Unmanned swarms can effectively improve the utility of tasks by collaboratively executing them in dynamic environments, but the heterogeneity of tasks and scarcity of spectrum resources lead to the dynamic matching of task resources becoming the core problem of improving utility. To address the interaction characteristics between the mission layer and the spectrum layer of unmanned swarm, this paper constructs a partially overlapping coalition formation game (POCFG) model, which is proposed to be an exact potential game with at least one Nash Equilibrium (NE). Inspired by the idea of parallel search of quantum superposition states and cooperative decision making of entangled states, a dual rationality guided quantum inspired partial overlapping coalition formation game (DRGQI-POCFG) algorithm is designed. The quantum entanglement state mechanism is utilized to correlate task allocation with the decision variables of spectrum resources. The simulation results show that the efficiency of joint task spectrum allocation is improved and the computational complexity is reduced. Compared with the selfish criterion algorithm, the Pareto algorithm and the non-joint allocation algorithm, the utility has increased by 12.2%, 26.4% and 34.6% respectively.

In space-air-ground integrated networks (SAGINs), the global energy efficiency (GEE) is a crucial metric for balancing the network throughput and energy consumption, and the maximization of GEE requires the optimizations of both user association and power allocation. Most existing methods optimize user association and power allocation separately or successively, relying on instantaneous non-local channel state information (CSI) exchanges. Nevertheless, both the separate and successive methods may fail to find the jointly optimal solution, and acquiring the instantaneous non-local CSI across the SAGINs is challenging due to the long communication distances between the access points (APs) and users. To address these issues, we leverage cloud-edge collaborations and propose an online delayed-interaction collaborative-learning independent-decision multi-agent DRL (DICLID-MADRL) algorithm. With the proposed algorithm, each AP can independently select users and configure transmit power with only local information to enhance GEE. Simulation results demonstrate that the proposed algorithm achieves a higher GEE with reduced time complexity compared to the state of the arts.

Joint channel selection and power control (JCSPC) is important to manage the interference in a heterogeneous network (HetNet), which consists of multiple base station (BS) and user equipment (UE) pairs. The JCSPC problem involves in a mixed continuous-discrete resource allocation and is typically NP-hard. Conventional methods usually obtain a quasi-optimal solution of the JCSPC problem in a centralized manner by assuming that the instantaneous global channel state information (CSI) is available. However, it is demanding to collect the instantaneous global CSI in practical scenarios. In this paper, we develop an intelligent cloud-edge collaboration assisted JCSPC algorithm. With the new algorithm, each BS can independently optimize its JCSPC policy with only local information, meanwhile enhance the sum-rate of the whole HetNet. Simulation results show that the proposed algorithm can achieve comparable and even better average sum-rate performance to the quasi-optimal method with a much lower time complexity.

This paper studies the global energy efficiency (GEE) optimization problem in a typical heterogeneous network (HetNet), where a macro base station (BS) and multiple micro BSs share the same spectrum band. Conventional optimization methods typically involve collecting global instantaneous channel state information (CSI) and utilizing centralized optimization algorithms to obtain the optimal transmit power and enhance the GEE. However, it is expensive to obtain the global instantaneous CSI in practical scenarios, and the optimization algorithms tend to be time-consuming. In this paper, we develop a lightweight cloud-edge collaboration framework based on the deep reinforcement learning (DRL) technique, such that the BSs in the edge do not need to exchange local instantaneous information with each other, and the core network in the cloud can collect only historical data rates and energy consumption information from each edge BS and feeds the calculated global reward back to them. Within the frame-work, we establish an independent actor-critic structure for each BS, and design a multi-agent independent actor-critic (MAIAC) power control algorithm, which enables each BS to determine its transmit power locally and enhance the GEE based on only local information. Simulation results indicate that the proposed MAIAC algorithm can achieve comparable GEE performance with conventional algorithms while requiring significantly less time complexity.

Human self-blockage can severely attenuate the mmWave signal and degrade the throughput, even in the absence of environmental blockages. Compared with environmental blockages, the human self-blockage is highly related to the direction of human movements, which has strong spatio-temporal correlations, and can be used to reduce beam training overheads meanwhile improve the throughput. In particular, we propose a convolutional long-short term memory (ConvLSTM) based deep spatio-temporal beam training algorithm, which can accurately infer the optimal beam by probing only a small portion of beams. Simulation results demonstrate that the proposed algorithm can provide a higher average throughput than the state of the arts.

