This paper addresses trajectory optimization, resource allocation, and access management in a multi-unmanned aerial vehicle (UAV) assisted collaborative mobile edge computing network for low-altitude economy. In the network, UAVs collaborate to compute offloaded tasks and improve fairness among time-varying UAV battery levels. The objective of this paper is to maximize the network utility defined by the size of successful offloaded tasks, the fairness among the user equipments, and the processing time and the energy consumption of the UAVs. In particular, we consider the time-varying UAV battery model, which affects the energy cost weights of the UAVs. Therefore, we propose a heuristic optimization framework which integrates utility partitioning two stage matching (UPTSM) algorithm and variables constrained whale optimization algorithm (VC-WOA). The UPTSM algorithm decomposes the original optimization problem into two sub-problems and models them as the bipartite graph matching problems. The VC-WOA achieves the search for legal solutions by limiting the variables which violate the task processing time constraints. Simulation results demonstrate the effectiveness of the proposed heuristic optimization framework in speeding up the convergence and improving the fairness among the UAV battery levels.

This paper proposes a channel decomposition method and a novel beamforming architecture for line-of-sight (LoS) multi-input multi-output (MIMO) systems with dual continuous apertures (CAPs). Specifically, we develop a three-dimensional (3D) dual-CAP geometric model through orthogonal basis projection, establishing a unified electromagnetic analysis framework for arbitrarily oriented non-coplanar CAPs and enabling consistent 3D wave propagation modeling. Building on this model, we derive the closed-form expression for the effective degrees of freedom (EDoF) of the dual-CAP LoS MIMO system by analyzing spherical wave phase differences via sampling theory. This explicitly connects EDoF to wavelength, propagation distance, and angular rotation. Our proposed channel decomposition method decouples dual-CAP LoS MIMO channels into multiple independent SISO subchannels, which enables a novel beamforming architecture. We implement this architecture through a wavefront degrees of freedom orthogonal subchannel decomposition (WDOSD) algorithm to maximize the achievable rate of the dual-CAP LoS MIMO system. Numerical results demonstrate that: i) both component-wise EDoF and total EDoF can be accurately obtained using our proposed closed-form expressions; ii) under varying transmitter CAP sizes with fixed power, our WDOSD algorithm achieves up to three times performance improvement over the minimum mean-squared error (MMSE) algorithm; and iii) under varying communication distance with fixed angle, the WDOSD algorithm achieves up to two times performance improvement over the MMSE algorithm.

Diverse and complex wireless interference is one of the most critical threats to modern wireless communication systems. The continuous shift of wireless interference toward higher frequency and wider bandwidth imposes sampling-rate limitations on wireless interference identification (WII). Existing compressed sensing-based WII methods designed for undersampling scenarios suffer from high-complexity signal reconstruction and low identification accuracy caused by fixed sampling strategies. To address these challenges, we propose a multi-domain fusion model which employs domain-specific hybrid sampling to extract interference features for multi-attribute WII without signal reconstruction. Given the respective classification advantages of random and uniform undersampling in time-domain sequences and time–frequency images, this paper proposes a dual-branch architecture to exploit their joint benefits. Specifically, we propose a learnable sparse sampler combined with a Transformer to extract time-domain features in the random undersampling branch. We further prove that the Restricted Isometry Property (RIP)-compliant undersampling largely preserves the feature class discriminability, which motivates the introduction of the RIP loss. In parallel, we propose a multi-scale feature extraction module and a cross-fusion module for dimensionality reduction in the uniform undersampling branch. Subsequently, an attribute correlation-driven graph convolution network is introduced to classify the fused features from both branches, further improving WII performance. Finally, we propose an adaptive multi-domain binary cross-entropy loss and prove that the optimal weight of the RIP loss effectively mitigates gradient conflicts. Experimental results show that the proposed model improves precision by 25.1% over the state-of-the-art, while maintaining effective multi-attribute WII performance at undersampling ratios up to 8.

Inter-satellite links (ISLs) are pivotal for information interaction and orbit determination in mega-constellation networks. Terahertz (THz) band, with its abundant spectral resources, exhibits significant potential for high-speed ISL establishment. However, due to frequent operations of Low earth orbit (LEO) satellites, the combined impacts of multi-source timevarying platform vibrations and two-dimensional asymmetrical features of each source on THz inter-satellite communication-ranging systems have yet to be revealed. This paper proposes a vibration-constrained THz-ISL system with LEO satellite attitude dynamics and spatial effects. Specifically, we establish a multi-peak frequency model using Semiconductor Inter-Satellite Link Experiment (SILEX) spectra, complemented by an optimized sum-of-sinusoids (SOS) time-domain model to address high dynamics and multi-source vibrations in LEO scenarios. Statistical link gain models are formulated, encompassing steady, sporadic, and universal scenarios. Subsequently, the proposed ISL gain model is applied to assess communication and ranging performance. We derive the signal-to-noise ratio (SNR)-vibration quantitative relationship through Stirling’s approximation, where SNR quantifies communication performance degradation. Ranging precision is evaluated via the Fisher information matrix (FIM), revealing vibration deterioration of Cram´er-Rao bounds (CRB). Under given power constraints, we define truncation distance as the maximum vibration-tolerant link range where performance remains unaffected, and derive its closed-form solution. Simulations utilizing SILEX orbit data indicate that under typical settings: 1) inter-satellite vibrations are on the order of hundreds of μrad and cause a 5% pointing error attenuation, 2) vibrations alter system performance boundaries, with 600 km communication distance reduction and 2.2 dB ranging accuracy degradation with 0.03° deviation, 3) vibration effects can be mitigated via phased array dimensions and beamforming design, achieving capacity-robustness trade-off.

This paper addresses the coalition formation and the resource allocation in a device-to-device assisted mobile edge computing network, where the user equipments (UEs) collaborate to share the communication bandwidth and the computation resources for the task offloading. Our goal is to minimize the task execution cost, which is defined as the weighted sum of energy consumption and processing delay. In particular, we model waiting time of UEs in a coalition for the task offloading and incorporate it in the task execution cost. Therefore, we propose a three-layer hierarchical optimization framework which integrates the convex optimization, the heuristic algorithm, and the coalition game theory. In particular, we propose a double weighted mutation genetic algorithm to enhance the convergence of the algorithm, which applies weighted mutations to the offloading leader and the offloading order in the coalition. Furthermore, the task execution costs in both middle and upper layers are analytically evaluated. Simulation results validate the effectiveness of our proposed algorithms in reducing the task execution costs and speeding up the convergence.

