minimum power for harvesting operation as well as saturation of the harvested power at high transmit power levels, conventional linear EH model may lead to performance mismatches for practical non-linear EH circuits in both low and high transmit power regimes.

This paper studies the physical layer security for a cooperative non-orthogonal multiple access system with energy harvesting (EH) and full-duplex (FD) relaying. In particular, the base station transmits a superposed message to a dedicated EH-FD relay according to NOMA principle. Then, the relay forwards the decoded message to users. At the same time, the message transmitted by the relay can be wiretapped by an passive eavesdropper. The lower bound on the ergodic secrecy rates and the approximated secrecy outage probabilities of users are analyzed with closed-form solutions considering imperfect SIC. Finally, simulation results demonstrate the correctness of theoretical analysis, reflect the influence of the key parameters on the system performance and verify the better secrecy performance of the proposed scheme than its counterpart.

In this paper, weighted sum energy efficiency (WSEE) in uplink and downlink of a multi-user non-orthogonal multiple access (NOMA) system is considered. we adopt a more realistic power consumption model where signal processing power is modeled as a linear function of transmit power. Rather than the well-known network-centric global energy efficiency (GEE) metric, which is a pseudo-concave (PC) function, the weighted sum energy efficiency metric is considered, which is not PC in general. To find the optimum user power allocation Dinkelbach-like algorithm is adopted, by which each individual fractional EE function is converted to a parametric function where under some conditions on the weights falls into a class of convex optimization problems and it is solved in the dual domain. The dual variables are updated using a sub-gradient and cutting plane-based algorithm, which here ellipsoid method is used. Since the optimum solution restricts user weights, a low complexity suboptimum algorithm that does not consider any condition on user's weights is proposed. The problem is non-convex in general; hence, epigraph form followed by successive convex approximation (SCA) is used to deal with that problem. Results demonstrate that with the user-oriented metric, one can provide different priorities to users, and by choosing proper weights entail fairness among users.

Non-orthogonal multiple access (NOMA) aided unmanned aerial vehicle (UAV) is becoming a promising technique for future wireless networks. However, its security remains a great challenge due to the line-of-sight in UAV communications and high transmit power for weak users in NOMA. Thus, in this paper, we propose a power allocation (PA) scheme for NOMA-UAV networks with circular trajectory, to maximize the sum rate of common users while guaranteeing the security for a specific user. To achieve this, we consider three cases based on the distance from the UAV to the secure user. Specifically, the lowest transmit power is assigned to the secure user in each time slot to guarantee its security, with the remaining power allocated to common users to maximize their sum rate. Due to the non-convexity of the problem, we analyze its monotonicity and derive the closed-form solutions for these three cases. To further improve the transmission rate of the secure user, we also derive the upper bound for its decoding threshold, and analyze the linear relationship between the secure decoding threshold and the sum rate of common users. Simulation results are demonstrated to evaluate the effectiveness of the proposed secure PA scheme in NOMA-UAV networks.

The drone has been applied in various areas due to its small size, high mobility and low price. However, illegal uses of drones have posed huge threats to both public safety and personal privacy. There is an urgent demand for technologies that can timely detect and counter the drones. In this paper, we propose an adaptive switching spatial-temporal fusion detection method for remote flying drones in the airspace using electrical-optical cameras, which can enhance the contrast between the target and background as well as suppressing the noises and clutters simultaneously. For each incoming video frame, a dark-attentive interframe difference method and a row-column separate black-hat method are proposed to generate temporal feature maps (TFM) and spatial feature maps (SFM), respectively, in parallel. Inspired by the phenomenon that the features in TFMs and SFMs both go strong at the regions of the intended target while they do not at other regions where noises and clutters locate, we design an adaptive switching spatial-temporal fusion mechanism to fuse the SFMs and TFMs, generating adaptive switching spatial-temporal feature maps (ASSTFM). Finally, an adaptive local threshold mechanism is used in ASSTFMs to segment the targets from backgrounds. In order to validate the effectiveness of our method, we conduct both offline experiments and field tests. The experiment results manifest that our method is superior to the other seven baseline methods and works more stably for different backgrounds and various types of drones.

