Wireless network optimization is essential for improving the network performance in mobile communications. However, due to the stochastic nature of wireless networks, existing schemes based on analytical models and deterministic optimization are less reliable. To this end, we design a framework for robust network optimization based on deep generative models and stochastic optimization. Inspired by the powerful diffusion process, we propose a deep generative simulator to capture the statistical distribution of the network performance. By sampling from the deep generative simulator, we can alleviate the inherent uncertainty related to the network performance and devise an innovative expectation-quantile-based stochastic objective function. The inner expectation is designed for the temporal statistics, while the outer quantile is developed for the spatial statistics. This designated two-tier objective function is capable of mitigating temporal fluctuations and ensuring satisfactory network performance across most geographical grids, thereby achieving robustness. To solve this stochastic optimization problem, a smooth zeroth-order approach is introduced by taking advantage of the unique structure of quantile functions. Through theoretical performance analysis and simulation experiments with real-world datasets, we demonstrate the superiority of our approach over other baseline schemes, highlighting its practical utility in robust network optimization.

The strict block error rate (BLER) requirement under the time-varying nature of wireless channels in Ultra-reliable low-latency communication (URLLC) systems pose sig-nificant challenges for link adaptation (LA). To tackle these challenges, we propose a novel LA method that adaptively selects the modulation and coding scheme (MCS) without requiring perfect channel knowledge which is unrealistic to obtain in URLLC. The goal is to maximize the coding rate while ensuring strict BLER constraints in URLLC systems. To achieve this, we utilize the Deep Q-Network (DQN) algorithm to select the MCS dynamically. Furthermore, we enhance the MCS selection process by using the Outer Loop Link Adaptation algorithm for transmission reliability improvement. Given the nature of URLLC, the samples of ACK and NACK are highly imbalanced, which can cause issues in the training process. To address it, we propose a novel training mechanism that improves the performance of DQN model and convergence speed during the training stage. Through extensive simulations, we demonstrate that our proposed algorithm outperforms existing methods regarding coding rate and imposing strict BLER constraints.

In this article, we consider a practical indoor 3-D mobile online visible light positioning (VLP) system, where the orientation of the user equipment (UE) is arbitrary. Based on the received signal strength (RSS) of multiple photodetectors (PDs), we formulate the 3-D VLP problem as a nonlinear least squares (NLSs) optimization problem, and then propose a sequential quadratic programming (SQP) positioning algorithm to efficiently calculate UE’s location. To obtain more accurate positioning solutions, we further leverage the advantages of deep learning and develop a stochastic gradient descent (SGD)-based VLP algorithm, and achieve an average positioning error of 1.77 cm, which significantly outperforms existing RSS VLP localization methods. Moreover, we design a 3-D mobile online VLP system prototype by using a portable RaspberryPi 4 Model B as the positioning signal processor and data memory, and establish the first publicly available 3-D VLP measured data set, including both RSS and orientation. The proposed positioning schemes are implemented and evaluated via the designed prototype system, which can achieve centimeter-level positioning accuracy (below 1 cm in certain condition).

Symbol-level precoding (SLP) based on the concept of constructive interference (CI) is shown to be superior to traditional block-level precoding (BLP), however at the cost of a symbol-by-symbol optimization during the precoding design. In this paper, we propose a CI-based block-level precoding (CI-BLP) scheme for the downlink transmission of a multi-user multiple-input single-output (MU-MISO) communication system, where we design a constant precoding matrix to a block of symbol slots to exploit CI for each symbol slot simultaneously. A single optimization problem is formulated to maximize the minimum CI effect over the entire block, thus reducing the computational cost of traditional SLP as the optimization problem only needs to be solved once per block. By leveraging the Karush-Kuhn-Tucker (KKT) conditions and the dual problem formulation, the original optimization problem is finally shown to be equivalent to a quadratic programming (QP) over a simplex. Numerical results validate our derivations and exhibit superior performance for the proposed CI-BLP scheme over traditional BLP and SLP methods, thanks to the relaxed block-level power constraint.

The indoor location based services are fascinating in many applications such as commercial recommendation, surveillance, and navigation. In this paper, we propose a high-precision indoor single-link passive tracking method based on Unscented Kalman Filter (UKF) using WiFi channel state information (CSI), namely UKFWiTr. In this method, both the CSI-quotient and Space-Alternating Generalized Expectation-maximization algorithm are used to estimate Doppler frequency shift and Time-of-Flight. Then, an Arrival-of-Angle optimization method and a tracking accuracy improvement method both based on UKF are put forward in UKFWiTr. The experimental results show that the average tracking error in different environments is less than 1.3m, and can even achieve 0.49m in particular scenarios.

In this paper, we propose a constructive interference (CI)-based block-level precoding (CI-BLP) approach for the downlink of a multi-user multiple-input single-output (MU-MISO) communication system. Contrary to existing CI precoding approaches which have to be designed on a symbol-by-symbol level, here a constant precoding matrix is applied to a collection of symbols within a given transmission block, thus significantly reducing the computational costs over traditional CI-based symbol-level precoding (CI-SLP) as the CI-BLP optimization problem only needs to be solved once per block. For both PSK and QAM modulation, we formulate an optimization problem to maximize the minimum CI effect over the block subject to a block- rather than symbol-level power budget. We mathematically derive the optimal precoding matrix for CI-BLP as a function of the Lagrange multipliers in closed form. By formulating the dual problem, the original CI-BLP optimization problem is further shown to be equivalent to a quadratic programming (QP) optimization. Numerical results validate our derivations, and show that the proposed CI-BLP scheme achieves improved performance over the traditional CI-SLP method, thanks to the relaxed power constraint over the considered block of symbol slots.