We devise a cutting-edge near-field terahertz (THz) covert communication strategy with noise uncertainty where the beam is focused at a specific location, defined by both distance and direction. Leveraging unique near-field properties, the strategy combats eavesdroppers by increasing the capacity gap between legitimate and eavesdropping channels. We first develop a novel performance analytical framework for the near-field THz covert communication system, based on which we derive closed-form expressions for key performance metrics and thresholds, including the average covert probability, covert outage probability, covert rate, optimal detection threshold, and received power threshold of the eavesdropper. To further improve the secrecy performance, we design a new true-time-delay (TTD)-based piecewise approximation hybrid beamforming scheme for maximizing the covert rate, while effectively mitigating the negative impact caused by the beam split effect. Our numerical results demonstrate that the near-field THz covert communication strategy effectively combats eavesdroppers at all distances in the sector covering the main beam, demonstrating its practical significance for future wireless networks.

In the integrated space-air-ground cooperative sensing and communication scenario, massive sensing data necessitates high-speed transmission, while ensuring reliable data delivery confronts the combined challenges of cross-polarization interference, IQ imbalance, and multipath effects. To address the efficient interference suppression requirements for zero-IF dual-polarized receiver architectures in integrated space-air-ground systems, this paper proposes a fully blind joint processing method in the time domain for broadband dual-polarized signals, incorporating XPIC, equalization, and IQ compensation. Compared to step-by-step processing, the joint processing method can achieve global optimization with faster convergence speed. For different scenarios, the adaptive coefficients of the transversal filter are updated using different algorithms, supporting various modulation schemes including PSK, APSK, and QAM. The reconfigurable loop topology architecture enables dynamic adjustment of processing sequence based on channel conditions. Simulations demonstrate that this method can adapt to multipath signals from LEO satellites and UAVs, tolerate 2 dB amplitude and 12° phase imbalance in IQ components, and provide over 30 dB XPI suppression gain, making it suitable for high-speed data transmission scenarios in integrated space-air-ground cooperative sensing and communication systems.

In this article, we develop a dynamic weighting strategy which considers the residual energy of different devices in aerial edge computing networks, and formulate a weighted energy consumption optimization problem aimed at extending device operating duration. To solve the formulated problem, we develop a clustering algorithm using K-means++ to establish optimal user-to-unmanned-aerial-vehicle access relationships, and the optimization problem is decomposed into trajectory, transmit power, and bandwidth subproblems. Each subproblem is sequentially solved by using the successive convex approximation algorithm, and the entire optimization problem is resolved by using the block coordinate descent algorithm. Simulation results demonstrate the effectiveness of our proposed weighting strategy in managing the energy levels of users, which prolongs the operational duration of the devices.

In MIMO systems, hybrid digital and analog precoding structures are gaining attention for reducing power consumption and costs compared to fully digital precoding. However, deployment expenses and circuit power consumption still pose challenges. Nonideal hardware in practical applications further impacts energy efficiency (EE), making research into high-energy-efficiency MIMO structures under such conditions essential. To tackle this issue, an exceedingly effective hybrid precoding strategy is developed, incorporating the implications of nonideal hardware characteristics. Initially, the research models the aforementioned hardware characteristics, formulates the signal model for the hybrid-structured MIMO communication system, and discerns the nonconvex problem relative to energy efficiency optimization. Subsequently, the optimization problem is effectively resolved by choosing analog radio frequency (RF) precoding and digital baseband precoding designs, including the precoding design for array gain. Furthermore, fractional programming is utilized to achieve digital precoding. An iterative optimization method is utilized to solve this problem. Finally, the mixed precoding scheme is formulated to showcase the system’s energy efficiency performance through simulation and explore the impact of imperfect hardware on the system. Additionally, to further diminish design complexity, the implementation of various channels is analyzed to propose a low-complexity method via the approximation of the equivalent channel matrix. Simulation results show that the performance of this method achieves performance comparable to existing hybrid precoding methods, when the number of antennas is sufficiently large.

Integrating feeder- and user-links in multi-beam satellite communications significantly enhances system flexibility but requires effective resource allocation to fully realize its potential. Multi-agent deep reinforcement learning (MADRL) has emerged as a scalable solution for beam hopping, by allowing each agent to optimize the transmission parameters for one beam. However, integrating feeder- and user-links introduces complicated dependencies, including resource competition between feeder- and user-links and data-flow coupling between uplinks and downlinks, dramatically deteriorating agent cooperation. To approach the performance limit, this paper introduces a dependency-elimination MADRL framework incorporating model decomposition, link decoupling, and novel agent-level collaboration mechanisms to allocate beams, power, and bandwidth with reduced complexity. Specifically, to facilitate beam-level agent reuse for complexity reduction under the heterogeneity of feeder- and user-links, characterized by data-flow aggregation and division, we decouple bandwidth allocation from the learning model. The uplink-downlink dependencies in the bandwidth allocation is then resolved using a generalized water-filling strategy based on the performance upper bounds. Furthermore, we improve agent cooperation efficiency through state and reward decomposition and a novel non-cooperation penalty. Evaluations show that our method improves the system performance by up to 57.7% compared to sota MADRL methods while reducing training complexity by more than 50%.

Terahertz (THz) hybrid analog-digital beamforming systems face significant performance degradation due to group delay mismatch in phase shifters, which distorts beam patterns and significantly reduces the array gain. This paper presents a comprehensive framework to model, analyze, and mitigate group delay mismatch in THz hybrid analog-digital beamforming systems, addressing a critical oversight in existing research. We first establish a theoretical model for group delay mismatch in THz hybrid analog-digital beamforming systems, revealing its frequency-dependent Gaussian distribution and its cascading impact on beam squint and capacity degradation. Our analysis explicitly incorporates spatial group delay gradients, ultra-wideband channel characteristics, and hardware non-idealities, deriving closed-form expressions for beam misalignment and capacity loss. These results demonstrate that group delay-induced degradation scales quadratically with center frequency and is insensitive to bandwidth or array size, redefining THz system design priorities. We then propose a robust frequency selective hybrid analog-digital beamforming algorithm that jointly optimizes analog and digital beamformers to counteract group delay distortions. The analog beamformer, designed via Riemannian manifold optimization, minimizes spatial group delay gradients under the constant modulus constraints, while the digital precoders employs minimax optimization to compensate residual phase errors across subcarriers. Simulations under realistic THz channel conditions validate the accuracy of our analysis and the effective of our proposed design. Specially, our algorithm achieves 33.91 bps/Hz spectral efficiency at 10 dB SNR, outperforming conventional methods by 31.5% without requiring additional expensive analog lines.

