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Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). The authors in (Chiang et al., 2007) proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g., tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small- and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm. [ABSTRACT FROM AUTHOR]

This paper studies how to apply game theory to realize a well-function device-to-device enabled vehicular (D2D-V) communication system, where the uplink channel allocated to the cellular user (CU) is reused by multiple D2D-V users. Considering a non-cooperative setting where the CU and D2D-V users are selfish and profit-driven, a novel Stackelberg game framework is proposed to model the single-leader-multiple-follower hierarchical competition, where the CU and D2D-V users act as the leader and the followers, respectively. For the current D2D-V networks, the interference in the dense vehicle scene often leads to extremely poor communication quality. Moreover, a vehicle's mobility leads to an uncertain channel state and further affects the stability of communication. To achieve effective communication, robust Stackelberg game-based resource allocation is developed, and a price-penalty mechanism is further proposed. Unlike previous Stackelberg games, the robust game is highlighted by handling the channel uncertainty which is embedded in the interference probability constraints. Besides, a game equilibrium (GE) is considered to be the solution and its existence and uniqueness are investigated. Also, a distributed robust power control and nonuniform price bargaining algorithm is proposed to approach the GE. Numerical simulations are performed to evaluate the algorithm performances, and the results indicate that the proposed algorithm is effective in high mobility vehicular networks under uncertain channel environments. [ABSTRACT FROM AUTHOR]

This paper proposes a flexible pilot assignment method to jointly optimize the uplink and downlink data transmission in multi-cell Massive multiple input multiple output (MIMO) systems with correlated Rayleigh fading channels. By utilizing a closed-form expression of the ergodic spectral efficiency (SE) achieved with maximum ratio processing, we formulate an optimization problem for maximizing the minimum weighted sum of the uplink and downlink SEs subject to the transmit powers and pilot assignment sets. This combinatiorial optimization problem is solved by two sequential algorithms: a heuristic pilot assignment is first proposed to obtain a good pilot reuse set and the data power control is then implemented. Numerical results manifest that the proposed algorithm converges fast to a better minimum sum SE per user than the algorithms in previous works. [ABSTRACT FROM AUTHOR]

It has been well-recognized that clustered Interference alignment (IA) can provide remarkable interference suppression performance for the existing small cell networks (SCNs). There is also a tendency that full-duplex (FD) radios would replace the half-duplex radios at future small base stations (SBSs). In this context, the intra-cell and inter-cell interference in SCNs would become much more serious, where the performance of clustered IA has not been evaluated yet. In this paper, we explore the maximum achievable sum-rate of the FD-based SCNs when clustered IA combined with power control strategy is applied. To achieve this, a mixed-integer optimization problem is formulated, which is furtherly decoupled into two subproblems for ease of handling. Then we propose the minimized rate loss (MRL) algorithm to address the clustering subproblem and a convex approximation method to address the power control subproblem. The two subproblems are performed alternatively till the sum-rate gains convergence. Preliminary simulations clearly demonstrate that the achievable sum-rate is limited by the number of antennas at the users. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been widely applied for classification tasks in wireless communications such as link scheduling, user association and base station (BS) muting. When training a DNN using datasets with highly-skewed class distribution where most data belong to a few majority classes, learning performance for minority classes will degrade since the imbalanced data forces the training process to be biased towards the majority classes. In many wireless problems, multiple decisions for classification are made jointly, say deciding which BSs in a network should be muted to avoid interference. In this paper, we employ DNN to learn an optimal predictive interference coordination policy, and strive to avoid biased learning in such a multi-label multi-class classification problem. The major contribution is proposing a training method to encourage fairness among classes by minimizing the maximal cost of decisions among classes, which is converted into a problem to optimize the weighting factors on the training cost of each class. We provide a gradient descent algorithm to optimize the weighting factors and the model parameters of the DNN by alternative updates. Simulation results show that the training method can improve the learning performance for the minority class and can achieve higher network utility than existing training methods. [ABSTRACT FROM AUTHOR]

Both Device-to-device (D2D) and full-duplex (FD) have been widely recognized as spectrum efficient techniques in the fifth-generation (5G) networks. By combining them, the FD-D2D aided underlaying networks (FN) has exhibited considerable technical advantages in terms of both spectral efficiency (SE) and energy efficiency (EE). Considering the fact that the performance of FN may be severely affected by users’ workload, the workload-driven FN (WFN) must be investigated. In this paper, a deep learning based transmit power allocation (TPA) method is proposed for automatically determining the optimal transmit powers of co-spectrum cellular users (CUs) and D2D users (DUs) relying on a deep neural network. Unlike the conventional transmit-power-control schemes, in which complex optimization problems must be addressed in an iterative manner (it usually requires a relative longer computational time), the proposed scheme enables each DU to determine its transmit power with a relatively shorter time. Furthermore, an improved iterative subspace-pursuit algorithm, as the performance benchmark, is formulated for WFN. In addition, to reflect the influence imposed by the workload, the penalty-based statistical sum-date-rate (PSS) can be employed as the performance metric of WFN. Numerical results show that the proposed scheme is capable of achieving a PSS comparable with that of the traditional iterative-based algorithms even under heavy-workload scenarios, but the computational complexity of the former can be significantly reduced. [ABSTRACT FROM AUTHOR]

This paper investigates subchannel allocation based on clustered interference alignment in dense small cell networks when all small cell user equipments (SUEs) have differentiated requirements for data streams. By imposing the condition that each cluster has a size not exceeding the maximum value achieved when each SUE needs only one data stream, we maximize the number of SUEs with guaranteed requirements for data streams, which is NP-hard. Hence, we propose a two-phase efficient solution with much lower complexity and reduced feedback overhead. First, similarity clustering is performed by graph partition, and then, subchannel allocation is done through a coloring algorithm. Numerical results show that the proposed solution offers a performance better than the related schemes and close to the approximate optimal solution. [ABSTRACT FROM AUTHOR]