We propose covert beamforming design frameworks for integrated radar sensing and communication (IRSC) systems, where the radar can covertly communicate with legitimate users under the cover of the probing waveforms without being detected by the eavesdropper. Specifically, by jointly designing the target detection beamformer and communication beamformer, we aim to maximize the radar detection mutual information (MI) (or the communication rate) subject to the covert constraint, the communication rate constraint (or the radar detection MI constraint), and the total power constraint. For the perfect eavesdropper’s channel state information (CSI) scenario, we transform the covert beamforming design problems into a series of convex subproblems, by exploiting semidefinite relaxation, which can be solved via the bisection search method. Considering the high complexity of iterative optimization, we further propose a single-iterative covert beamformer design scheme based on the zero-forcing criterion. For the imperfect eavesdropper’s CSI scenario, we develop a relaxation and restriction method to tackle the robust covert beamforming design problems. Simulation results demonstrate the effectiveness of the proposed covert beamforming schemes for perfect and imperfect CSI scenarios.

In this paper, we consider the design of a wireless sensor network (WSN) that aims at monitoring the environment and collecting data periodically. In view of the limited energy and computational capability of the sensor nodes, a mobile edge computing (MEC) server is deployed in the WSN as a data processing unit. The goal of the design is to maintain the freshness of the data, which is characterized by the criterion of the age of information (AoI). Therefore, we analyze the long-term average AoI of the considered network. Then, the energy and time constraints for the WSN are modeled with consideration of transmission and computation. Next, a non-convex average AoI minimization problem is formulated subject to the energy and time constraints by jointly optimizing the sampling rate, computing scheduling, and transmit power. To tackle the challenging problem, the geometric programming and successive convex approximation (SCA) technique are applied to develop an algorithm with convergence guarantee. Moreover, to exhibit the benefits of the MEC server, a joint design is investigated for the WSN without the MEC server. Finally, the numerical results demonstrate the efficiency of our proposed SCA-based algorithm and show the impact of the sampling rate on the AoI performance.

In this paper, we consider a beampattern design problem for a dual-functional radar-communication (DFRC) system with the aid of the reconfigurable intelligent surface (RIS). The goal of the design is to meet the communication requirements of the users while the beampattern can match the ideal beampattern. We formulate the beampattern design as a non- convex optimization problem by jointly optimizing the transmit and passive beamformers. To solve the problem, three methods, namely the semidefinite relaxation (SDR)-based method, penalty- based method, and Riemannian conjugate gradient (RCG)-based method are proposed. The simulation results illustrate that: i) the radar performance can be significantly enhanced with the aid of RIS; ii) a tradeoff should be made between the communication performance and radar performance; iii) 4 control bits are sufficient to make the performance close to that of continuous phase shift.

This paper investigates beam design in a millimeter wave (mmWave) network, where some obstacles block the link between the base stations (BSs) and the user equipments (UEs). There is a single cell in an area where the UEs communicate with the single BS through a line of sight (LoS) link. To provide services in the network, synchronization signal blocks (SSBs) are transmitted by the BS in a fixed beam pattern. We aim at minimizing the transmit power by designing the beam while meeting the coverage requirement, which depends on the reference signal received power (RSRP) at the UEs. A non-convex power minimization problem is then formulated by jointly optimizing the beam directions, beam width and beam number subject to the coverage constraints. To resolve this problem, an alternating optimization (AO) based algorithm is proposed and the successive convex approximation (SCA) method is applied. The simulation results demonstrate that the proposed algorithm can significantly improve the performance of power consumption compared with the other two heuristic algorithms.

This paper considers a positioning problem of high-speed trains (HST) in railway wireless network by utilizing the 5G new radio (NR) positioning reference signals (PRS). It is assumed that the train runs along the railroad at a fixed velocity and receives PRS signals from the base stations (BSs) deployed on the side of the railroad. To deal with the positioning problem, an iterative two-phase weighted least squares (I2WLS) method based on range difference of arrival (RDOA) measurements is proposed, which linearizes the RDOA equations to pseudo-linear ones. The accuracy gap between RMSE of the proposed approach and Cramer-Rao lower bound (CRLB) is small when the RDOA measurements noise level is sufficiently small. Furthermore, the simulation results illustrate that a high, centimeter-level accuracy can be achieved for small PRS intervals, velocities and base station distances.

In this paper, we investigate a robust beamforming design in a millimeter wave (mmWave) communication network with consideration of the random blockages. The network with multiple remote radio units (RRUs) is taken into account, where the joint transmission coordinated multi-point (JT-CoMP) scheme is adopted to improve the spectrum efficiency. To further enhance the communication performance and maintain the reliability of the network, an active intelligent reflecting surface (IRS) panel is deployed. We formulate the robust beamforming design of the mmWave communication network as an optimization problem aiming at minimizing the total transmit power at the RRUs subject to the average signal-to-interference-plus-noise ratio (SINR) constraints and power constraint over the active IRS. To deal with the challenge caused by the variables coupling, an algorithm based on the alternating optimization (AO) and semidefinite programming (SDP) is proposed. Numerical results illustrate that: i) the transmit power of the network can be reduced by deploying both the passive and active IRSs; ii) the integration of the active IRS and CoMP scheme can obtain a significant performance gain compared to that of the conventional passive IRS and CoMP.

