The integration of aerial reconfigurable intelligent surfaces (ARISs) into IoT networks offers transformative potential for enhancing secure and energy-efficient communication in the presence of blockages and eavesdropping threats. This paper proposes to integrate ARIS into Internet of Things (IoT) networks to simultaneously improve communication reliability, enforce information freshness, and defend against eavesdropping. We formulate a joint optimization problem to minimize the average total transmit energy of IoT devices through the coordinated design of unmanned aerial vehicle (UAV) trajectory, transmit power allocation, ARIS phase shifts, and device scheduling, subject to rigorous constraints on age of information (AoI), UAV energy budget, and secrecy rate guarantees. The optimization problem is formulated as a dynamic programming problem. To address the complexity of long-term dynamic optimization, we employ Lyapunov optimization to decompose it into a per-slot deterministic optimization problem, which can be solved without requiring future state information. However, the per-slot problem is a mixed-integer non-convex optimization problem, making it inherently challenging to solve optimally. To address this, we propose an efficient algorithm that effectively balances the tradeoff between minimizing average total energy consumption and stabilizing average total queue backlogs. Simulation results demonstrate that our algorithm reduces average transmit energy by 46% compared to the round-robin comparison scheme while strictly adhering to information freshness and UAV energy constraints.

Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. We first analyze the impact of UAV deployment on the sensing quality and identify a threshold value for the sensing elevation angle that guarantees a satisfactory quality of data samples. Due to the non-ideal sensing channels, we consider the probabilistic sensing model, where the successful sensing probability of each UAV is determined by its position. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. Theoretical results suggest that the convergence rate can be improved if UAVs have a uniform successful sensing probability. Based on this analysis, we formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we apply the alternating optimization technique, and propose the bandwidth, batch size, and position optimization (BBPO) scheme to optimize these three decision variables alternately. Simulation results demonstrate that our BBPO scheme outperforms other baseline schemes regarding convergence rate and testing accuracy. The simulation implementation is available at https://github.com/TheaSherlock/ISCC-UAV.

Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for federated edge learning (FEEL) due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Due to the nonideal sensing channels, we consider the probabilistic sensing model. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. We formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we propose our algorithm based on the alternating optimization technique. Simulation results demonstrate that our algorithm outperforms other baselines regarding convergence rate and testing accuracy.

Most of the existing generation models encounter notable challenges in complex scenes, particularly with inaccuracies in facial organs and textures that do not align with actual conditions. Traditional face restoration methods, heavily dependent on facial geometry and reference priors, often generate incorrect facial images that contribute misleading prior information. In this study, a Misinformation-Flitered GuideDenoising Diffusion Probabilistic Models (MF-GDDPM) is proposed to address these issue. Specifically, MF-GDDPM employs low-pass filtering to remove high-frequency details that contain misleading prior information. This process results in filtered low-dimensional facial contours that guide the diffusion model in generating high-quality facial images. To further enhance the fidelity of the generated results, a dualstream encoder within the Denoising Unet is constructed to process facial contours and high-dimensional details separately, while the Attention Feature Fusion (AFF) attention mechanism ensures the fidelity of image restoration. We have also incorporated the Natural Image Quality Evaluator (NIQE), a deep learning-based image quality assessment tool, into our framework as a novel loss function to crucially ensure the naturalness of restored images. Overall, the proposed method marks a significant improvement in generating accurate and clear facial images using diffusion models.

Mobile edge computing (MEC) is a promising tech-nology to support the latency-critical applications of mobile devices by offloading complex computation tasks to edge servers. However, the mobility of devices yield a great challenge on de-livering reliable continuous services, especially for those latency- critical applications. Motivated by the fact that the user's location changes slower than the task arrivals, we propose a two-timescale mobility management framework by joint service migration and power control. The management design is formulated as a long-term energy minimization problem, subject to the reliability requirement of the latency-critical application. Leveraging the Lyapunov optimization technique, we develop an online two- timescale control algorithm to solve the problem. The simulation results demonstrate that our proposed online algorithm can significantly improve the energy and reliability performance compared to the baselines.

The paper investigates the information freshness and energy harvesting (EH) in downlink Internet of Things (IoT) networks. Information freshness is measured by Age of Information (AoI). We consider a scenario where an access point periodically sends short packets to N independent IoT devices. All the devices are equipped with capacitors to store energy through wireless power transfer (WPT) when the devices receive signals conveying unwanted packets. Conventionally, short packets for multiple devices are jointly coded into one larger packet to improve reliability. However, whether short packets should be jointly coded to reduce average AoI and improve EH performance has not been well investigated. On the one hand, a larger number of packets jointly coded decreases the packet error rate (PER), which may reduce the average AoI because the time to successfully receive the next update may be shorter. On the other hand, more packets jointly coded cause less time for WPT, as the devices spend most of their time receiving update packets, resulting in poor EH performance. Therefore, we investigate the tradeoff between AoI and EH by examining the number of packets to be jointly coded. Closed-form AoI and EH formulas are derived. Numerical results show that there exist optimal numbers of jointly coded packets that can achieve both high information freshness and high EH at the same time.

Mobile edge computing (MEC) is a promising technology for enhancing the computation capacities and features of mobile users by offloading complex computation tasks to the edge servers. However, mobility poses great challenges on delivering reliable MEC service required for latency-critical applications. First, mobility management has to tackle the dynamics of both user’s location changes and task arrivals that vary in different timescales. Second, user mobility could induce service migration, leading to reliability loss due to the migration delay. In this paper, we propose a two-timescale mobility management framework by joint control of service migration and transmission power to address the above challenges. Specifically, the service migration operates at a large timescale to support user mobility in the multi-cell network, while the power control is performed at a small timescale for real-time task offloading. Their joint control is formulated as an optimization problem aiming at the long-term mobile energy minimization subject to the reliability requirement of computation offloading. To solve the problem, we propose a Lyapunov-based framework to decompose the problem into different timescales, based on which a low-complexity two-timescale online algorithm is developed by exploiting the problem structure. The proposed online algorithm is shown to be asymptotically optimal via theoretical analysis, and is further developed to accommodate the multiuser management. The simulation results demonstrate that our proposed algorithm can significantly improve the energy and reliability performance.