The millimeter wave (mmWave) communication has drawn intensive attention with abundant band resources. In this paper, we consider the popular content distribution (PCD) problem in the mmWave vehicular network. In order to offload the communication burden of base stations (BSs), vehicle-to-vehicle (V2V) communication is introduced into the PCD problem to transmit contents between on-board units (OBUs), and improve the transmission efficiency. We propose a full-duplex (FD) cooperative scheme based on coalition formation game, and the utility function is provided based on the maximization of the number of received contents. The contribution of each member in the coalition can be transferable to its individual profit. While maximizing the number of received contents in the fixed time, the cooperative scheme also ensures the individual profit of each OBU in the coalition. We evaluate the proposed scheme by extensive simulations in mmWave vehicular networks. Compared with other existing schemes, the proposed scheme has superior performances on the number of possessed contents, and system fairness. Besides, the low complexity of the proposed algorithm is demonstrated by the switch operation number, and CPU time. [ABSTRACT FROM AUTHOR]

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems. [ABSTRACT FROM AUTHOR]

Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency. In this paper, we focus on an IRS-assisted NOMA network and propose an energy-efficient algorithm to yield a good tradeoff between the sum-rate maximization and total power consumption minimization. We aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming at the BS and the reflecting beamforming at the IRS. Specifically, the transmit beamforming and the phases of the low-cost passive elements on the IRS are alternatively optimized until the convergence. Simulation results demonstrate that the proposed algorithm in IRS-NOMA can yield superior performance compared with the conventional OMA-IRS and NOMA with a random phase IRS. [ABSTRACT FROM AUTHOR]

In this paper, we present a civil aircrafts augmented space-air-ground integrated networks (CAA-SAGIN) architecture, where each mobile user can access multiple platforms simultaneously. With the target of ensuring fairness, we put forward the fair optimization strategies including resource allocation and resource auction. In the resource allocation, we formulate a service-oriented fair (SOF) resource allocation problem in order to minimize the difference between the allocated and required data rate. After introducing convex-relaxation and logarithmic approximation, the joint optimal solutions of subchannel and power allocation can be obtained by SOF iteration algorithm. Aiming at managing the terrestrial network overload, we present a user update mechanism. After applying SOF resource allocation for selected original users in sky access platforms, we propose a resource auction scheme so as to maximize the utility for both sides. First, we decompose the original problem into several subproblems and transform them into their lower bound by first-order approximation. Then, we can obtain the joint optimal solution with an alternating descent iterative method. From simulation results, we can find that CAA-SAGIN can bring great benefits to traditional terrestrial networks. In addition, the proposed resource allocation and auction schemes are significant in terms of telecommunication and economic parameters. [ABSTRACT FROM AUTHOR]

To be differentiated from the literature, in this paper, we aim to alleviate the impact of pilot contamination (PC) on the massive MIMO systems by cooperatively scheduling users (rather than just assigning) among neighboring cells to share the limited orthogonal pilots. To this end, we develop the multi-cell cooperative scheduling (MCCS) algorithm together with the cooperative scheduling indexes (COSIs) for maximizing the data rate, maximizing the Jain's fairness index and reaching a better tradeoff in-between. For convenience, they are denoted by the COSIs for the CMDR, CMMF and CPF schedulers, respectively. However, its high computational complexity may somehow resists its from practical applications. Thus, a low-complexity cooperative proportional fairness (LC-CPF) algorithm is designed to well approach the MCCS algorithm using the CPF COSI; and at the meantime, the order of computational complexity can be significantly reduced. Moreover, its ability to reach a better tradeoff can still be maintained under the impact of high spectrum-sharing interference. In addition, it is interesting to find that using the proposed cooperative scheduling methods, the open-loop power control mechanism is no longer required for compensating the differences of received signal quality between users. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has been deemed a promising technology to achieve high spectral efficiency. This paper investigates transmit power optimization for a multi-user multiple-input single-output (MISO) NOMA downlink system endowed with energy harvesting and physical layer security. More specifically, the system is designed to satisfy the data-link security requirements of its legitimate users while charging energy harvesting devices. To achieve that goal, we investigate a joint design on beamforming vectors and artificial noise (AN) covariance matrix. Then, an iterative difference-of-two-convex-function (D.C.) algorithm is proposed by applying the successive convex approximation (SCA) approach. To further reduce the computational complexity, a sub-optimal alternating D.C. (ADC) algorithm is devised by decoupling beamforming and mapping the AN to the null space of legitimate users. In the ADC algorithm, optimal null space and beamforming vectors are alternately solved by convex programming. Furthermore, the convergence of the proposed D.C. and ADC algorithms is derived. Extensive simulation results show that the two proposed AN-aided algorithms significantly reduce the transmit power cost over OMA and NOMA scheme without AN, especially when there are more eavesdroppers. [ABSTRACT FROM AUTHOR]

Incipient fault detection and diagnosis (FDD) is an important measure to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a data-driven FDD method, namely deep slow feature analysis and belief rule base method (DSFA-BRB), for the running gears of high-speed trains. The method uses two kinds of statistics to perform fault detection on the multi-dimensional data of the running gears. In addition, the characteristics of more accurate data are extracted, which greatly reduces the complexity of constructing a diagnostic and quantitative model. Further, by constructing a BRB model combining expert knowledge and data, it is possible to avoid misjudgment caused by data incompleteness. Compared with the traditional methods, the DSFA-BRB algorithm has better performance in reducing fault alarm probability. Finally, the validity of the algorithm is verified by the actual running gears system. [ABSTRACT FROM AUTHOR]

