Electrical Capacitance Tomography (ECT), as a rapidly developing process tomography technique, has been widely applied in multiphase flow monitoring, industrial process control, and medical imaging. However, due to the “soft-field” effect, the ill-posed nature of the inverse problem, and limitations in data acquisition. Existing reconstruction methods generally suffer from large reconstruction errors, low accuracy, blurred edges, and pronounced artifacts. To address these issues, this paper proposed a reconstruction method based on fractional-order particle filtering and image super-resolution. Firstly, a fractional-order formulation is introduced into the system equation to better characterize the dynamic behavior of the ECT system, by exploiting the short-term memory property of fractional-order systems, thereby accelerating convergence in the search space. Subsequently, the particle filter is employed to effectively alleviate the “soft-field” effect of the ECT system. By directly approximating the posterior probability distribution of the system state through Monte Carlo sampling, more informative particles can be selected, which enhances reconstruction accuracy and stability. Finally, an image super-resolution model based on Lorentzian function fitting is adopted, where the point spread function is estimated and combined with the Lucy–Richardson algorithm to achieve high-resolution imaging and mitigate edge blurring.In the experimental study, a three-dimensional simulation model was established using COMSOL, and a 12-electrode hardware platform was constructed for validation under various typical flow patterns. The results demonstrate that the proposed method achieves superior image quality while maintaining low computational cost (less than 0.3 s), providing an effective solution for ECT imaging in complex industrial environments.

In the oil and gas related fields, three-phase distribution flow in pipelines is one of the important aspects that need to be continuously monitored in the Industrial Pipeline Internet of Things (IoT). The dual-modality system of Electrical Capacitance Tomography (ECT) and Electromagnetic Tomography (EMT) can reconstruct the three-phase distribution within a pipeline’s cross-section. However, due to mutual interference among three-phase flow media and noise effects, the reconstruction quality of ECT and EMT images is degraded. In this paper, an ECT-EMT image fusion method based on cross-sensitive field optimization and super-resolution preprocessing is proposed. Firstly, unimodal ECT image reconstruction generates virtual image in solid-phase fluid regions, leveraging this information, a cross-sensitive field optimization method combined with the YOLOv8 network is proposed to improve the EMT reconstructed image quality. Compared with traditional fixed-sensitivity-field EMT reconstruction methods, the correlation coefficient increased by an average of 3.8%. Then, a Gaussian second-order derivative super-resolution method is introduced, which uses a Gaussian second-order derivative function to fit fuzzy kernel parameters and deconvolves the degradation matrices of ECT and EMT images, solving the edge-blurring problem in their fusion. Compared to traditional deblurring methods such as Wiener filtering and single Gaussian kernel modelling, PSNR improved by an average of 1.4 dB, while SSIM improved by an average of 0.03. Finally, an image fusion method based on Latent Low-Rank Representation is proposed by extracting global and local feature information from super-resolution preprocessed ECT and EMT grayscale matrices, respectively. Compared to mainstream pixel-level fusion methods such as wavelet transforms, it resolves the issue of performance degradation in traditional fusion methods when observational data is insufficient. All validations were conducted using simulation data from COMSOL, with sensitivity field optimisation adapted to the complex flow patterns of three-phase flow scenarios. The super-resolution module addressed reconstructed images exhibiting low signal-to-noise ratios and blurred edges, whilst the LatLRR fusion method maintained stable fusion performance even under small-sample conditions.

In this work, we investigate the frequency synchronization and system performance in massive multiple-input multiple-output (MIMO) systems with one-bit analog-to digital converters (ADCs). First, we tackle the challenges arising from severe multi-user interference (MUI) and the non-linearity inherent in one-bit ADCs in orthogonal frequency division multiplexing (OFDM) based on Bussgang decomposition and receive beamforming. To assess the accuracy of the CFO estimation, we analyze its theoretical mean square error (MSE) and investigate how quantization noise influences synchronization precision. Additionally, we introduce a multi-user inference (MUI)-plus-noise whitening technique to mitigate the correlation of the equivalent noise. Finally, we derive an approximate expression for the uplink achievable rate using maximal-ratio combining (MRC) detection scheme. Extensive numerical simulations confirm the effectiveness of the proposed approach.

