Mobile edge computing–space-air-ground integrated network (MEC-SAGIN) is emerging as a crucial component of future wireless systems. Despite its potential, addressing network fluctuations while ensuring continuous low-latency computing services in highly dynamic environments remains a significant challenge. To address this issue, this paper proposes a fluid antenna (FA)-assisted MEC-SAGIN system, which enhances channel transmission conditions and reduces uplink task offloading latency by flexibly adjusting the antenna ports of edge computing users equipped with FAs. Specifically, we aim to minimize the maximum total computational delay (TCD) of edge computing tasks for ground users (GUs) and the satellite user (SU) by jointly optimizing the task offloading strategies, computational resource allocation, FA port positions, unmanned aerial vehicle (UAV) location, and the receive beamforming matrix. To solve this non-convex problem, we employ the block coordinate descent (BCD) technique to decompose the original problem into four subproblems. The subproblems are optimized using a combination of low-complexity iterative algorithms and the projected gradient descent (PGD) method to refine communication and computation configurations as well as FA port selection. Simulation results demonstrate that the FA-assisted scheme significantly improves the TCD performance of the MEC-SAGIN system. It maintains transmission stability and reliability in dynamic environments while outperforming conventional fixed-position antennas (FPAs) and random-port antenna schemes.

This paper investigates mobile edge computing (MEC)-aided multi-static integrated sensing and communication (ISAC) systems. In the considered system, each device offloads a task to the MEC server for computation. Meanwhile, a sensing receiver (SR) has to detect a target. To enhance the detection capabilities, the devices and base station (BS) collaboratively transmit radar signals, and the SR detects the target based on the received radar echoes. Specifically, the task execution period is divided into two phases. In the first phase, the devices offload part of their tasks while simultaneously transmitting radar signals for target sensing. In the second phase, the MEC server at the BS processes the offloaded tasks, and meanwhile devices along with the BS transmit radar signals for sensing. Thus, the system entities form a multi-static ISAC framework. Radar signal processing schemes are designed for both scenarios—with and without knowledge of the target response amplitudes. The corresponding cooperative detection probabilities under a constant false alarm probability constraint are derived. To ensure high energy efficiency, optimization problem is formulated to minimize energy consumption across all devices, subject to task computation and detection probability constraints. This is achieved by adjusting beamforming at the devices and BS, as well as task partitioning and phase duration allocation. For scenarios with knowledge of target response amplitudes, the problem is efficiently solved by iteratively optimizing beamforming subproblem, time and task division subproblem using weighted minimum mean square error method, successive convex approximation, and golden section search method. For scenarios without knowledge of target response amplitudes, the formulated bi-level optimization problem is first equivalently transformed into a single-level problem, which is then tackled using alternating optimization method, semidefinite programming, and Lagrangian dual method. Simulations validate the benefits of the proposed MEC-aided cooperative sensing scheme compared with numerous benchmarking schemes including traditional non-cooperative sensing scheme.

Split inference divides a global deep neural network (DNN) into several sub-models and assigns them to different computing nodes, thereby leveraging distributed computational resources at network edge for executing complex artificial intelligence (AI) algorithms. As an emerging technique,over-the-air computation(AirComp) turns a multi-access channel into a processor for distributed computing. Specifically, by exploiting the waveform superposition of the multi-access channel, a receiver directly aggregates signals transmitted by different transmitters to obtain their average (or any nomographic function). In order to accelerate inference speed and reduce communication and computation loads, we propose a novelin-network AIframework that realizes inference through communications among devices. This distinctive feature of the framework is to generalize multiple-input-multiple output (MIMO) AirComp to realize over-the-air matrix-vector multiplications (MVM), the most computation intensive operations of a DNN. As a result, the participating devices act like neurons in the DNN but they are now linked through computation capable wireless channels, which gives the name ofOver-the-Air Neural Link(AirNeuralink). The proposed AirNeuralink techniques are associated with different system topologies including relay, star, and distributed topologies. Their design involves jointly optimizing the precoding and post-equalization to minimize the MVM errors of the estimated feature values at the receiver set with respect to their ground truth under the transmit power constraint for each transmitter. The optimal post-equalization is derived in the form of Wiener filter but with an aggregation matrix (weight matrix). Utilizing the property of Schur-concave function and matrix inequality, the optimal structure of precoding in each topology is designed to enable spatial-channel power allocation by efficiently solving a convex problem with scalar variables. Besides, the asymptotic MVM errors are analyzed to show that the errors can be sufficiently small if the rank of model-weight matrix is no larger than that of channel matrix. Simulations demonstrate the superior performance of the proposed framework in transmission latency reduction while maintaining the inference accuracy as compared with traditional digital transmission.

This paper investigates the delay minimization problem for multi-task federated learning (MTFL) systems at the network edge. We develop a novel MTFL framework, based on which the divergence bounds are derived for both task-related and task-unrelated scenarios, systematically quantifying the effects of user importance, user participation, and inter-task correlations on convergence behavior. Building upon these insights, a long-term joint optimization problem is formulated to minimize the overall training delay under the long-term divergence bounds and energy constraints. To address the coupling in multi-slot user scheduling, the optimization problem is decomposed into a single-slot joint resource allocation and task scheduling subproblem and a cross-slot user scheduling subproblem. The former is solved using block coordinate descent (BCD) combined with Johnson’s rule, while the latter is modeled as a constrained Markov decision process (CMDP) and addressed via a dueling double deep Q-network (D3QN) with cost shaping and prioritized experience replay. Numerical results verify the effectiveness of the proposed framework and convergence analysis, demonstrating its significant improvements over baseline schemes in terms of convergence and delay reduction.

In this paper, we investigate a joint communication and computation resource allocation strategy for an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system employing fluid antenna (FA). Specifically, each user is equipped with an FA to offload the entire computation tasks to the MEC server deployed on the UAV. By dynamically selecting antenna ports, users can achieve latency-efficient edge computing services, especially advantageous in dynamic environments. To minimize the maximum execution delay of all the users, we jointly optimize the UAV location, FA port selection, and computation resource allocation, subject to computational capacity constraints. The original non-convex optimization problem is decomposed into three tractable subproblems within a block coordinate descent (BCD) algorithm. The optimal computing frequencies are derived in closed form, while the UAV location and FA port selection are optimized using low-complexity iterative algorithms based on successive convex approximation (SCA) and linear programming (LP) techniques. In addition to conventional benchmarks with fixed-position antennas (FPAs), we also introduce a reconfigurable intelligent surface (RIS)-assisted system as a comparative baseline. Simulation results demonstrate that the proposed FA-assisted scheme significantly outperforms both FPAs and RIS-assisted counterparts, with performance gains becoming more pronounced in multi-task and highly dynamic scenarios, establishing FA-assisted UAV-MEC as a promising solution for future deployments.

In this paper, we propose a Sustainable Sensing Federated Learning (S2FL) system where Internet-of-Things (IoT) devices and mobile users harvest energy wirelessly to perform data sensing, local Federated Learning (FL) training, and model update transmissions. We formulate a joint optimization problem that considers transmission power, CPU frequency, bandwidth allocation, and time allocation to maximize long-term learning efficiency. To solve this complex and dynamic problem, we develop an algorithm that integrates Deep Reinforcement Learning (DRL) with optimization techniques, leveraging the Deep Deterministic Policy Gradient (DDPG) algorithm for time allocation and a Lagrangian-Based Block Coordinate Descent (BCD) Method for per-slot resource optimization. Simulation results demonstrate that our proposed DDPG-based DRL-S2FL algorithm significantly outperforms benchmark schemes, achieving up to 15 % higher average reward compared to Deep Q-Networks (DQN) and 40 % greater performance than Random strategies. This work highlights the effectiveness of combining advanced optimization techniques with deep reinforcement learning to enhance federated learning performance in dynamic wireless environments.

Hybrid beamforming (HBF) offers significant advantages for massive multiple-input multiple-output (mMIMO) multi-beam low earth orbit (LEO) satellite systems, due to its ability to reduce hardware complexity and increase spectral efficiency. However, in practical multi-beam LEO systems, angle errors and imperfect channel state information at the transmitter (CSIT) present major challenges for traditional HBF techniques, which assume perfect channel characteristics. To address these challenges, a robust HBF method designed for multi-beam LEO systems is introduced, integrating wide beam (WB)-based analog beamformer with a digital precoder assisted by rate-splitting multiple access (RSMA). Furthermore, to solve the digital precoding problem, we employ a semi-definite programming (SDP) method augmented with penalty functions to transform the original non-convex problem into a convex one and solve it iteratively. Finally, simulation results demonstrate that the proposed HBF method can significantly enhance ergodic sum rate, outperforming traditional HBF methods in terms of rate performance.

Orthogonal time frequency space (OTFS) modulation, a delay-Doppler (DD) domain communication scheme exhibiting strong robustness against the Doppler shifts, has the potentials to be employed in LEO satellite communications. However, the performance comparison with the orthogonal frequency division multiplexing (OFDM) modulation and the resource allocation scheme for multiuser OTFS-based LEO satellite communication system have rarely been investigated. In this paper, we conduct a performance comparison under various channel conditions between the OTFS and OFDM modulations, encompassing evaluations of sum-rate and bit error ratio (BER). Additionally, we investigate the joint optimal allocation of power and delay-Doppler resource blocks aiming at maximizing sum-rate for multiuser downlink OTFS-based LEO satellite communication systems. Unlike the conventional modulations relying on complex input-output relations within the Time-Frequency (TF) domain, the OTFS modulation exploits both time and frequency diversities, i.e., delay and Doppler shifts remain constant during a OTFS frame, which facilitates a DD domain input-output simple relation for our investigation. We transform the resulting non-convex and combinatorial optimization problem into an equivalent difference of convex problem by decoupling the conditional constraints, and solve the transformed problem via penalty convex-concave procedure algorithm. Simulation results demonstrate that the OTFS modulation is robust to carrier frequency offsets (CFO) caused by high-mobility of LEO satellites, and has superior performance to the OFDM modulation. Moreover, numerical results indicate that our proposed resource allocation scheme has higher sum-rate than existing schemes for the OTFS modulation, such as delay divided multiple access and Doppler divided multiple access, especially in the high signal-to-noise ratio (SNR) regime.

Service caching in mobile edge computing (MEC) networks involves pre-storing computation programs on MEC servers to efficiently handle users’ computational tasks. By pre-loading these programs, service caching can significantly reduce both computation and transmission delays, addressing the diverse computational requirements of users. However, optimal caching placement is essential due to limited caching capacity, which directly impacts the efficiency of computation offloading. Proper cache placement ensures that relevant programs are readily available, thereby maximizing offloading performance and minimizing delays. This paper investigates a multi-tier MEC network consisting of caching-enabled edge servers, a cloud server, and multiple users. Users offload computational tasks to proximate edge servers, where locally cached programs facilitate immediate processing, thereby mitigating delays. When programs are absent from the cache, tasks are offloaded to the cloud, leading to additional latency. We formulate an optimization problem to minimize the overall communication and computation delay by jointly optimizing caching placement, transmission power, bandwidth allocation, and computation capacity. To tackle the complexity of this mixed-integer nonlinear programming (MINLP) problem, we propose two novel algorithms. The first is a Dinkelbach and big-M-based algorithm that reformulates the problem into a mixed-integer second-order cone programming (MI-SOCP) problem, approximating a near-optimal solution. Recognizing the computational demands of MI-SOCP, we also develop a low-complexity algorithm based on successive convex approximation (SCA) and alternating methods, which efficiently yields a high-quality sub-optimal solution. Simulation results confirm the effectiveness of the proposed algorithms in reducing network delays and emphasize the critical role of caching in improving network performance.

Ultra-reliable low-latency communication (URLLC) is a critical type of service that imposes stringent latency requirements. Considering the random and burst URLLC packets arrival characteristics, incorporating spatial frequency reuse into multi-cell networks can significantly improve the system performance. Nevertheless, how to design the frequency refuse strategy for such a multi-cell URLLC system remains technically challenging. In this article, we investigate an online dynamic resource scheduling problem in a multi-cell downlink system with URLLC services. The long-term time-averaged effective throughput is maximized while guaranteeing the instantaneous transmission reliability and prolonged network stability. The formulated problem is a mixed integer nonlinear stochastic optimization problem, in which the Lyapunov optimization is first leveraged to transform the long-term maximization problem into sequential short-term online ones. To tackle the deterministic problem in each time-slot, we further propose a distributed algorithm that delegates computational processes to corresponding base stations for collaborative execution. In this framework, the user association is abstracted as a cooperative game model. Subsequently, each base station exploits alternating optimization and convex optimization approximation algorithms to address the remaining resource allocation problem. Simulation results validate the effectiveness of the proposed algorithm in throughput-backlog trade-off, showcasing that the multi-cell collaborative transmission can attain better performance compared with existing schemes.