With the diversification of wireless applications, the traffic patterns in the evolving wireless networks are becoming more dynamic and heterogeneous. Although numerous methods have been developed for spatio-temporal analysis, the impact of traffic patterns on spatial capacity has yet to be fully addressed. To this end, this paper studies the spatial outage capacity (SOC) in Poisson networks with Bernoulli traffic, which answers the question: “What is the maximum density of concurrently active links that satisfy a certain outage constraint?” We perform the analysis by integrating stochastic geometry with queueing theory and derive the meta distribution (MD) of signal-to-interference ratio (SIR). Unlike the conventional approaches that approximate the MDs by beta distributions, we consider the spatio-temporal correlations of the dominant interference exactly while treating the remaining interference in an average sense. Our analysis maintains tractability and achieves a highly accurate characterization of the SIR, especially for dense networks in the high-reliability regime that is particularly significant for network design. Moreover, we prove that the SOC in the high-reliability regime is achieved when all transmitters are always active. Simulations validate the accuracy of the theoretical results and show that the packet arrival rate has a marginal effect on the SOC as well as the corresponding SIR MD. We also show that the optimal density that maximizes the SOC is approximately inversely proportional to the packet arrival rate.

Distinguishing the near-field and far-field regions in line-of-sight (LoS) multiple-input multiple-output (MIMO) systems is crucial, as their distinct characteristics significantly impact performance and system design. In this paper, we propose a novel criterion for identifying the near-field and far-field regions based on the number of independent spatial streams. We introduce and derive a closed-form expression of effective degree of freedom (EDoF) for LoS MIMO systems by taking into account both the phase differences and path attenuation. Then, the spatial multiplexing distance (SMD) and resolvable distance (RD) are derived to define the boundary of the near field. Based on these boundaries, we propose a hybrid search-gradient descent (HSGD) algorithm to estimate the near-field channel information, which combines a coarse search through non-uniform step sizes with a precise estimation based on the gradient descent. Our numerical results unveil that i) the EDoF can be accurately calculated using the closed-form expression, ii) the HSGD algorithm achieves at least 28.72% improvement over the polar-domain simultaneous iterative gridless weighted (PSIGW) algorithm and 22.91% improvement over the orthogonal matching pursuit (OMP) algorithm across the different signal-to-noise ratio (SNR), and iii) the HSGD algorithm achieves at least 95.84% improvement over the PSIGW algorithm and 79.83% improvement over the OMP algorithm across varying communication distances.

This paper investigates an unmanned aerial vehicle (UAV)-base station (BS) integrated network, where a UAV transmits downlink secrecy data to multiple ground cognitive users while a ground BS utilizes jamming beamforming to help the UAV counter the eavesdropping attack of a ground eavesdropper. In particular, we consider the imperfect eavesdropping and jamming channel state information (CSI) related to the eavesdropper. To maximize the minimum sum secrecy rate of the cognitive users, a robust secure transmission scheme is proposed. The UAV trajectory, UAV transmit power, BS beamforming, and user scheduling are jointly optimized with the constraints of the communication quality of the primary users served by the BS and the UAV available propulsion energy. We formulate a non-convex optimization problem which is challenging to be solved mathematically, and we utilize an alternating optimization technique to divide the original problem into three sub-problems, i.e., UAV trajectory sub-problem, transmit power sub-problem, and user scheduling sub-problem. Besides, they can be solved by the successive convex approximation, semi-definite relaxation and S-procedure, and bivariate relaxation methods, respectively. Moreover, we explore the impact of different parameters of the proposed transmission scheme on the minimum sum secrecy rate of the cognitive users, and verify the superiority of the proposed robust secure transmission scheme design.

In the coming sixth-generation era, space-air–ground integrated network (SAGIN) is a technology with the potential for seamless coverage and high-data rate transmission. However, the inherent broadcast nature of wireless communication forces us to consider physical-layer security. This article explores secure communications with the aid of hybrid free space optical/radio frequency (FSO/RF) links in a two-phase uplink transmission. Specifically, in the first-phase transmission, a ground device transmits secrecy data to an unmanned aerial vehicle (UAV) via an radio frequency (RF) link, while the UAV emits artificial noise to confuse an eavesdropper. In the second-phase transmission, the UAV sends the secrecy data to a satellite via an FSO link to defend against RF eavesdropping. More specifically, we design two transmission schemes, i.e., slot-based scheme and period-based scheme, which are suitable for transmitting delay-sensitive data and delay-insensitive data, respectively. In order to maximize the average secrecy rate of the system, the trajectory and power allocation of the UAV are jointly optimized. The objective functions of these two schemes are both nonconvex, which are mathematically intractable to tackle by the interior-point method. Therefore, we use block coordinate descent and successive convex approximation techniques to obtain approximate solutions. Numerical results reveal the impact of the UAV trajectory and power allocation optimization on the average secrecy rate during different flight periods in different schemes. In addition, other benchmark schemes are considered for comparison, and the results indicate that our proposed schemes can achieve higher average secrecy rates.

In this study, a joint blind frame synchronization and decoding method is proposed based on the normalized syndrome satisfaction probability (NSSP) provided by the soft Low-Density Parity-Check (LDPC) codes decoder. To reduce complexity, a stopping criterion is introduced into the iterative process through the convergence of NSSP evolution patterns. In addition to achieving an excellent synchronization performance, this method also eliminates the redundancy caused by the pilot sequence. Furthermore, it is compared with the optimal pilot-based and other code-aided frame synchronization algorithms. According to analytical and simulation results, the proposed technique outperforms other advanced code-aided frame synchronization solutions in synchronization, which makes it applicable to achieve comparable performance to the pilot-based methods in specific coding gains. Due to the introduction of a novel stopping criterion, the average number of joint detection & decoding is reduced by up to 90%. Analog probability processing technology integrates sub-threshold region circuits with probability domain-based iterative message-passing algorithms inspired by high energy efficiency and low complexity. Hence, hardware implementation is presented based on the analog LDPC decoder chip as fabricated in a 0.35-μm CMOS technology for our proposed algorithms. The experimental results demonstrate the effectiveness of the proposed method and the realization loss is within 0.2 dB compared with the theoretical circuit simulation results.

This paper studies covert MIMO ambient backscatter communication (AmBC). In contrast to the prior covert AmBC works, we equip all devices with multiple antennas, where a backscatter tag works as a beamformer to hide both his transmission behavior and location from a warden. Technically, we derive two key metrics, namely the Kullback-Leibler divergence (KL divergence) measuring the transmission covertness and the covert rate measuring the transmission efficiency. We reveal that they both increase monotonically w.r.t the number of the tag’s antenna. This tradeoff makes the optimum parameter configuration non-trivial. To this end, we optimize the beamformer of the tag to maximize the covert rate subject to the covertness constraint under perfect knowledge of the warden’s channel state information (WCSI). We use the matrix permutation and semi-definite relaxation (SDR) method to make the formulated non-convex problem convex, and obtain the solution with the barrier method. We also study the impact of imperfect WCSI. To address the uncertain estimate error, we use the S-Procedure to reformulate the constraints linearly and the primal-dual interior-point method for a near-optimal solution. We conduct extensive numerical experiments and confirm that our work outperforms the state-of-the-art ones.