This article presents a novel under-sampling method for ECG signals, aimed at reducing the sampling rate and power consumption in IoT-based ECG wearable devices. The key contribution addresses the common issue of model mismatch in existing methods, which negatively impacts signal reconstruction accuracy. Initially, the ECG signal is modeled as a linear combination of several Gaussian second-order derivative functions, which can be efficiently represented with only a few parameters, thus mitigating the problem of large model matching errors. To further enhance reconstruction accuracy, an improved two-channel finite rate of innovation sampling framework is introduced, effectively addressing the nonideal effects caused by the low-pass filter during sampling. Additionally, a modified annihilating filter reconstruction algorithm is proposed, allowing high-precision signal reconstruction using a small number of sampling points to estimate parameters. The validity of the proposed method is confirmed through simulations with real ECG signals from the MIT-BIH arrhythmia database, and a hardware platform is developed to verify its feasibility in a practical system. Experimental results demonstrate that, compared to the existing methods, the proposed approach significantly reduces reconstruction error (achieving a PRD as low as 2.29% and an SRR of 11.77 dB), and exhibits better robustness in noisy environments.

In oil and gas related industries, multiphase flow in pipelines is one of the important elements that the Industrial Pipeline Internet of Things (IoT) should continuously monitor. However, existing reconstruction methods often are limited by low resolution and blurred edges. In this article, an electrical capacitance tomography (ECT) imaging system based on Lorentz deblurring and particle filtering is proposed to suppress ECT image blurring. First, a deblurring model based on Lorentz function fitting is proposed, capable of effectively improving the blurred edges, through point spread function (PSF) estimation and Lucy-Richardson algorithm. Then, in the image reconstruction process, it is reformulated as an iterative search for effective particles and their associated weights in the state space, combined with Lorentz deblurring model for the optimal solution. Finally, the virtual-instrument-based ECT hardware system based on the principle of modularity, creates the synergistic architecture between the ECT hardware and imaging software, which enables real-time visualization of imaging. Simulation experiments demonstrate that the image reconstruction algorithm outperforms existing methods in terms of relative error and correlation coefficient, effectively suppressing image blur. Moreover, the ECT imaging system proposed can enhance the measurement capacitance accuracy.

This paper investigates an active intelligent reflecting surface (IRS) assisted integrated sensing and communications (ISAC) system in a cloud radio access network (C-RAN). In particular, we focus on the IRS assisted target sensing for the blind area, wherein multiple IRSs are deployed to establish controllable reflective links between the targets and the sensing remote radio heads (RRHs). A novel IRS-assisted location-aware direction-of-arrival (DOA) estimation scheme is proposed, where the BBU (baseband unit) pool simultaneously recovers the DOA from each target to each IRS using the RRH received signals. In general, the channel knowledge between the IRSs and RRHs is hard to acquire, hence we utilize the location information of IRSs and RRHs instead. Specifically, we transform the DOA estimation problem into a mixed one-dimensional and two-dimensional atomic norm minimization (ANM) problem, by which the DOAs can be efficiently extracted. Moreover, a theoretical Cramér-Rao lower bound (CRLB) on the DOA estimation error is also derived to evaluate the performance of the proposed scheme. Finally, simulation results illustrate the effectiveness for DOA estimation by the proposed scheme.

Image deblurring is a technique employed to reduce or eliminate degradation resulting from the effects of impulse response and atmospheric turbulence during remote sensing image acquisition. However, the issue of low recovery accuracy due to inadequate point spread function (PSF) matching is a significant challenge in current deblurring methods. In this letter, a remote sensing image deblurring method based on the differential Lorentzian PSF model is proposed. First, a differential Lorentzian PSF model is proposed, which is able to model the line spread function (LSF) of a real image as a series of pulsewidth variable Lorentzian functions and their differential function combinations. Subsequently, a model parameter estimation algorithm based on the improved zeroing filter is proposed. This algorithm is able to reconstruct the target parameters by utilizing the edge information of the actual image, with the objective of eliminating the matching error of the PSF model in the actual complex scene and improving the parameter estimation accuracy. Finally, a remote sensing image deblurring method is proposed by using the differential Lorenz point diffusion function model combined with the L-R algorithm. The results of the simulation experiments demonstrate that the method proposed in this letter is more effective than existing remote sensing image deblurring methods in terms of image recovery accuracy.