With the emergence and development of mission-critical applications, the design of uplink URLLC transmission has become one of the issues of concern. In this article, we consider the ultrareliable uplink transmission design between multiple robots and a central controller in a smart factory with stringent delay requirements while multiple relays are deployed for cooperative transmission. Subject to the throughput and reliability requirements, a relay-assisted two-phase transmission protocol is proposed aiming at total transmit power minimization by jointly optimizing the decoding error probability, relay selection, resource block (RB) assignment, and transmit power allocation. To support the mission-critical applications in Internet of Things (IoT) with diverse throughput targets and make full use of RBs, we investigate the optimization problem under the single-RB allocation and multi-RB allocation schemes. The problems in these two cases have an analogous structure and can be solved with a unified framework. In the solution framework, we present the optimal design solved by the nonconvex penalty (NCP)- and quadratic penalty (QP)-based successive convex approximation (SCA) algorithms and the corresponding low-complexity design with near-optimal settings of decoding error probabilities. Numerical results demonstrate the effectiveness of the proposed penalty-based iterative algorithms and confirm that the near-optimal design only causes minor power performance loss compared to the optimal design. The power performance gap under these two allocation schemes and the impact of system parameters are revealed and analyzed.

In this article, we consider covert beamforming design for intelligent reflecting surface (IRS)-assisted Internet-of-Things (IoT) networks, where Alice utilizes IRS to covertly transmit a message to Bob without being recognized by Willie. We investigate the joint beamformer design of Alice and IRS to maximize the covert rate of Bob when the knowledge about Willie’s channel state information (WCSI) is perfect and imperfect at Alice, respectively. For the former case, we develop a covert beamformer under the perfect covert constraint by applying semidefinite relaxation. For the latter case, the optimal decision threshold of Willie is derived, and we analyze the false alarm and the missed detection probabilities. Furthermore, we utilize the property of the Kullback–Leibler divergence to develop the robust beamformer based on a relaxation,$S$-Lemma, and alternate iteration approach. Finally, the numerical experiments evaluate the performance of the proposed covert beamformer design and robust beamformer design.

Intelligent reflecting surface (IRS) and device-to-device (D2D) communication are two promising technologies for improving transmission reliability between transceivers in communication systems. In this paper, we consider the design of reliable communication between the access point (AP) and actuators for a downlink multiuser multiple-input single-output (MISO) system in the industrial IoT (IIoT) scenario. We propose a two-stage protocol combining IRS with D2D communication so that all actuators can successfully receive the message from AP within a given delay. The superiority of the protocol is that the communication reliability between AP and actuators is doubly augmented by the IRS-aided first-stage transmission and the second-stage D2D transmission. A joint optimization problem of active and passive beamforming is formulated, which aims to maximize the number of actuators with successful decoding. We study the joint beamforming problem for cases where the channel state information (CSI) is perfect and imperfect. For each case, we develop efficient algorithms that include convergence and complexity analysis. Simulation results demonstrate the necessity and role of IRS with a well-optimized reflection matrix, and the D2D network in promoting reliable communication. Moreover, the proposed protocol can enable reliable communication even in the presence of stringent latency requirements and CSI estimation errors.

For the ultra-reliable low-latency communication (URLLC), the existing joint design algorithms of channel training and data transmission are not applicable due to the stringent reliability requirement and limited blocklength. To address this issue, we develop a low-complexity joint design framework for MISO communication based on the finite blocklength code (FBC). Specifically, an approximate bound of the packet error probability (PEP) is first derived and validated by practical modulation and coding schemes. It reveals the inherent tension between reliability, latency, and information bit number. Then, we formulate the joint design into a nonconvex optimization problem with the objective to maximize the information bit number. By exploiting the monotonicity of the PEP approximate bound, we provide closed-form solutions of power and blocklength allocation. Thereby, we develop a low-complexity algorithm to support the URLLC services aiming at the information bit number maximization. Furthermore, we investigate the joint designs to optimize the reliability, latency, and total energy, respectively, to fully meet the diverse demands of URLLC services. Finally, numerical results are provided to validate the proposed joint designs. The results show the outage capacity-based design severely underestimates the required wireless resources.

In this work, we study a downlink multi-user network, where a single access point (AP) is supposed to accomplish data transmissions to all users under low latency constraints. To more effectively cope with the multiple access demand, we consider a hybrid strategy for the multi-user downlink service in finite blocklength (FBL) regime, which combines broadcasting with time-division multiple access (TDMA). In the hybrid strategy, the users are first clustered into different groups. Different groups are served in a TDMA manner with dedicated time slots, while users within each group are served together via a broadcasting signal from the AP. By taking into account the fairness of transmission reliability among all users, we formulate a problem minimizing the maximum error probability among users via jointly determining the user grouping and allocating blocklength among all groups. To address the complicated non-convex problem, we first characterize the optimal blocklength allocation under each given grouping decision, which leads to an optimal closed-form allocation solution via solving an equation system. Based on the characterized features, we are enabled to efficiently distill out the optimal grouping from all possible groupings, which forms the efficient optimal solution for the optimal joint design. Afterwards, aiming at a complexity reduction, we further propose a low-complexity iterative solution, in which the grouping is iteratively improved via the introduced operations until a convergence to a suboptimum. Finally, via simulations, we validate the proposed solutions and reveal the close optimality of the iterative solution. In addition, the hybrid strategy has shown a significant reliability advantage in comparison to pure broadcasting or TDMA, and this performance advantage becomes further enlarged in case of more users.