This paper presents a covert ambient backscatter communication (AmBC) system aided by the intelligent reflecting surface (IRS), where an IRS is used as a beamformer to help Tag for covert AmBC. Technically, we derive the expression of the Kullback-Leibler (KL) divergence to measure the detection performance of the warden. To fight against the warden’s detection, we propose a joint IRS’s beamforming and Tag’s reflection coefficient optimization scheme to maximize the covert AmBC rate subject to a key constraint metric of the KL divergence. To solve the non-convex problem, we formulate it as Fractional Programming (FP) for the linear iterations, and use the Majorization-Minimization (MM) algorithm to obtain the optimal parameters of IRS and Tag. Moreover, we investigate the covert performance with the imperfect channel state information of the warden’s link (WCSI) with Tag and IRS. Numerical results show the covert performance of the system, and illustrate the superiority of the IRS’s assistance to the covert backscatter efficiency. The simulations also show that the channel estimated error of Tag-Willie link has a negative impact on the covert backscatter efficiency, while the channel estimated errors of IRS-Willie link have the positive influence on the contrary.

In this paper, we investigate the mmWave beam tracking for vehicle-to-infrastructure (V2I) communications to find the optimal beam via sub-6 GHz channel state information (CSI). We consider two scenarios: 1) sub-6 GHz and mmWave transceivers are co-located on the same base station (BS), and 2) sub-6 GHz and mmWave BSs are separated in different places constituting heterogeneous networks (HetNets) where one sub-6 GHz BS controls multiple mmWave BSs. Considering the mobility of the vehicle and time-varying channels, we propose a predictive beam tracking method based on long short-term memory (LSTM) to construct the maps from historical sequential sub-6 GHz CSI to the future optimal mmWave beam. A single LSTM model can handle the beam tracking in the co-located scenario, since there is a one-to-one correspondence between the sub-6 GHz and mmWave transceivers, and the propagation of sub-6 GHz and mmWave signals is similar. However, in the HetNet scenario, it is difficult to select the best one among the beams of multiple mmWave BSs only via the CSI of one sub-6 GHz BS. To address this challenge, we design an LSTM fusion model, which exploits not only the historical sequential sub-6 GHz CSI but also a number of mmWave wide beam measurements, to obtain the optimal mmWave BS and beam in the HetNet. In this case, the collected sub-6 GHz CSI and mmWave wide beam measurements are analyzed by the LSTM and fully connected network (FCN) modules, respectively, providing two beam prediction results. Then the results are fused by an attention-based FCN module to accomplish the final prediction. Simulation results verify the effectiveness and superiority of our LSTM-based beam tracking models compared with other state-of-the-art deep learning beam tracking models that also leverage sub-6 GHz channels. Besides, the robustness and generalization of our proposed LSTM models are illustrated through simulations.

On the heels of orthogonal time frequency space (OTFS) modulation, the recently discovered affine frequency division multiplexing (AFDM) is a promising waveform for the sixth-generation wireless network. In this paper, we study the widely-used embedded pilot-aided (EPA) channel estimation in multiple-input multiple-output AFDM (MIMO-AFDM) system with fractional Doppler shifts. We first formulate the vectorized input-output relationship of MIMO-AFDM, and theoretically prove that MIMO-AFDM can achieve full diversity in doubly selective channels. Then we illustrate the implementation of EPA channel estimation in MIMO-AFDM and unveil that serious inter-Doppler interference (IDoI) occurs if we try to estimate the channel gain, delay shift, and Doppler shift of each propagation path. To address this issue, the diagonal reconstructability of AFDM subchannel matrix is studied and a low-complexity embedded pilot-aided diagonal reconstruction (EPA-DR) channel estimation scheme is proposed. The EPA-DR scheme calculates the AFDM effective channel matrix directly without estimating the three channel parameters, eliminating the severe IDoI inherently. Since the effective channel matrix is necessary for MIMO-AFDM receive processing, we believe this is an important step to bring AFDM towards practical communication systems. Simulation results validate the effectiveness of the proposed EPA-DR scheme.

With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this article, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterward, we derive the expressions of average distances from a typical terminal to different BSs and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions.

The dependable transmission of data and the efficacy of wireless communication depend critically on the detection and classification of interference. Traditional classification algorithms may not yield the requisite precision in identifying and categorizing diverse types of interference, whereas deep learning (DL) algorithms necessitate high-quality data and training samples, which prove unfeasible in real-time communication scenarios. In addressing these challenges, we present a novel approach that utilizes the denoising diffusion probabilistic model (DDPM) for offline processing of collected signals before feature extraction and subsequently sending the signals into a predefined classifier. Our experimental analyses show that our approach delivers up to 91% accuracy without any prior information, outperforming both generative adversarial network (GAN)-based and other traditional DL algorithms, even with limited signal samples of only 5. More significantly, our approach underscores the feasibility of employing generative models in signal processing and achieves state-of-the-art performance on high-precision recognition in real-world communication scenarios.

Full-duplex (FD) integrated sensing and communication (ISAC) has great potential in future vehicular networks. However, the FD requirement and the ISAC functions make the receiver processing extremely complicated, particularly when multiple transmissions are uncoordinated. In this paper, we study frequency-hopping (FH) based receivers in an FD ISAC system, where the arrivals of backscattered signals from one node may overlap with those of signals from another node. To mitigate the interferences caused by the overlapping signals, we consider two receiver options based on either conventional communications or frequency-modulated continuous-wave radars, and two signal modulations based on either fast FH or un-slotted ALOHA FH. Based on the different signal modulations, we develop two parameter estimation schemes via using FH-decoding and de-chirp operations, respectively. To further improve the sensing accuracy, we proceed to propose an iterative algorithm, which refines the estimates of all parameters via using short-time-Fourier transform and maximizing the received power in desired frequency bands. After obtaining all channel parameters in sensing, bilateral communications between two nodes are realized by differential phase-shift keying. Finally, simulation results are provided and verify that the proposed FD ISAC can obtain parameters in high resolution and realize robust communication links.

Mounting compact and lightweight base stations on unmanned aerial vehicles (UAVs) is a cost-effective and flexible solution to provide seamless coverage on the existing terrestrial networks. While the coverage probability in UAV-assisted cellular networks has been widely investigated, it provides only the first-order statistic of signal-to-interference-plus-noise ratio (SINR). In this paper, to analyze high-order statistics of SINR and characterize the disparity among individual links, we provide a meta distribution (MD)-based analytical framework for UAV-assisted cellular networks, in which the probabilistic line-of-sight channel and realistic antenna pattern are taken into account for air-to-ground transmissions. To accurately characterize the interference from UAVs, we relax the widely applied uniform off-boresight angle (OBA) assumption and derive the exact distribution of OBA. Using stochastic geometry, for both steerable and vertical antenna scenarios, we obtain mathematical expressions for the moments of condition success probability, the SINR MD, and the mean local delay. Moreover, we study the asymptotic behavior of the moments as network density approaches infinity. Numerical results validate the tightness of the theoretical results and show that the uniform OBA assumption underestimates the network performance, especially in the regime of moderate altitude of UAV. We also show that when UAVs are equipped with steerable antennas, the network coverage and user fairness can be optimized simultaneously by carefully adjusting the UAV parameters.