As the demand for mobile data traffic continues to grow, offloading data traffic to unlicensed spectrum is a promising approach that can relieve the pressure on cellular systems. Therefore, it is an urgent need to propose an unlicensed spectrum access method to guarantee the harmonious and efficient coexistence between cellular network technologies such as LTE and incumbent users such as WiFi in the unlicensed spectrum. However, existing coexistence schemes such as licensed assisted access (LAA) and LTE-unlicensed (LTE-U) still suffer from inefficient spectrum utilization and unsatisfactory fairness. In the paper, we formulate the optimization problem of the unlicensed spectrum access among multiple small bases (SBSs) as a game, and then solve the Nash Equilibrium (NE) with cooperative and distributed multi-agent deep reinforcement learning (MADRL). Specifically, a two level access framework for the coexistence scenario, which consists of feedback cycle and executive cycle, is first proposed, and then the key elements of MADRL including state, action, reward and Q-network are designed in detail based on the proposed access framework. To overcome the problems of learning divergence and prohibitive computation overhead in the coexistence scenario with multiple SBSs due to the non-stability phenomena, we adopt the mean field technology to solve the NE, which can simplify the process of solving NE by converting the interaction of an agent with the remaining multiple agents into an action with the average effect of them. Simulation results show that 1) the proposed algorithm can overcome the learning divergence problem and converge to the NE quickly, and 2) the proposed algorithm can achieve the bi-objective optimization of total throughput and fairness of the coexistence network, and can achieve better performance in terms of throughput and fairness compared with the baseline methods such as Cat-4 LBT, Cooperative LBT and Random schemes.

We consider a typical heterogeneous network (HetNet), which consists of a macro base station (BS) and multiple small BSs sharing the same spectrum band. Since the spectrum sharing among different BS-user links may cause severe mutual interference and degrades the global energy efficiency (GEE), it is important to optimize the transmit power of each BS and enhance the GEE. Conventional methods first collect the global instantaneous channel state information (CSI) and then optimize the transmit power in a centralized manner. Nevertheless, it is demanding to obtain the global instantaneous CSI in practical situations and the centralized optimization may easily overwhelm the coherence time of wireless channels. To tackle these issues, we leverage the strong computing capability of the (cloud) core network and the fast configuration capability of (edge) BSs and propose an intelligent cloud-edge collaboration framework. By properly designing the cloud-edge collaboration, we develop a deep reinforcement learning (DRL) based energy efficient power control algorithm. With the proposed algorithm, each BS can configure its transmit power independently and enhance the GEE. Simulation results reveal that, in both static-user and mobile-user scenarios, the proposed algorithm can provide comparable GEE performance with the conventional methods while requiring a much lower time complexity.

Highly directional beams in millimeter wave (mmWave) communications necessitate beam training or alignment between the access point (AP) and the user equipment (UE), and exhausted beam search (EBS) method is suggested in the current 3GPP standard. Nevertheless, EBS suffers from high overheads and inevitably lowers the throughput, especially when the beam space is large. In this paper, we utilize the spatial correlation among different beams as well as the strong feature extraction/representation capability of the deep convolutional neural network (CNN), and propose an intelligent beam training algorithm. With the proposed method, the AP can probe only a fixed subset of the whole beam space and identify the optimal beam intelligently. Simulation results show that, the proposed method can largely reduce the overheads for the beam training meanwhile boost the throughput performance compared with the state of the arts.

In a typical heterogeneous network (HetNet), in which a macro base station (BS) and multiple small BSs coexist on the same spectrum band, energy-efficiency (EE) performance is an important design metric and is highly related to the transmit power of BSs. Conventional methods optimize BSs’ transmit power to enhance the EE by assuming that the global channel state information (CSI) is available. However, it is challenging or expensive to collect the instantaneous global CSI in the HetNet. In this paper, we utilize deep reinforcement learning (DRL) technique to design an intelligent power control algorithm, with which each BS can independently determine the transmit power based on only local information. Simulation results demonstrate that the proposed algorithm outperforms conventional methods in terms of both EE performance and time complexity.