To enhance the secrecy of wireless transmission, we propose a cooperative user relaying NOMA scheme, in which the near user works in full-duplex mode by transmitting jamming signals and receiving desired signals in the first phase, and the base station sends jamming signals in the second phase. Two power allocation (PA) schemes between the legitimate and jamming signals are designed based on different knowledge of the eavesdropping channel state information (ECSI). The first scheme aims to jam the eavesdropping with highest power while meeting rate requirements without ECSI. In the second scheme, the closed-form expressions of secrecy outage probability (SOP) are first derived using the statistical ECSI. Then, the sum secrecy rate is maximized by optimizing the PA and redundant rate with SOP constraints. Simulation results are provided to show the effectiveness of the proposed schemes.

The calculation of eigenvalues of a matrix is required by many algorithms. Specifically, it is the key technique in subspace-based direction of arrival (DOA) estimation algorithms, e.g., multiple signal classification (MUSIC). The calculation of the eigenvalues therefore directly affects the real-time implementation of DOA estimation approaches. However, the classical Jacobi methods are time-consuming. In literature, a parallel implementation has been adopted to accelerate the calculation of eigenvalues. In this paper, we propose to further decrease the execution time of this parallel method. In particular, each parallel unit of the proposed method uses one coordinate rotation digital computer (CORDIC) period per iteration, while more are required by the traditional counterparts, such that the eigenvalue decomposition of the MUSIC algorithm can be accelerated. In addition, the proposed method is implemented in an FPGA platform. The experimental results show that the proposed method is more computationally efficient.

In this article, we investigate the cellular assisted mobile edge computing (MEC) via non-orthogonal multiple access (NOMA), where a group of edge-computing users (EUs) exploit NOMA to simultaneously offload their computation-workloads to an edge-server (ES), and conventional cellular-user (CU) allows the EUs to reuse its authorized frequency channel for the NOMA-transmission. We firstly characterize the transmit-powers of the CU and EUs and then formulate a joint optimization of the EUs’ offloaded computation-workloads, offloading-duration (i.e., how long for reusing the CU's channel), as well as the ES's computation-resource allocations (for processing different EUs’ offloaded workloads), with the objective of minimizing the overall-latency in completing all EUs’ computation-requirements, subject to the CU‘s and EUs’ limited power and energy capacities as well as the ES's limited computation-resource capacity. Despite the strict non-convexity of the formulated joint optimization problem, we propose an efficient algorithm to compute the optimal offloading solution. With the optimal offloading solution for the scenario of one CU, we further investigate the scenario of multiple CUs and investigate the optimal pairing of the EUs for reusing different CUs’ channels for computation-offloading. Taking into account the coupling effect due to the ES's limited computation-resource, we formulate a joint optimization of the EU-pairing and the ES's capacity allocation of the computation-resource for accommodating different EU-pairs. Despite the difficulty due to the mixed binary and non-linear non-convex programming of the formulated problem, we propose an efficient layered algorithm for solving the problem. Numerical results are provided to validate the accuracy of our proposed algorithms. We also show the performance advantage of our NOMA-assisted offloading in comparison with conventional orthogonal multiple access (OMA) based computation offloading.

Precise estimation of transmission input&output shaft torque and wheel speed is significant to the development of torque distribution and coordinated control method for the power split hybrid powertrain system. This paper studies an estimation method of transmission input and output torque as well as drive wheel speed using an unknown input observer in different operating modes for a compound power split hybrid powertrain system. Firstly, the configuration and dynamic characteristic of power split transmission is analyzed. Then, state equations in different operating modes are established, and a Takagi-Sugeno (T-S) fuzzy model is utilized to deal with the nonlinearities of load resistance torque. Furthermore, an unknown input reduced-order observer is designed to estimate the transmission input&output shaft torque and wheel speed. The asymptotical stability of the developed observer is verified based on Lyapunov theory. Finally, the performance of the unknown input observer is assessed by comparing with the Luenberger observer based on Matlab/Simulink simulation platform and test bench. Simulation and experiment results show that, the proposed observer can estimate the transmission input&output shaft torque and wheel speed more effectively in different operating modes.