Remote first-aid treatment on ambulances is a promising application of 5G. However, there still exist gaps between the capabilities of current 5G networks and the stringent requirements of remote emergency on ambulances. Dual connectivity (DC) is an efficient technology to fill these gaps by integrating 5G millimeter wave (mmWave) with Sub-6GHz networks. In this paper, we investigate a dual-connectivity handover scheme to enhance the transmission rate of the wireless links for a 5G-enabled ambulance. Due to the long delay caused by signal transmission and processing, the conventional handover schemes based on reference signal received power (RSRP) measured by users are not sufficiently sensitive to the rapidly changing propagation environments surrounding the 5G-enabled ambulance. Instead, considering the randomness of environments and the delay caused by the handover process, we employ a deep Q network (DQN)-based algorithm to find a far-sighted policy for solving the handover problem. However, due to the drawbacks of single-step bootstrapping, value overestimation, and low-efficiency exploration, the vanilla DQN is performance-limited. To this end, we adopt effective techniques including multi-step learning, double DQN, and NoisyNet to improve learning performances, and propose a noisy double DQN (NDDQN)-based dual-connectivity handover scheme. Simulation results verify the effectiveness and superiority of our NDDQN-based handover scheme compared with the vanilla DQN and upper confidence bound (UCB)-based handover schemes, and then show that our handover scheme can adapt to various handover models.

In wireless-powered device-to-device (D2D) networks with backscatter-aided passive relays, the cooperation among D2D pairs is of special necessity. With the cooperation among D2D pairs, the devices can act as passive relays to enhance the signal reception, and share their active transmission time to improve the resource utilization. Therefore, how the D2D pairs form coalitions and how to reallocate the active transmission time to the D2D pairs after forming coalitions should be studied. In this article, we develop a hierarchical cooperative game framework for the network to investigate how the D2D pairs cooperate with each other. Specifically, in the upper layer of the game, we employ the coalition game to formulate how the D2D pairs form coalitions and analyze the stability of the coalition structure and the complexity of the proposed strategy. In the lower layer of the game, we employ the bargaining game to investigate how the D2D pairs share the active transmission time, and analytically evaluate the complexity of deriving the Nash bargaining solution. Simulation results demonstrate the effectiveness of our proposed scheme. The results also show that the computational complexity of our proposed scheme is only a small fraction of that of the exclusive search-based scheme. Nevertheless, compared with the exclusive search-based scheme, the total effective amount of the transmitted data in the proposed cooperative scheme reduces no more than 2%, which is very minor.

Joint communication and radar sensing (JCAS) integrates the two functions into one system, sharing one transmitted signal. In this paper, we investigate JCAS waveform optimization in communication-centric systems, where a base station (BS) detects radar targets and communicates with mobile users simultaneously. Different from existing works, we study multi-metric optimizations for a practical low-cost system and establish their connections. To relax the requirement of full-duplex technology, we add a single receive antenna for sensing at the BS, which is synchronized with and spatially separated from the JCAS transmit array. We first optimize precoders for communications and radar, individually. Then, we formulate a JCAS waveform optimization problem that constrains either mutual information (MI) or Cramér-Rao bound (CRB) of radar and maximizes the relaxed signal-to-interference-plus-noise rate (SINR) of communications. Exploiting the geometric characteristic of the relaxed SINR, we provide a closed-form solution under certain conditions and propose a numerical iteration algorithm that works in all situations. We also disclose the connections between optimizations with constraining MI and CRB, using numerical results. Finally, simulation results are provided and validate the proposed optimization solutions.

We propose a cluster-based multi-carrier beam division multiple access (MC-BDMA) scheme to enable massive device terahertz (THz) communications with the dynamic-subarray hybrid beamforming (HBF) architecture. This scheme is motivated by a limitation of conventional HBF architectures, i.e., the number of users cannot exceed the number of radio frequency (RF) chains. By exploiting the unique properties of THz channels, such as high distance-and-frequency dependence, high sparsity, and small angular spread, we propose multiple users in the same cluster to be supported by a single RF chain with distance-aware multi-carrier modulation. Moreover, we propose different clusters to be divided by BDMA that is accomplished through HBF design. In our proposed scheme, we first design a novel antenna selection and subarray partition algorithm for the dynamic-subarray HBF architecture to compensate for performance loss caused by diverse channels among users. We then design an algorithm for multi-carrier HBF which maximizes the achievable throughput and eliminates the inter-beam interference. Numerical results show that our proposed cluster-based MC-BDMA scheme with the dynamic-subarray HBF architecture provides an almost 100% spectral efficiency gain and a 60% energy efficiency gain over the conventional non-cluster HBF scheme in massive device communications.

In order to satisfy the requirement of extremely high data rate in traffic hotspot regions, millimeter wave (mmWave) has attracted significant attention in wireless communication networks. While the coverage performance of mmWave networks based on the distribution of signal-to-interference-plus-noise ratio (SINR) has been widely studied, it provides only very limited information on the link reliability. In this paper, we provide a fine-grained performance analysis of the mmWave networks with hotspots. Specifically, we first establish a general and tractable framework to investigate the performance of mmWave networks using the Poisson cluster process integrated with several features of the mmWave band. Both open and closed association strategies are considered. To show what fraction of users in the networks achieves target reliability when the SINR is given, we derive the tier association probability and the moments of the conditional SINR distribution, based on which the exact meta distributions of SINR are given. Interestingly, in clustered mmWave networks, the widely used standard and generalized beta approximations do not work well when the blockage effect is severe. To resolve this issue, we provide a modified approximation by scaling the standard beta distribution, which is shown to be closer to the exact results. We conduct extensive simulations to study the impact of mmWave and deployment features on the performance of clustered mmWave networks. Numerical results reveal that the optimal scattering variance of mmWave base stations scales with the cluster size to maximize the number of concurrent reliable links, and increasing the antenna directivity results in more reliable communications than elevating the transmit power of mmWave base stations.