This paper investigates a joint beamforming design in a multiuser multiple-input single-output (MISO) communication network aided with an intelligent reflecting surface (IRS) panel. The symbol-level precoding (SLP) is adopted to enhance the system performance by exploiting the multiuser interference (MUI) with consideration of bounded channel uncertainty. The joint beamforming design is formulated into a nonconvex worst-case robust programming to minimize the transmit power subject to single-to-noise ratio (SNR) requirements. To address the challenges due to the constant modulus and the coupling of the beamformers, we first study the single-user case. Specifically, we propose and compare two algorithms based on the semidefinite relaxation (SDR) and alternating optimization (AO) methods, respectively. It turns out that the AO-based algorithm has much lower computational complexity but with almost the same power to the SDR-based algorithm. Then, we apply the AO technique to the multiuser case and thereby develop an algorithm based on the proximal gradient descent (PGD) method. The algorithm can be generalized to the case of finite-resolution IRS and the scenario with direct links from the transmitter to the users. Numerical results show that the SLP can significantly improve the system performance. Meanwhile, 3-bit phase shifters can achieve near-optimal power performance.

This paper investigates an unmanned aerial vehicle (UAV) aided integrated radar sensing and communications (ISAC) network, where the UAV senses multiple targets via onboard radar devices and transmits the data to a ground base station (BS) for decision. The freshness of sensed data significantly affects the accuracy of BS decision and can be quantified by the peak age of information (PAoI). To guarantee the PAoI performance of the UAV-aided system, we design a joint optimization problem with consideration of the UAV trajectory, the scheduling order of the targets, and the time and power allocation for packet sensing and communication. Since the joint programming is a non-convex problem mainly due to the variable coupling and binary constraints, an efficient successive convex approximation (SCA) iterative algorithm is proposed, which transforms this non-convex problem into a convex problem with the help of fractional programming and penalty method. Simulation results show that the proposed algorithm outperforms the alternate optimization (AO) based algorithm in terms of the PAoI and convergence rate.

Scenario identification plays an important role in improving communication system performance. Considering that the scenarios of vehicle communications are dynamic due to movements of vehicles, and there are obvious differences in channel characteristics, vehicle speeds, traffic densities between various scenarios, the requirement for real-time scenario identification of vehicular communications is increasingly urgent. Vehicular communication systems can select appropriate channel models and transmission mode by correctly identifying the current scenarios to maintain an effective and reliable operating state. This paper presents a machine-learning-based scenario identification model for intelligent vehicular communications. Channel characteristics extracted from channel measurements in different scenarios form the datasets used to training, then a back-propagation neural network (BPNN) is trained, and a scenario identification model is obtained. Furthermore, the model configuration scheme is explored and presented which can make the proposed identification model achieves optimal performance. Subsequently, identification accuracy is verified by using validation data of the corresponding scenarios. The results show that the identification accuracies are all above 98 % in four typical scenarios of urban areas, highways, tunnels, and vehicle obstructions, which indicates that the model proposed in this paper shows good performance in scenario identification for intelligent vehicular communications.

In this paper, we consider the power minimization problem of joint physical resource block (PRB) assignment and transmit power allocation under specified delay and reliability requirements for ultra-reliable and low-latency communication (URLLC) in downlink cellular orthogonal frequency-division multiple-access (OFDMA) system. To be more practical, only the imperfect channel state information (CSI) is assumed to be available at the base station (BS). The formulated problem is a combinatorial and mixed-integer nonconvex problem and is difficult to tackle. Through techniques of slack variables introduction, the first-order Taylor approximation and reweighted ℓ1-norm, we approximate it by a convex problem and the successive convex approximation (SCA) based iterative algorithm is proposed to yield sub-optimal solutions. Numerical results provide some insights into the impact of channel estimation error, user number, the allowable maximum delay and packet error probability on the required system sum power.

Multi-access edge computing (MEC) can enhance the computing capability of mobile devices, while non-orthogonal multiple access (NOMA) can provide high data rates. Combining these two strategies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task delay minimization in multi-user NOMA-MEC networks, where multiple users can offload their tasks simultaneously through the same frequency band. We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts. We aim to minimize the task delay among users by optimizing their tasks partition ratios and offloading transmit power. The delay minimization problem is first formulated, and it is shown that it is a nonconvex one. By carefully investigating its structure, we transform the original problem into an equivalent quasi-convex. In this way, a bisection search iterative algorithm is proposed in order to achieve the minimum task delay. To reduce the complexity of the proposed algorithm and evaluate its optimality, we further derive closed-form expressions for the optimal task partition ratio and offloading power for the case of two-user NOMA-MEC networks. Simulations demonstrate the convergence and optimality of the proposed algorithm and the effectiveness of the closed-form analysis.