The integration of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) has created new opportunities for efficient data processing and calculating services within the Internet of Things. However, the presence of the active eavesdropper brings serious vulnerabilities to the security calculation of terminal users (TUs), which can eavesdrop on TUs’ confidential content and compromise the quality of offloading calculation. In this paper, we propose an efficient energy consumption minimization scheme for the considered secure UAV-enabled MEC network including an active UAV eavesdropper. While ensuring security calculation for all TUs’ data, the network’s weighted energy consumption is achieved through trajectory and resource optimization, including time, local calculation and offloading calculation allocation. Due to the coupling of multi-variables and the non-convexity of the constraints, the problem is highly challenging to solve directly. To address this, an auxiliary variable is introduced to transform the problem into a more tractable form. The optimizing solution is then obtained through iterative updates, allowing for the convergence towards an optimizing solution. Simulation results show that the proposed scheme exhibits superior performance of reducing the network’s energy consumption compared to the benchmark scheme.

Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure for an intelligent and simplified sixth-generation (6G) wireless network. Nevertheless, to further enhance the capacity and coverage, more radio remote heads (RRHs) as well as high-fidelity and low-latency fronthaul links are required, which may lead to high implementation cost. To address this issue, we propose to exploit the intelligent reflecting surface (IRS) as an alternative way to enhance the C-RAN, which is a low-cost and energy-efficient option. Specifically, we consider the uplink transmission where multi-antenna users communicate with the baseband unit (BBU) pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs. RRHs can conduct either point-to-point (P2P) compression or Wyner-Ziv coding to compress the received signals, which are then forwarded to the BBU pool through fronthaul links. We investigate the joint design and optimization of user transmit beamformers, IRS passive beamformers, and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding. By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation (SDR), we propose a successive convex approximation approach to solve non-convex problems, and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided. Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.

This paper proposes an angle domain framework for the design of an intelligent reflecting surface (IRS) system. The maximum likelihood (ML) principle is applied to derive the estimators for the effective angles among the base station (BS), IRS and user. It is demonstrated that the accuracy of the estimated angles improves with the number of BS antennas. Also, deploying the IRS closer to the BS increases the accuracy of the estimated angle from the IRS to the user. Then, exploiting the estimated angles, we propose a joint design of BS beamforming and IRS beamforming. Simulation results show that our proposed algorithm, which only needs few angle information, achieves nearly the same performance as the algorithm requiring full channel state information (CSI). Moreover, the optimized BS beam becomes more focused towards the IRS direction as the number of reflecting elements increases.

This paper proposes a novel location information aided design framework for multiple intelligent reflecting surface (IRS) systems. Assuming practical and imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order NM2is achieved, where N is the number of BS antennas and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs.

This article proposes a novel location information aided multiple intelligent reflecting surface (IRS) systems. Assuming imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order N M2is achieved, where N is the antenna number and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M, while becomes proportional to M2as non-line-of-sight (NLOS) paths vanish. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs. Finally, an optimal power allocation scheme has been proposed to improve the system performance.

As one of the 5G physical-layer technique candidates, universal filtered multi-carrier (UFMC) supports sliced spectrum communication by filtering a series of subcarriers. The filter design greatly affects the inter-sub-band interference in UFMC and hence is crucial to the overall system performance. Here we first propose a novel UFMC scheme, namely UFMC-AIC, which incorporates active interference cancellation (AIC) into UFMC to considerably reduce the inter-sub-band interference. Then we propose a modified version of UFMC-AIC which optimizes the weighting factors of the interference cancellation subcarriers (ICSs) under a total power constraint. Simulation results show that the modified UFMC-AIC scheme can achieve higher transmission rate and better BER performance than the conventional UFMC either for AWGN channel or for Rayleigh channel. Moreover, we validate that our proposal can significantly reduce the filter design requirements.