Mobile-edge computing (MEC) enhances the capacities and features of mobile devices by offloading computation-intensive tasks over wireless networks to edge servers. One challenge faced by the deployment of MEC in cellular networks is to support user mobility. As a result, offloaded tasks can be seamlessly migrated between base stations (BSs) without compromising the resource-utilization efficiency and link reliability. In this paper, we tackle the challenge by optimizing the policy for migration/handover between BSs by jointly managing computation-and-radio resources. The objectives are twofold: maximizing the sum offloading rate, quantifying MEC throughput, and minimizing the migration cost. The policy design is formulated as a decision-optimization problem that accounts for virtualization, I/O interference between virtual machines (VMs), and wireless multi-access. To solve the complex combinatorial problem, we develop an efficient relaxation-and-rounding based solution approach. The approach relies on an optimal iterative algorithm for solving the integer-relaxed problem and a novel integer-recovery design. The latter outperforms the traditional rounding method by exploiting the derived problem properties and applying matching theory. In addition, we also consider the design for a special case of “hotspot mitigation”, referring to alleviating an overloaded server/BS by migrating its load to the nearby idle servers/BSs. From simulation results, we observed close-to-optimal performance of the proposed migration policies under various settings. This demonstrates their efficiency in computation-and-radio resource management for joint service migration and BS handover in multi-cell MEC networks.

Mobile-edge computing (MEC) enhances the capacities and features of mobile devices via offloading computation-intensive tasks over wireless networks to the edge servers. One challenge faced by the deployment of MEC in cellular networks is to support user mobility, so that the offloaded tasks can be seamlessly migrated between base stations (BSs) without compromising the resource-utilization efficiency and link reliability. In this paper, we tackle the challenge by optimizing the policy for migration/handover between BSs by jointly managing computation-and-radio resources. The policy design is formulated as a multi-objective optimization problem that maximizes the sum offloading rate, quantifying MEC throughput, and minimizes the migration cost, where the issues of virtualization, I/O interference between virtual machines (VMs), and wireless multi-access are taken into account. To solve the complex combinatorial problem, we develop an efficient relaxation-and-rounding based approach, including an optimal iterative algorithm for solving the integer-relaxed problem and a novel integer-recovery design that exploits the derived problem properties. The simulation results show the close-to-optimal performance of the proposed migration policies under various settings, validating their efficiency in computation-and-radio resource management for joint service migration and BS handover in multi-cell MEC networks.

This paper puts forth an interference mitigation scheme, named signal-aligned network coding (SNC), for both uplink and downlink cloud radio access networks (C-RANs). The base stations (BSs) are connected to a central processor (CP) via digital links with individual limited capacities. The user equipments (UEs) communicate with the CP through BSs serving as relay nodes. We focus on the situation that the digital links have moderate rate constraint. Loosely speaking, the capacities of the digital links and the wireless links are of the same order of magnitude. The SNC scheme strategically aligns the signals by signal alignment (SA) and then decodes the aligned signals by physical-layer network coding (PNC). With the proper designs of the network-coded messages transmitted and the alignment of signals, the SNC scheme significantly mitigates the inter-cell interference. No matter the size of the network, the SNC scheme achieves full degrees of freedom (DoF) asymptotically. For the finite signal-to-noise ratio (SNR) performances, simulation results show that the SNC scheme achieves superior sum-rate than the conventional compute-and-forward scheme in the two-user and the three-user cases, especially in the high SNR regime.

Unmanned aerial vehicles (UAVs) can enhance the performance of cellular networks, due to their high mobility and efficient deployment. In this paper, we consider a single-UAV assisted wireless communication system, where the UAV is deployed as an aerial base station (BS) to serve ground users. We maximize the transmission rate of ground users in the downlink communication by optimizing the UAV trajectory. To account for the impact of the ground BS on the UAV trajectory design, we provide a higher reward for the UAV to serve at a cell edge position. The cost function takes into account both the energy consumption during moving and hovering. We formulate our problem as a route selection problem in an acyclic directed graph, where each vertex and each edge are associated with a reward and a cost, respectively. The shortest path (SP) scheme is used to determine the optimal trajectory. Simulation results show that the SP scheme achieves the highest payoff among the compared schemes. Finally, we provide an application scenario based on our campus map to illustrate how the UAV determines the optimal trajectory under the SP scheme.

Mobile-edge computing (MEC) is an emerging technology for enhancing the computational capabilities of mobile devices and reducing their energy consumption via offloading complex computation tasks to the nearby servers. Multiuser MEC at servers is widely realized via parallel computing based on virtualization. Due to finite shared I/O resources, interference between virtual machines (VMs), called I/O interference, arises that degrades the computation performance. In this paper, we study the problem of joint radio-and-computation resource allocation (RCRA) in multiuser MEC systems in the presence of I/O interference. Specifically, we formulate a sum offloading rate maximization problem by joint offloading-user scheduling, the offloaded size control, and time allocation for communication (offloading and downloading) and computation. The problem is a non-convex mixed-integer programming problem. An optimal algorithm with low complexity is designed based on a decomposition approach and Dinkelbach method. The simulation results demonstrate considering of I/O interference can endow on an offloading controller robustness against the performancedegradation factor.