With the rapid development of the backscatter technology, backscatter-aided passive relays can be employed to improve the performance of wireless-powered device-to-device (D2D) networks. In this paper, we investigate the strategy of how the D2D pairs cooperate with each other in the network, and formulate the problem as a coalition game. In the coalition game, the players are the D2D pairs, the payoff of a D2D pair is the amount of its transmitted data, and the action is that each D2D pair can choose to stay or switch its coalition. We then develop the preference relation and the switch rule, and further design the switch algorithm for the D2D pairs to form coalitions. Moreover, we analyse the stability of the coalition game and the complexity of the proposed strategy. Simulation results demonstrate that compared with the non-cooperative strategy, our developed cooperative strategy can efficiently improve the amount of the transmitted data.

We propose a novel hybrid beamforming (BF) scheme with distance-aware multi-carrier (DAMC) modulation and beam division multiple access (BDMA) to enable massive connections in terahertz (THz) micro-scale networks. This scheme breaks a fundamental limitation in hybrid BF, i.e., the number of users that are simultaneously supported cannot exceed the number of RF chains. Some unique properties of THz channels, such as high distance-and-frequency dependence, high sparsity, and small angular spread, are exploited in this scheme. First, we propose a user grouping scheme with rough beam pre-scanning and a DAMC spectrum allocation scheme to eliminate intragroup interference. Then, we propose a wideband hybrid BF designing algorithm using the principles of BDMA to control inter-group interference. Furthermore, we propose an iterative power allocation strategy to maximize the achievable sum-rate of the network. Simulation results are presented to show that our proposed hybrid BF DAMC-BDMA scheme achieves higher sum-rate than the fully digital BF scheme in the high transmit power regime, due to the high sparsity of THz channels. Simulation results also demonstrate that our iterative power allocation strategy has strong robustness against uncertain interferences.

Angle of Arrival (AoA) estimation with localized hybrid arrays is challenging in millimeter-wave (mmWave) communication systems. Most existing solutions quantize AoAs into limited values with relatively low accuracy. This paper presents a multi-AoA estimation scheme which is capable of estimating multiple AoAs from multiple users with low complexity. Specifically, we design a path filter via combining the received signals for each subarray. Each path filter enables a certain range of AoAs to pass through while suppressing the rest. Then we can use low-complexity cross-correlation operations to obtain continuous AoA estimates. Association of paths to users is further achieved by a follow-up pseudo-random codes based correlation operation. The scheme is first presented for a narrowband system and then extended to wideband with frequency selectivity. We also introduce new metrics and derive the lower bound of mean square error for evaluating the accuracy of AoA estimates, as conventional metrics face difficulties in the presence of multiple closely located AoAs. Extensive simulation results are provided and validate the effectiveness of the proposed multi-AoA estimation scheme.

We propose novel hybrid beamforming schemes for the terahertz (THz) wireless system where a multi-antenna base station (BS) communicates with a multi-antenna user over frequency selective fading. Here, we assume that the BS employs sub-connected hybrid beamforming and multi-carrier modulation to deliver ultra high data rate. We consider a three-dimensional wideband THz channel by incorporating the joint effect of molecular absorption, high sparsity, and multi-path fading, and consider the carrier frequency offset in multi-carrier systems. With this model, we first propose a two-stage wideband hybrid beamforming scheme which includes a beamsteering codebook searching algorithm for analog beamforming and a regularized channel inversion method for digital beamforming. We then propose a novel wideband hybrid beamforming scheme with two digital beamformers. In this scheme, an additional digital beamformer is developed to compensate for the performance loss caused by the constant-amplitude hardware constraints and the difference of channel matrices among subcarriers. Furthermore, we consider imperfect channel state information (CSI) and propose a probabilistic robust hybrid beamforming scheme to combat channel estimation errors. Numerical results demonstrate the benefits of our proposed schemes for the sake of practical implementation, especially considering its high spectral efficiency, low complexity, and robustness against imperfect CSI.

This paper investigates the joint base station (BS) association and beamforming for energy efficiency maximization in coordinated multi-cell multiuser downlink systems. In particular, we assume that only the channel distribution information is known to the BSs. The considered problem is difficult to be solved optimally due to the non-smooth and non-convex functions in the formulation. Therefore, we propose an iterative suboptimal algorithm to solve the problem efficiently based on the successive convex approximation (SCA). More specifically, the convex approximation of the original problem at each iteration can be solved efficiently by the second-order cone programming and the solution obtained by the proposed algorithm satisfies the generalized Karush-Kuhn-Tucker (KKT) conditions. To facilitate the implementation of decentralized beamforming, we transform the convex approximation problem at each iteration of the SCA into an equivalent form, which is amenable to applying the alternating direction method of multipliers (ADMM). By combining the SCA and the ADMM, a decentralized energy-efficient beamforming algorithm is proposed. Numerical results are presented to show the performance of the proposed algorithms.

Recently, unmanned aerial vehicle (UAV) has been considered as a promising technique in mobile edge computing networks, and enhances the performances of ultra-reliable and low-latency services. In this paper, we propose a UAV-aided low latency mobile edge computing network with millimeter wave (mmWave) backhaul. There are two types of communication links in our network, the UAV link and the ad hoc link. We jointly consider the network resource allocation and the UAV trajectory design in our proposed problem, which is a non-convex mixed integer nonlinear programming (MINLP). For solving the proposed problem, we give a novel algorithm framework. We adopt generalized Benders decomposition (GBD) as the outer loop algorithm to separate the integer variables and continuous variables. In the inner loop, the continuous primal problem is solved by the joint alternating direction method of multipliers (ADMM), Dinkelbach algorithm and successive convex approximation (SCA) algorithm. The simulation results show that our proposed system architecture and algorithm framework can achieve low latency for the time-sensitive task in mobile edge computing.

In this paper, we investigate the dynamic access point and service selection in a backscatter-assisted radio-frequency-powered cognitive network, where many secondary transmitters (STs) can choose different transmission services provided by multiple access points. To analyze the access point and service selection of the STs, we formulate the problem as an evolutionary game. The STs act as the players and adjust their selections of the access points and services based on their utilities. Specifically, we model the access point and service adaptation of the STs by the replicator dynamics, and analytically prove the existence and uniqueness, and the stability of the evolutionary equilibrium. We also consider the delay of information used by the STs to adapt their selection and perform the analysis by using delayed replicator dynamics. In particular, the stability region of the delayed replicator dynamics in a special case is derived. Furthermore, we develop a low-complexity algorithm for the access point and service selection in the network based on evolutionary game. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed access point and service selection strategy in the network.