This paper investigates the joint optimal allocation of radio and computational resources aiming to minimize global average task offloading age (TOA) over all time slots and mobile devices (MDs) for long-term multi-cell MEC systems with continuous arrival of MDs. TOA represents the total number of offloading time slots, including both transmission and computation. The joint resource allocation problem cannot be solved online because its objective function is long-term average of TOA over all time slots. We transform the long-term resource allocation problem into an online one by the Lyapunov method, then an iterative algorithm is proposed to solve the online problem. The idea of this algorithm is computing iteratively the two sub-problems which optimize sub-channel allocation and offloading power and computational resources joint allocation based on an initial resource allocation scheme. The alternating direction method of multipliers (ADMM) method is employed to solve the first sub-problem. For the second sub-problem, a closed-form expression of optimal power is deduced by solving a convex optimization problem using the Lagrange multiplier method, then the sub-problem is simplified into a linear programming (LP) problem about computational resource allocation. The improved iterative greedy (IIG) algorithm is applied to solve the LP problem. Simulation results demonstrate that the proposed algorithm approaches the performance of the optimal branch-and-bound (BnB) algorithm in the MEC systems with one-time arrival of MDs, and outperforms two benchmark schemes such as first in first out (FIFO) and Chang’s algorithm.

This paper studies the joint on-ground precoding and on-board beamforming design problem in multi-gateway multibeam satellite communications with feeder link interference. Compared with pure on-ground systems, the hybrid on-ground on-board architecture provides a good trade-off between the total throughput and the bandwidth requirements of the feeder link. Moreover, we propose a two-timescale design strategy which significantly reduces the computational burden on the satellite payload. To be specific, the long-term on-board beamforming network (BFN) is designed based on the statistical channel state information (S-CSI), while the short-term on-ground precoding matrices are designed to cater to the instantaneous CSI (I-CSI) with optimized on-board BFN. We formulate an average sum rate maximization problem which incorporates the lossless constraint of the BFN, as well as the average power constraints at the satellite. To tackle this two-timescale non-convex stochastic optimization problem, we propose an algorithm called two-timescale joint on-ground and on-board beamforming (TJGBB). At each iteration, the TJGBB algorithm first solves the short-term on-ground precoding subproblem based on the weighted minimum mean square error (WMMSE) method. Then, it constructs convex surrogate functions for the objective and constraints of the long-term on-board beamforming primary problem, creating an approximate version. Finally, the long-term variables are updated by solving this convex approximation problem. The proposed algorithm is proven to almost surely converge to a stationary solution of the original problem within an acceptable error margin. Simulations show that our proposed method can significantly improve the overall system throughput compared to existing schemes.

In this paper, we introduce a novel uplink semanticaware wireless communication system, catering to multiple users by leveraging a shared probability graph between an access point (AP) and a base station (BS). Under imperfect channel state information (CSI), all users transmit data information to the AP through conventional bit transmission and the lower bound of signal estimation error is derived. An edge server equipped with the AP and a cloud server equipped with the BS execute information compression and recovery based on the shared probability graph, respectively. While semantic information compression incurs computational resource consumption, it significantly reduces communication resource usage. This paper addresses the challenge of minimizing system latency through jointly optimizing communication resource allocation, channel truncation threshold, and data compression ratio, considering limited wireless resources, signal estimation error, and the system’s energy budget. To solve the formulated non-convex time minimization problem, we decompose the optimization problem into four subproblems and solve each of them iteratively. In particular, the power allocation subproblem is transformed into a convex time allocation optimization problem. The bandwidth allocation subproblem is proven to be convex. The optimal channel truncation threshold is obtained through a bisection search. The data compression ratio optimization subproblem is a non-convex integer programming problem solved by the Gurobi optimizer. Numerical results show the effectiveness of the proposed algorithm, validating the necessity to consider imperfect CSI and semantic transmission and the superior performance of semantic communication compared to conventional bit transmission.

This study investigates scalable uplink cell-free massive multiple-input multiple-output networks, comprising user equipments (UEs), radio units (RUs), data routers, and decentralized processing units (DUs). In our model, UEs are served by dynamically allocated user-centric clusters of RUs. The corresponding cluster processors, implementing the physical layer for each user, are hosted as software-defined virtual network functions by the DUs. In our paradigm, RUs, data routers, and DUs are not fully connected, necessitating a holistic approach to address the joint challenges of cluster processor placement (at one of the DUs) and the allocation of fronthaul data links among RUs, routers, and DUs. We simultaneously consider the fronthaul topology, the limited fronthaul communication capacity, and computation constraints at the DUs. Specifically, we formulate the joint optimization of fronthaul load balancing and cluster processor placement as a mixed-integer linear problem. Furthermore, we present numerical results that shed light on the interplay between these elements under finite resolution of the A/D quantization at the RUs.

In this paper, we introduce a novel uplink semantic relay (SemRelay)-aided wireless communication system, catering to multiple users by leveraging a shared probability graph between the SemRelay and the base station (BS). In this system, users transmit text information to the SemRelay through conventional bit transmission, and the SemRelay compresses this information using a knowledge based characterized by probability graph before transmitting it to the BS through semantic communication. Then, the BS recovers the information based on the shared probability graph. While the semantic information compression incurs computational resource consumption, it significantly reduces communication resource usage. This paper addresses the challenge of minimizing overall system latency through jointly optimizing communication and computation re-source allocation, considering limited wireless resources and the system's energy budget. To address this problem, we introduce an efficient iterative algorithm, which employs block coordinate descent for communication resource allocation and exhaustive searching for determining the optimal data compression scheme. In particular, both power allocation subproblem and bandwidth allocation subproblem are proved to be convex. The complexity analysis of the proposed algorithm are also provided. Numerical results validate the effectiveness of the proposed algorithm and the superior performance of semantic communication compared to the conventional bit transmission.

This paper investigates a mobile edge computing (MEC) aided multi-static integrated sensing and communication (ISAC) system. In the considered system, each device has a task to be offloaded to MEC server for computation. Meanwhile, a sensing receiver (SR) has to detect a point target. To enhance detection probability, devices and base station (BS) collaboratively transmit radar signals and then SR detects the target of interest based on received radar echoes. Specifically, the whole task execution period is divided into two phases. In the first phase, devices offload partial of their tasks and simultaneously transmit radar signals for target sensing. In the second phase, the MEC server at the BS computes offloaded tasks and meanwhile devices along with BS transmit radar signals for sensing. Therefore, the entities in the system constitute a multi-static ISAC framework. Radar signal processing for multi-static sensing system is designed and the corresponding cooperative detection probability is derived. To ensure high energy efficiency, an optimization problem is formulated to minimize energy consumption of all devices under task computation and detection probability constraints by adjusting devices’ and BS’s beamforming, task partitioning, and phase duration allocation. This problem is efficiently solved by iteratively optimizing beamforming subproblem, time and task division subproblem through weighted minimum mean square error, successive convex approximation and search methods. Simulations verify the effectiveness of the proposed MEC aided cooperative sensing algorithm compared with non-cooperative sensing scheme.

This paper aims at designing a joint on-ground and on-board beamforming scheme for multi-gateway multibeam satellite systems. The proposed scheme exploits the degrees of freedom of on-board processing to mitigate feeder link interference and enhance satellite resource flexibility without requiring additional feeder link bandwidth, thereby improving the overall throughput. Based on the practical consideration of not escalating the complexity and real-time computational burden of the satellite payload, the two-timescale design is employed. Specifically, the long-term on-board beamforming network (BFN) is designed based on the statistical channel state information (S-CSI) of all links, while the short-term on-ground precoding is designed based on the instantaneous CSI (I-CSI) of the effective channel with optimized BFN. We formulate an average sum rate maximization problem that incorporates the lossless constraint of the BFN, as well as the average power constraints at the satellite. We propose an algorithm called two-timescale joint on-ground and on-board beamforming (TJGBB) to tackle this mixed-timescale non-convex stochastic optimization problem. Simulation results demonstrate the effectiveness of the proposed algorithm and its advantages over the benchmark schemes.

Optimizing resource allocation in satellite communication systems is crucial due to the limited availability of satellite resources. In order to address this challenge, various techniques have been developed in the literature to optimize transmission power, bandwidth, and time slots independently. This paper proposes an algorithm that jointly optimizes GEO satellite downlink power, bandwidth, and time slots to maximize energy efficiency. The algorithm employs a two-step approach, utilizing first linear programming to allocate a time slot to each user and then solving a non-convex optimization problem using the Dinkelbach and Successive Convex Approximation (SCA) methods to determine each user's corresponding power and bandwidth. Our findings demonstrate that joint resource allocation can significantly enhance energy efficiency and reduce power consumption more effectively than independent resource allocation.

Multi-beam GEO satellites are integral to modern satellite communication systems. This paper focuses on a multi-beam GEO satellite downlink TDMA/FDMA system, exploring flexible beam allocation to users. Our objective is to maximize the sum system rate, which involves joint optimization of beams, time, carriers, and power allocation. Then we address this mixed-integer non-convex problem through an iterative optimization algorithm based on the Successive Convex Approximation (SCA) and Quadratic Transformation (QT) algorithms. Our algorithm significantly reduces the average user unmet degree by 20% and enhances the system’s sum rate by 23% compared to the baseline scheme, demonstrating its effectiveness in scenarios with non-uniform user traffic distribution.

Multibeam geostationary (GEO) satellites play a pivotal role in contemporary satellite communication by ad-dressing the escalating demands. In this paper, we assume a flexible beam-user mapping scheme, wherein users have the liberty to select a specific beam for access, while power and bandwidth allocation is performed on a per-beam basis. To effectively manage radio resources and cater to user demands, we employ a Time Division Multiple (TDM) technique within each beam. To address the resource management challenge, we propose an iterative algorithm for optimizing time fraction and power. Each subproblem is transformed and solved using the Successive Convex Approximation (SCA) method and branch and bound algorithm. The results demonstrate that the proposed algorithm outperforms fixed user mapping and fixed time fraction algorithms by reducing the average user unmet degree by 4.0% and 9.9%, respectively. The flexibility inherent in user mapping and resource allocation enhances resource utilization and alleviates the pressure on satellite from higher user demands.

An orbit design method for repeat ground track (RGT) satellites aiming at maximizing average access ratio (AAR) of terrestrial-satellite terminals (TSTs) is proposed in this paper. Several TSTs are located in different places, their AAR is deduced and employed as the objective to design the orbit, constraints of the regression error in orbit are also deduced and the optimization problem which the optimal orbit satisfies is obtained. The objective is transformed by using the penalty function and the Particle Swarm Optimization (PSO) is adopted to solve the optimization problem. Compared with previous work, ours has two contributions: one is that the expression of the objective to design the orbit is deduced, the other is that the adopted algorithm to solve the optimization problem has a better performance for outputting the global optimal solution.

Mobile edge computing (MEC) in the next generation networks will provide computation services at the network edge to enrich the capabilities of mobile devices and lengthen their battery lives. However, the performance of MEC cannot be guaranteed, when large size local tasks are uploaded to the server simultaneously causing network congestion. As a new paradigm that focuses on transmitting the meaning of messages, semantic communications reveals the significant potential to reduce the network traffic. In this paper, we propose a semantic-aware joint communication and computation resource allocation framework for MEC systems. In the considered system, random tasks arrive at each terminal device (TD), which needs to be computed locally or offloaded to the MEC server. To further release the transmission burden, each TD sends the small-size extracted semantic information of tasks to the server instead of the original large-size raw data. An optimization problem of joint semantic-aware division factor, communication and computation resource management is formulated. The problem aims to minimize the energy consumption of the whole system, while satisfying long-term delay and processing rate constraints. To solve this problem, an online low-complexity algorithm is proposed. In particular, Lyapunov optimization is utilized to decompose the original coupled long-term problem into a series of decoupled deterministic problems without requiring the realizations of future task arrivals and channel gains. Then, the block coordinate descent method and successive convex approximation algorithm are adopted to solve the current time slot deterministic problem by observing the current system states. Moreover, the closed-form optimal solution of each optimization variable is provided. Simulation results show that the proposed algorithm yields up to 41.8% energy reduction compared to its counterpart without semantic-aware allocation.

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart.

With the dramatic increase in the diverse service requirements and mobile devices, the application-specific data tends to span multiple consecutive timeslots to complete the transmission, while the demand for spectrum resources is further exacerbated. Recent works have suggested that integrating spatial frequency reuse with multi-cell networks can enhance the spectral efficiency and alleviate the scarcity of spectrum. Hence this paper considers a downlink multi-cell multi-timeslot orthogonal frequency division multiple access (OFDMA) cellular system where the users keep downloading data from the base stations (BS) until reaching a predetermined cache size. Specifically, we aim to minimize the transmission delay by jointly optimizing the BS selection, subcarrier assignment, and transmit power allocation, taking into account the current cache size. Due to inter-cell interference and multi-timeslot coupling, this problem is challenging to solve directly. We prove that this problem can be transformed into sequential online sum rate maximization subproblems under causal channel state information (CSI). To solve the subproblems, we first develop a centralized dynamic resource allocation algorithm based on the parameter transformation and the majorization-minimization (MM). In view of the trade-off between performance and complexity, we further propose a distributed algorithm by a designed BS selection scheme and the MM approach. Simulation results demonstrate that the distributed algorithm achieves comparable performance to the centralized algorithm, while they both outperform the benchmark schemes in terms of transmission delay.