In conventional mobile data plans, the data is associated with a fixed period (e.g., one month) and the unused data will be cleared at the end of each period. To take advantage of consumers' heterogeneous demands across different periods and meanwhile to provide more time flexibility, some mobile data service providers (SP) have offered data plans with different lengths of period. In this paper, we consider the data plan design problem for a single SP, who provides data plans with different lengths of period for consumers with different characteristics of data demands. We propose a contract-theoretic approach, wherein the SP offers a period-price data plan contract which consists of a set of period and price combinations, indicating the prices for data with different periods. We study the optimal data plan contract designs under two different models: discrete and continuous consumer-type models, depending on whether the consumer type is discrete or continuous. In the former model, each type of consumers are assigned with a specific period-price combination. In the latter model, the consumers are first categorized into a finite number of groups, and each group of consumers (possibly with different types) are assigned with a specific period-price combination. We systematically analyze the incentive compatibility (IC) constraint and individual rationality (IR) constraint, which ensure each consumer to choose the data plan with the period-price combination intended for his type. We further derive the optimal contract that maximizes the SP's expected profit, meanwhile satisfying the IC and IR constraints of consumers. Our numerical results show that our proposed optimal contract can increase the SP's profit over 35% comparing with the conventional monthly-period data plan.

Mobile-edge computing (MEC) is an emerging technology for enhancing the computational capabilities of the mobile devices and reducing their energy consumption via offloading complex computation tasks to the nearby servers. Multiuser MEC at servers is widely realized via parallel computing based on virtualization. Due to finite shared I/O resources, interference between virtual machines (VMs), called I/O interference, degrades the computation performance. In this paper, we study the problem of joint radio-and-computation resource allocation (RCRA) in multiuser MEC systems in the presence of I/O interference. Specifically, offloading scheduling algorithms is designed targeting two system performance metrics: sum offloading rate maximization and sum mobile energy consumption minimization. Their designs are formulated as non-convex mixed-integer programming problems, which account for latency due to offloading, result downloading, and parallel computing. A set of low-complexity algorithms are designed based on a decomposition approach and leveraging classic techniques from combinatorial optimization. The resultant algorithms jointly schedule offloading users, control their offloading sizes, and divide time for communication (offloading and downloading) and computation. They are either optimal or can achieve close-to-optimality as shown by simulation. The comprehensive simulation results demonstrate that considering of I/O interference can endow on an offloading controller robustness against the performance-degradation factor.

Interference alignment (IA) has been shown as an important technique to achieve a linear capacity scaling in wireless communications. However, the IA scheme over finite signaling dimensions for a general multiple-input-multiple-output (MIMO) X channel is still rarely studied. The main challenge of MIMO X channels is that the two sets of conditions for IA, namely the interference nulling conditions and the rank preservation conditions, get coupled. The usual IA methods for the interference channel and the broadcasting channel cannot be applied anymore. In this paper, we show that the rank preservation conditions can be replaced by a group of specific rank conditions, under which the IA problem is simplified. Then, based on this technique, an iterative algorithm of IA is designed for the MIMO X channel. The algorithm is designed with limited signaling dimensions. From the simulation results, we find that the algorithm has good performances even under limited signal-to-noise ratio.

This paper considers the downlink channel of multicell multiuser multiple-input single-output systems with arbitrary architecture. We aim to maximize the minimum weighted signal-to-interference-plus-noise ratio (SINR) through user-base station (BS) association, coordinated beamforming among BSs, and power control subject to per BS power constraints. The problem is known to be NP-hard. In the high-SNR regime, where the performance is interference-limited, we globally optimize the upper bound, which is achieved asymptotically, of the balanced SINR. In the low-SNR regime, where the performance is restricted by the tightest per BS power constraint, we balance the transmit power using relaxed integer programming and group sparse optimization techniques. The two goals can be integrated to achieve a universally better performance. From the perspective of optimization methods, the corresponding algorithms are based on Lagrangian relaxation and are guaranteed to converge. Simulation results show that the proposed algorithms outperform the existing algorithms designed for the downlink single-input single-output systems.

We study the user association and scheduling problem in multi-cell multi-user multiple-input multiple-output systems with dynamic traffic. Two successive linear beamforming schemes, namely, zero forcing-successive interference cancellation and adaptive orthogonal beamforming, are adopted in the uplink channel and the downlink channel, respectively. The confidence level of signal-to-interference-plus-noise ratio of a random user is analyzed, in order for base stations (BSs) to decide on the maximum number of active users while guaranteeing the worst-case quality of service. We also derive the conditional expected rate of a random user knowing its local channel state information, in order for users to evaluate the performance and select the BS. Noticing that the performance can be divided into different classes, we model the user association and scheduling problem as an auction game. The auction is equipped with a strategy-proof pricing scheme and can be conducted in a distributed fashion with a limited information exchange. Shown by the simulation, the proposed mechanism has the dominant performance in respect of social welfare, throughput, and load balancing.

In this paper, we study Pareto optimality for multiuser relay networks. We adopt single-stream transmission and amplify-and-forward relays. First, with fixed relay processing matrices and transmit and receive beamforming vectors, we study Pareto optimality with respect to the power of the transmitters. Based on the signal-to-noise-plus-interference ratio (SINR) balancing analysis, we give a necessary and sufficient condition for a set of SINRs to be Pareto optimal. Second, we consider Pareto optimality with respect to the relay processing matrices, where the power of the transmitters and the transmit and receive beamforming vectors is fixed. Taking advantage of multi-objective optimization analysis, we present a necessary and sufficient condition for a set of SINRs to be Pareto optimal. We also give a necessary condition to check whether Pareto optimality is fulfilled. Finally, with fixed relay processing matrices, we study Pareto optimality with respect to the transmit and receive beamforming vectors. Simulations show that our proposed algorithms outperform the compared schemes.

This paper proposes the ideas of interference alignment with physical-layer network coding (IAPNC). While conventional interference alignment (IA) aligns interfering signals and then treats them as noise, IAPNC treats the aligned signals as combined signals and utilizes them to convey data streams to receivers. We show the ideas of IAPNC in multi-hop multiple-input multiple-output (MIMO) channels consisting of 3 transmitter-receiver pairs and 2 half-duplex decode-and-forward (DF) relays in each intermediate layer, i.e., 3-2 ... 2-3 MIMO channels. We consider three users want to convey independent data streams to distinct destinations with the aid of relays. Simulation results show that IAPNC scheme in the 3-2-3 MIMO channel achieves the same degrees of freedom (DoF) as conventional IA scheme in the 3-3-3 MIMO channel which has one more relay. The results also show that the end-to-end sum-rate of IAPNC scheme outperforms that of zero-forcing (ZF) filtering scheme in medium-to-high signal-to-noise ratio (SNR) regime for the 3-2-3 MIMO channel. The performance improvement of IAPNC scheme mainly comes from efficient utilization of signals in interfering signal subspaces.