Device-to-device (D2D) technology, which allows direct communications between proximal devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic explosion problem. In this paper, we consider an overlay D2D network, in which multiple D2D pairs coexist on several orthogonal spectrum bands, i.e., channels. Due to spectrum scarcity, the number of D2D pairs is typically more than that of available channels, and thus multiple D2D pairs may use a single channel simultaneously. This may lead to severe co-channel interference and degrade network performance. To deal with this issue, we formulate a joint channel selection and power control optimization problem, with the aim to maximize the weighted-sum-rate (WSR) of the D2D network. Unfortunately, this problem is non-convex and NP-hard. To solve this problem, we first adopt the state-of-art fractional programming (FP) technique and develop an FP-based algorithm to obtain a near-optimal solution. However, the FP-based algorithm requires instantaneous global channel state information (CSI) for centralized processing, resulting in poor scalability and prohibitively high signalling overheads. Therefore, we further propose a distributed deep reinforcement learning (DRL)-based scheme, with which D2D pairs can autonomously optimize channel selection and transmit power by only exploiting local information and outdated nonlocal information. Compared with the FP-based algorithm, the DRL-based scheme can achieve better scalability and reduce signalling overheads significantly. Simulation results demonstrate that even without instantaneous global CSI, the performance of the DRL-based scheme can approach closely to that of the FP-based algorithm.

We consider a typical heterogeneous network (HetNet), in which multiple access points (APs) are deployed to serve users by reusing the same spectrum band. Since different APs and users may cause severe interference to each other, advanced power control techniques are needed to manage the interference and enhance the sum-rate of the whole network. Conventional power control techniques first collect instantaneous global channel state information (CSI) and then calculate sub-optimal solutions. Nevertheless, it is challenging to collect instantaneous global CSI in the HetNet, in which global CSI typically changes fast. In this article, we exploit deep reinforcement learning (DRL) to design a multi-agent power control algorithm, which has a centralized-training-distributed-execution framework. To be specific, each AP acts as an agent with a local deep neural network (DNN) and we propose a multiple-actor-shared-critic (MASC) method to train the local DNNs separately in an online trial-and-error manner. With the proposed algorithm, each AP can independently use the local DNN to control the transmit power with only local observations. Simulations results show that the proposed algorithm outperforms the conventional power control algorithms in terms of both the converged average sum-rate and the computational complexity.

To manage the interference and enhance the sum-rate of the heterogeneous network (HetNet), conventional power control algorithms first collect instantaneous global channel state information (CSI) and then design sub-optimal power control solutions. But, the global CSI in the HetNet typically changes fast and it is demanding to collect instantaneous global CSI. In this paper, we exploit deep reinforcement learning to design a multi-agent non-cooperative power control algorithm. Particularly, a deep neural networks (DNN) is established at each access point (AP) and a multiple-actor-shared-critic (MASC) method is developed to effectively train the DNNs. Then, each AP can use the DNN to independently optimize the transmit power by feeding only local information into the DNN. Simulation results show that, the proposed algorithm can rapidly converge to an average sum-rate higher than those of conventional power control algorithms.

Operating LTE networks in unlicensed bands together with legacy WiFi systems is deemed as a promising technique to support explosively growing mobile traffic. In conventional LTE/WiFi spectrum sharing schemes, LTE systems need to know WiFi traffic demands for optimizing system parameters to protect WiFi systems, for which the two systems are required to cooperate with each other via signallings exchanges. However, it is difficult to establish a dedicated channel among the two independent systems for exchanging signallings. Hence, in this paper, we propose an intelligent duty-cycle medium access control protocol to realize the effective and fair spectrum sharing between LTE and WiFi systems without requiring signalling exchanges. Specifically, we first design a duty-cycle spectrum sharing framework, which allows an LTE system to share the spectrum with a WiFi system by using time sharing. After that, we develop deep reinforcement learning (DRL)-based algorithms to learn WiFi traffic demands by analyzing WiFi channel activity, e.g., the idleness/business of WiFi channels, which can be observed by the LTE system via monitoring WiFi channels. Based on the learnt knowledge, the LTE system can adaptively optimize LTE transmission time to maximize its own throughput and meanwhile to provide sufficient protection to the WiFi system. Simulation results show that, in terms of LTE throughput and WiFi protection, the performance of the proposed intelligent scheme can approach that of the genie-aided exhaustive search algorithm, which needs the perfect knowledge of WiFi traffic demands through massive signalling exchanges and is of high computational complexity.