In this paper, we aim to design an adaptive power allocation scheme to minimize the average transmit power of a hybrid automatic repeat request with chase combining (HARQ-CC) enabled non-orthogonal multiple access (NOMA) system under strict outage constraints of users. Specifically, we assume that the base station only knows the statistical channel state information of the users. To achieve power efficient design and cope with the reliable transmissions of users, a partial HARQ-CC scheme is proposed. We first focus on the two-user case. To evaluate the performance of the two-user partial HARQ-CC enabled NOMA system, we first analyze the outage probability of each user. Then, an average power minimization problem is formulated. However, the attained expressions of the outage probabilities are nonconvex, and thus make the problem challenging to solve. Hence, we propose to use a successive convex approximation (SCA) based algorithm to solve the problem iteratively. Meanwhile, we prove that the proposed algorithm can converge to a Karush-Kuhn-Tucker point of the original problem. For more practical applications, we also investigate the partial HARQ-CC enabled transmissions in the multi-user scenario. The user pairing and power allocation problem is considered. With the aid of matching theory, a low complexity algorithm is presented to first handle the user pairing problem. Then the power allocation problem for each user pair is solved by the proposed SCA-based algorithm. Simulation results show the efficiency of the proposed transmission strategy and the near-optimality of the proposed algorithms.

This paper investigates the transmission energy minimization problem for the two-user downlink with strictly heterogeneous latency constraints. To cope with the latency constraints and to explicitly specify the trade-off between blocklength (latency) and reliability the normal approximation of the capacity of finite blocklength codes (FBCs) is adopted, in contrast to the classical Shannon capacity formula. We first consider the non-orthogonal multiple access (NOMA) based transmission scheme. However, due to heterogeneous latency constraints and channel conditions at receivers, the conventional successive interference cancellation may be infeasible. We thus study the problem by considering heterogeneous receiver conditions under different interference mitigation schemes and solve the corresponding NOMA design problems. It is shown that, though the energy function is not convex and does not have closed form expression, the studied NOMA problems can be globally solved semi-analytically and with low complexity. Moreover, we propose a hybrid transmission scheme that combines the time division multiple access (TDMA) and NOMA. Specifically, the hybrid scheme can judiciously perform bit and time allocation and take TDMA and NOMA as two special instances. To handle the more challenging hybrid design problem, we propose a concave approximation of the FBC rate/capacity formula, by which we obtain computationally efficient and high-quality solutions. Simulation results show that the hybrid scheme can achieve considerable transmission energy saving compared with both pure NOMA and TDMA schemes.

Mobile edge computing (MEC) can provide considerable computing capabilities for Internet of Things (IoT) devices, especially for applications with latency sensitive tasks. By applying non-orthogonal multiple access (NOMA) in MEC, multiple users can offload their tasks simultaneously on the same frequency band. In this paper, the minimization problem of task completion time is investigated for the NOMA enabled multi-user MEC networks. We adopt \emph{partial offloading}, in which each user's task can be partitioned, while the formulated problem is quasi-convex. Thus a bisection search (BSS) algorithm is proposed to achieve the minimum task completion time for the multi- user case. To reduce the complexity and evaluate the optimality of the BSS algorithm, we further derive closed- form expressions for the optimal task partition ratio and offloading power for a two-user NOMA-MEC network. Simulations demonstrate the convergence and optimality of the proposed BSS algorithm and the effectiveness of the optimal approach.

In this paper, we investigate the feasible signal-to-interference-plus-noise-power-ratio (SINR) region of a multi-cell downlink non-orthogonal multiple access (NOMA) system with successive interference cancellation (SIC) technique. Based on the Perron-Frobenius Theory, we first derive a necessary and sufficient condition for an SINR vector to be feasible under a fixed SIC decoding order. The feasible SINR region of the system is then given by the union of the feasible SINR regions under all possible decoding orders. Next, we derive a necessary condition for an optimal SIC decoding order in the sense that it can achieve the SINR region boundary. Based on this condition, we further propose an efficient algorithm that can approximate the SINR region boundary. Simulation results are provided to validate the efficiency of the proposed algorithm and compare the performance of NOMA and orthogonal multiple access.

In this paper, a unified framework for 3-hop unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network is proposed. Aim at characterizing the performance of proposed framework, by using stochastic geometry, the analytical expressions for the outage probability of uplink/downlink transmissions are derived in closed- form for randomly deployed NOMA users. To obtain more insights of the network performance, the asymptotic analyses for the outage probability in the high signal- to-noise ratio (SNR) regime are carried out. These results reveal that for the uplink transmission, there exists an error floor due to the interference from the far user, while the performance of the far user can outperform the near one for the downlink transmission, which can be explained by the fact that the far user has a higher receiving power.

In a full-duplex (FD) multi-user network, the system performance is not only limited by the self-interference but also by the co-channel interference due to the simultaneous uplink and downlink transmissions. Joint design of the uplink/downlink transmission direction of users and the power allocation is crucial for achieving high system performance in the FD multi-user network. In this paper, we investigate the joint uplink/downlink transmission direction assignment (TDA), user paring (UP), and power allocation problem for maximizing the system max-min fairness (MMF) rate in an FD multi-user orthogonal frequency division multiple access (OFDMA) system. The problem is formulated with a two-time-scale structure, where the TDA and the UP variables are for optimizing a long-term MMF rate while the power allocation is for optimizing an instantaneous MMF rate during each channel coherence interval. We show that the studied joint MMF rate maximization problem is NP-hard in general. To obtain high-quality suboptimal solutions, we propose efficient methods based on simple relaxation and greedy rounding techniques. The simulation results are presented to show that the proposed algorithms are effective and achieve higher MMF rates than the existing heuristic methods.