We study the simultaneous wireless information and power transfer (SWIPT) problem for the multiuser two-way relay network. We adopt power splitting (PS) receivers and one amplify-and-forward (AF) relay. Aiming at minimizing the total relay transmit power under constraints of received SINR and harvested power, we optimize the PS ratios and the relay processing matrix at the same time. We first reformulate the optimization problem as a quadratically constrained quadratic program (QCQP) and analyze the feasibility of this QCQP. We find several necessary conditions for the problem to be feasible. Since solving the problem or just finding a feasible point of this problem is NP-hard, we use the feasible point pursuit-successive convex approximation (FPP-SCA) to obtain an approximately optimal solution.

We study the uplink multi-user multiple-input multiple-output (UL MU-MIMO) channel in a wireless local area network (WLAN) with multiple access points (APs) and multiple stations (STAs). APs are allocated with different orthogonal frequency-division multiplexing (OFDM) subcarriers. Each AP has multiple antennas and applies zero-forcing successive interference cancellation (ZF-SIC) as in [1] for spatial multiplexing in its own MU-MIMO channel. STAs arrive and depart dynamically. The joint AP selection and STA scheduling problem is modeled as an auction game. STAs perform AP selection to maximize their utility, which is a function of the expected rate, the valuation and the price charged by APs. APs schedule STAs to maximize the social welfare and set price to ensure truthful revelation of STAs' valuation. The auction is conducted in a distributed fashion and requires subtle information exchange. Shown by the simulation, the proposed mechanism has superior performance in respect of social welfare, throughput and load balancing.

We study the multiuser relay network and propose a distributed power-beamforming optimization algorithm to optimize the relay revenue and the utility of each user pair at the same time. The distributed manner of this algorithm is due to the pricing mechanism that provides the possibility of matching between user utility optimization and relay revenue maximization. Our algorithm consists of a power allocation stage and a beamforming stage. In the former stage, the relay controls the power of different users via pricing. In the latter stage, beamforming vectors are updated to maximize the individual SINR based on the network duality derived for the multiuser relay network. Simulation results show that higher relay revenue and user utilities are achieved by our power-beamforming algorithm than by the compared algorithms.

We investigate a system consisting of a single source, a single destination and multiple layers of parallel relays. The system can be considered as a combination of a broadcast channel, a multi-user multiple input multiple output (MIMO) interference channel and a multiple access channel. Interference alignment (IA) scheme is used throughout the whole system and perfect channel state information (CSI) is assumed available at all transmitters and receivers. In this paper, we consider two layers of parallel relays and our objective is to optimize the power allocation of each transmitter to maximize the total transmission rate of the whole system. Iterative algorithms are derived to find the optimal precode and decode vectors.

Interference alignment (IA) scheme for multiple input multiple output (MIMO) system has been widely studied. In this paper, we introduce a novel metric known as Group-SINR (GSINR), which is also the average SINR per stream of one group under perfect interference alignment. Based on the GSINR characterization, we develop a Max-GSINR algorithm to jointly optimize Transmitter-Receiver (Tx-Rx) beamformers and allocate group power subject to total power constraint. The Max-GSINR algorithm is guaranteed to converge and implementable in a distributed manner in reciprocal channels. Results show that the proposed algorithm achieves higher average SINR per stream under fixed group power allocation, and higher sum rate under uneven group power allocation.

We study a system where a base station (BS) with an antenna array serves multiple users. Each user has a single antenna. Due to practical limitations, each antenna of the BS has an individual power constraint. Assume that only long-term channel state information (CSI) is available. The objective is to minimize the downlink transmission power under the users' signal-to-interference-plus-noise ratio (SINR) constraints and the per-antenna power constraints. Mathematically, the objective is to find the optimal solution of a non-convex optimization problem. With dual decomposition, the problem is decomposed into two levels. For the lower level problem, we propose a novel iterative method based on the theory of matrix pencils. For the higher level problem, we propose a subgradient method and prove its convergence. Assembling all the computation methods, we obtain a beamforming and power control algorithm. At last, numerical results are provided to illustrate the performance of the algorithm.

In this work, we consider a cellular cell with D2D links being activated. Besides, the transmission scheme is assumed to be OFDM. With the underlay in-band spectrum sharing scheme, the D2D links can access the spectrum occupied by the cellular links. Hence, the D2D links may interfere with the existing cellular links. Moreover, since the D2D links' channels could overlap, the D2D links are modeled as parallel Gaussian interference channels. Our objective is to design a power control algorithm to maintain the existing cellular links' performance and to maximize each D2D links' transmission rate. The existing cellular links' performance is measured by the outage probability of each subchannel. We formulate a generalized Nash game which is analyzed with the variational inequality theory. The players of the game is the D2D links and the coupled constraints of the players are the outage probability constraints of the cellular links. However, the problem is a non-convex one. By observing the problem structure, we are able to propose an efficient heuristic algorithm which can be implemented distributively. In the end, the performance of the algorithm is demonstrated with simulation results.