Cooperative transmission is an efficient method for wireless sensor networks (WSNs) to decrease power consumption of sensor nodes and combat the fast fading inherent to wireless multipath channels. During the course, a key issue is how a sensor relay handles the balance between assisting other nodes’ communication and accomplishing its own transmission tasks. To address this problem, this study investigates the potential bargaining for resource allocation in WSNs over Rayleigh fading environments by considering energy fairness in cooperative transmission. Specifically, based on the characteristics of Rayleigh fading channel, a relevant system model is built. Then, after analysing the system capacity with a given outage probability over the fading channels, a system utility function for transmit‐time allocation is proposed to address the participants’ energy concern. An iterative algorithm is subsequently achieved by solving the Nash equilibrium. Furthermore, essential analysis and discussion on the pure equilibrium and convergence conditions of the iterative algorithm are provided. Finally, numerical results are presented to evaluate the feasibility and system performance of the proposed framework.

Universal filtered multi-carrier (UFMC) is one of the enabling techniques for 5G, which supports sliced spectrum by filtering successive sub-bands. The filter design is an essential and challenging issue, which is critical to the performance of UFMC. In this paper, we propose a novel UFMC scheme, namely, UFMC-AIC, which incorporates active interference cancellation into UFMC so as to mitigate the inter-sub-band interference. A non-convex optimization problem is formulated, which aims to maximize the overall signal-to-interference-plus-noise ratio performance by adaptively optimizing the weighting factors of the interference cancellation subcarriers under the power constraints. The minorization-maximization method is applied to solve the problem in the ideal case when the sub-band states and the channel state information are perfectly known at the base station. A practical distributed solution is further obtained in a closed form by the Lagrangian dual method, which largely reduces the computational complexity with only slight performance loss compared with the ideal case. Simulation results show that our proposal with lower cost of filter can still achieve better bit error rate performance than the conventional UFMC under different carrier frequency offsets for the Rayleigh block-fading channel.

In the future Internet of Things (IoT), a multitude of objects are to be provided with always-on-line connections and dynamic access to the network services, which brings new challenges to multiple access protocol design. In this paper, a novel multiple access protocol, termed as "multi-carrier rateless multiple access (MC-RMA)'', is proposed to overcome such challenges. Instead of assigning the available resource elements (REs) to the users in a fixed and centralized manner, the base station (BS) simply assigns a specific distribution profile to each user, with which each user randomly chooses a certain number of coded symbols, linearly combines them, and then transmits the resultant signal to BS until receiving an acknowledgement (ACK) from BS. The transmission process of each user, as well as those of all the users as a whole, resembles a special form of linear superposition rateless coding and the user information can be decoded using the low-complexity belief propagation (BP) algorithm. Due to the inherent rate adaptation property of rateless codes, the proposed RMA protocol is quite suitable for dynamic massive access in future IoT.

This paper studies the capacity scaling law of the multi-pair relay network with K source-destination pairs and M relays, where each node is equipped with a single antenna and works in half duplex mode. With the conventional two-slot relaying, the capacity was found to scale as K/2 log (M)+O(1) for fixed K and M → ∞. This paper shows that the capacity scaling law can be further improved to K log (M)+O(1) with successive relaying, as if the relays were full duplex. This scaling law can be achieved by a distributed coherent amplify-and-forward scheme, which only requires local channel state information (CSI) at each relay and statistical CSI at the sources and destinations.

A novel rateless coded Vector Orthogonal Frequency Division Multiplexing (V-OFDM) system for adaptive transmission over wireless channels is studied in this paper. In particular, an iterative receiver with joint V-OFDM equalization and ratelss decoding is designed, which exchanges the logarithmic likelihood information between the belief propagation decoder and the maximum likelihood equalizer or the hard/soft decision based equalizer recursively. Both analytical and numerical results show that the proposed algorithm significantly improves the system performance with a modest complexity.