The approach of factor-graphs (FGs) is applied in the context of power control and user pairing in Device-to-Device (D2D) communications as an effective underlay concept in wireless cellular networks. D2D communications can increase the spectral efficiency of wireless cellular networks by establishing a direct link between devices with limited help from the evolved node base stations (eNBs). A well-designed user pairing and power allocation scheme with low complexity can remarkably improve the system's performance. In this paper, a simple and distributed FG based approach is utilized for power control and user pairing implementation in an underlay cellular network with D2D communications. A max-min criterion is proposed to maximize the minimum rate of all active users in the network, including the cellular and multiple D2D co-channel links in the uplink direction. An associated message-passing (MP) algorithm is presented to distributedly solve the resultant NP-hard maximization problem, with a guaranteed convergence compared to game theoretic and Q-learning based methods. The complexity and convergence of the proposed method is analyzed and numerical results confirm that the proposed scheme outperforms alternative algorithms in terms of complexity, while keeping the sum-rate of users nearly the same as centralized counterpart methods. [ABSTRACT FROM AUTHOR]

In the heterogeneous networks (HetNets), the downlink-uplink decoupled (DUDE) user association is a very promising technology that can employ different association strategies for uplink and downlink. The DUDE mode has great potential for improving system capacity and frequency efficiency. However, interference between the DUDE user equipment (DUDE UE), macro user equipment (MUE) and small user equipment (SUE) still needs to be considered and properly tackled. Therefore, this paper presents an optimization scheme of frequency allocation and power control to improve the communication quality of users. Firstly, the multiple region frequency allocation (MRFA) scheme is proposed for non-uniform user distribution to alleviate interference among users and allocate spectrum resources equally to dense users. This scheme is modelled as a sub-region partition problem with maximum sum rate, which can disperse densely distributed users to different sub-bands for transmission. Secondly, the convergent power control (CPC) algorithm is considered to improve the transmission performance of each user. It can converge the transmit power to the optimal transmit power for reducing interference among co-channel users when there is no disturbance in the network. The CPC algorithm can also make the signal-to-interference-plus-noise-ratio (SINR) of each active user always greater than the target threshold until the end of communication by dynamically adjusting the transmit power of users. Simulation results show that the joint scheme of MRFA and CPC achieves a higher system throughput and better user performance than existing frequency allocation schemes or power control schemes. [ABSTRACT FROM AUTHOR]

This paper studies the problem of user association, deadline scheduling and channel allocation in dense cellular networks with energy harvesting base stations. The objective is to maximize the number of associated and scheduled users while allocating the available channels to the users and respecting the energy and deadline constraints. First, the computational complexity of this problem is characterized by studying its NP-hardness in different cases. Next, efficient algorithms are proposed in each case. The case of single channel and single base station is solved by proposing polynomial-time optimal algorithms. The case of single channel and multiple base stations is solved by proposing an efficient constant-factor approximation algorithm. The case of multiple channels is solved by proposing efficient heuristic algorithms. Our theoretical analysis are supplemented by simulation results to illustrate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the joint signal to interference plus noise ratio (SINR) thresholds optimization and resource allocation to maximize the sum-rate of Device-to-Device (D2D) communications while still retaining the rate requirements for active cellular users (CUs), when the inactive CUs are used as opportunistic relays under three operational modes: without using network-coding (NNC), using traditional high-layer network-coding (HNC), and using physical-layer network-coding (PNC). Under Rayleigh fading, we show that, given the selections of relays, this sum-rate maximization in no-relay scheme, NNC, HNC, and PNC opportunistic relay schemes can be formulated as a mixed integer non-linear programming (MINLP), which is NP-hard in general. To find the solution to the MINLP, we propose a two-step approach to solve the problem: 1) for each possible pairing of a D2D pair and a CU, we derive the optimal SINR thresholds to obtain the maximum transmission rate of the D2D pair while satisfying the rate requirement of the CU; 2) based on the maximum transmission rates of D2D pairs for each possible pairing in the first step, we develop a bipartite-matching method to find the optimal pairing CUs for D2D pairs. Finally, according to the solution to the MINLP, we propose an iterative relay selection algorithm to find out the relays that can further improve the sum-rate of D2D communications. Extensive simulation results demonstrate that, compared with the scenario without relaying, the NNC, HNC, and PNC opportunistic relay schemes achieve a maximum performance enhancement of 106%, 138%, and 168%, respectively. [ABSTRACT FROM AUTHOR]

Sparse code multiple access (SCMA) is one type of non-orthogonal multiple access techniques for the fifth-generation wireless communication networks. Existing multiuser detection algorithms for SCMA uplink systems are based on the assumption that signals are in synchronization. However, asynchrony cannot be neglected due to the propagation delays in practical environments. In this paper, we present three detection algorithms for uplink asynchronous SCMA systems, namely belief propagation message passing algorithm, belief propagation with equal gain combining message passing algorithm, and partial phase-coherent algorithms. Simulation results show that our proposed asynchronous detection algorithms are able to offer descent bit error rate performances, with only a small performances loss compared to the synchronous case. Moreover, our proposed algorithms converge well in the uplink asynchronous SCMA systems. [ABSTRACT FROM AUTHOR]

The deployment of large-scale UAV clusters for cluster advantages will lead to high competition and excessive congestion of spectrum resources, which results in mutual interference. This paper investigates the interference-aware online spectrum access problem for multicluster Flying Ad-Hoc Network (FANET) under different network topologies, i.e., the locations of UAV clusters are varying. First, the problem is formulated as a data-assisted multistage channel access game with the goal of mitigating interference of all UAV clusters and decreasing channel switching cost during each slot. As we prove that the game is an exact potential game that guarantees at least one pure-strategy Nash equilibrium and interference-aware online channel preserving based concurrent best response (IOCPCBR) algorithm, an online distributed algorithm is proposed to achieve the desirable solution. Finally, the simulation results demonstrate the validity and effectiveness of the multistage channel access game as well as IOCPCBR algorithm. [ABSTRACT FROM AUTHOR]