In this paper, we aim to design a power allocation strategy to minimize the average consumed power of a hybrid automatic repeat request with chase combining (HARQ-CC) enabled system under a strict outage constraint for each user. In particular, the non-orthogonal multiple access (NOMA) is incorporated to further improve the system spectrum efficiency and we assume the base station only knows the statistical channel state information of the users. To evaluate the performance of the HARQ-CC enabled NOMA system, we first analyze the outage probability of each user. Then, based on the derived outage probabilities, by optimizing the transmit power, an average power minimization problem is considered. However, the attained expressions of the outage probabilities are nonconvex and extremely complicated, and thus make the formulated problem difficult to deal with. To efficiently solve the original problem, we first conservatively approximate it by a tractable one and then use a successive convex approximation based algorithm to handle the relaxed problem iteratively. And the presented algorithm can be guaranteed to converge to at least a stationary point of the problem. The simulation results show the efficacy of the proposed transmission strategy and the near-optimality of the proposed approximation approach and algorithm.

In this paper, we study the problem of data collection by an unmanned aerial vehicle (UAV) from a set of sensors located on a straight line. The objective is to minimize the UAV's total aviation time while allowing each of the sensors to successfully upload a certain amount of data using a given amount of energy. The whole trajectory is divided into non-overlapping intervals, in each of which one sensor is served by the UAV. The division of the intervals, the UAV speed and the sensors' power allocation policy are sequentially optimized. We show that the optimal power allocation follows the classical water-filling policy, the optimal UAV speed can be obtained by bisection search, and the optimal division of the intervals can be determined by employing the dynamic programming (DP) approach. Numerical results show that for a single sensor case, the optimal transmission interval is symmetric over the location of the sensor. For multiple sensors, the optimal UAV speed is proportional to the given energy and inversely proportional to the data upload requirement.

In this paper, we consider a scenario where an unmanned aerial vehicle (UAV) collects data from a set of sensors on a straight line. The UAV can either cruise or hover while communicating with the sensors. The objective is to minimize the UAV's total flight time from a starting point to a destination while allowing each sensor to successfully upload a certain amount of data using a given amount of energy. The whole trajectory is divided into non-overlapping data collection intervals, in each of which one sensor is served by the UAV. The data collection intervals, the UAV's speed, and the sensors' transmit powers are jointly optimized. The formulated flight time minimization problem is difficult to solve. We first show that when only one sensor is present, the sensor's transmit power follows a water-filling policy and the UAV's speed can be found efficiently by bisection search. Then, we show that for the general case with multiple sensors, the flight time minimization problem can be equivalently reformulated as a dynamic programming (DP) problem. The subproblem involved in each stage of the DP reduces to handle the case with only one sensor node. Numerical results present the insightful behaviors of the UAV and the sensors. Specifically, it is observed that the UAV's optimal speed is proportional to the given energy of the sensors and the inter-sensor distance, but it is inversely proportional to the data upload requirement.

In this paper, a novel joint design of beamforming and power allocation is proposed for a multi-cell multiuser multiple-input multiple-output non-orthogonal multiple access network. In this network, base stations adopt coordinated multipoint for downlink transmission. We study a new scenario where the users are divided into two groups according to their quality-of-service requirements, rather than their channel qualities as investigated in the literature. Our proposed joint design aims to maximize the sum rate of the users in one group with the best effort while guaranteeing the minimum required target rates of the users in the other group. The joint design is formulated as a non-convex NP-hard problem. To make the problem tractable, a series of transformations is adopted to simplify the design problem. Then, an iterative suboptimal resource allocation algorithm based on successive convex approximation is proposed. In each iteration, a rank-constrained optimization problem is solved optimally via semidefinite program relaxation. Numerical results reveal that the proposed scheme offers significant sum-rate gains compared to the existing schemes and converges fast to a suboptimal solution.

This paper introduces downlink non-orthogonal multiple access (NOMA) into short-packet communications. NOMA has great potential to improve fairness and spectral efficiency with respect to orthogonal multiple access (OMA) for low-latency downlink transmission, thus making it attractive for the emerging Internet of Things. We consider a two-user downlink NOMA system with finite blocklength constraints, in which the transmission rates and power allocation are optimized. To this end, we investigate the trade-off among the transmission rate, decoding error probability, and the transmission latency measured in blocklength. Then, a 1-D search algorithm is proposed to resolve the challenges mainly due to the achievable rate affected by the finite blocklength and the unguaranteed successive interference cancellation. We also analyze the performance of OMA as a benchmark to fully demonstrate the benefit of NOMA. Our simulation results show that NOMA significantly outperforms OMA in terms of achieving a higher effective throughput subject to the same finite blocklength constraint, or incurring a lower latency to achieve the same effective throughput target. Interestingly, we further find that with the finite blocklength, the advantage of NOMA relative to OMA is more prominent when the effective throughput targets at the two users become more comparable.

In this paper, we consider a wireless network with one full-duplex (FD) base station (BS) and a set of half-duplex (HD) user equipments (UEs). In such scenario, in addition to the self-interference, the co-channel interference from uplink UEs to downlink UEs is the main bottleneck for the network performance. To overcome this, we consider the problem of maximizing the minimum fairness rate among all UEs by jointly determining the UE uplink/downlink directions and pairing the UEs over different resource blocks. We first show that the UE pairing problem is NP-hard in general. To develop efficient suboptimal algorithms, we formulate the considered problem as a mixed integer linear program and handle it by the iterative reweighted ℓq-norm minimization (IRM) method. In particular, we propose a two-stage IRM algorithm that determines the UE transmission directions in the first stage followed by optimizing the UE pairs in the second stage. Simulation results are presented to show the efficacy of the proposed algorithm over some heuristic methods.