Edgeartificial intelligence(AI) in the sixth-generation networks will provide inference services at the network edge to enrich the capabilities of mobile devices and lengthen their battery lives. As a well-known technique in computing, batching can boost the computation throughput at an edge server by assembling multiple tasks into a batch that is fed into a pre-trained prediction model. This reduces the memory-access frequency and hence accelerates the execution of each task. In this paper, we study joint batching and (task) scheduling to maximise the throughput (i.e., the number of completed tasks) under the practical assumptions of heterogeneous task arrivals and deadlines. The design aims to optimise the number of batches, their starting time instants, and the task-batch association that determines batch sizes. The joint optimisation problem is complex due to multiple coupled variables as mentioned and numerous constraints including heterogeneous tasks arrivals and deadlines, the causality requirements on multi-task execution, and limited radio resources. Our approach of solving the formulated mixed-integer problem is to transform it into a convex problem via integer relaxation method and ℓ0-norm approximation. This results in an efficient alternating optimization algorithm for finding a close-to-optimal solution. Specifically, it iterates between solving two sub-problems, optimal task-batch association and optimal batch starting time. The former is a linear program whose solution can be found using a derived scheme of greedy task selection while that of the latter is derived in closed form. In addition, we also design the optimal algorithm from leveragingspectrum holes, which are caused by fixed bandwidth allocation to devices and their asynchronized multi-batch task execution, to admit unscheduled tasks so as to further enhance throughput. Simulation results demonstrate that the proposed framework of joint batching and resource allocation can substantially enhance the throughput of multiuser edge-AI as opposed to a number of benchmarking schemes.

We consider scalable cell-free massive multiple-input multiple-output networks under an open radio access network paradigm comprising user equipments (UEs), radio units (RUs), and decentralized processing units (DUs). UEs are served by dynamically allocated user-centric clusters of RUs. The corresponding cluster processors (implementing the physical layer for each user) are hosted by the DUs as software-defined virtual network functions. Unlike the current literature, mainly focused on the characterization of the user rates under unrestricted fronthaul communication and computation, in this work we explicitly take into account the fronthaul topology, the limited fronthaul communication capacity, and computation constraints at the DUs. In particular, we systematically address the new problem of joint fronthaul load balancing and allocation of the computation resource. As a consequence of our new optimization framework, we present representative numerical results highlighting the existence of an optimal number of quantization bits in the analog-to-digital conversion at the RUs.

In this paper, we investigate the energy minimization problem in cell-free mobile edge computing (MEC) networks under dynamic channel conditions and random arrival tasks. We aim to minimize the total energy consumption of user equipment (UEs) and MEC servers (MECSs) by jointly optimizing MECS active/sleep mode selection, UE-MECS association decision (MSAD), and task offloading, communication, and computation resource allocation (TORA) across two different timescales. Considering that the involved optimization variables affect the system performance in different timescales, we decouple the formulated stochastic optimization problem into two subproblems: MSAD operating in the large timescale and TORA in the small timescale. Leveraging the Lyapunov method, the stochastic TORA problem is decoupled into a series of deterministic problems, of which the closed-form solutions are presented. Furthermore, the MSAD problem is reformulated as a constrained Markov decision process (MDP). Then, we propose a double dueling deep Q-network (D3QN) to learn the optimal MSAD based on the TORA results. Numerical results demonstrate that the proposed online TORA-assisted MSAD learning algorithm has effective convergence and achieves substantial energy reductions for the MEC networks compared with the benchmark schemes.

Channel estimation is one of the fundamental topics in practical multi-antenna systems. With the progress of artificial intelligence, deep learning (DL)-based schemes have presented enormous the potential for performance and efficiency. In this paper, we propose an attention-aided approach to achieve channel estimation for multi-user single input multiple output (SIMO) system. Specifically, the multi-attention graph learning network (MAGLN) is conducted to estimate the uplink channel, which concentrates on the partial more important information in the different dimensions. The channel attention and graph attention mechanisms are adopted to enhance the quality of extracted features and finally output the estimated channel information. Numerical results show that the proposed scheme has better estimation performance compared with the traditional algorithms and other candidate DL-based architectures.

In this paper, we investigate the power allocation problem to maximize the long-term average downlink sum-rate for orthogonal frequency division multiplexing (OFDM) based multi-cell networks. The traditional centralized training and centralized execution (CTCE) scheme is impractical to solve this complex problem, because it is hard for training center to obtain the global information, and the signaling overhead is also unbearable. To this end, a centralized training and distributed execution (CTDE) framework for wireless power control based on deep reinforcement learning (DRL) is proposed. Specifically, we first transform this problem into a sequential decision problem by designing appropriate state, action and reward. Then, a CTDE scheme, where each agent only requires its own local observations for power allocation, is devised through combining QMIX algorithm and Normalized Advantage Functions (NAF) algorithm. In particular, QMIX network is used to fit the total Q-value function and NAF network is deployed to handle continuous power output. Simulation results show that the proposed scheme converges fast and achieves better rate performance compared with conventional CTDE algorithms.

The low earth orbit (LEO) satellite constellation network can realize the global seamless coverage, and provide data offload services to remote areas, such as mountain areas. This paper considers a LEO satellite-ground network, in the network each user can choose a basestation equipped with a special antenna through C-band, and then the basestation will transmit the collected data to the LEO satellite through backhaul link over Ka-band, so as to realize the data offloading services for mobile users. We aim to maximize the system energy efficiency within the constraints of meeting the backhaul capacity determined by the LEO satellite backhaul link and the users minimum data rate requirements. The optimization problem is a nonconvex problem, which is solved by an iterative algorithm based on successive convex approximation(SCA). The final simulation results show that the proposed allocation scheme for data offloading transmission has higher system energy efficiency than the traditional access allocation schemes.

In this paper, the problem of the deployment and resource management for visible light communication (VLC)-enabled, reconfigurable intelligent surfaces (RISs)-assisted unmanned aerial vehicle (UAV) networks is investigated. In the considered model, UAVs provide terrestrial users with wireless services and illumination simultaneously. Moreover, RISs are utilized to further improve the channel quality between UAVs and users. This joint placement and resource management problem is constructed aiming at acquiring the optimal UAV deployment, RISs phase shift, user and RIS association that satisfies the users’ needs with minimum consumption of the UAVs’ energy. An iterative algorithm that alternately optimizes continuous and binary variables is proposed to solve this mixed-integer programming problem. Specifically, RISs phase shift optimization is solved by phases alignment method and semidefinite program algorithm. Next, the successive convex approximation algorithm is proposed to settle the UAV deployment problem. The user and RIS association variables are relaxed to the continuous ones before adopting the dual method to find the optimal solution. Moreover, a greedy algorithm is proposed as an alternative to RIS association optimization with low complexity. Simulation results show that the proposed two schemes harvest the superior performance of 34.85% and 32.11% energy consumption reduction over the case without RIS, respectively.

In Vehicle-to-Everything (V2X), cellular Device-to-Device (D2D) communication can improve spectrum efficiency, and non-orthogonal multiple access (NOMA) can further strengthen system capacity. However, the execution for NOMA may be affected by the additional interference introduced by cellular links in V2X. In this paper, we study the energy-efficient optimization problem in cellular D2D-aided V2X networks with NOMA. To efficiently meet the quality of service (QoS) requirements while accounting for the maximum performance from the user’s perspective, we attempt to maximize the minimum energy efficiency (EE) of each matching link by performing power allocation and spectrum sharing. Since the formulated problem belongs to a non-convex mixed integer non-linear programming (MINLP), a novel resource allocation scheme is proposed to solve this complex coupling problem. Finally, simulation results verify the proposed algorithm’s effectiveness as compared to different benchmark schemes.

This paper investigates the lower bounds on the location estimation error for multiple reconfigurable intelligent surfaces (RISs)-aided millimeter-wave (mmWave) positioning systems. The error lower bound is quantified by Cramer-Rao lower bounds (CRLB), of which two are decisive, namely, the position error bound (PEB), and the rotation error bound (REB). This paper begins by deriving the analytical expressions of the PEB and REB as functions of the RIS phase shifts. Then, the lowest achievable PEB and REB are obtained by optimizing the phase shifts of all RISs using the particle swarm optimization (PSO) algorithm. Numerical results have shown that a three-RIS-aided system generates 38.6% lower PEB and REB with the most basic beam-alignment phase shifts strategy compared to the single-RIS system. With the RIS phase shifts optimized by the PSO algorithm, the PEB and REB can be further reduced by another 41.2%.

In this paper, the problem of power allocation is investigated for a multi-user downlink visible light communication (VLC) system with non-orthogonal multiple access (NOMA). In this considered system, not only input-independent Gaussian noise but also input-dependent shot noise are considered due to the properties of realistic VLC channels. This problem is posed as a joint problem of alternating current power and direct current (DC) power under the optical and electrical domain constraints in VLC as well as specific power constraints for NOMA decoding, whose goal is to maximize the minimum signal to interference plus noise ratio (SINR) among all users. Although this problem is non-convex, a geometric programming (GP) based algorithm is proposed to convert the original problem into a convex one, thus the optimal solution is obtained. Furthermore, special cases where lower DC offset is preferred are investigated. Simulation results verify that the DC offset has significant impacts on the performance of NOMA VLC systems with shot noise, and our proposed scheme achieves better SINR performance over the conventional schemes.

The multi-task federated learning (FL) problem in the wireless communication system is investigated in this paper. The base station (BS) and wireless users cooperatively perform a two-task FL algorithm in the established model. Users use their local datasets to train two local models of two different tasks. The trained local model of only one task is transmitted to the BS at each time and the BS aggregates the obtained models to calculate a global model, which will be sent back to all users. Since the resources for wireless transmission, such as transmit power and number of subcarriers are limited, the BS have to allocate resources reasonably to minimize the time consumption of the FL procedure while meeting the required learning performance. On the other hand, users are dynamically arranged to participate in different tasks in each iteration. This resource allocation and users arrangement problem is formulated as an optimization problem which aims to minimize time consumption of the two-task FL procedure. To address this nonconvex problem, we first decompose it into two convex sub-problems. Then we propose an iterative algorithm to solve this problem via iteratively obtaining the optimal solution of the joint power control and communication round optimization subproblem, and user arrangement subproblem. Simulation results of this multi-task FL system show that the proposed algorithm can reduce 7.02% and 9.67% completion time compared to the uniform and random user selection schemes respectively.

This paper investigates the problem of secrecy energy efficiency for an unmanned aerial vehicle (UAV) assisted joint sensing and communication system. In the considered system, there exists one UAV, one legal user, and one eavesdropper. The UAV needs to complete multiple tasks in multiple time cycles. In each time cycle, the UAV first flies to sense one task and then transmits the sensing results to the legal user. To maximize the secrecy energy efficiency of the system, a joint sensing and transmission time and UAV location optimization problem is formulated. To solve this non-convex fractional programming problem, the original problem is first divided into two subproblems. Each sub-problem can be easily transformed to a convex one by using the successive convex approximation (SCA) method and the Dinkelbach’s approach. Then, an iterative algorithm based on the alternating method is proposed. Simulation results reveal that our proposed algorithm is superior to the conventional algorithms in terms of secrecy energy efficiency.

In this article, we investigate the energy minimization problem of an unmanned-aerial-vehicle (UAV)-assisted data collection sensor network. We jointly optimize the trajectory of the UAV and the power consumption of the sensors for data uploading with the power and energy constraints of sensors. The trajectory design consists of two parts: 1) the serving orders for sensors and 2) the UAV’s hovering positions, where the latter is highly coupled with the power consumption of the sensors. To find the optimal serving orders of sensors, we formulate the problem as a standard traveling salesman problem (TSP), which can be optimally solved by the efficient Cutting-Plane method. To solve the UAV position and sensor uploading power optimization problem, we propose the PSPSCA algorithm that optimizes the transmit power by the pattern search method, while the UAV’s hovering positions are optimized by the successive-convex-approximation (SCA) method in the inner loop. To deal with the high computational complexity of the PSPSCA algorithm, we analyze the analytical relationship between optimal sensor uploading power and the UAV’s hovering positions, based on which we simplify the optimization problem and propose the AQSCA algorithm as an alternative approach. Simulation results have validated that the proposed algorithm outperforms the existing benchmark schemes.

The visible light communication (VLC) technology has attracted much attention in the research of the sixth generation (6G) communication systems. In this paper, a novel three dimensional (3D) space-time-frequency non-stationary geometry-based stochastic model (GBSM) is proposed for indoor VLC channels. The proposed VLC GBSM can capture unique indoor VLC channel characteristics such as the space-time-frequency non-stationarity caused by large light-emitting diode (LED) arrays in indoor scenarios, long travelling paths, and large bandwidths of visible light waves, respectively. In addition, the proposed model can support special radiation patterns of LEDs, 3D translational and rotational motions of the optical receiver (Rx), and can be applied to angle diversity receivers (ADRs). Key channel properties are simulated and analyzed, including the space-time-frequency correlation function (STFCF), received power, root mean square (RMS) delay spread, and path loss (PL). Simulation results verify the space-time-frequency non-stationarity in indoor VLC channels. In addition, the influence of light source radiation patterns, receiver rotations, and ADRs on channel characteristics have been investigated. Finally, the accuracy and practicality of the proposed model are validated by comparing the simulation result of channel 3dB bandwidth with the existing measurement data. The proposed channel model will play a supporting role in the design of future 6G VLC systems.