Unmanned aerial vehicles (UAVs) have recently been proposed as moving base stations to collect data from ground IoT nodes in remote areas. Since IoT nodes are normally battery-limited, energy efficiency is an important metric in IoT systems. In order to improve energy efficiency in UAV-enabled IoT systems, it is necessary to allocate both channels and transmit power properly for IoT nodes. Motivated by the superior performance of deep reinforcement learning (DRL) in decision-making tasks, we propose a DRL-based channel and power allocation framework in a UAV-enabled IoT system. With the proposed framework, the UAV-BS is able to intelligently allocate both channels and transmit power for uplink transmissions of IoT nodes to maximize the minimum energy-efficiency among all the IoT nodes. Simulation results validate the effectiveness of the proposed algorithm and show its superiority over the- state-of-the-arts.

In this paper, we study the resource allocation design for downlink multicarrier non-orthogonal multiple access systems with short-packet communications (MC-NOMA-SPC). In contrast to long- packet communications in conventional wireless systems, SPC suffers from a transmission rate degradation and a significant decoding error rate. Thus conventional resource allocation design based on the Shannon capacity assuming infinite blocklength is no longer optimal. In this paper, we employ the effective-throughput as the performance metric to evaluate the tradeoff between the transmission rate and the decoding error rate. Then, we jointly optimize the subcarrier assignment, transmission power allocation, and transmission rate adaptation of each user to maximize the total weighted effective-throughput subject to various practical constraints. Since the problem formulated belongs to a non-convex mixed integer non-linear programming (MINLP) problem, we develop an efficient algorithm based on the dynamic programming (DP) recursion framework to obtain its optimal solutions. In addition, we analyze the complexity of the proposed algorithm theoretically. Finally, simulation results show that the proposed optimal algorithm outperforms the suboptimal baseline schemes significantly.

As a promising candidate to alleviate the mobile traffic explosion, device-to-device (D2D) technology enables the direct communications between proximal devices. To mitigate the mutual interference among D2D pairs and improve the spectrum efficiency, this paper investigates a weighted-sum-rate (WSR) maximization problem in multi-channel D2D networks. Particularly, we propose a deep reinforcement learning based scheme for each D2D pair to make decisions independently on the channel selection and power control. In contrast to the conventional methods that require instantaneous global network information, the proposed scheme only needs local information and outdated feedbacks. Simulation results demonstrate that, in terms of the WSR, the proposed scheme outperforms the conventional suboptimal fractional programming algorithm that requires the instantaneous global network information.

Unmanned Aerial Vehicles (UAVs) have recently been proposed as flying base stations, called UAV-BSs, to provide reliable connections and extend the coverage of the existing wireless networks. The mobility of UAV-BSs leads to a dynamic network environment, in which the global network information is hard to be obtained. Since frequent information exchanges cause huge signaling overheads, it is difficult to deploy centralized algorithms in UAV networks. Hence, we propose a distributed deep reinforcement learning (DRL) framework for multi-user access control in UAV networks. In particular, each user makes its own access decisions independently based on the local network information, and maximizes the long-term throughput while avoiding frequent handovers. Simulation results have validated the effectiveness of the proposed algorithm and shown the superiority of the proposed DRL framework over the state of arts.

As a promising technique to handle the conflict between the explosive mobile traffic and scare spectrum resource, license-assisted access (LAA) has been proposed to operate the LTE network on the unlicensed band. This paper considers the LAA-LTE system coexisting with an unsaturated WiFi system. Specifically, deep reinforcement learning (DRL) is adopted to enable the LAA-LTE system to learn the traffic pattern of the WiFi system and adaptively optimize its transmission time in each frame. Different from conventional coexistence schemes, which require massive signaling exchanges between the two systems to achieve fairness, the proposed DRL-based algorithm can maximize the spectrum usage while protecting the WiFi system without such signaling requirements. Simulation results demonstrate that the proposed scheme can achieve almost the same LAA-LTE throughput and protection to the WiFi system of the genie-aided exhaustive search algorithm, which has high complexity and requires to know the WiFi information perfectly.