The secrecy outage probability (SOP) performance is investigated for a cooperative non-orthogonal multiple access system under eavesdropping attack, where a base station transmits signals to a far user via a full-duplex relay, and to a near user directly at the same time, in the presence of an eavesdropper. All nodes are configured with multiple antennas and antenna selection is adopted by the base station and relay to enhance the security performance. The exact and asymptotic closed-form SOP expressions are derived, which incorporate the impact of imperfect channel state information and apply to general cases considering the quality of service of the far and near users, whether or not the wiretap channel is known, and the location of the eavesdropper. Monte Carlo simulations corroborate the analysis and the superiority of the proposed system over the single-antenna system and the one without antenna selection. It is also revealed that the asymptotic SOP performance of the near user is nearly independent of the power allocation.

This paper proposes a novel probabilistic buffer-aided (BA) relay selection (RS) scheme for cooperative relay networks. Different from most existing BA RS schemes that are determined by the wireless channel condition and the buffer state, the proposed scheme also utilizes additional randomness to decide the link selection outcome. In particular, we first select the strongest available source-to-relay and relay-to-destination links, respectively, and then probabilistically select one from these two links. Some previously proposed BA RS schemes can be viewed as special cases of the proposed scheme, such as the max-link scheme and the scheme with reduced packet delay. Then, the outage probability and average packet delay are analyzed. Both the analytical and simulation results show that different tradeoffs between the outage probability and average packet delay can be easily achieved by adjusting a single corresponding parameter.

Due to cost reduction and device miniaturization, amateur drones are now widely used in numerous civilian and commercial applications. However, the abuse of amateur drones has resulted in emerging threats to personal privacy and public security. To alleviate these threats, we design an acoustic-based surveillance system, which can achieve the capacity of amateur drones detection and localization with 24/7 (24 hours per day and 7 days per week under normal circumstances) availability. In the designed system, a detection fusion algorithm and a TDOA estimation algorithm based on the Bayesian filter are applied to improve the performance of drone detection and localization. Field experiments are carried out, and the results demonstrate that the designed system can detect and locate an amateur drone in real time with high accuracy and 24/7 availability.

In this letter, we propose a novel cooperative non-orthogonal multiple access (NOMA) scheme to guarantee the secure transmission of a specific user via two time slots. During the first time slot, the base station (BS) transmits the superimposed signal to the first user and the relay via NOMA. Meanwhile, the signal for the first user is also decoded at the second user from the superimposed signal due to its high transmit power. In the second time slot, the relay forwards the signal to the second user while the BS retransmits the signal for the first user as interference to disrupt the eavesdropping. Due to the fact that the second user has obtained the signal for the first user in the first slot, the interference can be eliminated at the second user. To measure the performance of the proposed cooperative NOMA scheme, the outage probability for the first user and the secrecy outage probability for the second user are analyzed. Simulation results are presented to show the effectiveness of the proposed scheme.\ud

Unmanned aerial vehicles (UAVs) technique has been recognized as a promising solution in future wireless connectivity from the sky, and UAV navigation is one of the most significant open research problems, which has attracted wide interest in the research community. However, the current UAV navigation schemes are unable to capture the UAV motion and select the best UAV-ground links in real-time, and these weaknesses overwhelm the UAV navigation performance. To tackle these fundamental limitations, in this paper, we merge the state-of-the-art deep reinforcement learning with the UAV navigation through massive multiple-input-multiple-output (MIMO) technique. To be specific, we carefully design a deep Q-network (DQN) for optimizing the UAV navigation by selecting the optimal policy, and then we propose a learning mechanism for processing the DQN. The DQN is trained so that the agent is capable of making decisions based on the received signal strengths for navigating the UAVs with the aid of the powerful Q-learning. Simulation results are provided to corroborate the superiority of the proposed schemes in terms of the coverage and convergence compared with those of the other schemes.