In cellular heterogeneous networks (HetNets), a number of distributed base stations cooperatively provide services to multiple mobile users. This paper addresses joint base-station activation and coordinated beamforming design for downlink transmission in HetNets. To this end, a mixed integer program is formulated to optimize the total power consumption of a HetNet. A novel approach based on Benders’ decomposition is then put forth to obtain the optimal solution for the problem of interest with guaranteed convergence. Building on our new formulation, a dual-subgradient algorithm is also proposed to find an approximate solution in polynomial time. The proposed approaches can be generalized to more general setups, including robust beamforming designs under channel uncertainty, and coordinated beamforming for multi-cell scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose two groups of recursive codes, namely the Hadamard recursive carrier interferometry (HRCI) codes and diagonal recursive carrier interferometry (DRCI) codes, to simultaneously reduce the peak-to-average power ratio (PAPR) and suppress the phase noises and residual carrier frequency offset (RCFO) for high-speed orthogonal frequency division multiplexing (OFDM) systems. We exploit the recursion property of the Hadamard and diagonal matrices to constitute the proposed HRCI code and DRCI code using the carrier interferometry (CI) code, and present the system model in details to spread OFDM symbols in the frequency domain. Then, we prove that both HRCI and DRCI generation matrices keep their invertibility during the recursions. As a direct result of the new code design, the phase intervals are enlarged and the signals are shifted in the time domain, thus both the PAPR and the phase noises are reduced, and the RCFO can be mitigated. Moreover, the enlarged phase intervals enable the transceiver blocks including the channel estimator, which estimate the phase noise based on the received signals, to improve the performance. Furthermore, we present the expressions for the HRCI and DRCI embedded transmitted and received signals, and derive the analytical signal-to-interference-plus-noise ratio (SINR) expressions for bit error rate (BER) performance analysis. Then, we analyze the effects of presented codes on the phase differences and provide the computational complexity analysis. Numerical simulations verify the effectiveness of our theoretical analysis of SINR. Additionally, under different parameter settings, which consider the working conditions of practical systems, we demonstrate that the presented robust HRCI and DRCI coded OFDM systems can suppress the PAPR satisfactorily and better BER performances can be achieved by the recursive CI-aided spread OFDM systems with phase noise rejection and RCFO compensation when compared with counterpart systems. [ABSTRACT FROM AUTHOR]

A $K$ -tier heterogeneous downlink millimeter wave (mmWave) cellular network with user-centric small cell deployments is studied in this paper. In particular, we consider a heterogeneous network model with user equipments (UEs) being distributed according to a Poisson cluster process (PCP). Distinguishing features of mmWave communications including directional beamforming and a sophisticated path loss model incorporating both line-of-sight and non-line-of-sight transmissions, are taken into account. Initially, we determine general expressions for the association probabilities of different tier BSs. Using tools from stochastic geometry, we then characterize the Laplace transform of the interference and derive a general expression for the signal-to-interference-plus-noise ratio coverage probability. While these expressions are applicable to any PCP, we later specialize the results to two popular PCPs, namely, first, Thomas cluster process, where the UEs are clustered around the base stations (BSs) according to a Gaussian distribution, and second, Matérn cluster process, where the UEs are scattered according to a uniform distribution. Subsequently, upper and lower bounds for the coverage probability are provided. Special cases are also addressed, providing the insight that when the cluster size grows without bound, our PCP-based model specializes to a Poisson point process-based model. Area spectral efficiency is investigated as well. Moreover, we have discussed extensions to cases in which more practical antenna gain patterns are taken into account and also the shadowing is considered. Via numerical and simulation results, we explore the effects of the beamwidth of the main lobe, the main lobe directivity gain, the biasing factor and the transmit power of the small-cell BSs to get insight on the performance in practical scenarios. Performance in dense networks is also analyzed numerically. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new constellation design for a non-coherent massive single input multiple output (m-SIMO) uplink system based on M-DPSK suitable for Rician channels. Under Rician propagation, the non-coherent systems proposed until now cannot completely remove the interference. This new design takes into account the inter user and inter symbol interferences in order to convert them into useful terms that help us in the non-coherent detection. We propose an algorithm to non coherently detect the joint symbol with the information from all users leveraging the characteristics of the new constellation. In addition, the signal-to-interference-plus-noise ratio is derived and is used to present analytical bounds for the symbol error rate. It is shown that the analytical results tightly match the numerical simulation. We show that with the proposed constellation and detection algorithm we are able to leverage the line-of-sight component of the Rician propagation, obtaining a better performance than with Rayleigh channels. [ABSTRACT FROM AUTHOR]

This paper proposes a widely linear processing framework for multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) downlink systems. In the framework, a widely linear MIMO-NOMA (WL-MIMO-NOMA) model is derived by assuming that the base station transmits real-valued downlink signals. WL-MIMO-NOMA adopts complex-valued power allocation coefficients to stagger the user signals in phase. The main features of WL-MIMO-NOMA are the following: first, in general case, WL-MIMO-NOMA can remove all the inter-cluster interference and at least half of the intra-cluster interference; and second, in user pairing case, both interferences can be completely eliminated. This is distinct from the existing work where real power coefficients are used, which cannot guarantee the complete separation of the paired user signals because the signals transmitted to the paired users are overlapped in phase. In addition, the closed-form expressions of outage probabilities are derived. The phase difference of the complex power coefficients is optimized to minimize the outage probability. It is proven that, with the optimal phase difference, successive interference cancellation is unnecessary in user pairing case. Finally, the framework is extended to the mixed case of real/complex circular signals. Simulation results show that the proposed framework outperforms the existing work, and the numerical results agree well with the analytical analysis. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a promising scheme for the fifth generation (5G) of mobile communication systems. In this scheme, transmission to multiple users is performed on the same subchannel using superposition coding and successive interference cancellation. In this paper, we focus on a multi-cell network with two users’ data traffic models, namely elastic and streaming. We exploit the NOMA scheme in order to maximize the download elastic traffic rate at cells, without degrading the download streaming traffic rates. Since elastic traffic rates at different cells are interactive, we maximize the total elastic traffic rates assuming either perfect or partial channel state information available at each base station subject to a target rate for streaming users. Because of the interference due to NOMA as well as the interference among different cells, subchannel assignment and power allocation affect the system performance significantly. For this reason, we propose an iterative algorithm to jointly solve the subchannel assignment and the power allocation problem via the Hungarian algorithm and successive convex approximation, respectively. Finally, our simulation results show the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) and spectrum efficiency (SE) have received significant attentions on optimizing the network performance in cognitive radio networks. In this paper, an EE+SE tradeoff based target is considered for the primary users (PUs) and the secondary users (SUs). First of all, considering the orthogonal frequency division multiple access-based resource allocation (RA) for the underlying SUs, we formulate an objective function through minimizing a weighted sum of the secondary interference power, where the network performance of both PUs and SUs are guaranteed by the constraints on quality of service, power consumption and data rate. However, it is a NP-hard problem. In order to solve it, we propose a damped three dimensional (D3D) message-passing algorithm (MPA) based on deep learning. Specifically, a feed-forward neural network is devised and an analogous back propagation algorithm is developed to learn the optimal parameters of the D3D-MPA. To improve the computational efficiency of the allocation and the learning, a suboptimal RA scheme is deduced based on a damped two dimensional MPA. Finally, simulation results are provided to confirm the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