This paper investigates the optimization of physical layer security in multiple-input multiple-output (MIMO) enabled visible light communication (VLC) networks. In the considered model, one transmitter equipped with light-emitting diodes (LEDs) intends to send confidential messages to legitimate users while one eavesdropper attempts to eavesdrop on the communication between the transmitter and legitimate users. This security problem is formulated as an optimization problem whose goal is to minimize the sum mean-square-error (MSE) of all legitimate users while meeting the MSE requirement of the eavesdropper thus ensuring the security. To solve this problem, the original optimization problem is first transformed to a convex problem using successive convex approximation. An iterative algorithm with low complexity is proposed to solve this optimization problem. Simulation results show that the proposed algorithm can reduce the sum MSE of legitimate users by up to 40% compared to a conventional zero forcing scheme.

This paper investigates the deployment of federated learning (FL) over an over-the-air computation (AirComp) and intelligent reflecting surface (IRS) based wireless network. In the considered system, devices transmit locally trained machine learning (ML) models to the base station (BS) which aggregates the received ML models and generates a shared global ML model. The devices can directly transmit ML models to the BS or using IRS. Meanwhile, AirComp is used to aggregate ML models that are transmitted from the devices to the BS. To minimize the energy consumption of devices, an energy minimization problem is formulated, which jointly optimizes the device selection, phase shift matrix, decoding vector, and power control. To seek the solution, the original optimization problem is divided into four sub-problems. Then the fractional program, greedy algorithm, matrix derivation, and weighted minimum mean square error methods are used to compute the phase shift matrix, device selection vector, decoding vector, and transmit power, respectively. Simulation results show that the proposed algorithm can reduce 11.2% energy consumption of devices compared to an FL algorithm that is implemented at a network without any IRSs.

In this paper, the problem of joint subcarrier and power allocation is studied for multi-cell orthogonal frequency-division multiplexing (OFDM) systems. This joint subcarrier and power resource allocation problem is formulated as an optimization problem whose goal is to maximize the system spectral efficiency. To solve the proposed problem, the original optimization problem is first decomposed into two subproblems: subcarrier allocation and power allocation. By solving these two subproblems, an initial subcarrier and power allocation scheme is accordingly obtained. An multi-agent reinforcement learning (MARL) algorithm is proposed to further increase the spectral efficiency. In particular, using the proposed MARL algorithm, each BS can adapt its allocation scheme according to the wireless environmental states. Numerical results show that the proposed MARL method can achieve up to 53.6% gain in terms of spectral efficiency compared to the conventional scheme. The proposed MARL scheme also converges more rapidly than the conventional single-agent Q-learning approach.

In this paper, we employ Cooperative Rate-Splitting (CRS) technique to enhance the Secrecy Sum Rate (SSR) for the Multiple Input Single Output (MISO) Broadcast Channel (BC), consisting of two legitimate users and one eavesdropper, with perfect Channel State Information (CSI) available at all nodes. For CRS based on the three-node relay channel, the transmitter splits and encodes the messages of legitimate users into common and private streams based on Rate-Splitting (RS). With the goal of maximizing SSR, the proposed CRS strategy opportunistically asks the relaying legitimate user to forward its decoded common message. During the transmission, the eavesdropper keeps wiretapping silently. To ensure secure transmission, the common message is used for the dual purpose, serving both as a desired message and Artificial Noise (AN) without consuming extra transmit power comparing to the conventional AN design. Taking into account the total power constraint and the Physical Layer (PHY) security, the precoders and timeslot allocation are jointly optimized by solving the nonconvex SSR maximization problem based on Sequential Convex Approximation (SCA) algorithm. Numerical results show that the proposed CRS secure transmission scheme outperforms existing Multi-User Linear Precoding (MU-LP) and Cooperative Non-Orthogonal Multiple Access (C-NOMA) strategies. Therefore, CRS is a promising strategy to enhance the PHY security in Multi-antenna BC systems.

Reconfigurable intelligent surface (RIS) is a promising solution to improve spectrum efficiency and promote cost- effectively wireless communication in the future. In this paper, RIS is deployed between a single-antenna base station (BS) and multiple single-antenna users to assist downlink non-orthogonal multiple access (NOMA) transmission. Considering the fairness among users, our goal is jointly optimizing the power allocation, decoding order, and the phase shifts to maximize the minimum user rate under total power constraint. To solve this minimum rate maximization problem, the optimal power allocation and the optimal fair rate are first revealed with a given phase shift vector. Then, the phase shift vector is optimized via maximizing the worst channel gain, which can determine the lower bound of the fair rate. The phase shift vector optimization problem is relaxed to a convex semidefinite program (SDP) and an efficient algorithm is proposed to obtain a rank-one solution. Simulation results show that our proposed algorithm can enhance the fair rate compared to the conventional scheme.

Reconfigurable intelligent surface (RIS) is an emerging technology to achieve energy-efficient wireless communication. This technology has the potential of turning the wireless environment, which is highly probabilistic in nature, into a programmable and partially deterministic space. This paper focuses on the joint user clustering, passive beamforming and power allocation for the downlink RIS-assisted nonorthogonal-multiple-access (NOMA) system, with the target of maximizing energy efficiency. This is an optimization problem which is solved by optimizing three sub-problems iteratively. In particular, user clustering sub-problem is solved with a matching algorithm, power allocation sub-problem is solved with the difference of two convex functions (DC) programming, and passive beamforming sub-problem is solved with univariate search technique. Simulation results demonstrate that the downlink RIS-assisted NOMA system can improve the energy efficiency by 7.8%-23.1%, compared with NOMA system without RIS and traditional orthogonal-multiple-access (OMA) system without RIS.

This paper investigates the energy efficiency maximization problem in a downlink reconfigurable intelligent surface (RIS) assisted visible light communication (VLC) system. The energy efficiency maximization problem is formulated via jointly optimizing time allocation, power control, phase shit matrix under the unique power constraints in VLC. To solve this non-convex energy efficiency maximization problem, the original problem is first simplified to an equivalent problem with a smaller number of variables. Then, an alternating algorithm with low complexity is accordingly proposed to obtain a suboptimal solution via iteratively solving the joint time allocation and power control subproblem, and the phase shift matrix adjustment subproblem. The simulation results show that the proposed algorithm can achieve up to 0.127dB gain in terms of energy efficiency compared to the conventional interior point method.

In this paper, the optimization of the system energy efficiency (EE) is studied for a reconfigurable intelligent surface (RIS) assisted full-deplex (FD) communication system. In the studied model, two devices communicate with each other using one RIS under the FD mode. Each of the devices will receive not only the message from the other device but also the self-interference. The main problem of this work is to maximize EE by jointly optimizing the reflection coefficients matrix and the transmit power of devices. To solve this problem, a nonlinear fractional programming based algorithm is used to transform the fractional optimization problem into a subtractive problem. Then the transmit power and the RIS phase shifts matrix are optimized by an iterative method. Simulation results show that the proposed scheme can achieve up to 200% gain in terms of EE compared to a conventional RIS assisted half-duplex mode.

With the rapid development of cloud computing, the user-centric networks with the baseband unit pool have attracted a great deal of attentions in academic and industrial fields. However, limited fronthaul capacity and computation resource have become the bottlenecks inevitably. Thus, this paper investigates the power-efficient transmission in user-centric networks by considering both fronthaul capacity and computation resource constraints, where multiple access points (APs) and user equipments (UEs) are distributed. Specifically, a joint optimization of the beamforming vectors, AP-UE association strategy and transmission time is proposed to minimize the total power consumption (TPC). The formulated mixed integer non-linear problem (MINLP) is NP-hard. To address this problem, the MINLP is first transformed into a convex one via the successive convex approximation and semidefinite relaxation methods. Then, an iterative but effective algorithm is designed by using the property of the solution and applying the Lagrangian dual method. Simulation results show that the proposed algorithm converges rapidly and outperforms benchmark algorithms in terms of TPC.

This paper investigates the secrecy rate performance of a device-to-device (D2D) underlaid cellular network with an eavesdropper. Both the base station (BS) and the eavesdropper are assumed to have multiple antennas and apply minimum mean-square error (MMSE) receivers for detection. Different from the literature, mainly focused on enhancing the security of cellular communication using D2D jammers while neglected the secrecy performance of D2D communication, this paper aims to maximize the instantaneous weighted sum secrecy rate (IWSSR) of both cellular and D2D users. The maximization of IWSSR with respect to the transmit power of mobile users is a non-convex problem with non-differentiable objective function. In order to obtain efficient feasible solutions, we transform the IWSSR maximization problem to a min-max problem, relax the non-negative operator in secrecy rate expressions and then propose an alternative algorithm to solve the remaining problem. Simulation results show that the IWSSR of the network can be effectively increased by the proposed algorithm and, compared with exhaustive search (ES), the proposed algorithm performs very close to ES and involves much less computational complexity.

Device-to-Device (D2D) communications incorporated with content caching have been regarded as a promising way to offload the cellular traffic data. In this paper, the caching strategy is investigated to maximize the D2D offloading gain with the comprehensive consideration of user collaborative characteristics as well as the physical transmission conditions. Specifically, for a given content, the number of interested users in different groups is different, and users always ask the most trustworthy user in proximity for D2D transmissions. An analytical expression of the D2D success probability is first derived, which represents the probability that the received signal to interference ratio is no less than a given threshold. As the formulated problem is nonconvex, the optimal caching strategy for the special unbiased case is derived in a closed form, and a numerical searching algorithm is proposed to obtain the globally optimal solution for the general case. To reduce the computational complexity, an iterative algorithm based on the asymptotic approximation of the D2D success probability is proposed to obtain the solution that satisfies the Karush-Kuhn-Tucker conditions. The simulation results verify the effectiveness of the analytical results and show that the proposed algorithm outperforms the existing schemes in terms of offloading gain.

With the development of Internet of Things, the mobile edge computing has become a promising technology for real-time communications. This paper investigates a mobile edge computing system that consists of an access point integrated with a mobile edge computing server, multiple mobile stations, and a malicious eavesdropper. By offloading part of the computing tasks to the mobile edge computing server, the energy consumption of mobile stations can be reduced significantly and the lifetime is prolonged as well. Moreover, the physical layer security is an effective technique to guarantee the secure transmission of the offloading data. Based on the proposed system model, we formulate an optimization problem to minimize the energy consumption of the system by jointly optimizing the allocations of local computing tasks, local central processor's frequency, offloading power, and offloading timeslots. A difference of convex algorithm based scheme is proposed to solve the problem. The performance of the proposed scheme is superior to the benchmark schemes, which is demonstrated by simulation results.

This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments' movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments.

This paper analyzes the ergodic capacity of a user-centric virtual cell (VC) dense network, where multiple access points (APs) form a VC for each user equipment (UE) and transmit data cooperatively over millimeter wave (mmWave) channels. Different from traditional microwave radio communications, blockage phenomena have an important effect on mmWave transmissions. Accordingly, we adopt a distance-dependent line- of-sight (LOS) probability function and model the locations of the LOS and non-line-of-sight (NLOS) APs as two independent non-homogeneous Poisson point processes (PPP). Invoking this model in a VC dense network, new expressions are derived for the downlink ergodic capacity, accounting for: blockage, small-scale fading and AP cooperation. In particular, we compare the ergodic capacity for different types of fading distributions, including Rayleigh and Nakagami. Numerical results validate our analytical expressions and show that AP cooperation can provide notable capacity gain, especially in low- AP-density regions.

This paper investigates the physical layer security issue of a device-to-device (D2D) underlaid cellular system with a multi-antenna base station (BS) and a multi-antenna eavesdropper. To investigate the potential of D2D communication in improving network security, the conventional network without D2D users (DUs) is first considered. It is shown that the problem of maximizing the sum secrecy rate (SR) of cellular users (CUs) for this special case can be transformed to an assignment problem and optimally solved. Then, a D2D underlaid network is considered. Since the joint optimization of resource block (RB) allocation, CU-DU matching and power control is a mixed integer programming, the problem is difficult to handle. Hence, the RB assignment process is first conducted by ignoring D2D communication, and an iterative algorithm is then proposed to solve the remaining problem. Simulation results show that the sum SR of CUs can be greatly increased by D2D communication, and compared with the existing schemes, a better secrecy performance can be obtained by the proposed algorithms.

We study the uplink achievable rate of full-duplex (FD) multiuser multi-input multi-output (MIMO) systems with a base station (BS) and users over Rician fading channels, based on maximum ratio combining (MRC) receivers. Contrary to previous related works over Rayleigh fading channels, this paper assumes that the fast fading MIMO channel matrix follows the Rice distribution which will be more common in future 5G wireless communication systems. A lower bound expressions of the uplink achievable rate for perfect channel state information (CSI) can be derived when the number of BS antennas grows large. Based on the theoretical analysis, it is found that when the antennas of the BS are large enough and the power scaling law is applied properly, the impact of multi-user interference (MUI), loop interference (LI) and noise can be suppressed. In addition, the simulation results show that the uplink sum achievable rates increase with the number of BS antennas and they will converge to fixed values with the increasing Rician K-factor.