In this paper, we consider a multi-cell network where every base station (BS) serves multiple users with an antenna array. Each user is associated with only one BS and has a single antenna. Assume that only long-term channel state information (CSI) is available in the system. The objective is to minimize the network downlink transmission power needed to meet the users' signal-to-interference-plus-noise ratio (SINR) requirements. For this objective, we propose an asynchronous distributed beamforming and power control algorithm, which provides the same optimal solution as given by centralized algorithms. To design the algorithm, the power minimization problem is formulated mathematically as a non-convex problem. For distributed implementation, the non-convex problem is cast into the dual decomposition framework. Resorting to the theory about matrix pencil, a novel asynchronous iterative method is proposed for solving the dual of the non-convex problem. The methods for beamforming and power control are obtained by investigating the primal problem. Finally, simulation results are provided to demonstrate the convergence and performance of the algorithm.

We study the downlink beamformer design problem considering different real-time requirements of users. The problem is formulated mathematically as a non-convex optimization problem. The objective is to maximize the minimum signal to interference plus noise ratio (SINR) of the non-real-time service users, considering the SINR constraints of the real-time users and the total transmission power constraint. We carefully study the feasibility conditions for the original non-convex optimization problem. The results provide both theoretical and physical insights into the beamformer design problem. After a series of equivalence and relaxation of the original problem, the solution can be found via bisection feasibility checking and solving a set of geometric programming (GP) problems obtained from randomization. Combining the feasibility checking algorithm and the randomization procedure, we have our algorithm for computing the beamforming vector. Numerical results are provided for illustration.

In this paper, we study the competition among the primary users (PUs) in a Dynamic Spectrum Leasing (DSL) system where multiple PUs lease spectrum to the secondary users (SUs) for monetary rewards. Considering the uncertainties of the PUs' channel gains and of the SUs' demands for spectrum, the competition among the PUs is formulated as a stochastic Nash game. Due to the uncertainties, the PUs aim to maximize their long term utilities which are related to the income from leasing spectrum and to their quality of service (QoS) conditions. Resorting to the stochastic variational inequality (SVI) theory, we investigate the existence and uniqueness of the stochastic Nash equilibrium (SNE). Based on the stochastic approximation theory, we propose a distributed learning algorithm for computing the SNE of the game. Rigorous convergence proof of the algorithm is provided. Besides, the features of the algorithm are demonstrated via numerical results.

In this paper, we extend the distributed power control problem for one-to-one transmissions in Gaussian interference channels to one-to-many transmission scenarios. We assume a user-centric wireless network where the end users play the roles of decision makers. We formulate the power control problem as a noncooperative game. New challenges arise due to the coupling issues among power strategy spaces of distributed end users, which make standard Nash equilibrium based noncooperative game approach inapplicable. Indeed, our problem turns out to be a generalized Nash equilibrium problem (GNEP). Resorting to variational inequality theory, we show several fundamental properties of the GNEP. Then we propose a penalty-based distributed algorithm IP^2JA, which possesses favorable properties for practical implementation. Numerical results are provided to verify our arguments and proposed algorithm.

Spectrum sharing and power allocation are interesting issues in cognitive radio networks. In our paper, we introduce a pricing scheme to demonstrate the spectrum sharing scenario between primary users and secondary users. We demonstrate the joint optimal allocation scheme of both spectrum and power for secondary users buying spectrum from multiple primary users. The joint optimal allocation scheme maximizes secondary users' utilities while primary users' utilities meet certain requirements. Moreover, we illustrate a distributed resource allocation scheme to jointly optimize usage of power and spectrum when each secondary user does not know others' buying strategies. Our simulation results show that under some utility requirements, the joint optimized resource allocation scheme has 3dB gain over that under even resource allocation.

Adaptive cooperation with conflict-free relay assignment in a network setting is a challenging problem. The problem becomes even more difficult when users have quality-of-service (QoS) requirements but the available spectrum resource is limited, where admission control may be required. In this paper, we jointly study relay assignment and admission control in cooperative radio networks (CRNs). A one-stage optimization problem is formulated to integrate our multiple objectives. Since the problem in question is prohibitively difficult, we resort to an appropriate decomposition approach after a careful analysis on the structure of the formulated problem. A simple distributed algorithm is also proposed to overcome the inherent drawbacks of the centralized scheme. Numerical results demonstrate the benefits attained by jointly considering relay assignment and admission control, as well as the effectiveness of our proposed algorithms.

Relayed transmission is an interesting topic in cooperative diversity. Some researchers propose power allocation schemes for relayed transmission by only considering transmission through a fixed relay node. In our paper, we analyze a relayed transmission through a mobile relay node. We build up a transmission system model and determine the long term distribution characteristics of the movement of the mobile node. We also calculate the time-varying data rate for transmission through the mobile relay node and demonstrate the improvements due to mobility. Moreover, we illustrate the optimal power allocation for relayed transmission through the mobile relay node. Our results show that under some practical data rate requirements, we have 3 dB gain compared with transmission under even power allocation.

We investigate the cooperative routing problem in the single-source single-destination systems for the delay-sensitive case by using the proposed “Cooperative Graph” approach. The “Cooperative Graph” enumerates all possible cooperative routes in the problem. In a “Cooperative Graph”, a vertex represents a route. For each route, optimal scheduling is required under a total end-to-end duration constraint. As a result, the “Minimum Energy Cooperative Route”, which is the cooperative route with the minimum total energy consumption, can be obtained. “Maximum Hops Cut” with maximum number of hops to be K (K-MRC), which is a heuristic scheme to enumerate cooperative routes with no more than K hops, is suggested in the paper. Simulations indicate that the route obtained by using 4-MHC consumes energy that is close to the optimal value.

We investigate a scheme called multi-flow transmission, which extends selection diversity. Unlike selection diversity, a user receives/transmits distinct information from/to multiple base stations. We allow different amounts of information to be transmitted by/to different base stations. Moreover, we consider MIMO channels. As examples, we consider max-min throughput and proportional fairness for the utility functions. For the MIMO transmissions, we consider capacity-achieving scheme and beamforming. We compare the total utility for both multi-flow transmission and selection diversity. Multi-flow transmission provides an upper bound on the total utility for selection diversity. In our simulations, multi-flow transmission outperforms selection diversity by at most $52\%$ and $30\%$ in terms of the average total utility for max-min throughput and proportional fairness, respectively.