Radio frequency (RF) energy harvesting technique has received high attention as a promising technology for Internet of Things (IoT) based industrial systems due to its flexibility of energy supply. When multiple IoT gateways are used as directional RF energy sources, the direction and phase of RF energy transmissions influence the RF energy transmission efficiency. In this paper, we propose a phase-aware directional energy transmission algorithm (PA-DETA) where directional RF energy sources (e.g., IoT gateways) are connected to a controller and their operation parameters (i.e., RF energy transmission direction and initial phase) are decided by the controller to maximize the total harvested energy while maintaining the energy consumption in RF energy sources and the number of discarded packets due to energy depletion of harvesting nodes below certain levels. To optimize the performance of PA-DETA, we formulate a constraint Markov decision process and the optimal policy on the direction and initial phase of RF energy transmissions is obtained by a linear programming. Evaluation results demonstrate that PA-DETA with the optimal policy outperforms the comparison schemes in terms of the total harvested energy, the energy consumption in RF energy sources, and the packet discard rate. [ABSTRACT FROM AUTHOR]

As an important complement and enhancement technique for cellular communication systems, the device-to-device (D2D) technique has exhibited a number of benefits in terms of, for instance, offering an improved spectral efficiency, enabling a reduced transmit power and providing an enhanced channel capacity, etc. However, a severe interference may be imposed on the conventional cellular users by the activated D2D links, thus, substantially eroding the performance of the former. In this paper, we propose an interference limited area (ILA) based D2D-management scheme along with an appropriately designed power control algorithm for mitigating the above-mentioned interference. We first derive the closed-form expressions of the average coverage probability for both the cellular and D2D links under the proposed ILA scheme, followed by proposing a resource-allocation algorithm subject to a power control mechanism. Numerical results show that the proposed ILA scheme is capable of outperforming the conventional scheme in terms of both the coverage probability (i.e., for both cellular and D2D links) and the sum data rate, particularly in the scenarios with a high D2D density and a high signal-to-interference-plus-noise ratio. [ABSTRACT FROM AUTHOR]

To ensure link reliability and signal receiving quality, robust adaptive beamforming (RAB) is vital important in mobile communications. In this paper, we propose a new RAB algorithm based on interference-plus-noise covariance (INC) matrix reconstruction and steering vector (SV) estimation. In this method, the INC matrix is reconstructed by estimating all interferences SVs and powers, as well as the noise power. The interference SVs are estimated by using the Capon spatial spectrum together with robust Capon beamforming principle, subsequently the interference powers are estimated based on the orthogonality between different signal SVs. On the other hand, the desired signal SV is estimated via maximizing the beamformer output power by solving a quadratic convex optimization problem. The proposed algorithm only needs to know in advance the array geometry and angular sector, in which the desired signal lies. Simulation results indicate that the proposed algorithm outperforms the existing RAB techniques in terms of the overall performance in cases of various mismatches. [ABSTRACT FROM AUTHOR]

Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to tackle the capacity crunch in cellular networks. However, imbalanced load among small and macrocells and poor resource utilization as a consequence in HetNets remains a long-standing problem. This paper addresses this problem by presenting a solution for maximization of coverage and capacity while minimizing load imbalance among macro and small cells. Most recent studies on the topic focus on either optimization of coverage, capacity or load, or a combination of two of these three intertwined objectives. We formulate the optimization problem as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity, and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization (CCO) and load balancing (LB) self-organizing network (SON) functions. In the presented joint CCO-LB solution, a conflict free operation of CCO and LB is ensured by designing a novel load aware user association methodology and resolving the effects of shadowing on coverage probability using stochastic approximation. The problem is proven to be nonconvex and is solved using genetic algorithm, sequential quadratic programming, and pattern search algorithms. The proposed CCO-LB solution is compared against two recently proposed CCO and CCO-LB solutions in the literature. Results show that the proposed solution can yield significant gain in terms of throughput, spectral efficiency, and load distribution. [ABSTRACT FROM AUTHOR]

This paper considers a heavy loaded full-duplex (FD) small cell consisting of one FD base station and multiple half-duplex users where the number of subchannels is less than the number of uplink and downlink users. With the objective of maximizing the total sum-rate, a joint power allocation, and user pairing problem is formulated. To solve this problem, a novel decomposition method is proposed, which decouples the power allocation and user pairing problem into two subproblems. It is proven that the optimal solution of subproblems is the optimal solution of the original problem. Then, optimal algorithms are developed for the two subproblems. Numerical results show that the proposed algorithm achieves better performance compared with the baselines of the greedy algorithm and random pairing algorithm. [ABSTRACT FROM AUTHOR]