Non-orthogonal multiple access (NOMA) is envisioned to be a key technology to enhance the spectrum efficiency for 5G cellular networks. Meanwhile, ambient backscatter communication (AmBC) is considered as a promising solution to Internet-of-Things (IoT), due to its high spectrum- and power-efficiency. In this paper, we are interested in an integrated system of cellular and IoT networks, and propose a backscatter-NOMA system, which incorporates a downlink NOMA system with backscatter devices. In this system, the base station (BS) transmits information to two cellular users according to the NOMA protocol, while a backscatter device transmits its information over the BS signals to one cellular user using passive radio technology. We derive the closed-form expressions of the outage probabilities and analyze the diversity orders of all relevant transmissions in the proposed system. Finally, we provide numerical results to verify the theoretical analysis.

We study a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users coexist with a pair of primary users on a certain spectrum band. To protect primary transmissions, secondary transmitters (STs) adopt a sensing-based approach to access the spectrum band. Nevertheless, STs may cause uncertain interference to the primary receiver (PR) due to imperfect spectrum sensing, which is particularly significant when the wireless links between the primary transmitter (PT) and STs are extremely weak and the wireless links between STs and the PR are non-ignorable. This makes it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with the issue, we propose an intelligent deep reinforcement learning (DRL) based MCS selection algorithm for the primary transmission. With the proposed algorithm, the DRL agent at the PR is able to learn the pattern of the interference from the STs and predict the interference in the future. Simulation results show that the transmission rate of the proposed algorithm can converge to 90% ^ 100% transmission rate of the optimal MCS selection algorithm, which assumes that the interference from the STs is perfectly known at the PR as prior information. Meanwhile, the transmission rate of the proposed algorithm is around 100% higher than the transmission rate of the benchmark algorithm, which selects the MCS without the information about interference.

Massive wireless connections are emerging in Internet of Things (IoT) and will lead to a severe spectrum scarcity issue. To deal with this issue, we introduce the cognitive radio technology into the IoT, namely, cognitive IoT. Different from a conventional cognitive network, the cognitive IoT is dominated by short-packet transmissions, which suffer from a significant packet error rate even when the transmission rate is smaller than the Shannon capacity. In this paper, we jointly optimize the spectrum sensing time and packet error rate to maximize the cognitive effective-throughput, which is defined as the effective transmission rate by considering the packet error rate. First, we formulate an instantaneous effective-throughput maximization problem with the instantaneous channel state information (CSI) between cognitive transceivers, and develop a successive optimization algorithm. Second, we formulate an average effective-throughput maximization problem with the statistical CSI between cognitive transceivers. Due to the complicated expression of the average effective-throughput, we analyze its closed-form expression and adopt an exhaustive search method to obtain the optimal solution. Numerical and simulation results reveal that, the packet length has a significant impact on the optimal design. Meanwhile, the proposed algorithms can almost maximize the instantaneous/average effective-throughput.

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to coherently detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit a Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital-domain channel estimation and SIC. The frame structure of the designed FDT contains two successive phases: labeling phase and data transmission phase. In particular, the designed FDT performs cluster labeling in the labeling phase and performs GMM clustering based on an expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the theoretical analysis about the detection performance, computational complexity, and convergence performance for the designed FDT are studied. Finally, simulation results show that the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) or least mean square (LMS) channel estimator.

Internet-of-Things (IoT) is a promising technology to connect massive machines and devices in the future communication networks. In this paper, we study a wireless-powered IoT network (WPIN) with short packet communication (SPC), in which a hybrid access point (HAP) first transmits power to the IoT devices wirelessly, then the devices in turn transmit their short data packets achieved by finite blocklength codes to the HAP using the harvested energy. Different from the long packet communication in conventional wireless network, SPC suffers from transmission rate degradation and a significant packet error rate. Thus, conventional resource allocation in the existing literature based on Shannon capacity achieved by the infinite blocklength codes is no longer optimal. In this paper, to enhance the transmission efficiency and reliability, we first define effective-throughput and effective-amount-of-information as the performance metrics to balance the transmission rate and the packet error rate, and then jointly optimize the transmission time and packet error rate of each user to maximize the total effective-throughput or minimize the total transmission time subject to the users' individual effective-amount-of-information requirements. To overcome the non-convexity of the formulated problems, we develop efficient algorithms to find high-quality suboptimal solutions for them. The simulation results show that the proposed algorithms can achieve similar performances as that of the optimal solution via exhaustive search, and outperform the benchmark schemes.