Cooperative transmission protocols using a flow optimization approach for a general multi-node half-duplex wireless relay network are presented. The proposed design involves solving a convex flow optimization problem on a graph that models the relay network. Two protocols are developed using the techniques of broadcasting (BC), multiple-access (MA), and time sharing (TS), and both are shown to achieve the optimal diversity-multiplexing tradeoff (DMT). Simulation results are used to quantify the performances of the proposed protocols in terms of outage probabilities, and to compare them against the max-flow-min-cut bound.

This paper investigates how much the rate region of the two-user Gaussian interference channel can be enlarged by allowing the two source nodes to cooperate. Two cooperative transmission schemes are proposed, based on dirty-paper coding and the assumption that the radio bandwidth is partitioned into two parts, and each part is utilized by one source node. The achievable rate regions and the outage performance of these two schemes are compared with the simplified Han-Kobayashi scheme, which is an efficient coding scheme for the interference channel. Simulation results show that in some channel realizations, the rate region of the Han-Kobayashi scheme is a subset of the rate regions of our two proposed cooperative transmission schemes. Furthermore, a significant gain in outage performance can be obtained, as the cooperative schemes have twice the diversity order of the simplified Han-Kobayashi scheme. While both cooperative schemes are able to yield large diversity gain, one of them can be implemented by simple decoder. Besides, it has an efficient algorithm for maximizing its weighted sum rate, and can be extended easily to the multi-channel case.

We consider the downlink transmission with multiple sources. Each source transmits over an orthogonal channel by using cooperative broadcasting. Each destination is required to receive and decode a given amount of information. A destination can receive different partitions of information from multiple sources. The sources cooperate and allocate the flow rates along each link to minimize the maximum individual transmit power of each source. To simplify the computations involved in the optimization process, we suggest a sub-optimal algorithm. Simulation results indicate that our optimal scheme gives improvements between 4.2 dB and 5.6 dB over the single-source scheme, where a destination selects only one source for transmission, in our considered networks and rate requirements. Our sub-optimal algorithm suffers a loss of below 0.4 dB compared with the optimal one. The expressions involved in the sub-optimal algorithm are less complicated and it requires M iterations, where M is the number of destinations, and a bisection search in each iteration.

We investigate the power allocation problem in interfering wireless networks. In the network, there are multiple sources with each transmitting to multiple destinations. By proposing a game, we are able to allocate power for each destination in a distributed way. Due to the possibility that the feasible set can be empty, our game can terminate the power allocation process upon detecting any infeasibility. We show that our game allocates power in a way such that the total transmit power is minimized. In general, the game works for interfering networks with arbitrary interference cancellation for the transmissions from the same source. As examples, we consider two schemes, namely interference transmission and cooperative broadcasting interference transmission. For converging cases, the schemes require within 60 iterations for convergence for over 90% of realizations. For cases without feasible solutions, the schemes require within 6 iterations for detecting the infeasibility for over 90% of realizations.

We investigate the relaying problem in parallel relay networks consisting of a source sending information to a destination via multiple relays. To exploit the broadcasting nature of the wireless channel, the source broadcasts to the relays and the relays forward the decoded information to the destination. The flow rates on all links in the network are selected to minimize the average transmit power required. We consider two optimal protocols, respectively in which the relays forward the decoded information phase-synchronously and phase-asynchronously. It turns out that not all relays forward information in the optimal phase-asynchronous protocol. Using this fact, we propose a sub-optimal protocol employing at most two relays. In phase-synchronous relaying, the optimal protocol gives around 1dB and over 3 dB improvements compared at the outage probability of 10-4with respect to the single-relay optimal protocol at low-rate and high-rate requirements, respectively. Again, we propose a two-relay sub-optimal protocol for phase-synchronous transmission. Simulation results indicate that these sub-optimal protocols give performances that are close to the performances of the optimal protocols and superior to that of the single-relay optimal protocol.

We investigate the problem of flow optimization in a parallel relay network which consists of a single source sending information to a single destination through multiple relays. The source transmits to the relays using cooperative broadcast and the relays decode and forward the received information to the destination using multiple-access transmission. The relays are operating in half-duplex mode. The flow rates on all links in the network are assigned in a way such that the minimum average transmit power required is minimized. It turns out that not all relays participate in optimal transmission. Using this fact, we propose a sub-optimal relay protocol that employs at most two relays. Simulation results indicate that this sub-optimal protocol gives performance that is close to the performance of the optimal protocol employing multiple relays and superior to that obtained by existing single-relay selection protocols.

A pricing game is derived for cooperative transmission among selfish nodes. Each source node is assigned an orthogonal channel. By overhearing other channels, the source nodes can help relaying other nodes’ messages at a rate based on the received payments. At Nash Equilibrium, the total transmission power is minimized. We also propose a simplified implementation for low rate regime. Simulation results illustrate the significant improvement of power consumption over direct transmission scheme and the high convergence rate of our implementation.

In this paper, a cooperative transmission design for a multi-node wireless cluster network is presented. It is assumed that the nodes operate in half-duplex mode and that channel information is available at the nodes. The proposed design involves solving a convex flow optimization problem on a fully connected graph that models the cluster network. A much simpler generalized routing protocol based on the above design is also presented. Both the proposed flow-optimized protocol and the generalized routing protocol are shown to achieve the optimal diversity-multiplexing tradeoff for the cluster network. Moreover, simulation results are presented to quantify the gap between the performance of the proposed protocols and that of a max-flow-min-cut type bound, in terms of outage probability.

There is recently an increasing popularity in the use of wireless ad hoc networks, especially for sensor networks. However, these networks are susceptible to fading, interference and limited power supply. In this paper, we consider the issue of cooperative routing under the effect of both multi-user interference (MUI) and fading in ultra-wideband (UWB) networks. We first generate a single path route from any available routing algorithms. Based on this single path route, our cooperative routing algorithm is executed to see whether nodes which 'overhear' the information should cooperate to alleviate the effect of fading, and thus improve outage performance. From our result, it is shown that our cooperative routing algorithm reduces the average transmit energy by 8dB at 3% of outage.