As a novel statistical model, Málaga distribution is proposed recently, which covers many commonly used fading models as special cases. The secure transmission of information over the Málaga fading channels has not been discussed in literature. In this paper, the secrecy outage probability (SOP) over the Málaga fading channels is investigated. Initially, an exact expression of the SOP is derived, which is with an integral term and can not be easily used in practice. To reduce its computational complexity, a closed-from expression for the lower bound of the SOP is then obtained. Numerical results that illustrate the effect of the Málaga fading on system performance are provided. The accuracy of the performance analysis is verified by simulations.

Network coding is widely adopted in ad hoc networks to enhance the network throughput while few attention has been paid on applying it in device- to-device (D2D) networks. In this paper, we consider a relay-assisted D2D network with inter- session network coding, where multiple D2D users coexist in an underlaying manner. We aim to maximize the sum throughput via joint power and resource group allocation, which is formulated as a mixed integer programming problem. To solve this problem, an iterative algorithm with low complexity is accordingly proposed. Simulation results verify the effectiveness and superiority of the proposed algorithm compared with existing algorithms.

This paper studies energy efficient resource allocation for a machine-to-machine enabled cellular network with nonlinear energy harvesting, especially focusing on two different multiple access strategies, namely nonorthogonal multiple access (NOMA) and time division multiple access (TDMA). Our goal is to minimize the total energy consumption of the network via joint power control and time allocation while taking into account circuit power consumption. For both NOMA and TDMA strategies, we show that it is optimal for each machine type communication device (MTCD) to transmit with the minimum throughput, and the energy consumption of each MTCD is a convex function with respect to the allocated transmission time. Based on the derived optimal conditions for the transmission power of MTCDs, we transform the original optimization problem for NOMA to an equivalent problem which can be solved suboptimally via an iterative power control and time allocation algorithm. Through an appropriate variable transformation, we also transform the original optimization problem for TDMA to an equivalent tractable problem, which can be iteratively solved. Numerical results verify the theoretical findings and demonstrate that NOMA consumes less total energy than TDMA at low circuit power regime of MTCDs, while at high circuit power regime of MTCDs TDMA achieves better network energy efficiency than NOMA.

In this paper, we investigate the transceiver design for multiple-input multiple-output visible light communication (VLC) systems that employ the integer-forcing (IF) lattice decoding technique. To facilitate the joint design of the transmitter precoder, the integer matrix and the receiver equalizer, we first give a necessary and sufficient condition for the integer matrix to be invertible over 1-D lattice. Based on this condition, we then propose a new method for choosing the integer matrix which achieves better performance than the existing lattice reduction method. Moreover, taking into account two typical constraints in VLC, we optimize transmit and receive matrices using the conditional gradient method or the projected gradient method. Finally, the integer matrix and the transmit and receive matrices are jointly optimized in an iterative manner. Simulation results are provided to demonstrate the performance improvement achieved by the proposed framework.

This letter investigates joint power control and user clustering for downlink nonorthogonal multiple access systems. Our aim is to minimize the total power consumption by taking into account not only the conventional transmission power but also the decoding power of the users. To solve this optimization problem, it is firstly transformed into an equivalent problem with tractable constraints. Then, an efficient algorithm is proposed to tackle the equivalent problem by using the techniques of reweighted ℓ1-norm minimization and majorization-minimization. Numerical results validate the superiority of the proposed algorithm over the conventional algorithms including the popular matching-based algorithm.

In this paper, we aim to minimize the average file transmission delay via bandwidth allocation and cache placement in two-tier heterogeneous networks with limited storage capacity, which consists of cache capacity and buffer capacity. For average delay minimization problem with fixed bandwidth allocation, although this problem is nonconvex, the optimal solution is obtained in closed form by comparing all locally optimal solutions calculated from solving the Karush-Kuhn-Tucker conditions. To jointly optimize bandwidth allocation and cache placement, the optimal bandwidth allocation is first derived and then substituted into the original problem. The structure of the optimal caching strategy is presented, which shows that it is optimal to cache the files with high popularity instead of the files with big size. Based on this optimal structure, we propose an iterative algorithm with low complexity to obtain a suboptimal solution, where the closed-from expression is obtained in each step. Numerical results show the superiority of our solution compared with the conventional cache strategy without considering cache and buffer tradeoff in terms of delay.

In this paper, we consider the sum α-fair utility maximization problem for joint downlink (DL) and uplink (UL) transmissions of a wireless powered communication network via time and power allocation. In the DL, the users with energy harvesting receiver architecture decode information and harvest energy based on simultaneous wireless information and power transfer. While in the UL, the users utilize the harvested energy for information transmission, and harvest energy when other users transmit UL information. We show that the general sum α-fair utility maximization problem can be transformed into an equivalent convex one. Trade-offs between sum rate and user fairness can be balanced via adjusting the value of α. In particular, for zero fairness, i.e., α = 0, the optimal allocated time for both DL and UL is proportional to the overall available transmission power. Trade-offs between sum rate and user fairness are presented through simulations.

In this paper, we investigate the problems of sum power minimization and sum rate maximization for multi-cell networks with non-orthogonal multiple access. Considering the sum power minimization, we obtain closed-form solutions to the optimal power allocation strategy and then successfully transform the original problem to a linear one with a much smaller size, which can be optimally solved by using the standard interference function. To solve the nonconvex sum rate maximization problem, we first prove that the power allocation problem for a single cell is a convex problem. By analyzing the Karush-Kuhn-Tucker conditions, the optimal power allocation for users in a single cell is derived in closed form. Based on the optimal solution in each cell, a distributed algorithm is accordingly proposed to acquire efficient solutions. Numerical results verify our theoretical findings showing the superiority of our solutions compared with the orthogonal frequency division multiple access and broadcast channel.

In this paper, we consider the network utility maximization problem with various user priorities via jointly optimizing user association, load distribution, and power control in a load-coupled heterogeneous network. In order to tackle the nonconvexity of the problem, we first analyze the problem by obtaining the optimal resource allocation strategy in closed form and characterizing the optimal base station load distribution pattern. Both observations are shown essential in simplifying the original problem and making it possible to transform the nonconvex load distribution and power control problem into convex reformulation via exponential variable transformation. An iterative algorithm with low complexity is accordingly presented to obtain a suboptimal solution to the joint optimization problem. Simulation results show that the proposed algorithm achieves better performance than conventional approaches.

This paper considers the problem of transmission scheduling of delay-sensitive data over a point-to-point correlated Rayleigh fading channel with channel estimation errors. According to the imperfect channel state information (CSI) and the buffer state, the transmit power and the modulation and coding scheme (MCS) are determined to jointly maximize the energy efficiency, and minimize transmission delay and overflow probability. To account of the effects of the channel estimation errors, the CSI imperfection is modeled as uncertain sets using the ellipsoidal approximation. Then the joint optimization problem is formulated using the weighted sum method. Using the idea of online learning, two algorithms are proposed to schedule the delay-sensitive data for the situations with and without the uncertainty bound of channel estimation, respectively. The numerical results indicate that the proposed online learning based scheduling algorithms can tackle the imperfect CSI issue and improve the system performance in terms of the energy efficiency, transmission delay and overflow probability. Moreover, the convergence times are very short, which highlights the feasibility of the proposed online learning based scheduling for practical systems.

Recently, optical spatial modulation (OSM) has been proposed for indoor optical wireless communications (OWC), which is considered as a feasible complementary solution for high data rate transmission. This paper investigates the constellation optimization problem for OSM based OWC. An OWC system with multiple transmitters and multiple receivers is considered. By using OSM, only a single transmitter is active at each time instance. Considering the non-negativity and the peak optical intensity constraints, the constellation space for OSM based OWC system is established. By exploiting the channel state information at the transmitter, this paper designs the multi-dimensional constellations by maximizing the minimum Euclidean distance between the received constellation points. Two feasible algorithms are proposed to solve the optimization problem. To evaluate the performance of the proposed algorithms, the symbol error rate is also derived. Simulation results show that the derived constellations using the proposed two algorithms outperform the existing uniformly distributed constellations.

As a promising solution to offload cellular traffic, device-to-device (D2D) communication has been adopted to help disseminate contents. In this paper, the D2D offloading utility is maximized by proposing an optimal content pushing strategy based on the user interests and sharing willingness. Specifically, users are classified into groups by their interest probabilities and carry out D2D communications according to their sharing willingness. Although the formulated optimization problem is nonconvex, the optimal solution is obtained in closed-form by applying Karush-Kuhn-Tucker conditions. The theoretical and simulation results show that more contents should be pushed to the user group that is most willing to share, instead of the group that has the largest number of interested users.

In this paper, an indoor visible light communication (VLC) system with the input-dependent noise is considered. In the system, the main noise is caused by Gaussian noise, however, with a noise variance depending on the current input signal strength. In the presence of the input-dependent noise, the theoretical expression of the bit error rate (BER) for the VLC using on-off keying is derived. Based on the derived BER, an optimization problem is formulated to improve the BER performance by tilting the receiver plane. The proposed optimization problem is proven to be a convex optimization problem, which can be efficiently solved by using the specialized solver such as the CVX toolbox for MATLAB. To verify the accuracy of the derived expression of the BER, all theoretical results are thoroughly confirmed by using the Monte-Carlo simulations. Moreover, simulation results show that the larger the variance of the input-dependent noise is, the worse the BER performance becomes. Additionally, the BER performance can be dramatically improved by tilting the receiver plane properly.

Hardware-efficient low-complexity precoding is very important in the downlink of Massive MIMO systems for mitigating interference and optimizing performance. In this paper, we propose a correlation-driven optimized Taylor expansion (CD-OTE) precoding scheme to simplify linear minimum mean square error (MMSE) precoding. In order to simplify the hardware-expensive matrix inversion involved in the linear MMSE pre-coder, a Taylor expansion with optimized polynomial coefficients and selection of the most relevant correlation coefficients is proposed. We take into consideration the correlation between different users' channels and develop a general design criterion. Both convergence and complexity analyses are carried out. Simulation results show that the proposed CD-OTE precoder is significantly better than previously reported techniques, while requiring a similar cost.

Device-to-Device (D2D) communication has been proposed as a promising way to offload traffic from the cellular network. In this paper, with the joint impact of user preference and selfishness, the D2D assisted content dissemination process is investigated in order to maximize the offloading gain via D2D communications. An alternative group pushing optimization (AGPO) algorithm is proposed to solve the formulated nonconvex problem. In addition, for the special case of two groups, the optimal solution is derived in closed-form to help validate the algorithm. Finally, the simulation results show that the AGPO algorithm converges to the global optimum and has a much lower complexity compared to exhaustive search.

We consider the problem of minimizing the total transmission power for a OFDM network where mutual interference exists among cells, with the power and load constraints for each base station (BS) and the rate demand constraint for every user. To solve the power minimization problem, we develop a distributed resource allocation and power control algorithm with low complexity. The complexity of the proposed algorithm is also analyzed. Numerical results show that the proposed algorithm is superior to the conventional schemes in terms of power consumption.

In this paper, we investigate the sum energy minimization problem in an uplink machine-to- machine (M2M) system with energy harvesting. To solve this nonconvex sum energy minimization problem, we first transform it into an equivalent convex problem and provide the optimal condition of the original problem. Then, we propose a low- complexity iterative scheme, which yields the optimal solution. The complexity of the proposed scheme is also analyzed. Numerical results show that the proposed scheme can achieve good performance.

Full‐duplex relay‐assisted device‐to‐device (D2D) communication underlaying fifth generation cellular networks allow devices to exchange information directly and extend coverage via relay strategy. In this study, the authors consider such a scenario, where the D2D communications assisted by fixed‐location full‐duplex relays in interference existing circumstance. Different from previous works, they assume that there are two types of users, cellular users and D2D users. They investigate power control problem and coverage probability performance in the previously assumed situation. Therefore, an effective power control scheme is of great importance to suppress interference between D2D and cellular communications, which can improve the total system throughput and spectral efficiency. To describe it, they formulate a power control optimisation problem for cellular communication and propose a simple on–off power control algorithm for D2D communication. They also obtain an analytic expression for the coverage probability of the cellular link using stochastic geometry according to the proposed algorithm. Simulation results follow to show the rates of both cellular and D2D links in the various numbers of D2D transceivers.

Device-to-device (D2D) assisted offloading heavily depends on the participation of human users. The content preference and sharing willingness of human users are two crucial factors in the D2D assisted offloading. In this paper, with consideration of these two factors, the optimal content pushing strategy is investigated by formulating an optimization problem to maximize the offloading gain measured by the offloaded traffic. Users are placed into groups according to their content preferences and share content with intergroup and intragroup users at different sharing probabilities. Although the optimization problem is nonconvex, the closed-form optimal solution for a special case is obtained, when the sharing probabilities for intergroup and intragroup users are the same. Furthermore, an alternative group optimization (AGO) algorithm is proposed to solve the general case of the optimization problem. Finally, simulation results are provided to demonstrate the offloading performance achieved by the optimal pushing strategy for the special case and AGO algorithm. An interesting conclusion drawn is that the group with the largest number of interested users is not necessarily given the highest pushing probability. It is more important to give high pushing probability to users with high sharing willingness.