Traffic offloading via device-to-device communications is expected to play a major role to meet the exponential data traffic growth in wireless networks. In this paper, we focus on the problem of user capacity maximization in ultra-dense heterogeneous networks with device-to-device cooperation, where a large number of users in a given geographical area request common data content, such as video on demand streaming, with strict quality of service guarantees. We aim at finding the best strategy for delivering the content either over long range connectivity from the access points or short range connectivity from peer mobile terminals while meeting a target rate per served user. We formulate the multiuser resource management problem as an optimization problem including traffic offloading and channel allocation considering a very high user density reaching up to 1.75 users per square meter. We extend the system model to include mobile terminals acting as content owners by having the content cached locally, in order to further enhance the network capacity and coverage. We then propose an iterative resource management solution, which first solves the optimization subproblem of traffic offloading with orthogonal channels, and then optimally allocates channels to the transmitters with minimized interference due to channel reuse. We also propose efficient suboptimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra-dense networks. For performance evaluation, we consider a realistic scenario consisting of a stadium topology with thousands of mobile terminals active simultaneously. We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity. [ABSTRACT FROM AUTHOR]

Cognitive small-cell networks have great potential in improving spectrum efficiency and mitigating inter-cell interference. Comprehensively classifying the channel states in cognitive small-cell networks is important for efficiently reusing the spectrum bands that are licensed to a macrocell. In this paper, we investigate channel state classification in cognitive small-cell networks with multilevels of transmission powers, including occupation detection of spectrum bands and transmission power classification of a macrocell base station (MBS). Specifically, two scenarios including a priori known signaling features and unknown signaling features are both studied. For the former scenario, we propose an optimal spectrum sensing and power classification algorithm, based on coherent classification, to achieve accurate sensing performance by fully exploiting the inherent information of the signaling features. Optimal sensing threshold and decision regions are derived for detecting and classifying the transmission power of the MBS. For the scenario without signaling features, a generic spectrum sensing and power classification algorithm is proposed based on noncoherent classification with low implementation complexity. A new performance metric, i.e., classification probability, is introduced to comprehensively evaluate the classification capability of the proposed algorithms. Finally, extensive simulations are provided to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

Multiple linear constraint water filling problem (MLWFP) is solved innovatively in this paper, in which powers allotted to the $T$ resources of the transmitting user maximize the capacity of the transmitting user while controlling the interference to all the surrounded users of the wireless network. MLWFP is solved previously using iterative algorithms that take more number of iterations, thus, increasing the computational complexity. Unlike prior algorithms, the uniqueness of the proposed algorithm is in detecting the tight bounds for the number of nonzero powers $S$. Subsequently, $S$ is calculated from its bounds. This makes it possible for the proposed algorithm to compute $S$ nonzero powers alone (using the closed-form solution), whereas all the prior algorithms find powers iteratively for all $T$ ( $>$ $S$ ) resources. Thus, the worst case computational complexity of the proposed algorithm is reduced manyfold and is considerably lower than that of the prior algorithms by a factor of $O(T^2)$. Simulation results justify the analysis of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study the downlink resource assignment problem of maximizing the total data rate subject to minimum satisfaction guarantees in multiservice scenarios assuming the use of multiuser (MU) multiple-input–multiple-output (MIMO) techniques. With the use of MU MIMO techniques, the same frequency resource can be shared by different terminals at the same time by assigning different spatial channels to each terminal. Different from single-user (SU) access per frequency resource where the resource assignment problem consists of searching for the best association between frequency resources and terminals, with MU access per resource, we aim at finding which space-division multiple-access (SDMA) group should be associated with each frequency resource. The first contribution of this paper is the formal presentation of this generalized problem as an optimization problem. Then, after some mathematical manipulations, we managed to convert this problem from a nonlinear integer to an integer linear problem (ILP) that can be optimally solved by standard techniques instead of resorting to brute-force methods. Motivated by the high computational complexity of the optimal solution, we propose a low-complexity algorithm. By analyzing outage rate and total data rate performance metrics, we show that the proposed solution presents a good performance–complexity tradeoff compared with the method for obtaining the optimal solution. [ABSTRACT FROM PUBLISHER]

Recently, there has been an increasing interest in offloading the 3GPP LTE data by using device-to-device (D2D) communications between devices. However, the peer discovering is challenging, especially in the indoor environment, since traditional users use a cellular signal to find peers, leading to incurring interference to other cellular users. In this paper, we propose ROOMMATEs, a novel approach for indoor peer discovery process, which is the enabler for indoor D2D communications in long term evolution (LTE) networks. It is a centralized approach utilizing, but not limited to, the ubiquitous WiFi network/femtocell network, combining with eNodeB in order to deliver the best results. ROOMMATEs is an unsupervised, yet energy efficient algorithm that can find surrounding user equipments (UEs) while minimizing interference and consuming much less energy. Based on the results, ROOMMATEs proves to be highly energy efficient, saving on average 24% per UE and improving signal-to-interference-plus-noise ratio in order of tens of decibels compared to other approaches. Moreover, ROOMMATEs provides indoor place identification for UEs with high accuracy using a small number of observations. ROOMMATEs is robust to missing and noisy data, works with various UEs’ brands/models, and requires no user interactions. [ABSTRACT FROM AUTHOR]

In a pricing-based spectrum trading market, an interesting issue is to study how secondary users make their selections in a rational manner when there are available spectrum of different qualities in the market. In this case, primary systems inherently require a precise pricing strategy to maximize their profits especially when the secondary users’ selection preferences are not uniform nor predictable during the dynamic access. In this paper, we propose a novel spectrum pricing mechanism for primary systems by taking into account the selection preferences of heterogeneous secondary users and quality diversity of leased spectrum caused by different interference levels and channel fading characteristics. Our work mainly focuses on exploring the optimal pricing solution in the case that the selection preference of secondary buyers is stochastic. In the proposed system model, the spectrum to be leased is divided into a series of uniform subchannels of various qualities for spectrum trading. We analyze the impact of secondary users’ preference on spectrum trading and propose an iterative algorithm for spectrum pricing by introducing the Hotelling model to describe the relationship between the spectrum diversity and the primary system's utility. Proofs of the integrability of the utility function and existence of Nash equilibrium used in the proposed algorithm are provided. Numerical results demonstrate the effectiveness of the proposed pricing method in improving the primary system's profits. [ABSTRACT FROM AUTHOR]