In this paper, we study the multiple-source multiple-relay cooperation system. In the transmission protocol we proposed, the relay nodes not only decode and forward the symbols from the source nodes, but also encode the incoming symbols according to the cooperative code, resulting in higher efficiency and flexibility compared with previous repetition-coded cooperation system. We analyze the impact of the noisy interuser channel between the source nodes and the relay nodes on the system performance, and then propose an adaptive cooperative coding scheme to compensate for it. The analysis and the simulation results show that the proposed system achieves full diversity.

In this paper, we analyze the decode-and-forward cooperative multihop transmission. Different from conventional multihop transmission, each node achieves the spatial diversity by combining all the independently fading symbols from previous nodes on the route line. We consider the possibility for the relay node to forward error-detected symbols, and propose an adaptive transmission protocol to compensate for it. The bit error rate is derived for the cooperation system, and the comparison with the conventional multihop transmission shows that this system can achieve full diversity order. Practical cooperative range and the impact of relay nodes distribution on the system performance are discussed for the practical implementation

In this paper, a modified learning algorithm to obtain better generalization performance is proposed. The cost terms of this new algorithm are selected based on the second-order derivatives of the neural activation at the hidden layers and the first-order derivatives of the neural activation at the output layer. It can be guaranteed that in the course of training, the additional cost terms for this algorithm can penalize both the input-to-output mapping sensitivity and the high frequency components to obtain better generalization performance. Finally, theoretical justifications and simulation results are given to verify the efficiency and effectiveness of the proposed learning algorithm.

In this paper, we derive closed-form expressions for the bit error rate (BER) for a distributed space-time block coded system, considering both perfect and imperfect channel estimation. For the special case of single relay, under the assumption of an appropriate power control, we show that the diversity order of two is obtained, achieving the maximum diversity order. Our analysis also demonstrates the existence of an error floor due to channel estimation errors.

In this paper, a goal programming model is proposed for general downlink channel assignment. Unlike many channel assignment schemes with quality of service (QoS) requirements constraints, QoS requirements are formulated as mathematical functions in the objective functions of our schemes. This approach allows greater flexibility for QoS formulation. With this formulation, when there is insufficient amount of resource, the proposed schemes can provide the compromise solutions more conveniently without explicit admission controls. As a result, resource can be allocated more efficiently according to this model. Based on this model, the channel swapping algorithm and the best-first-assign algorithm are proposed. Simulation results show that with the proposed algorithm, less channel resource is required to meet the client demand.

This paper presents a novel BP-based image retrieval (BPBIR) system, which is based on the observation that the images users need are often similar to a set of images with the same conception instead of one query image and the assumption that there is a nonlinear relationship between different features. If users aren't satisfied with the retrieved results, relevance feedback method is used to enhance the performance of the proposed system by changing the weights of the BP neural networks. In addition, we discuss some divisional methods to give rough information on the spatial color composition. Finally, we compare the performance of the proposed system with other systems. Experimental results show the efficacy of the proposed system.

This paper considers the phase control problem in multiple-input single-output antenna system (MISO) with finite number of control bits. We propose an optimal algorithm which requires only O(nTlog nT) of computations, where nTis the number of transmit (input) antennas

We consider the convergence issues of distributed power-control algorithms for mobile cellular systems. A convergence theorem for power-control algorithms of canonical type is proved. Our result generalizes Yates' framework and provides a new outlook on the problem. The general applicability of the theorem is demonstrated by showing that many well-known distributed algorithms are canonical. Furthermore, by devising some new discrete algorithms, we exemplify how the theorem can be used to aid new design.

Code assignment on the downlink is considered for multi-carrier CDMA systems. In a fading channel, a user may experience different performance with different assigned codes. Therefore, a good assignment, which effectively utilizes the channel characteristics, may improve the performance for the user. We determine optimal assignments that maximize the total throughput or minimize the total latency. Simulations show that major improvements can be obtained through suitable code design and optimal assignment.

We consider the convergence issues of distributed power control algorithms for mobile cellular systems. A convergence theorem for power control algorithms of canonical type is proven. Our result generalizes Yates' (1995) framework and provides a new outlook on the problem. The general applicability of the theorem is demonstrated by showing that all the well-known algorithms are canonical. Furthermore, by devising a new discrete algorithm, we exemplify how the theorem can be used to aid new design.

The problem of transmitter adaptation in the form of adapting the spreading sequences and the transmission powers of different users for a multicode direct-sequence code division multiple access (DS-CDMA) system is considered. Particular attention is given to a distributed algorithm, which updates each pair of transmitter and receiver without information from other users. The transmitter adaptation problem and the algorithm are studied from the viewpoint of a single user, as well as the viewpoint of the whole system. The algorithm is shown to give either the optimal sequences or a choice of sequences that is close to the optimal one. Simulation results show that major improvement in performance can be obtained with the proposed transmission adaptation scheme. The effect of restricting the choice of sequences to polyphase sequences is also considered.

The problem of adapting the spreading sequences and the transmission powers of different users in a multicode direct-sequence code division multiple access (DS-CDMA) system is considered. Particular attention is given to a distributed algorithm which updates each pair of transmitter and receiver without information from other users. The sequence adaptation problem and the algorithm are studied from from the viewpoint of a single user as well as the viewpoint of the whole system. The algorithm is shown to give either the optimal sequences or sequences that are close to the optimal ones. The effect of restricting the choice of sequences to polyphase sequences is also considered.

We investigate practical issues for implementing power control together with signature sequence adaptation in direct sequence code division multiple access (DS-CDMA) systems. A practical decentralized power control algorithm with sequence adaptation is proposed. In this algorithm, each user independently and simultaneously implements power control and alters his/her signature sequence according to the information collected from the channel (simply by sampling the received signal). No information from the other users is required. A simplified version of the algorithm with low complexity is also developed in this paper. Simulations show that with a small number of samples per iteration, our decentralized algorithms with matched filters perform much better than power control with minimum mean square error (MMSE) receivers only.