This paper investigates the application of simultaneous wireless information and power transfer (SWIPT) to nonorthogonal multiple access (NOMA). A new cooperative multiple-input-single-output (MISO) SWIPT NOMA protocol is proposed, where user 2 who has a strong channel condition acts as an energy-harvesting relay to help user 1 who has a poor channel condition. Our objective is to maximize the data rate of user 2 while satisfying the QoS requirement of user 1. The formulated problem boils down to a complicated nonconvex problem. We first use the semidefinite relaxation (SDR) technique to relax the nonconvex problem. Then, an iterative algorithm with the successive convex approximation (SCA) method is proposed to solve the relaxed problem. As a result, a local optimal solution is obtained. Motivated by the practical applications, the cooperative SWIPT NOMA protocol design in SISO case is investigated and a semiclosed-form solution is derived, which can strictly guarantee the global optimality. It is worth pointing out that the SCA method also admits a global optimal solution in SISO case. Simulation results show that the SWIPT-aided NOMA protocol outperforms the existing transmission protocols.

Ultra dense networks are a promising technique to achieve increasing 1000 times data rate requirements of the fifth generation (5G) wireless communications. However, due to the ‘tidal effect’ of mobile Internet traffic, the energy efficiency of the 5G communication system decreases dramatically, especially in off-peak hour, such as midnight. The software-defined wireless networks (SDWN) architecture provides a solution to reduce the energy consumption via cells management. To facilitate wide usage of cells management in SDWN, we consider in this paper the problem of joint ultra dense cells management and resource allocation, with the objective to minimize the network power consumption while guaranteeing the quality of service requirements of users and the coverage rate requirement. The problem is formulated as a mixed-integer nonlinear programming problem. To deal with this problem, we utilize the sparse characteristics of the beamforming vector and the method of reweighted l1 norm to approximate l0 norm. Because the coverage rate requirement constraint is still non-convex, we propose an iterative algorithm to derive the lower bound of the problem, and then propose a heuristic iterative algorithm to obtain a practical solution. Simulation results confirm that minimizing the total network power consumption results in sparse network topologies, and some of the small cells are inactivated when possible. The performance of the proposed heuristic algorithm is only increased by 13.09% compared with the lower bound, while the target signal-to-interference-plus-noise ratio grow from 0 to 8 dB. And the proposed heuristic algorithm can achieve good performance compared with the conventional sparsity-based algorithm.

With the rapid development of high-speed railway (HSR) system, there is an increasing demand on providing high quality multimedia services for HSR passengers. In this paper, we investigate the downlink resource allocation problem for multimedia services delivery in HSR orthogonal frequency division multiple access (OFDMA) system. Taking the statistical delay-QoS requirements into account, we formulate the problem as a mixed integer non-linear programming (MINLP), which aims at maximizing the system throughput over a trip of the train. With the help of integer constraint relaxation, this problem can be simplified into a convex optimization problem. To solve it, a stochastic approximation based dynamic resource allocation algorithm is developed. Finally, the numerical results are presented to show the effectiveness of the proposed resource allocation algorithm.

In this paper, we investigate the delay-aware dynamic resource management problem for multi- service transmission in high-speed railway wireless communications, with a focus on resource allocation among the services and power control along the time. By taking account of average delay requirements and power constraints, the considered problem is formulated into a stochastic optimization problem, rather than pursuing the traditional convex optimization means. Inspired by Lyapunov optimization theory, the intractable stochastic optimization problem is transformed into a tractable deterministic optimization problem, which is a mixed-integer resource management problem. By exploiting the specific problem structure, the mixed-integer resource management problem is equivalently transformed into a single variable problem, which can be effectively solved by the golden section search method with guaranteed global optimality. Finally, we propose a dynamic resource management algorithm to solve the original stochastic optimization problem. Simulation results show the advantage of the proposed dynamic algorithm and reveal that there exists a fundamental tradeoff between delay requirements and power consumption.

This paper considers an energy-efficient packet scheduling problem over quasi-static block fading channels. The goal is to minimize the total energy for transmitting a sequence of data packets under the first-in-first-out rule and strict delay constraints. Conventionally, such a design problem is studied under the assumption that the packet transmission rate can be characterized by the classical Shannon capacity formula, which, however, may provide inaccurate energy consumption estimation, especially when the code blocklength is finite. In this paper, we formulate a new energy-efficient packet scheduling problem by adopting a recently developed channel capacity formula for finite blocklength codes. The newly formulated problem is fundamentally more challenging to solve than the traditional one, because the transmission energy function under the new channel capacity formula neither can be expressed in closed form nor possesses desirable monotonicity and convexity in general. We analyze conditions on the code blocklength for which the transmission energy function is monotonic and convex. Based on these properties, we develop efficient offline packet scheduling algorithms as well as a rolling-window-based online algorithm for real-time packet scheduling. Simulation results demonstrate not only the efficacy of the proposed algorithms but also the fact that the traditional design using the Shannon capacity formula can considerably underestimate the transmission energy for reliable communications.