In this letter, we revisit the pre-coder design problem for the amplify-and-forward two-way relay channel with multiple antennas equipped at each node. Based on generalized singular value decomposition (GSVD), we propose a novel pre-coding technique named Hermitian Relay Pre-coding (HRP) with a relatively low design complexity. Compared with the existing GSVD pre-coding method, HRP can achieve a higher rate by avoiding zero-forcing operation but keeping the same design complexity.

Provisioning reliable wireless services for railway passengers is becoming an increasingly critical problem to be addressed with the fast development of high speed trains (HST). In this paper, exploiting the linear mobility inherent to the HST communication scenario, we discover a new type of spatial-temporal correlation between the base station and moving antenna array on the roof top of the train. Capitalizing on the new spatial-temporal correlation structure and properties, an improved differential space-time modulation (DSTM) scheme is proposed. Analytical expressions are obtained for the pairwise error probability of the system. It is demonstrated that the proposed approach achieves superior error performance compared with the conventional DSTM scheme. In addition, an adaptive method, which dynamically adjusts the transmission block length is proposed to further enhance the system performance. Numerical results are provided to verify the performance of the proposed schemes.

In this paper, we propose a new physical layer network coding (PNC) scheme based on rateless coding for two-way relay systems which consist of two end nodes and one relay. The two-way relay channel is studied from a new perspective of end-to-end channel equivalence, and a novel end-to-end demodulator is proposed based on the resultant end-to-end equivalent channel. Unlike the conventional demodulator for the point-to-point channel, our proposed demodulator takes into account the potential error brought by the PNC mapping at the relay, thus improves the system reliability and throughput. The theoretical end-to-end achievable rate is further derived, which serves as an upper bound for the proposed demodulation scheme. Simulation results validate the high efficiency of the proposed scheme in terms of BER and system throughput.

In this paper, we propose a three-stage rateless coded protocol for a half-duplex time-division two-way relay system, where two terminals send messages to each other through a relay between them. In the protocol, each terminal takes one of the first two stages respectively to encode its message using rateless code and broadcast the result until the relay acknowledges successful decoding. During the third stage, the relay combines and re-encodes both messages with a joint network-channel coding scheme based on rateless coding which provides incremental redundancy. Together with the packets received directly in previous stages, each terminal then retrieves the desired message using an iterative decoder. The degree profiles of the specific rateless codes, i.e., Raptor codes, implemented at both terminals and the relay, are jointly optimized for both the AWGN channel and the Rayleigh block fading channel through solving a set of linear programming problems. Simulation results show that, the system throughput as well as the error rate achieved by the optimized degree profiles always outperforms those achieved by the conventional degree profile optimized for Binary Erasure Channel (BEC) and the previous network coding scheme with rateless codes.

In this paper, we consider an asymmetric time-division multiple access relay system consisting of two sources, one relay and one destination, where the channel conditions and message lengths of the two sources are allowed to be different. To enhance the link robustness and the system throughput, joint network-channel coding (JNCC) is employed with a specially designed rateless code which conducts both the channel coding and network coding simultaneously. In particular, at the sources, messages are rateless coded and then broadcasted to the relay and destination. While at the relay, a novel two-dimensional (2-D) LT code is proposed, which jointly encodes the precoded message bits of the two sources using a 2-D degree profile and at the meantime completes the network coding inherently. Interestingly, the proposed scheme can be degraded to several conventional JNCC schemes based on rateless coding. To further approach the theoretical limit, the corresponding degree profiles implemented at both sources and the relay are jointly designed based on the extrinsic information transfer (EXIT) function analysis. Simulations show that our proposed JNCC scheme with the optimized degree profiles outperforms other JNCC schemes with the conventional profiles both on the BER and throughput performances.

In this paper, we design a joint channel-network coding scheme based on rateless code for the three-stage two-way relay system, where two terminals send messages to each other through a relay between them. Each terminal takes one of the first two stages to encode its message using a Raptor Code and then broadcasts the result into the air, respectively. In the third stage, upon successfully decoding the corresponding messages, the relay node re-encodes them with the new Raptor Codes, and then XORs the outputs and broadcasts the result to both terminals. Together with the packets received directly in previous stages, each terminal then retrieves the desired message using an iterative decoder. Here, the degree profiles of the Raptor Codes used at each node are jointly optimized through solving a set of linear programming problems. Simulations show that, the system throughput achieved by the optimized degree profiles always outperforms the one with conventional degree profile optimized for binary erasure channel (BEC) and the conventional network coding scheme with rateless coding.