In this paper, we consider fixed wireless access (FWA) systems with antenna arrays. We consider both the downlink and the uplink. Multiple antennas are used at the transmitters, as well as the receivers. We demonstrate that with antenna arrays, the user capacity of a system can be dramatically increased, allowing multiple users to use the same narrowband spectrum simultaneously. In general, it may be difficult to determine the optimal transmission vectors. We consider a simple algorithm that often yields desirable solutions. We also consider the special case of co-located users. The communication system then reduces to a point-to-point communication link where the optimal transmission vectors can be determined under some conditions.

We consider transmitter and receiver optimization in multicarrier code-division multiple-access (MC-CDMA) systems under Rayleigh fading channels. Receiver optimization is performed in a decentralized manner, while transmitter optimization can be performed through either centralized or decentralized control of the powers of different carriers. Results show that when the number of users is smaller than or equal to the number of carriers, each transmitter often tends to concentrate its power on a different carrier which does not suffer deep fading. The MC-CDMA system then tends to a frequency-division multiple-access system with near-optimal frequency assignment. When the number of users gets large, each user tends to choose more than one carrier, which do not suffer deep fading, while interference suppression is performed across the chosen carriers by the corresponding receiver.

In this paper, we consider transmitter and receiver optimization in multicarrier code division multiple access (MC CDMA) systems. Receiver optimization is performed in a decentralized manner while transmitter optimization is performed through centralized control of the power of different carriers. Results show that when the number of users is smaller than or equal to the number of carriers, each transmitter should concentrate its power on a different carrier which does not suffer deep fading. The MC-CDMA system then tends to a frequency division multiple access system with near optimal frequency assignment. When the number of users gets large, each user tends to choose more than one carriers, which do not suffer deep fading, while interference suppression is performed across the chosen carriers by the corresponding receiver.

We consider signal reception in multicarrier code-division multiple-access (MC-CDMA) systems. A blind adaptive algorithm is proposed to determine a weight vector which optimally combines the desired signal contributions from different carriers while suppressing noise and interference. No knowledge of the channel conditions (fading coefficients, signature sequences and timing of interferers, statistics of other noises, etc.) nor any training sequence is required. The performance is examined for Rayleigh fading channels. Results show that the proposed algorithm performs well and is robust to the near-far problem. Hence, the results show that MC-CDMA systems are attractive candidates for future CDMA systems.

A linear decentralized receiver capable of suppressing multiple-access interference (MAI) for asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems with aperiodic random signature sequences is proposed. Performance bounds on this receiver are also obtained. Using them as performance measures, the problem of chip waveform selection in DS-CDMA systems with the proposed receiver under the near-far scenario is investigated. In particular, the performance of several practical chip waveforms is compared. An LMS-type adaptive algorithm is developed to obtain the parameters needed in the receiver, which only requires the signature sequence and coarse timing information of the desired user.

We investigate direct-sequence spread-spectrum multiple-access (DS/SSMA) communication systems with general linear modulation and error control coding. By the central limit theorem for martingale difference arrays, it is proved that under general conditions, the overall multiple-access interference (MAI) across different symbol intervals tends to a jointly complex Gaussian random vector as both the number of chips per symbol interval and the number of interferers tend to infinity. The result allows the calculation of the limiting exact error probabilities, and justifies the use of the popular standard Gaussian approximation. The asymptotic properties of the MAI from each individual interferer are also examined. The results lead to another approximation, the conditional Gaussian approximation, which provides better estimates to the error probabilities, especially in near-far situations.

A linear receiver for direct-sequence spread-spectrum multiple-access communication systems under the aperiodic random sequence model is considered. The receiver consists of the conventional matched filter followed by a tapped delay line with the provision of incorporating the use of antenna arrays. It has the ability of suppressing multiple-access interference (MAI) and narrowband interference in some weighted proportions, as well as combining multipath components without explicit estimation of any channel conditions. Under some specific simplified channel models, the receiver reduces to the minimum variance distortionless response beamformer, the RAKE receiver, a notch filter, or an MAI suppressor. The interference rejection capability is made possible through a suitable choice of weights in the tapped delay line. The optimal weights can be obtained by straightforward but computationally complex eigenanalysis. In order to reduce the computational complexity, a simple blind adaptive algorithm is also developed.

Methods to determine the average error probabilities in quadriphase direct-sequence spread spectrum multiple-access (DS/SSMA) communication systems are proposed. The systems being considered employ random signature sequences and arbitrary chip waveforms that are time-limited to one chip interval. The methods range from an algorithm that determines upper and lower bounds with arbitrary accuracy, to simple formulas that provide accurate estimates efficiently. Each method will find applications in different areas. Numerical examples are provided to illustrate each of the methods.

Direct-sequence spread-spectrum multiple-access (DS/SSMA) communication systems with random m-phase sequences, for even m, are considered. By examining the asymptotic behavior of the normalized multiple-access interference (MAI), the authors find that a system with random m-phase sequences, for m>4, should have the same performance as one with random quadriphase sequences asymptotically. However, a system, with random m-phase sequences, for m/spl ges/4, may perform better than one with random binary sequences when the number of simultaneous users is relatively small. A new Gaussian approximation is proposed to estimate the probabilities of error in these systems. In two cases, the new approximation reduces to established results which have been shown to provide close estimates to the probabilities of error.

A direct-sequence spread spectrum multiple access (DS/SSMA) communication system employing continuous phase modulation (CPM) and trellis coding is described and analyzed to obtain an estimate of the error probability. The trellis code provides significant coding gain while the CPM signal format gives desirable spectral properties. The analysis shows that significant improvement in error probability over the standard binary DS/SSMA system can be achieved with relatively simple codes.>