In this paper, we study widely linear precoding techniques to mitigate in-phase/quadrature-phase (IQ) imbalance (IQI) in the downlink of large-scale multiple-input multiple-output (MIMO) systems. We adopt a real-valued signal model, which considers the IQI at the transmitter, and then develop widely linear zero-forcing (WL-ZF), widely linear matched filter, widely linear minimum mean-squared error, and widely linear block-diagonalization (WL-BD) type precoding algorithms for both single- and multiple-antenna users. We also present a performance analysis of WL-ZF and WL-BD. It is proved that without IQI, WL-ZF has exactly the same multiplexing gain and power offset as ZF, while when IQI exists, WL-ZF achieves the same multiplexing gain as ZF with ideal IQ branches, but with a minor power loss, which is related to the system scale and the IQ parameters. We also compare the performance of WL-BD with BD. The analysis shows that with ideal IQ branches, WL-BD has the same data rate as BD, while when IQI exists, WL-BD achieves the same multiplexing gain as BD without IQ imbalance. Numerical results verify the analysis and show that the proposed widely linear type precoding methods significantly outperform their conventional counterparts with IQI and approach those with ideal IQ branches.

This paper presents an enhanced hybrid asymmetrically clipped optical orthogonal frequency division multiplexing (OFDM) scheme referred to as EHACO-OFDM. The scheme uses asymmetrically clipped optical OFDM (ACO-OFDM) on the odd subcarriers, pulse-amplitude-modulated discrete multitone modulation (PAM-DMT) on the imaginary parts of even subcarriers, and direct-current-biased optical OFDM (DCO-OFDM) on the real parts of even ones. Since the entire available bandwidth is utilized for data modulation, this scheme can achieve higher spectral efficiency than HACO-OFDM and ACO-OFDM. Moreover, as a smaller DC bias is introduced in our scheme, it is more power efficient than asymmetrically clipped DC-biased optical OFDM (ADO-OFDM) and DCO-OFDM. Simulation results show that EHACO-OFDM performs much better than ADO-OFDM and HACO-OFDM at high bit rates.

In this paper, we investigate energy-efficient hybrid precoding which consists of baseband precoding and radio frequency (RF) precoding for Millimeter Wave (mmWave) systems over multi-input multi-output (MIMO) interference channels. The considered optimization problem is intractable because both the object function and the constraint are non-convex. Instead of solving the multivariate optimization problem directly, we propose a near-optimal algorithm which includes two steps. First, the original problem is reformulated into an equivalent univariate optimization problem. Second, a near-optimal hybrid precoder is obtained via an orthogonal matching pursuit (OMP) based algorithm. Numerical results verify that the proposed hybrid precoding algorithm achieves a near-optimal EE performance. Moreover, the energy efficiency (EE) maximization algorithm outperforms the sum rate maximization algorithm in terms of the EE performance, especially at high transmit power.

This paper studies pilot reuse and data transmit power control in a D2D underlay massive MIMO system over fading channels. In order to reduce the length of pilots, we propose to reuse a set of orthogonal pilots among DUEs, and the graph coloring based pilot allocation (GCPA) algorithm is utilized to allocate pilots to DUEs. Linear minimum mean square error (LMMSE) filters are used for signal detection. We then derive the lower bound of D2D links' average signal-to-interference-plus-noise ratio (SINR), and formulate a power control problem to minimize D2D links' data transmit power under the target SINR constraints. An iterative method converging to the unique optimal solution is proposed. Simulation results show that the analytical lower bound of the average SINR closely matches the simulated average SINR. What's more, pilot resources can be saved greatly by pilot reuse, and the effect of pilot contamination to the system can be almost neglected by allocating proper number of pilots to DUEs and applying GCPA algorithm.

In this paper, we consider the problem of maximizing the sum energy efficiency (EE) for LTE networks where the interferences occur between cells. Cell load, transmit rate and transmit power, where cell load demonstrates the average resource usage in a cell, are considered in the signal-to- interference-and-noise-ratio (SINR) model. Exploiting the properties of sum EE, we prove that operating at full load is optimal and provide a distributed power control algorithm. With all other powers fixed, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. In high SINR situation, the transformed problem in subtractive form is proved a convex problem. Numerical results demonstrate the remarkable improvements in terms of EE.

The problem is to obtain the optimal number of base stations (BSs) and their locations on minimized power consumption with requirements of coverage rate and cell load. In this paper, we propose a mathematical model of evaluation index for cell planning and identify the optimization problem. The system model characterizes the total power consumption of planning area, taking into account the radio power, baseband power and cooling system loss. Two other problems, base station number search algorithm and cell sites planning with fixed cell number, are provided to solve the original problem. Simulation result proves that the algorithm enables to find a suboptimal solution, which relatively decreases the total power consumption under the planning constraints.

In this paper, we consider the problem of maximizing the weighted sum energy efficiency (WS-EE) for multi-input single-output (MISO) interference channels (ICs), which are well acknowledged as general models of heterogeneous networks (HetNets), multicell networks, etc. To address this problem, we develop an efficient distributed beamforming algorithm based on a pricing mechanism. Specifically, we carefully introduce a price metric for distributed beamforming design, which fortunately allows efficient closed-form solutions to the per-user beam-vector optimization problem. The convergence of the distributed pricing-based beamforming design is theoretically proven. Furthermore, we present an implementation strategy of the proposed distributed algorithm with limited information exchange. Numerical results show that our algorithm converges much faster than existing algorithms, while yielding comparable, sometimes even better performance in terms of the WS-EE. Finally, by taking the backhaul power consumption into account, it is interesting to show that the proposed algorithm with limited information exchange achieves better WS-EE than the full information exchange-based algorithm in some special cases.

In this paper, we consider the problem of maximizing the weighted sum energy efficiency (WS-EE) of a multi-input single-output (MISO) ad hoc network. To solve this problem, we develop one low-complexity distributed beamforming algorithm based on pricing mechanism. Specifically, each node updates its price information and broadcasts it to all the other nodes. Having collected all the information, each node selects its beam-vector in closed-form with low computational complexity. The convergence of this algorithm is strictly proved. Compared with the existing two-layer optimization algorithm, our algorithm has lower computational complexity without performance loss. Simulation results show that the proposed algorithm performs slightly worse than the centralized algorithm, but requires much less information exchange overhead.

In this paper, we propose widely-linear blockdiagonalization (BD) type precoding techniques to alleviate the impact of IQ imbalance in the downlink Massive multi-input multi-output (MIMO) systems. We first introduce a real-valued signal model and then develop widely-linear BD (WL-BD) type precoding algorithms, i.e., WL-BD, widely linear regularized BD (WL-RBD) and widely linear simplified generalized MMSE channel inversion (WL-S-GMI). We also present analysis of the sum-rate and multiplexing gain achieved by the proposed WLBD for scenarios with and without IQ imbalance. Numerical results verify the analysis and show that WL-BD type precoding methods significantly outperform their conventional counterparts with IQ imbalance and approach the ideal case.

The joint estimation and compensation of IQ imbalance (IQI) parameters at both transmitter (TX) and receiver (RX) is studied in this paper. We develop a generalized system model with a reduced number of parameters (RNP) that covers a wide range of mobile communications scenarios. We devise efficient direct least-squares (DLS) and alternating least-squares (ALS) techniques for IQI parameter estimation based on the generalized system model. For the ALS based method, we prove that the algorithm will converge to a local optimal solution of the optimization problem. Numerical results show that compared with a previously reported method, the proposed DLS-RNP achieves similar performance with a reduced computational complexity, and the proposed ALS-RNP algorithm has significantly better performance with comparable complexity, with a gain over 5 dB for QPSK and 10 dB for 64QAM in the high signal-to-noise-ratio (SNR) region.

This paper exploits the noncentral communication architecture for increasing system throughput with popular video files cached in user equipments, and transmitted through D2D links under control of base station. A cell is divided into equilateral hexagon clusters among which frequency resources are reused. In order to reduce inter-cluster interference, we propose to limit the transmit power of each user and adopt relay-assisted D2D communication when direct D2D link can not be established in a cluster. Simulation results show that the proposed scheme can achieve higher spectral efficiency without much degradation in system throughput when compared with the scenario where different frequency resources are used by different clusters.

This paper studies the impact of hardware mismatch (11M) between the base station (BS) and the user equipment (UE) in the downlink (DL) of large-scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e., matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the upper bounds on achievable rates of MF and RZF with 11M are investigated, which are related to the statistics of the circuit gains of the mismatched hardware. Moreover, we present a study of 11M calibration, where we take zero-forcing (ZF) precoding as an example to compare two 11M calibration schemes, i.e., Pre-precoding Calibration (Pre-Cal) and Post-precoding Calibration (Post-Cal). The analysis shows that Pre-Cal outperforms Post-Cal schemes. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

In this paper, we consider the problem of maximizing the energy efficiency (EE) for multiple-input-multiple-output (MIMO) interference channels (ICs), subject to the per-link power constraint. To avoid extensive information exchange among all links, the optimization problem is formulated as a noncooperative game, where each link maximizes its own EE. We show that this game always admits a Nash equilibrium (NE) and the sufficient condition for the uniqueness of the NE is derived for the case of large enough maximum transmit power constraint. To reach the NE of this game, we develop a totally distributed EE algorithm, in which each link updates its own transmit covariance matrix in a completely distributed and asynchronous way. Some players may update their solutions more frequently than others or even use the outdated interference information. The sufficient conditions that guarantee the global convergence of the proposed algorithm to the NE of the game have been given as well. We also study the impact of the circuit power consumption on the sum EE performance of the proposed algorithm in the case when the links are separated sufficiently far away. Moreover, the tradeoff between the sum EE and the sum spectral efficiency (SE) is investigated with the proposed algorithm under two special cases: 1) low transmit power constraint regime; and 2) high transmit power constraint regime. Finally, extensive simulations are conducted to evaluate the impact of various system parameters on the system performance.

We consider the problem of maximizing the weighted sum energy efficiency (WS-EE) in ad hoc networks. To solve this problem in a distributed manner, one novel distributed adaptive-pricing algorithm is developed based on limited information exchange among the nodes. Specifically, each node updates its current interference information and broadcasts it to the other nodes. Having collected all this information, each node can adjust its transmit power accordingly with simple arithmetical operations. Then iterate these two steps. This algorithm is strictly proven to be convergent and can attain the KKT optimality conditions of the problem. Moreover, an alternative centralized algorithm based on gradient projection method is proposed to serve as the performance benchmark. Simulation results show that the proposed distributed algorithm converges rapidly. Furthermore, this distributed algorithm performs as well as the centralized one and significantly outperforms the existing algorithm in terms of the WS-EE.

We consider the problem of maximizing the energy efficiency (EE) for a MIMO interference channel (IC), with the power constraint on each link. To obtain totally distributed solutions, this problem is formulated as a noncooperative game. We show that this game always admits a Nash equilibra (NE). Importantly, the sufficient condition that one can check to guarantee the uniqueness of the NE is derived. To reach the NE of this game, we provide a totally distributed EE algorithm, in which each player employs the fractional programming to update his own solution. These updates can be performed in a completely distributed and asynchronous fashion. Sufficient conditions that guarantee the convergence of the algorithm have been given as well. Simulation results show that the proposed algorithm converges fast and significantly outperforms the existing algorithms in terms of the sum-EE or the sum-rate.

In this paper, we focus on a dimmable visible light communication (VLC) system using PIN photodiodes. In such a system, the main distortion is caused by additive Gaussian noise, however, with a noise variance depending on the current signal strength. Under the non-negativity, peak power and dimmable average power constraints, the lower and upper bounds on the channel capacity are derived, respectively. Specifically, the derivation of the lower bound is based on the fact that the entropy of the output is always larger than the entropy of the input, while the derivation of the upper bound relies on the dual expression of the channel capacity and the notion of capacity-achieving input distribution that escape to infinity. Numerical results show that the gap between the lower and upper bounds is very small in the VLC application zone.

This paper studies the impact of hardware mismatch (HM) between base station (BS) and user equipment in the downlink of large scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e. matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the asymptotic downlink signal to interference plus noise ratio (SINR) under HM is investigated, which is only related to the variances of circuit gains in most practical scenarios. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

This paper aims to derive the upper and lower bounds for the channel capacity of dimmable visible light communications (VLC) systems. Because the information is modulated into the instantaneous optical intensity of light emitting diodes (LEDs), the transmitted optical intensity signal is represented by a nonnegative input, which is corrupted by an additive white Gaussian noise. Considering the illumination support and LED device ability in VLC systems, the transmitted optical intensity signal must satisfy the illumination and the allowed peak optical intensity constraints. With these constraints, a lower bound on the channel capacity is derived through the maximum mutual information between the channel input and output, which is determined by the source entropy maximization. By applying the dual expression of capacity, an upper bound on channel capacity is derived. Both the upper and lower bounds are presented as the closed forms. The numerical results show that the presented bounds are very tight. Moreover, the peak optical intensity constraints will result in the loss of channel capacity. However, such capacity loss is so small that it can be negligible when the allowed peak optical intensity is twice of the nominal optical intensity of LED devices.