This paper considers the power allocation problem in a relay-assisted Time-Division-Duplex-based multiple-cell system. In such a system, the achievable data rate of each user is not only coupled with those of others due to the inherent dynamic spatial frequency reuse therein, but also coupled between consecutive time slots due to the required two-hop transmission, which results in the so-called "spatial-coupling" and "time-coupling" nature, respectively. To address both of the two coupling effects in a relay assisted TDD cellular system, we formulate an optimal power allocation problem for the asynchronous scenario of the system where different cells work independently. We observe that the problem is non-convex and NP-hard, but can be converted to Geometric Programming (GP) problems and solved by interior-point methods. In case there is no central controller and/or massive information exchange among cells is prohibitive, game theory is employed to derive the distributed solutions. Finally, we validate the proposed algorithms by extensive simulations.

To improve the performance of a relay system with multiple channels, the following issues should be addressed. Namely, how to allocate the power at source and relay to subchannels, how to pair subchannels of the first and second hops, and which users should be scheduled to which subchannel pair. Considering these issues in the design of an optimal joint resource allocation scheme in orthogonal channels, in this paper we study a multi-user network with single regenerative relay node. A stochastic optimization problem to maximize system ergodic throughput with joint transmission power constraint and user average data rate is formulated. To satisfy user average data rate request, a scheme with a weighted factor associated to each user at each time slot is proposed. The stochastic approximation method is utilized to estimate this weighted factor and the proof of optimality is given. With the help of this weighted factor, the problem is converted into a deterministic optimization problem in each time slot and the Lagrange dual method can be employed to derive the optimal solution. Finally, the Stochastic Optimal Programming (SOP) is used to evaluate the performance by computer simulations.

In this paper we study the problem of relay-enhanced cell (REC) coverage for which relay location is optimized to maximize the achievable REC radius. The problem is investigated for both Rayleigh and Rician relay fading channels, under a pre-determined user's outage probability constraint. We propose a statistical approach to formulate the problem and develop an optimization algorithm for it. The analytical derivation is justified by numerical simulations.

In this paper, we will study the joint problem of power allocation, relay selection and subchannel pairing in OFDM based amplify-and-forward multiple relays system. The optimization problem of maximizing system capacity under joint power constraint at source and relays is firstly formulated. Then, based on Lagrangian dual method, an optimal algorithm to the problem is derived with high SNR assumption. Both computational complexity and dual gap are analyzed. Through simulations, we show that the performance of the proposed algorithm is perfectly matched with that of exhaustive search method and the computational complexity of the proposed algorithm is acceptable for practical implementation.

In the presence of multiple non-regenerative relays, we derived optimal joint power allocation, relay selection, and subchannel pairing schemes in orthogonal frequency division multiplexing (OFDM) based wireless networks. The Lagrange dual method was employed to design the optimal algorithm. First, the optimization problem was formulated for the single-relay system and the optimal centralized algorithm was presented by resolving the dual problem. Next, the optimal algorithm for a multi-relay system was proposed in a similar way. Compared with the exhaustive search method, the computational complexity of the proposed optimal algorithms was reduced from non-polynomial to polynomial time. Finally, the centralized algorithm was extended to the distributed algorithm, which was more feasible for the practical system. Simulation results verify our analysis.

Relay selection and subchannel pairing are important issues in OFDM based cooperative wireless systems to improve the system performance and reliability. Most of the relay selection schemes in modern literatures are centralized under the assumption that the overall channel condition is known at the source node, each relay node and the destination node which is impractical. In this paper, we will propose a low computational complexity distributed relay selection and subchannel pairing algorithms under limited channel state information. Through the computer simulation, we found that under the limited channel state information constraint, the proposed algorithm can achieve a much better system performance than the traditional relay selection and subchannel matching schemes and earn most of the system performance of the optimal one.