In this correspondence paper, an underlay cognitive radio multicast network, consisting of a cognitive base station (CBS) and multiple multicast groups of secondary users (SUs), is considered. All SUs, belonging to a particular multicast group, are served by the CBS using a common primary user (PU) channel. The goal is to maximize the energy efficiency (EE) of the system, through dynamic adaptation of target rate and transmit power for each multicast group, under the PUs’ individual interference constraint. The optimization problem formulated for this is proved to be nonquasi-concave with respect to the joint variation of the CBS's transmit power and target rate. An efficient iterative algorithm for EE maximization is proposed along with its complexity analysis. Simulation results illustrate the performance gain of our proposed scheme. [ABSTRACT FROM AUTHOR]

A cognitive radio is a smart software-defined radio that can adapt its transmission parameters, such as transmit power level and modulation type, based on the wireless channel conditions. In this paper, we introduce a joint power allocation and constellation design algorithm for cognitive radios assuming spectrum sensing imperfections. The proposed algorithm minimizes the symbol error rate of the secondary user by designing optimized 2-D constellation points and assigning transmit power levels. The constellation points are assumed to be equally probable with zero-mean and unit average symbol energy. The transmit power levels are assigned such that they do not exceed a predefined maximum transmit power threshold, and that the interference resulting from the secondary user to the primary user does not exceed a predefined value. The outcomes of the proposed algorithm, which are constellation points and transmit power levels, can be stored in a lookup table that the secondary user can access to adapt its transmission parameters to the environment based on sensing decisions, maximum transmit powers, and interference levels allowed by the primary user. Numerical results are provided to show the symbol error rate performance of the designed constellation points and compared to the performance of the conventional square grid M-ary quadrature amplitude modulation under the same operational parameters. [ABSTRACT FROM PUBLISHER]

In this paper, the performance of an underlay multiple-input multiple-output (MIMO) cognitive radio system is analytically studied. In particular, the secondary transmitter operates in a spatial multiplexing transmission mode, while a zero-forcing detector is employed at the secondary receiver. Additionally, the secondary system is interfered by multiple randomly distributed single-antenna primary users (PUs). To enhance the performance of secondary transmission, optimal power allocation is performed at the secondary transmitter with a constraint on the interference temperature (IT) specified by the PUs. The outage probability of the secondary receiver is explicitly derived in an exact closed-form expression. Also, some special cases of practical interest, including colocated PUs and massive MIMO, are discussed. Further, to mitigate instantaneous excessive interference onto PUs caused by the time-average IT, an iterative antenna reduction algorithm is developed for the secondary transmitter and, accordingly, the average number of transmit antennas is analytically computed. Extensive numerical and simulation results corroborate the effectiveness of our analysis. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the energy efficiency of downlink transmissions in heterogeneous networks. Our objective is to satisfy the rate requirement of users while maximizing the energy efficiency of the network. We employ the fractional frequency reuse (FFR) scheme to increase the energy efficiency of downlink transmissions and to eliminate outages for the cell-edge users. We formulate the joint cell-center boundary selection for FFR, scheduling, and power allocation problems. This formulation gives us a mixed discrete (selection of the cell-center boundary selection for FFR and scheduling) and continuous (power allocation) optimization problem which is hard to solve jointly. In order to solve this problem, we propose a three-stage resource allocation algorithm. In the first stage, we propose a dynamic method to determine the cell-center region boundaries. In the second stage, we employ the Lagrangian-directed scheduler algorithm to incorporate the rate requirements of users. The third stage solves the power allocation subproblem using the Levenberg–Marquardt method combined with dual decomposition. In order to make the base stations further reduce intercell interference, interference pricing mechanism is applied. This scheme penalizes the utility of a base station with the interference it creates. The performance of the proposed algorithm is simulated in a long-term evolution (LTE) network simulation tool. Our numerical results reveal that significant gains in terms of energy efficiency can be achieved with the proposed algorithm. The outage probability is also significantly reduced. [ABSTRACT FROM AUTHOR]

Multicarrier low-density-spreading multiple access (MC-LDSMA) is a promising multiple access technique that enables near-optimal multiuser detection. In MC-LDSMA, each user's symbol is spread over a small set of subcarriers, and each subcarrier is shared by multiple users. The unique structure of MC-LDSMA makes the radio resource allocation more challenging compared with some well-known multiple access techniques. In this paper, we study the radio resource allocation for a single-cell MC-LDSMA system. First, we consider the single-user case and derive the optimal power allocation and subcarriers partitioning schemes. Then, by capitalizing on the optimal power allocation of the Gaussian multiple-access channel, we provide an optimal solution for MC-LDSMA that maximizes the users' weighted sum rate under relaxed constraints. Due to the prohibitive complexity of the optimal solution, suboptimal algorithms are proposed based on the guidelines inferred by the optimal solution. The performance of the proposed algorithms and the effect of subcarrier loading and spreading are evaluated through Monte Carlo simulations. Numerical results show that the proposed algorithms significantly outperform conventional static resource allocation, and MC-LDSMA can improve the system performance in terms of spectral efficiency and fairness in comparison with orthogonal frequency-division multiple access. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a downlink multiuser multiple-input–multiple-output (MU-MIMO) system with multiple antennas at the transmitter and multiple antennas at each user, where the transmitter can send one or more data streams to each user. We propose a non-iterative method by combining Tomlinson–Harashima precoding (THP) with pre- and post-processing and selecting processing vectors based on the maximization of instantaneous signal-to-noise ratio (SNR) of the data stream at the input of the detector of each user. The postprocessing vectors for all users are found to be eigenvectors corresponding to the maximum eigenvalue of a certain matrix involving the channel matrix of the user. The transmitter computes the vectors of the linear processing matrix through an orthonormalization procedure in a single step. The feedback matrix at the transmitter is then obtained from the effective channel matrix and the linear processing matrix. We express the instantaneous SNR of all data streams in terms of eigenvalues of a Wishart matrix, obtained from the channel matrix of the user, and find the diversity order for each data stream. Considering multiple scenarios, we find the outage probability of the instantaneous SNR for all data streams and the cumulative distribution function (cdf) of the sum-rate capacity for all users using the proposed method and compare the results with those of recently proposed methods that provide a closed-form solution and with that of block diagonalization, channel inversion, and THP in scenarios where they are applicable. [ABSTRACT FROM PUBLISHER]