With the increasing demands for large-scale computing in Internet of Things network, fog computing emerges as a potential solution. However, the time and energy costs are the bottlenecks for developing fog computing. In this paper, we investigate the green fog computing by maximizing the network utility function considering energy efficiency with the constraints of power and interference. The proposed problem is a large-scale mixed integer nonlinear programming. To deal with such kind of problems, we design an algorithm framework to solve the problem in a distributed and parallel manner. The outer loop of the problem is based on the Benders decomposition to divide the integer variables and continuous variables into the master problems and subproblems, respectively. In the subproblem, we use the Dinkelbach algorithm to transform the fractional programming into an equivalent solvable form. In the inner loop, the large-scale problem with only continuous variables is handled by the alternating direction method of multipliers algorithm. For the master problem, we propose a centralized branch-and-bound algorithm to deal with the complexity. We also discuss the properties and performances of our algorithm. Finally, the simulation results indicate that our proposed algorithm is energy-efficient and time-saving.

Deploying Sub-6GHz networks together with millimeter wave (mmWave) is a promising solution to achieve high data rates in traffic hotspots while guaranteeing sufficient coverage, where mmWave small cells are densely deployed to provide high quality of service. In this paper, we propose an analytical framework to investigate the integrated Sub-6GHz-mmWave cellular networks, in which the Sub-6GHz base stations (BSs) are modeled as a Poisson point process, and the mmWave BSs are clustered following a Poisson cluster process in traffic hotspots. We conduct stochastic geometry-based analysis and derive the performance metrics including the association probability, signal-to-interference-plus-noise ratio coverage probability and average achievable rate, which are validated to be accurate by Monte Carlo simulations. We analyze the impact of various deployment parameters on the network performance to give insights on the network design. In particular, it is shown that deploying mmWave small cells in traffic hotspots will outperform both traditional Sub-6GHz heterogeneous network and isolated mmWave system in terms of the coverage probability. It can also be shown that extremely high and extremely small association weight for mmWave BSs will deteriorate the performance for cell edge users and cell interior users, respectively. Moreover, there exists an optimal pre-decided dispersion parameter of mmWave BSs that contributes to the maximum coverage probability.

This paper investigates the time scheduling for a backscatter-aided radio-frequency-powered cognitive radio network, where multiple secondary transmitters transmit data to the same secondary gateway in the backscatter mode and the harvest-then-transmit mode. With many secondary transmitters connected to the network, the total transmission demand of the secondary transmitters may frequently exceed the transmission capacity of the secondary network. As such, the secondary gateway is more likely to assign the time resource, i.e., the backscattering time in the backscatter mode and the transmission time in the harvest-then-transmit mode, to the secondary transmitters with higher transmission valuations. Therefore, according to a variety of demand requirements from secondary transmitters, we design two auction-based time scheduling mechanisms for the time resource assignment. In the auctions, the secondary gateway acts as the seller as well as the auctioneer, and the secondary transmitters act as the buyers to bid for the time resource. We design the winner determination, the time scheduling, and the pricing schemes for both the proposed auction-based mechanisms. Furthermore, the economic properties, such as individual rationality and truthfulness, and the computational efficiency of our proposed mechanisms are analytically evaluated. The simulation results demonstrate the effectiveness of our proposed mechanisms.

We propose a novel hybrid beamforming (BF) scheme for the Terahertz (THz) wireless communication system over frequency selective channels. In the system, a multi-antenna base station which employs the sub-connected architecture adopts orthogonal frequency division multiplexing to serve a multi- antenna user. By building a wideband THz channel model, we design a beamsteering codebook searching algorithm for analog BF in which the channel state information of all subcarriers in the radio frequency domain is considered. We then design the digital BF by using the regularized channel inversion method for eliminating inter-band interference at the baseband. Numerical results demonstrate that our proposed hybrid BF scheme achieves a significant spectral efficiency advantage over the existing hybrid BF scheme which adopted the zero-forcing digital beamformer. The results also demonstrate that the spectral efficiency achieved by our proposed low-complexity scheme is very close to that achieved by the high- complexity fully digital BF scheme, especially when the average received power at the user is low.

Fog computing is a promising approach to alleviate the computation burden in traditional mobile networks to meet the increasing application demands. Such a complicated system is typically challenging and requires distributed solutions. In this paper, we investigate the resource allocation problem in fog computing to maximize the utility function from the energy efficiency perspective. The formulated problem is a mix integer nonlinear programming problem, which is NP-hard. We adopt a modified distributed inner convex approximation (NOVA) to approximate the problem first. Then, the Benders decomposition algorithm is applied to deal with integer variables. In the subproblem, we use the Dinkelbach algorithm to transform the fractional programming into an equivalent parametric subtractive form. Furthermore, the subproblem is decomposed distributedly, which enables users to update without information exchange. The simulation results indicate the effectiveness of the proposed algorithm.

Deploying sub-6-GHz network together with millimeter wave (mm-wave) is a promising solution to simultaneously achieve sufficient coverage and high data rate. In heterogeneous networks, the traditional coupled access, i.e., users are constrained to be associated with the same base station in both downlink and uplink, is no longer optimal, and the concept of downlink and uplink decoupling (DUDe) has recently been proposed. In this paper, we analyze the coverage probability and area throughput for both the downlink and uplink of sub-6-GHz/mm-wave cellular networks with decoupled access. Compared with the existing works, we take uplink power control and mm-wave interference into account. Using the tools from stochastic geometry, the expressions of signal-to-interference-plus-noise ratio coverage probability, user-perceived rate coverage probability and the area throughput are derived. The impact of decoupled access and different small cells (SCells) is investigated. In particular, analytical results reveal that with decoupled access, UEs are more likely to be associated with SCells in uplink when the network is sparse, and the uplink traffic will be offloaded from sub-6-GHz SCells to mm-wave SCells when the network is dense. Moreover, the dense deployment of mm-wave SCells rather than sub-6-GHz SCells is more reasonable, and the DUDe is a key factor in improving the performance of dense cellular networks with multi-band.

In this paper, we propose an energy-efficient joint resource allocation and power control scheme for downlink multi-cell orthogonal frequency division multiple access (OFDMA) networks with imperfect channel state information (CSI). Considering the maximum allowed transmit power, the tolerable outage probability, and the fact that each resource block is allocated to at most one user in each cell, we formulate the energy-efficient joint resource allocation and power control problem as a probabilistic mixed non-convex fractional programming problem, which is hard to be tackled. In order to solve the problem efficiently, we first substitute the probabilistic constraints into the objective function and then introduce new auxiliary variables to transform the original problem into a difference of two convex functions (D.C.) programming problem. As a result, we resort to D.C. algorithm to obtain a solution satisfying Karush-Kuhn-Tucker conditions of the D.C. problem. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

In this paper, we investigate the energy-efficient power control for device-to-device (D2D) communications underlaying cellular networks with max-min fairness, where uplink resource blocks allocated to one cellular user equipment are reused by multiple D2D pairs to improve the frequency reuse factor, and the minimum individual energy efficiency (EE) is maximized. This is a generalized fractional programming (GFP) problem, and is hard to tackle due to its non-concave nature, which means the complexity of global optimal solution is unaffordable. In order to give sub-optimal solution with reasonable complexity, we first transform the GFP problem into equivalent optimization problem in a parametric subtractive form, and then add constraints on the co-channel interferences to convert the non-concave GFP problem into concave one. The sub-optimal solution, which can be obtained through solving the deduced concave problem based on sophisticated convex optimization methods, gives a tight lower bound on the optimal EE. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