This paper considers an energy-efficient packet scheduling problem over green data networks, aiming at minimizing the transmission energy subject to the First-In-First-Out and strict delay constraints. Traditionally, such a problem is studied based on the classical Shannon capacity formula. However, Shannon capacity is valid only when the code blocklength approaches infinity and therefore is not practical for some applications in 5G system which allow short delays only. In this paper, we formulate the packet scheduling problem using the recently developed channel capacity formula for the finite blocklength code. It turns out that the newly formulated problem is much more challenging to solve than the traditional ones. Nevertheless, we analytically show that our scheduling problem can possess certain desirable monotonic and convex properties. Based on these properties, by applying a successive upper bound minimization (SUM) method, an iterative packet scheduling algorithm is proposed to efficiently solve the considered problem. Simulation results show that, compared with the proposed design using the finite blocklength channel capacity, the traditional design based on Shannon capacity will seriously underestimate the required transmission energy for reliable communications.

With the rapid development of high-speed railway (HSR) system, there is an increasing demand on providing high throughput and continuous multimedia (CM) services for HSR passengers. In this paper, we investigate the downlink resource allocation problem for on-demand CM services in HSR OFDMA systems with a cellular/infostation integrated network architecture. The resource allocation problem is formulated as a two-stage optimization programming, which aims at maximizing the total reward of delivered services then minimizing the weighted total number of cumulative discontinuity packets over the trip of the train. An equivalent one-stage programming is proposed to resolve the difficulty of multi-stage optimization. The resultant mixed integer programming (MIP) is NP-hard in general, we thus reformulate it as a sparse ℓ0-minimization problem and then relax it to a linear programming (LP). Furthermore, a reweighted ℓ1-minimization technique is applied to improve the system performance. Simulation results are provided to validate the proposed algorithms.

With the rapid development of high-speed railway (HSR), how to provide the passengers with multimedia services has attracted increasing attention. A key issue is to develop an effective scheduling algorithm for multiple services with different quality of service (QoS) requirements. In this paper, we investigate the downlink service scheduling problem in HSR network taking account of end-to-end deadline constraints and successfully packet delivery ratio requirements. Firstly, by exploiting the deterministic high-speed train trajectory, we present a time-distance mapping in order to obtain the highly dynamic link capacity effectively. Next, a novel service model is developed for deadline constrained services with delivery ratio requirements, which enables us to turn the delivery ratio requirement into a single queue stability problem. Based on the Lyapunov drift, the optimal scheduling problem is formulated and the corresponding scheduling service algorithm is proposed by stochastic network optimization approach. Simulation results show that the proposed algorithm outperforms the conventional schemes in terms of QoS requirements.

This paper considers robust multi-cell coordinated beamforming (MCBF) design for downlink wireless systems, in the presence of channel state information (CSI) errors. By assuming that the CSI errors are complex Gaussian distributed, we formulate a chance-constrained robust MCBF design problem which guarantees that the mobile stations can achieve the desired signal-to-interference-plus-noise ratio (SINR) requirements with a high probability. A convex approximation method, based on semidefinite relaxation and tractable probability approximation formulations, is proposed. The goal is to solve the convex approximation formulation in a distributed manner, with only a small amount of information exchange between base stations. To this end, we develop a distributed implementation by applying a convex optimization method, called weighted variable-penalty alternating direction method of multipliers (WVP-ADMM), which is numerically more stable and can converge faster than the standard ADMM method. Simulation results are presented to examine the chance-constrained robust MCBF design and the proposed distributed implementation algorithm.

This paper considers the sum rate maximization problem of a two-user multiple-input single-output interference channel with receivers that can scavenge energy from the radio signals transmitted by the transmitters. We first study the optimal transmission strategy for an ideal scenario where the two receivers can simultaneously decode the information signal and harvest energy. Then, considering the limitations of the current circuit technology, we propose two practical schemes based on TDMA, where, at each time slot, the receiver either operates in the energy harvesting mode or in the information detection mode. Optimal transmission strategies for the two practical schemes are respectively investigated. Simulation results show that the three schemes exhibit interesting tradeoff between achievable sum rate and energy harvesting requirement, and do not dominate each other in terms of maximum achievable sum rate.

Multicell coordinated beamforming (MCBF) has been recognized as a promising approach to enhancing the system throughput and spectrum efficiency of wireless cellular systems. In contrast to the conventional single-cell beamforming (SBF) design, MCBF jointly optimizes the beamforming vectors of cooperative base stations (BSs) (via a central processing unit (CPU)) in order to mitigate the intercell interference. While most of the existing designs assume that the CPU has the perfect knowledge of the channel state information (CSI) of mobile stations (MSs), this paper takes into account the inevitable CSI errors at the CPU, and study the robust MCBF design problem. Specifically, we consider the worst-case robust design formulation that minimizes the weighted sum transmission power of BSs subject to worst-case signal-to-interference-plus-noise ratio (SINR) constraints on MSs. The associated optimization problem is challenging because it involves infinitely many nonconvex SINR constraints. In this paper, we show that the worst-case SINR constraints can be reformulated as linear matrix inequalities, and the approximation method known as semidefinite relation can be used to efficiently handle the worst-case robust MCBF problem. Simulation results show that the proposed robust MCBF design can provide guaranteed SINR performances for the MSs and outperforms the robust SBF design.