Small cells are introduced to cellular systems to enhance coverage and improve capacity. Densely deploying small cells can not only offload the traffic of macrocells but also provide an energy- and cost-efficient way to meet the sharp increase in traffic demands in mobile networks. However, such a cell deployment paradigm also leads to heterogeneous network (HetNet) infrastructure and raises new challenges for cell planning. In this paper, we study the cell planning issue in the HetNet. Our optimization task is to select a subset of candidate sites to deploy macro or small cells to minimize the total cost of ownership (TCO) or the energy consumption of the cellular system while satisfying practical constraints. We introduce approximation algorithms to cope with two different cell-planning cases, which are both NP-hard. First, we discuss the macrocell-only case. Our proposed algorithm achieves an approximation ratio of O(\log R) in this scenario, where R is the maximum achievable capacity of macrocells. Then, we introduce an O(\log \widetildeR)-approximation algorithm to the small-cell scenario, where \widetildeR is the maximum achievable capacity of a macrocell with small cells overlaid on it. Numerical results indicate that the HetNet can significantly reduce the TCO and the energy consumption of the cellular system. [ABSTRACT FROM PUBLISHER]

Sparse signatures have been proposed for the code-division multiple-access (CDMA) uplink to reduce multiuser detection complexity, but they have not yet been fully exploited for its downlink counterpart. In this paper, we propose multicarrier CDMA (MC-CDMA) downlink communication whereby regular sparse signatures are deployed in the frequency domain. Taking the symbol detection point of view, we formulate a problem appropriate for the downlink with discrete alphabets as inputs. The solution to the problem provides a power-efficient precoding algorithm for the base station (BS), subject to minimum symbol error probability (SEP) requirements at the mobile stations (MSs). In the algorithm, signature sparsity is shown to be crucial for reducing precoding complexity. Numerical results confirm system-load-dependent power reduction gain from the proposed precoding over the zero-forcing (ZF) precoding and the regularized ZF (RZF) precoding with optimized regularization parameter under the same SEP targets. For a fixed system load, it is also demonstrated that sparse MC-CDMA with a proper choice of sparsity level attains almost the same power efficiency and link throughput as that of dense MC-CDMA yet with reduced precoding complexity due to the sparse signatures. [ABSTRACT FROM PUBLISHER]

To efficiently trade off system sum rate and link fairness, power allocation in wireless spectrum-sharing networks often concentrates upon proportional fairness. The corresponding problem has been proved to be convex for a single tone but NP-hard for more than two tones. However, in the two-tone case, the complexity of the problem for achieving proportional fairness has not been addressed yet. In this paper, we prove that the issue of proportional-fairness optimization for the two-tone situation is NP-hard by reducing the problem of finding the maximum independent set in an undirected graph to it. Moreover, a computationally efficient algorithm is proposed to provide an efficient suboptimal solution. Simulation results are presented to illustrate the effectiveness of our proposal. [ABSTRACT FROM PUBLISHER]

This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit–receive pairs (users) shares a common collision channel via slotted-Aloha-like random access. The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability. While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information. The fundamental of our algorithm is that the users will first self-organize into a number of nonoverlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL). Each LL then adjusts its MAP according to a parameter R, which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value. We show that by ensuring R \le \text{2} at the LLs, the stability of the entire network can be assured, even when each user only has partial network information. For practical implementation, we propose each LL to use R=\text{2}$ as the constant reference signal to its built-in proportional and integral controller. The settings of the control parameters are discussed, and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput. [ABSTRACT FROM PUBLISHER]

Deploying femtocells underlaying macrocells is a promising way to improve the capacity and enhance the coverage of cellular systems. However, such a two-tier network also gives rise to a cross-tier and intratier interference issue that should be properly addressed to acquire the potential performance gain. In this paper, we study the resource-allocation (RA) problem in a two-tier orthogonal frequency-division multiplexing (OFDM) access-based heterogeneous cellular network, where the femtocells that employ a closed access strategy are equipped with a cognitive radio (CR) function to identify radio environment so that they can share subchannels with the macrocells without generating excessive interference to the macrocell users (MUs), which fall into the coverage of the femtocells. We formulate an optimization task to maximize the sum throughput of the femtocell users (FUs) under the consideration of imperfect spectrum sensing and channel uncertainty while controlling the interference to the MUs below their bearable thresholds in the sense of probability. We introduce a conservative convex approximation to the formulated problem and develop a fast algorithm to solve it by exploiting its structure. Simulation results show that our proposal can improve the throughput of the FUs with almost no changes in the infrastructure of the cellular network. [ABSTRACT FROM PUBLISHER]

Recent advances in loop interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same band with high spectrum efficiency. Meanwhile, wireless virtualization has attracted a lot of attention from both academia and industry because it can provide more flexibility, diversity, and other benefits in the process of wireless network design, construction, operation, and management. In this paper, we introduce the idea of wireless virtualization into FDR cellular networks. Then, the problem of energy-aware virtual resource management is formulated as a three-stage Stackelberg game. The subgame perfect equilibrium for each stage is analyzed. In addition, the interplays of the three-stage game are discussed, and an iterative algorithm is proposed to obtain the Stackelberg equilibrium solution. Simulation results are presented to show the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]