We consider a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users adopt sensing-based approaches to coexist with a pair of primary users on a certain spectrum band. Due to imperfect spectrum sensing, secondary transmitters (STs) may cause interference to the primary receiver (PR) and make it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with this issue, we exploit deep reinforcement learning (DRL) and propose an intelligent MCS selection algorithm for the primary transmission. To reduce the system overhead caused by the MCS switchings, we further introduce a switching cost factor in the proposed algorithm. The simulation results show that the primary transmission rate of the proposed algorithm without the switching cost factor is 90% ~ 100% of the optimal MCS selection scheme, which assumes that the interference from the STs is perfectly known at the PR as prior information, is 30% higher than that of the upper confidence bandit (UCB) algorithm, and is 100% higher than that of the signal-to-noise ratio (SNR)-based algorithm. Meanwhile, the proposed algorithm with the switching cost factor can achieve a higher primary transmission rate than those of the benchmark algorithms without increasing system overheads.

Since it is challenging to position the noncooperative receiver (Rx), especially when the backward frequency band of the Rx cannot be obtained by the anchors. In this paper, we propose a novel noncooperative Rx positioning method using full-duplex relay technique, where the anchors act as full-duplex relays for the Rx. It can trigger the close-loop-power-control (CLPC) between the transmitter (Tx) and the Rx, which contains the information of the Rx's location. By measuring the received signal from the Tx, the anchors can estimate the location of the Rx. Simulation results demonstrate the performance of the proposed Rx positioning method, and the root-mean-square-error (RMSE) can reach about 30%, which is similar to the conventional Tx positioning using received-signal-strength (RSS).

In underlay heterogeneous networks (HetNets), the distance between a macro base station (MBS) and a macro user (MU) is crucial for a small-cell based station (SBS) to control the interference to the MU and achieve the coexistence. To obtain the distance between the MBS and the MU, the SBS needs a backhaul link from the macro system, such that the macro system is able to transmit the information of the distance to the SBS through the backhaul link. However, there may not exist any backhaul link from the macro system to the SBS in practical situations. Thus, it is challenging for the SBS to obtain the distance. To deal with this issue, we propose a median based (MB) estimator for the SBS to obtain the distance between the MBS and the MU without any backhaul link. Numerical results show that the estimation error of the MB estimator can be as small as $4\%$.

In this paper, a multi-user multi-relay network with wireless energy harvesting (EH) and transferring (ET) is studied. In our system, a simultaneous two-level cooperation, i.e., information-level and energy-level cooperation is conducted for uplink data transmissions (from the users to a destination). Specifically, network coding is employed at the relays to facilitate the information-level cooperation; meanwhile, ET is adopted to share the harvested energy among the users for the energy-level cooperation. The energy minimization problem that takes into account the energy causality and outage probability constraints is formulated. However, the optimization problem is non-convex and hard to be solved directly. Alternatively, an approximation technique is adopted to convert it into a convex one. By solving the convex problem, efficient power allocation and ET policies are designed. Numerical results show that the proposed algorithm is able to achieve a near-optimal performance and outperforms the state of arts.

We study a downlink non-orthogonal multiple access (NOMA) system with cooperative full-duplex relaying, where the near user in terms of the base station (BS) is enabled to act as a full-duplex relay for the far user. In particular, we first derive the outage probability with closed-form expressions when the power allocations at the BS and relay (or the near user) are fixed. Then, we analytically obtain the optimal power allocations with closed-form expressions at the BS and relay to minimize the outage probability. Numerical results validate the correctness of the theoretical analysis and demonstrate the advantages of the proposed algorithms over the state of arts.

In an underlay cognitive radio network, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-channel gain. In particular, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the instantaneous cross-channel gain by observing the power adaption. Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of art, we show the advantages of the proposed proactive estimator.

In cognitive radio networks, the channel gain between primary transceivers, namely, primary channel gain, is crucial for a cognitive transmitter (CT) to control the transmit power and realize spectrum sharing. To obtain the primary channel gain, a backhaul between the primary system and the CT is needed. However, the backhaul is usually unavailable in practice. To deal with this issue, the CT is enabled to sense primary signals and estimate the primary channel gain in this paper. In particular, two estimators, namely, a high-complexity maximum likelihood (ML) estimator and a low-complexity median based (MB) estimator are proposed. Numerical results show that the ML estimator outperforms the MB estimator in terms of the accuracy if the signal to noise ratio (SNR) of the received primary signals at the CT is no smaller than 4 dB. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and accuracy.