We consider a multiuser scenario with a center node for data fusion, where simultaneously transmitting is required in real time and with low probability of interception. We established a novel multiple access scheme, named matrix division multiple access (MDMA), based on OFDM technology. Precoded with different matrices, the signal from different end nodes can be distinguished due to the signal space division. At the receiver, the zero-forcing (ZF) and minimum mean square error (MMSE) decoding schemes are developed. Additionally, we analyze the sum capacity of the MDMA system. Simulations show that the MDMA scheme achieves the better performance compared to the OFDMA.

In the last 2 years, all‐optical relaying techniques for free‐space optical (FSO) communications have drawn considerable attention. In this study, the outage performance of an FSO communication system with parallel all‐optical relays is investigated. A composite channel is established, which includes atmospheric loss, pointing error and atmospheric turbulence. To show the impact of the channel state information (CSI) on system performance, either full CSI or semi‐blind CSI at the relay nodes is considered. After that, the theoretical expressions of the outage probabilities for different scenarios are derived. Numerical results show that the derived expressions are quite accurate to evaluate the system performance. Moreover, the derived results demonstrate that the systems with semi‐blind CSI relaying can provide comparable performance to systems with full CSI relaying, and also indicate that the performance over strong turbulence channels with clear weather outperforms that over weak turbulence channels with light fog.

This paper aims to design an optimal set of beam-vectors for multi-band multi-antenna cognitive radio networks that jointly allocate power over both space and frequency, so that the sum power of secondary users (SUs) is minimized, subject to rate demands at the SUs, as well as the interference constraints imposed by primary users. Unlike the rate maximization problems, which are always feasible, this power minimization (PM) problem may be infeasible due to the rate constraints. Therefore, we provide a complete analysis of the PM problem by splitting the solution into two separate phases. In phase I, a novel method is developed to check the feasibility of the PM problem by considering an alternative problem, where one additional variable is introduced. This alternative problem is always feasible and one algorithm based on network duality and geometric programs is developed to solve it. In phase II, a novel algorithm is developed to solve the PM problem. This algorithm can be implemented in an online fashion. Furthermore, this algorithm is proved to converge to a Karush-Kuhn-Tucker point of the PM problem. Simulation results show that the proposed algorithms converge in only a few iterations and significantly outperform the existing single-band method in terms of both the feasibility probabilities and power savings.

In this paper, the downlink signal-to-interference-plus-noise ratio (SINR) distribution in multiuser large scale antenna systems with conjugate beamforming and Rayleigh fading is investigated. The probability density function (PDF) is derived and the distribution in high signal-to-noise ratio (SNR) regime is studied. Results indicate that the PDF of downlink SINR converges to F distribution when the interference is dominant over noise. It is interesting that the asymptotic SINR is just the reciprocal of the ratio of the number of users U to the number of transmit antennas N, and is irrelevant to the average transmit power when N and U grow with fixed ratio. However, when U is a large constant, the transmit power could be proportional to equation to maintain a specified quality of service (QoS), as a result of the large scale antenna system effect. Simulation results validate the derived PDF and analytical results.

In this paper, a novel optimization objective namely weighted sum rate is proposed for beamformer design in single group multicast systems. As an upper bound and special case of maximizing minimum rate, it can overcome previous disadvantages of computation complexity and sensitivity to channel state when the weights are chosen properly. Although the corresponding optimization problem is nonconvex, some properties of the optimal solution and closed-form solutions under some usual special cases are derived. In addition, an iterative algorithm with low computation complexity for general case is proposed. Simulation results show that the beamformer obtained by the iterative algorithm not only improves the minimum rate and average rate, but also gets better fairness and overall performance compared with previous schemes.

The problem of energy-efficient joint beamforming and antenna selection for multicast systems is consider in this paper. Under the performance objective of maximizing the number of bits per joule of energy consumed, beamformer design, optimal transmit power and antenna selection is discussed respectively. The beamforming problem is converted to an equivalent form of the max-min problem and we propose a suboptimal solution with low complexity whose validity is verified by the numerical results. The existence and uniqueness of the optimal transmit power is proved. Then, a simple antenna selection scheme is presented. In the end, we combine the above algorithms into a joint beamforming and antenna selection algorithm. Simulation results show the performance gain of the proposed scheme. A great deal of energy can be saved at the expense of acceptable throughput loss especially with low number of users or short distance. In addition, antenna selection is necessary for energy-efficient design in multi-antenna systems through the performance comparison.

Distributed multi-input multi-output (D-MIMO) system is a promising system to greatly improve the spectral efficiency and power efficiency of the cellular system. The performance analysis of the spectral efficiency of D-MIMO system is a fundamental problem for both theoretical study and technique evaluation. In this paper, the theoretical performances of spectral efficiency for D-MIMO system and traditional collocated MIMO (C-MIMO) system are studied and compared. First, a composite D-MIMO channel including path loss, shadow fading and multipath fading is given. Conditioned on the desired user position, by using the tight bounds and central limit theory, the analytical approximations of the mean and the cumulative distribution function (CDF) of the mutual information (MI) are derived for C-MIMO and D-MIMO channels at both high signal to noise ratio (SNR) and low SNR. Assuming that the users are randomly distributed in the cell, the CDFs of the spectral efficiency are also given for the C-MIMO and D-MIMO cellular system, and the closed-form expressions for the mean spectral efficiency, mean outage spectral efficiency are derived. Finally, the theoretical comparisons between C-MIMO and D-MIMO for large number of antennas are given, and simulation results are presented which validate the analytical results.

In this paper, the impact of path loss exponents on antenna location design in generalized istributed antenna system (GDAS) is investigated, with the goal of maximizing a lower bound of the average uplink capacity. A simple case when antenna ports (AP) are uniformly placed along a circle is studied first. Both the upper and lower bounds for the optimal placement radius are given. An approximate expression of the optimal radius with sufficient accuracy is derived using Newton-Cotes integration formula with a degree of 5. The given expression indicates that with different path loss exponents the optimal locations of APs differ. The interesting thing is that the optimal radius decreases as the path loss exponent grows when the number of APs is large, while it is the opposite when the number of APs is small. Analytical results are then extended to more general scenarios and a new criterion is proposed for antenna location design. Simulation results show that the given criterion outperforms those in previous literature, and the achieved capacity gain can be as much as 10%.

This paper studies the system uplink outage probability in distributed antenna systems (DAS). Firstly, owing to the complexity of actual wireless environments, this paper establishes a composite channel model which takes path loss, lognormal shadowing and Rayleigh fading into account. Then, the probability density function (PDF) of the output signal-to-noise ratio (SNR) is derived. After that, an analytical expression of the uplink outage probability for the MS over a given position is obtained after employing the selective transmission (ST) scheme. Further, considering the distribution of MSs in the cell, a closed-form expression of the system outage probability is derived. Numerical results show that the closed-form expression of the system outage probability can provide sufficient precision for evaluating the outage performance of DAS.

The proof of Proposition 2 in the above-mentioned paper was incorrect due to some flaws in the prerequisite analysis of the proposed hybrid feedback scheme. We fix the flaws and correct the proof in this comment. From the corrected proof, we arrive at a new observation that the hybrid feedback scheme has exactly the same feedback overhead as the pure opportunistic feedback scheme if the threshold is adaptively set for a target utilization. Simulation results demonstrate the correctness of our analysis.

This paper analyzes the spectral efficiency of the distributed multi-input multi-output (D-MIMO) system with antenna cooperation over the rayleigh-log-normal fading channel. Under the cooperative transmission scheme (CTS), analytical expressions of the ergodic normalized capacity and outage capacity are derived. Moreover, to optimize the system performance, an adaptive CTS (ACTS) is proposed by the criterion of minimizing the outage capacity, and a theoretical expression is derived to operate the ACTS. Simulation results are presented to verify the theoretical outcomes.

This paper studies the problem of subcarrier grouping in the downlink of multi-carrier code division multiple access (MC-CDMA) systems with linear minimum mean square error (MMSE) receiver. Further, the problem is considered as combinatorial optimization problem. By theoretical analysis, an optimal subcarrier grouping scheme is proposed. Moreover, the proposed scheme is efficient in computational time. According to the instantaneous channel conditions, the proposed optimal scheme can be applied adaptively subcarrier grouping to maximize the system capacity. The simulation results show that the proposed scheme outperforms the conventional static schemes and is efficient in improving the system performance.

In the scenario of dynamic spectrum access application for cognitive radio (CR), the spectrum-sidelobe problem of OFDM (orthogonal frequency division multiplexing) must be considered. A precoding ahead of IDFT (inverse discrete Fourier transformation) is presented to suppress the in-band-out-of-subband (IBOSB) radiation. Its design is based on generalized eigenvalue problem. In order to improve its performance for practical application, one column of the precoding matrix can be inverted to reduce the signal peak-to-average-power-ratio (PAPR) to a lower level. At the receiver, iterative soft detection with interference cancelation is adopted to detect the precoded data and further employs the frequency diversity. Both methods introduce some additional transceiver complexity compared with pure precoding, whereas simulations show that joint design not only provides improved PAPR statistics, but also achieves the markedly gain of Bit-Error-Rate (BER) performance over multipath fading channel.

This paper investigates the channel capacity of the distributed antenna system (DAS) over shadowed fading channels. To facilitate the analysis, the distribution of the received signal to noise ratio (SNR) is approximated firstly by a log- normal distribution. Then, by using Gauss-Hermite integration, an approximate analytical expression of the channel capacity is derived for the downlink of DAS. The numerical results show that the derived analytical expression of capacity can provide very well approximation to the simulation results.

In this paper, an channel and queue aware fair (CQAF) packet scheduling scheme is proposed for the downlink packet transmission in multiuser orthogonal frequency division multiplexing (OFDM) systems. By making use of the information on the channel conditions and the queue lengths, the proposed CQAF packet scheduling scheme efficiently allocates the subcarriers, transmission power and modulation level to users under the constraints of total transmission power, the number of subcarriers, bit-error-rate (BER) requirement and generalized processor sharing (GPS)-based fairness requirement. The numerical results show that the proposed CQAF packet scheduling scheme can reduce the transmission delay and queue length significantly while maximizing system throughput and maintaining fairness among users.

Motivated by the increasing popularity of multicarrier code division multiple access (MC-CDMA) systems, this letter studies the throughput maximization of MC-CDMA as a downlink multi-carrier multiple-access scheme and presents the theoretical analysis for the optimal solution to throughput optimization problem of downlink MC-CDMA systems. Then, a low-complexity allocation method is proposed with negligible throughput loss. Finally, several resource allocation principles are presented.

This letter considers minimum mean square error beamforming (MMSEBF) for the downlink of a multiuser multiple-input single-output (MISO) system with limited feedback, where the transmitter only has quantized information regarding the direction of the channel. We present an improved approach which is robust to the channel uncertainties arising from the quantization errors and the lack of channel magnitude information. Simulation results show that the proposed robust MMSEBF scheme provides improved performance over the conventional beamforming schemes in limited feedback systems.

In spectrum pooling applications, the spectral leakage of DFT-based OFDM systems should be divided into two parts, in-band-out-of-subband (IBOSB) leakage and out-of- band (OOB) radiation. A scheme is proposed to suppress the IBOSB leakage and redistribute OOB radiation through the frequency domain correlative coding with appropriate weighting. It introduces the correlation between adjacent subcarriers, and the DFT-based spectrum is reshaped through mutually cancelation of the sidelobes. The optimum weighting factor is given. Analysis and simulation show that the proposed scheme significantly suppresses IBOSB spectral leakage compared to conventional OFDM. At the same time, carrier-interference ratio (CIR) is increased and maximum likelihood sequence detection (MLSD) can mostly compensate the BER degradation caused by error propagation at the expense of a little computation complexity.

QR decomposition based M algorithm (QRD-M) is a type of maximum likelihood signal detecting (MLSD) scheme to provide a tradeoff detecting method between the system performance and complexity in multi-input multi-output (MIMO) communications. But for the conventional QRD-M algorithm, only one parameter, the maximum number of the survived branches, is used to make tradeoff, which cannot provide more valuable tradeoff options with better performance to complexity ratio. In this paper, an improved QRD-M algorithm with two independent parameters is proposed. It can provide more and better tradeoff options, which is proved through complexity analysis and numerical simulations.

Although frequency domain equalization (FDE) is very efficient to resist frequency selective fading in single-carrier communications, its performance will be greatly degraded due to high Doppler impact in a fast moving environment. To enhance the system performance of single-carrier communication system with FDE (SC-FDE), the self- cancellation technique is migrated from orthogonal frequency division multiplexing system (OFDM). It can obtain significant performance gain in scenarios with high Doppler in numerical simulations.