Crowdsensing has been recognized as a promising data collection paradigm, in which a platform outsources sensing tasks to a large number of users. However, requesting users to report raw data may give rise to many practical concerns, such as a significant overhead of communication and central processing, besides users’ privacy concerns. In many scenarios (e.g, advertising and recommendation), the data collector directly benefits from statistical aggregation of raw data. Thus motivated, we consider the data collection problem based on user's local histograms, which is intimately related to the fundamental trade-off between the platform's accuracy and users’ privacy. Because of users’ social relationship, their data are often correlated, indicating that users’ privacy may be leaked from others’ data. To tackle this challenge, we first utilize Gaussian Markov random fields to model the correlation structure embedded in users’ data. The data collection is modeled as a Stackelberg game where the platform decides its reward policy and users decide their noise levels while taking into account the social coupling among users. For the reward policy design, we first establish the relationship between users’ Nash equilibrium and the payment mechanism, and then optimize the platform's accuracy under a budget constraint. Further, since the noise levels are users’ private information, they may use falsified noise levels to achieve higher payoffs, which in turn impairs the crowdsensing performance. It turns out that with the insight into the correlation structure among users’ data, the information asymmetry can be overcome based on peer prediction. We revisit the payment mechanism to guarantee dominant truthfulness of each user's strategy. Theoretical analysis and numerical results demonstrate the effectiveness of the proposed mechanism.

In this article, a comprehensive strategy of user clustering and power allocation is investigated in downlink hybrid non-orthogonal multiple access (NOMA) networks. More particularly, users in the same cluster can receive signals simultaneously by using NOMA techniques, while time-division multiple access (TDMA) schemes are utilized among different clusters. By employing the weight factor, a weighted sum rate maximization problem is formulated and decoupled into user clustering and power allocation problems, where two different schemes for time slot allocation are proposed. The formulated user clustering problems with user-based and cluster-based time slot allocation schemes are respectively considered as coalitional games in characteristic and partition formations, and solved by two different algorithms, where both low-complexity and global optimal methods are proposed. The properties, including complexity, convergence, stability and optimality, are analyzed. To further improve the system performance, the formulated power allocation problem is solved by a successive convex approximation (SCA) based iterative algorithm. Simulation results reveal that: i) the proposed hybrid NOMA system is capable of achieving promising gains over centralized NOMA-based and conventional TDMA-based frameworks; and ii) the developed algorithms can significantly improve the weighted sum rate compared with the random user structure and the fixed power allocation.

In this paper, resource allocation for a multi-carrier uplink non-orthogonal multiple access (NOMA) system is studied. Unlike the existing works on multi-carrier uplink NOMA, in which each user is assumed to access only one subcarrier, we consider a more general scenario where the number of subcarriers allocated to a single user is not constrained. We first aim to maximize the system's sum rate, which requires selecting the appropriate subcarriers for each user and distribute the transmission power. The formulated non-convex problem is transformed into a convex one, and further, an optimal and low-complexity iterative water-filling solution is proposed. Nonetheless, it is shown that maximum transmit power is employed by each user to maximize the sum rate. Motivated by the fact that the users are power constrained, the energy efficiency (EE) maximization problem is also studied. Based on fractional programming, the EE maximization problem is transformed into a series of sum rate maximization subproblems, and the proposed iterative water-filling solution is applied to each subproblem. The proposed schemes are compared with other NOMA-based and orthogonal multiple access based algorithms, and its superiority is fully validated.