In this paper, we investigate the energy-efficient power control for device-to-device (D2D) communications underlaying cellular networks, where uplink resource blocks allocated to one cellular user equipment are reused by multiple D2D pairs and co-channel interference caused by resource sharing becomes a significant challenge. We consider both the total energy efficiency (EE) and individual EE optimization problems, which are fractional programming and generalized fractional programming problems, respectively, and are hard to tackle due to their non-concave nature. We first transform them into equivalent optimization problems in parametric subtractive forms, which fit in a class of non-concave optimization methods known as difference of two concave functions programming, and then solve them using Dinkelbach and branch-and-bound methods to give global optimal solutions. Due to the unaffordable complexity of the global optimal solution, we further propose sub-optimal schemes through adding constraints on the interferences to convert the non-concave problems into concave ones and to give sub-optimal solutions with reasonable complexity. The sub-optimal solution gives a tight lower bound on the optimal EE. Simulation results are presented to demonstrate the effectiveness of the proposed schemes.

In this paper, we investigate the energy-efficient resource allocation problem for the device-to- device (D2D) communications overlaying LTE networks, where the D2D user equipment (UE) shares the spectrum with the cellular UE in an orthogonal way such that the interference between them is completely eliminated. We consider both the non- orthogonal and orthogonal resource allocation strategies for D2D communications, where the resources allocated to different D2D pairs are non-orthogonal and orthogonal respectively. In the non-orthogonal strategy, the interference exists among different D2D pairs, and the resource allocation only concerns the transmit power control for each D2D pair; whereas in the orthogonal strategy, there is no interference, and the resource allocation concerns both the resource block (RB) allocation and the transmit power control. In the two strategies, the related resource allocation problems are firstly formulated as a fractional programming (FP) problem and a mixed-integer nonlinear fractional programming (MINLFP) problem respectively, both of which are then transformed into equivalent optimization problems in parametric subtractive form by exploiting the property of FP. As the transformed equivalent problems are non-concave, we develop the sub-optimal energy-efficient resource allocation schemes by solving them based on Dinkelbach and Powell-Hestenes-Rockafellar augmented Lagrangian methods. Simulation results demonstrate the effectiveness of the proposed schemes and show that the non-orthogonal strategy outperforms the orthogonal one in terms of the energy efficiency.

In this paper, we investigate space-time network coding (STNC) in cooperative multiple-input multiple-output networks, where U users communicate with a common destination D with the aid of R decode-and-forward relays. The transmit antenna selection with maximal-ratio combining (TAS/MRC) is adopted in user-destination and relay-destination links where a single transmit antenna that maximizes the instantaneous received signal-to-noise ratio is selected and fed back to transmitter by receiver and all the receive antennas are combined with MRC. In the presence of perfect feedback, we derive new exact and asymptotic closed-form expressions for the outage probability (OP) and the symbol error rate (SER) of STNC with TAS/MRC in independent but not necessarily identically distributed Rayleigh fading channels. We demonstrate that STNC with TAS/MRC guarantees full diversity order. To quantify the impact of delayed feedback, we further derive new exact and asymptotic OP and SER expressions in closed form. We prove that the delayed feedback degrades the full diversity order to (R + 1)ND, where ND is the antenna number of the destination D. Numerical and Monte Carlo simulation results are provided to demonstrate the accuracy of our theoretical analysis and evaluate the impact of network parameters on the performance of STNC with TAS/MRC.

In this paper, we investigate a multi-cell Long Term Evolution (LTE) uplink resource allocation problem to mitigate the inter-cell interference based on interference graph, in which the vertices represent the user equipments (UEs) and the edges represent critical interference relations between the UEs. Besides, to avoid increasing the overhead of LTE system, we derive the interference graph based on the channel statistics of cellular links. Given that the uplink resource allocation is NP-hard, two heuristic algorithms are proposed to give solutions with reasonable complexity. The first one is centralized algorithm based on the global interference graph, and the other one is distributed algorithm based on local interference graph. Both the centralized and distributed algorithms can mitigate the inter-cell interference evidently. We show by simulations that the centralized algorithm outperforms the distributed one in terms of throughput and fairness at the cost of higher overhead and complexity. As well, simulation results show that the cell-edge throughput improvements of the algorithms are more than 100% compared with improved riding peaks algorithm and the fairness factors are greater than 0.88.

We investigate the energy-efficient resource allocation problem for the downlink in long-term evolution heterogeneous networks through maximizing the energy efficiency (EE) under the per-user throughput and per-eNB power constraints in this paper. We demonstrate that EE is an increasing function in channel gain, and that EE is continuously differentiable and strictly quasiconcave in transmit power associated with each resource block (RB). Due to the non-convexity of the optimization problem, we develop a two-step resource allocation scheme composed by RB allocation and transmit power control. In the first step, we allocate RBs to users through maximizing the minimum EE of individual user and satisfying the throughput requirement of each user. In the second step, by enforcing the per-user throughput and per-eNB power constraints and exploiting the strict quasiconcavity of EE in transmit power associated with each RB, the power control algorithm is developed to maximize the EE. Simulation results demonstrate the effectiveness of the proposed resource allocation scheme and show that it greatly improves the EE compared with conventional spectral-efficient scheme.

In this paper, we investigate the performance of cooperative decode-and-forward (DF) multiple-input multiple-output (MIMO) relaying system with orthogonal space-time block code (OSTBC) transmissions over correlated Nakagami-m fading channels. For maximal ratio combining (MRC) receiver at the destination, we provide the compact closed-form expressions for cumulative distribution function (CDF) and probability density function (PDF) of the instantaneous end-to-end signal-to-noise ratio (SNR). In addition, the exact analytical expressions are also derived for the outage probability (OP) and symbol error rate (SER) relying on CDF. Furthermore, we present the asymptotic expressions for OP and SER in the high SNR regime, from which we gain an insight into the system performance and derive the achievable diversity order and array gain. The analytical expressions are validated through Monte-Carlo simulations.

Location of a source is of considerable interest in wireless sensor networks. A novel algorithm for source location by utilizing the time-of-arrival (TOA) measurements of a signal received at spatially separated sensors under non-line-of-sight (NLOS) environments is proposed. The algorithm is based on quadratic programming, which is a special type of mathematical optimization problem. Comparisons of performance with other algorithms are made, and Monte Carlo simulations are performed. Simulation results show that the proposed algorithm gives better results.