We study a downlink non-orthogonal multiple access system with cooperative full-duplex relaying, where the near user in terms of the base station (BS) is enabled to act as a full-duplex relay for the far user. In particular, we first derive the outage probability and ergodic sum rate with closed-form expressions when the power allocations at the BS and relay (or the near user) are fixed. Then, we analytically obtain the optimal power allocations with closed-form expressions at the BS and relay to minimize the outage probability. Furthermore, by taking the fairness between the near user and far user into account, we characterize the optimal power allocations with closed-form expressions at the BS and relay to maximize the minimum achievable rate of users. Simulation results validate the correctness of the theoretical analysis and demonstrate the advantages of the proposed algorithms over the state of the art.

In cognitive radio networks, the channel gain between primary transceivers, namely, primary channel gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary channel gain is estimated in the primary system, and thus unavailable at the CT. To deal with the issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signal, an ML estimator is first developed. To reduce the computational complexity of the ML estimator, a median-based (MB) estimator is then proposed. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Simulation results show that the estimation errors of both estimators can be as small as 0.015. Meanwhile, the ML estimator outperforms the MB estimator in terms of the estimation accuracy if the sensed primary signal at the CT is weak. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and the estimation accuracy.

In this paper, a multi-user multi-relay network with integrated energy harvesting and transferring (IEHT) strategy is studied. In our system, a simultaneous two-level cooperation, i.e., information- and energy-level cooperation is conducted for uplink data transmissions (from the users to a destination). Specifically, network coding is employed at the relays to facilitate the information-level cooperation; meanwhile, ET is adopted to share the harvested energy among the users for the energy-level cooperation. For generality purposes, the Nakagami-m fading channels that are independent but not necessarily identically distributed (i.n.i.d.) are considered. The problem of energy efficiency maximization under constraints of the energy causality and a predefined outage probability threshold is formulated and shown to be non-convex. By exploiting fractional and geometric programming, a convex form-based iterative algorithm is developed to solve the problem efficiently. Close-to-optimal power allocation and energy cooperation policies across consecutive transmissions are found. Moreover, the effects of relay locations, wireless energy transmission efficiency, battery capacity as well as the existence of direct links are investigated. The performance comparison with the current state of solutions demonstrates that the proposed policies can manage the harvested energy more efficiently.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access the licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the EE maximization as a joint optimization problem of the spectrum sensing duration and the transmit power of the cognitive transmitter. Specially, we consider the constraints of both the collision probability between the primary and cognitive transmission and the outage probability of the cognitive transmission. Since the joint optimization problem of the sensing duration and the transmit power is hard to be solved directly, we decompose it into two sub-problems with the spectrum sensing duration and the transmit power as variables, respectively. Based on the analytical solvers of the two sub-problems, we propose an iterative-based algorithm to solve the joint optimization problem. Finally, numerical results are provided to validate the analysis and performance of our proposed algorithms.

The problem of the centralized caching is studied in a two-layer network. The first layer of the network is constructed by a server and K1helpers, and the second layer consists of K1orthogonal sub-networks, each of which contains a helper and K2users. The pioneer caching design in the two-layer network is to directly apply the Maddah-Ali & Niesen (MAU) centralized caching [1] into individual layers, such that single-layer multicast opportunities (SMO) are deployed. In this paper, a joint caching (JC) algorithm is developed by exploiting both the SMO and the correlations of caching contents across two layers, namely, cross-layer storage correlations (CSC). Furthermore, cross-layer multicast opportunities (CMO) can also be created by applying the MAU scheme between the server and users. In order to simultaneously obtain the caching gains from SMO, CSC, and CMO, a hybrid caching scheme is proposed and demonstrated to be order-optimal when the storage sizes at both helpers and users are limited. In other words, the achievable rate region lies within a constant multiplicative and additive gap of the information-theoretic bounds. In particular, the multiplicative and additive factors can be carefully characterized to be 1/48 and 4, respectively.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access a licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the average EE maximization problem in fading channels as a joint optimization problem of the spectrum sensing duration and the transmit power of cognitive users. Meanwhile, we consider the constraints of both the collision probability between the primary and cognitive transmissions and the outage probability of cognitive transmissions. However, the joint optimization problem subject to the constraints is complicated and it is computationally hard to obtain the optimal solution. Alternatively, we develop two algorithms, i.e., a linear search algorithm and an iterative-based algorithm, with considerable complexity to solve the problem. Numerical results verify the correctness of both algorithms and show that the proposed algorithms can achieve the performance close to that of the exhaustive search algorithm and outperform the state of arts.