A novel turbo codec with hybrid modulation is proposed in this paper. Instead of brutal puncturing, we apply different modulation alphabets to the original not-punctured turbo coded bits, where the systematic bits are modulated by a lower order constellation and the parity bits are mapped by a higher order constellation, respectively. Numerical results show that the proposed scheme without puncturing can noticeably improve the bit error rate (BER) and frame error rate (FER) performance of turbo codes in Rayleigh flat fading channels compared with the conventional turbo codes with puncturing and uniform modulation with the same spectral efficiency.

Frequency domain equalization (FDE) is a very efficient way to resist frequency selective fading in single-carrier communications, but its performance will be greatly degraded when suffering serious Doppler impact due to time varying channel state. Based on the single-carrier system with FDE (SC-FDE), a segmentation FDE scheme is proposed to resist high Doppler in this paper without the price of system bandwidth efficiency. Through numerical simulations, its significant performance improvement in high Doppler is proved.

To enhance the bandwidth efficiency in cyclic prefix (CP) assisted OFDM system, we propose a complexity reduced blind channel equalization scheme based on the finite alphabet property of the transmitted signal and we also present its simplification scheme in this paper. According to the numerical simulation results, our proposals can perform as well as the conventional pilot assisted frequency domain channel estimation (PFDCE) in high Eb/N0, but a little worse in low Eb/N0

Cyclic Prefix (CP) has been applied in the single-carrier systems to resist the inter-block interference (IBI) due to the frequency selective fading channel and equalize the channel by one-tap frequency domain equalizer. However, there still exists the residual inter-symbol interference (ISI) due to imperfect channel equalization, which will severely degrade the system performance. In this paper the residual ISI is analysis in CP-SC with linear minimum mean square error (LMMSE) equalizer. With the initial estimates of LMMSE equalizer, the interference cancellation (IC) technique is adopted to alleviate the residual ISI so that the system performance can be considerably improved.

In general, cyclic prefix (CP), zero padding (ZP) or fixed symbol padding (FP) can be seen as the guard interval (GI) for data transmitting. By inserting enough long GI, we can remove inter-block interference (IBI) completely, and the signal distortion due to multi-path environment can be simply compensated by frequency domain equalization (FDE). But on the other hand, the system bandwidth efficiency will be decreased due to the redundancy of GI and additional channel estimation pilots. This paper proposes a new scheme to estimate channel state with the help of our special type GI and presents the FDE scheme based on the proposed GI system

The adaptive frequency domain interference cancellation and channel equalizer for MIMO-CP-CDMA systems is proposed to cancel the residual interference stage by stage. At each stage, the proposed scheme performs the adaptive forward filtering and interference cancellation in frequency domain so that the signal to interference plus noise ratio can be improved and the error propagation of the interference cancellation can be avoided. The achieved SINR of each code channel is utilized to compute the normalized correlation coefficient. Simulation results show that the proposed scheme can achieve the satisfactory performance and avoid the error propagation

This paper considers the design of iterative receivers for space-frequency block-coded orthogonal frequency division multiplexing (SFBC-OFDM) systems in unknown wireless dispersive fading channel with outer channel coding. The iterative joint channel estimation and symbol detection algorithm is derived. A simple decision-error judgment criterion is proposed and an improved training scheme is applied in the system to suppress the error propagation. Both theoretical analysis and computer simulation show that this algorithm gives better BER performance and saves the overhead of OFDM systems

When MIMO channel correlation information is available at the transmitter, closed-loop precoding can be introduced to improve system performance. In this paper, we proposed a novel closed-loop precoding algorithm based on code distance allocation for transmit correlated spatial multiplexing MIMO systems. We show that pairwise error probability of transmit correlated spatial multiplexing MIMO systems is determined by the minimum transmit code distance allocated to each eigenmode subchannel. We give the structure of code distance allocation matrix to optimize transmit code distance. Our proposed structure of code distance allocation matrix can simultaneously optimize minimum code distance allocated to each subchannel. Computer simulations show that our proposed procoding algorithm can improve the performance of transmit correlated spatial multiplexing systems

Bell Lab vertical layered space-time architecture (V-BLAST) is an approach for exploiting high MIMO channel capacity. A power allocation algorithm has been proposed to improve bit error rate performance when using ordering successive interference cancellation (OSIC) detection. However, the BER performance of proposed power allocation algorithm degrades greatly with the increase of transmit antenna correlation. In this paper, we proposed a closed-loop precoding algorithm based on OSIC detection. Our proposed precoding algorithm combines decorrelation with power allocation algorithm to improve the BER performance of transmit correlated V-BLAST systems. Computer simulations demonstrated that our proposed precoding method has better performance than mere power allocation algorithm

Orthogonal frequency division multiplexing (OFDM) is the projected modulation of choice for fourth-generation broadband multimedia wireless systems. However, for mobile applications, channel variations during one OFDM symbol introduce intercarrier interference (ICI), which degrades the performance. This gets more severe as mobile speed, carrier frequency or OFDM symbol duration increases. In this paper, we first analyze the ICI caused by time-variant channel in terms of the complex weighting coefficients, which give the contribution of each transmitter subcarrier to each demodulated subcarrier. Then, we propose a high efficiency intercarrier interference cancellation scheme to reduce the effects of Doppler frequency shift. Finally we derive the signal-to interference power ratio (SIR) for the proposed OFDM system. From the comparison between the normal OFDM and the proposed system, we can see that such an ICI cancellation scheme gives more than 5 dB SIR improvement. The simulation results confirm the analysis and show the SIR improvement.

We investigate the difference between extrinsic and posteriori information fed back from outer decoder to inner detector in a turbo-BLAST system for a quasistatic channel. With the help of extrinsic information transfer (EXIT) chart and simulation, it is observed that a faster convergence rate is obtained to feed back posteriori information, and especially, a better steady state performance is attained when antenna number is small.

Clipping is a popular method to overcome large peak-to-mean envelope power ratios (PMEPRs) in orthogonal frequency-division multiplexing (OFDM) systems, but it results in nonlinear distortion, which degrades signal detection performance. In this paper, clipping effects on signal detection with a simple channel estimation in OFDM are investigated, signal-to-noise and distortion ratio (SNDR) results of detected signals are derived, and symbol error rates (SERs) of clipped OFDM in frequency selective Rayleigh fading channels are evaluated. The evaluated SER results are supported by simulations.

An improved channel estimator with inter-symbol interference (ISI) free window measurement for OFDM systems is proposed by exploiting the inherent correlation between the cyclic prefix and portion of the received OFDM signal. This scheme make use of real time measured size of ISI free window to estimate the maximum delay spread of channel, then a transform domain channel estimator is adopted to further reduce the noise variance. Due to the ISI-free window can be estimated before the application of FFT to the received signal, it is a parallel processing, and very suitable for practical systems. Simulation result demonstrated that the proposed method can track the variation of delay spread of wireless channel, and improve the MSE of channel estimation significantly, thus give at least 1-db improvement in TU channel and 0.5-db in HT channel.

Multiple-input multiple-out (MIMO) wireless transmission has been proved to be a promising technique to support high data rate transmission. Among proposed schemes, space-time codes are designed to improve transmission reliability, while BLAST architectures are proposed to improve spectrum efficiency. To optimize both channel capacity and error probability, linear dispersion codes (LDCs) were proposed. However, original LDCs are designed for quasi-static fading channels, which don't guarantee optimal error probability in fast fading channels. In this paper, we discuss performance of LDCs both in quasi-static and fast fading channels. And we propose a modified LDCs transmission scheme, in which we introduce an interleaver in each antenna branch to turn quasi-static channels into equivalent fast fading channels. Then we present codes optimization criterion to search for LDCs which optimal both in quasi-static and fast fading channels. Computer simulations show that the performance of proposed LDCs design method is better than original LDCs.

Space-time transmission techniques, which make full use of the rich scattering channel with multiple transmit and receive antennas, can greatly increase the spectral efficiency. The conventional V-BLAST (Bell laboratories layered space-time) detection algorithm estimate symbols with the expected SNRs ordering. In this paper, a novel BLAST detection algorithm based instantaneous error ordering (IEO-BLAST) is proposed. The proposed BLAST detection algorithm obtains the additional diversity with instantaneous error ordering and reduces the effect of the error propagation. Therefore, the performance of the BLAST architecture is greatly improved. In addition, the simplified version of IEO-BLAST detection algorithm (SIEO-BLAST) is derived. And the performance of simplified algorithm has little degradation. The selection capacity (SC) is defined to represent the performance of the algorithm with successive interference cancellation.

Space-time transmission techniques, which make full use of the rich scattering channel with multiple transmit and receive antennas, can greatly increase the spectral efficiency. In this paper, a novel parallel multistage equalizer with partial decision feedback (PDF) is proposed for the layered space-time frequency-selective channel. It is comprised of a partial parallel interference canceller, a MIMO decision feedback equalizer and a linear combiner. Performance analysis shows that the proposed receiver is equivalent to the partial parallel interference cancellation (PPIC) using MMSE for initial estimation and tentative decisions. By using past- and post-symbol estimation to regenerate intersymbol and co-antenna interference, the proposed receiver can be further improved with partial decision feedback. Moreover, the proposed equalization schemes could be implemented by negligible complexity increase comparing with the other well-known approaches. Simulation results show that the proposed equalizers with PDF significantly outperform the conventional MIMO decision feedback equalizer without interference cancellation (IC) and MIMO equalizer with ordering successive interference cancellation (OSIC).

Space-time transmission techniques, which make full use of the rich scattering channel with multiple transmit and receive antennas, can greatly increase the spectral efficiency. In this paper, parallel multistage decision feedback equalizer (DFE), which is composed of parallel interference cancellation, multiple input single output decision feedback equalizer (MISO-DFE) and linear combination of the soft outputs of MISO-DFE and the previous soft decisions, is proposed for a single-carrier system over layered space-time frequency-selective channels. In addition, an algorithm is proposed to obtain the soft and hard decisions for initialization. And the proposed parallel multistage DFE can be implemented with lower complexity than MIMO-OFDM. Simulation results show that the parallel multistage DFE outperforms the MIMO-OFDM and various previous equalizers for a single-carrier system.

The Gauss-Seidel iterative algorithm for linear asynchronous decorrelation or minimum mean square error (MMSE) multiuser detector can be viewed as a kind of linear soft decision feedback multiuser detection. Based on this idea, a linear decorrelating or MMSE detector can be implemented by using a much simpler pipeline structure compared with the conventional scheme of computing the inverse of the corresponding correlation matrix. Through this pipeline structure, a lot of computations can be reduced.

Multicode CDMA with V-BLAST architecture is an attractive scheme to achieve high data rate transmission. We propose an advanced detection scheme, order partial parallel interference cancellation with decision feedback (DF-OPPIC), to combat co-antenna interference and multiple access interference in the system. Compared with conventional V-BLAST detection algorithms, the scheme has better performance on the frame error ratio in flat Rayleigh fading channel and multipath Rayleigh fading channel. Moreover, due to avoiding calculating pseudo-inverse and optimal detection order at every time of iteration, the scheme has lower arithmetic complexity.

Multicode CDMA with V-BLAST architecture is an attractive scheme to achieve high data rate transmission. We propose an advanced detection scheme, order partial parallel interference cancellation with decision feedback (DF-OPPIC), to combat co-antenna interference and multiple access interference in the system. Compared with the original reference, the scheme has better performance on the frame error ratio in flat Rayleigh fading channel and multipath Rayleigh fading channel. Moreover, due to the avoidance of calculating pseudo-inverse and optimal detection order in every time of iteration, the scheme has lower arithmetic complexity.

Two bit estimators, the weighted average bit estimator and shrinkage bit estimator, used in DS-CDMA partial parallel interference cancellation (PPIC) proposed by Divsalar et al. (see IEEE Trans. on Comm., vol.46, no.2, 1998) , are compared. It is shown that the two bit estimators improve the performances by decreasing the power of the residual interferences, remaining in the decision statistic of each stage after interference cancellation, but in different capabilities. We also justify the assumption that the residual interference is Gaussian, under which the two bit estimators are derived by Divsalar et al. Furthermore, theoretical optimal weights of the above two estimators minimizing the BER are derived.

The performance comparison and analysis between space-time trellis code (STTC) and space-time block code (STBC) is given. It is observed that the 2STBC scheme has a fixed performance, while in contrast the 2STTC can improve its performance by increasing states. Furthermore, we prove that coding advantage is not always reliable for analyzing the performance of a space-time code (STC).

Space-time spreading (STS) is considered as one of the transmit diversity schemes in the current cdma2000 standard. Space-time transmit diversity (STTD) has been proposed and specified in WCDMA standardization. In this paper, the performance of STS and STTD have been evaluated and compared in forward link CDMA2000 systems. The performance analysis and simulation results show that these two schemes have approximately the same performance on the frame error ratio in multipath fading channels even with imperfect channel estimation. But STS has more implementation complexity and higher peak to average ratio compared with STTD.

Space-time trellis codes can achieve the best tradeoff between bandwidth efficiency, diversity gain, constellation size and trellis complexity. We propose some optimum low rate space-time trellis codes. Performance analysis and simulation show that the low rate space-time trellis codes outperform space-time block codes concatenated with convolutional code at the same bandwidth efficiency, and is more suitable for the power limited wireless communication system.