In an underlay cognitive radio network, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-channel gain. Specifically, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the cross-channel gain by observing the power adaption. To demonstrate the accuracy of the estimation, we analytically characterize both an upper bound and a lower bound of the estimation performance. Furthermore, we study the impact of CT's relaying on the primary transmission and observe that the impact is related to the CT's location. By introducing a factor φ (0 ≤ φ ≤ 1) to denote the probability that the CT's relaying improves the primary transmission instead of causes interference, we design the CT location as a function of φ. Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of the art, we show the advantages of the proposed estimator.

We study a one-hop device-to-device (D2D)-assisted wireless caching network, where popular files are randomly and independently cached in the memory of end users. Each user may obtain the requested files from its own memory without any transmission, or from a helper through a one-hop D2D transmission, or from the base station. We formulate a joint D2D link scheduling and power allocation problem to maximize the system throughput. However, the problem is non-convex, and obtaining an optimal solution is computationally hard. Alternatively, we decompose the problem into a D2D link-scheduling problem and an optimal power allocation problem. To solve the two subproblems, we first develop a D2D link-scheduling algorithm to select the largest number of D2D links satisfying both the signal to interference plus noise ratio and the transmit power constraints. Then, we develop an optimal power allocation algorithm to maximize the minimum transmission rate of the scheduled D2D links. Numerical results indicate that both the number of the scheduled D2D links and the system throughput can be improved simultaneously with the Zipf-distribution caching scheme, the proposed D2D link-scheduling algorithm, and the proposed optimal power allocation algorithm compared with the state of the arts.

Recently, a proactive estimation method has been proposed to obtain the cross-channel gain from cognitive transmitter (CT) to primary receiver (PR), where CT acts as a full-duplex amplified-and-forward (AF) relay for primary users. However, since the cognitive and primary users usually have no cooperation, it has no guarantee that the direct and relay signals are synchronized, i.e, the time delay between direct and relay signals may be randomly large or small. This may cause interference to PR. In this paper, we analyze the impacts of the small and large time delays on proactive estimation, and propose an interference-free method to estimate the cross-channel gain. Simulation results demonstrate that the proposed method obtains about 5% estimation error with about 99% interference-free probability.

In this paper, we develop a new proactive estimation method to obtain the cross-channel gain from cognitive transmitter to primary receiver without any backhaul between cognitive and primary users. In conventional proactive methods, the jamming signal is used for probing, which introduces the extra interference to primary receivers. In our method, the relayed primary signal is used for probing, which instead assists the primary transmission. Simulation results demonstrate that the proposed method with 2% estimation errors can obtain up to about 72% throughput improvement introduced by the cross-channel gain.

In this paper, we will propose a novel method to estimate the cross channel gain between cognitive transmitter to primary receiver as well as the primary channel gain between primary transceivers, where the cognitive user is acting as a relay to proactively trigger the primary link adaptation. But, this kind of estimator may obtain two possible estimations for each channel, which may confuse the cognitive user. Thus, we will further develop a selection method to pick the estimation with less errors. Simulation results show that the proposed method can effectively improve the estimation performance.

In cognitive radio systems, the channel gains between primary users (PUs) and cognitive users (CUs) and that between PUs are critical for the coexistence of CUs and PUs. In this paper, we propose a proactive channel gain estimation approach by using the received primary signal for probing, which obtains both kinds of channel gains without information exchange between CUs and PUs. In average, the probing in our proactive approach does not introduce interference to PUs while conventional ones usually do. Simulation results show that the relative estimation errors of the proposed approach are below 0.02 with a proper CU location, where the channels suffer path loss and shadowing, and their gains range from about -120 dB to about -50 dB.