This paper considers recovering a continuous angular power spectrum (APS) from the channel covariance. Building on the projection-onto-linear-variety (PLV) algorithm, an affine-projection approach introduced by Miretti \emph{et. al.}, we analyze PLV in a well-defined \emph{weighted} Fourier-domain to emphasize its geometric interpretability. This yields an explicit fixed-dimensional trigonometric-polynomial representation and a closed-form solution via a positive-definite matrix, which directly implies uniqueness. We further establish an exact energy identity that yields the APS reconstruction error and leads to a sharp identifiability/resolution characterization: PLV achieves perfect recovery if and only if the ground-truth APS lies in the identified trigonometric-polynomial subspace; otherwise it returns the minimum-energy APS among all covariance-consistent spectra.

Orthogonal Time Frequency Space (OTFS) modulation is a promising technology for high-mobility 6G communications, yet its practical implementation faces challenges due to fractional delay and Doppler shifts, which cause energy leakage, SNR loss, and phase rotation mismatches in the delay-Doppler (DD) domain. Traditional compressed sensing methods suffer from grid mismatch and computational inefficiency in such doubly dispersive channels. The proposed CNN-based doubly fractional channel estimation network (DFCE-Net) introduces: (1) geometric-aware encoder-decoder enabling single-stage sparse reconstruction and spread texture learning, (2) multi-scale modules resolving delay-Doppler ambiguities, and (3) phase-constrained hybrid loss. The framework significantly outperforms OMP and SBL in NMSE across multipath scenarios and SNR variations. The proposed framework offers a high-precision, real-time solution for OTFS channel estimation, advancing reliability in high-mobility applications such as vehicular and satellite communications.

Ambient Internet of Things (IoT), with its low-cost and battery-free characteristics, holds substantial promise for future green IoT. However, such characteristics usually impose a strict constraint on the adopted radio frequency (RF) devices, resulting in undesirable RF link. One of the most important issues is carrier frequency offset (CFO), which adversely affects communication reliability. Noticing the signal sparsity in frequency domain, compressed sensing (CS) provides potential solutions to CFO estimation. In this paper, we decompose CFO into on-grid frequency and off-grid deviation, based on which two estimation algorithms are derived. In particular, the fast maximum likelihood-approximate message passing (FML-AMP) algorithm estimates the off-grid deviation with maximum likelihood estimator (MLE) and the on-grid frequency with AMP algorithm. The key is the derivation of the closed-form marginal distribution of the off-grid deviation, which enables the fast implementation of MLE and so facilitates the design of FML-AMP. To reduce complexity, a Newtonized orthogonal matching pursuit (NOMP) algorithm is further designed, which alternately applies Newton's method to estimate the off-grid deviation and orthogonal matching pursuit (OMP) to estimate the on-grid frequency. Numerical results demonstrate that both algorithms outperform existing methods and approach the Cramér-Rao bound at medium to high signal-to-noise ratio (SNR) regime.

Massive Multiple Input Multiple Output (MIMO) is a cornerstone technology for achieving high capacity and spectral efficiency. A key challenge in frequency division duplex (FDD) massive MIMO systems lies in obtaining accurate downlink (DL) channel state information (CSI), as the absence of uplink (UL)DL reciprocity hinders conventional estimation methods, creating a bottleneck in system performance. To address this issue, various approaches have been developed, broadly categorized into Conversion and Feedback approaches. However, partial reciprocity and quantization loss in feedback codebook design make accurate DL CSI acquisition a persistent challenge. In this paper, we propose a novel CGAN (Conditional Generative Adversarial Network)-based CSI-fusion framework that integrates both the UL channel statistics obtained from sounding reference signals (SRS) and the DL feedback from user equipments (UEs). These two sources of DL CSI-related information are fused and utilized as conditional inputs to CGAN to map the UL channel statistics to DL CSI. The proposed CGAN-based CSI-Fusion framework significantly enhances DL CSI acquisition accuracy, offering a practical solution to overcoming DL CSI acquisition challenges.

We consider the identifiability issue of maximum-likelihood based activity detection in massive MIMO-based grant-free random access. An intriguing observation by (Chen et al., 2022) indicates that the identifiability undergoes a phase transition for commonly-used random user signatures as$L^{2}$,$N$and$K$tend to infinity with fixed ratios, where$L$,$N$and$K$denote the user signature length, the total number of users, and the number of active users, respectively. In this letter, we provide a precise analytical characterization of the phase transition based on a spectral universality conjecture. Numerical results demonstrate excellent agreement between our theoretical predictions and the empirical phase transitions.

Oblique stripe noise widely appears in remote sensing images after image correction, exhibiting arbitrary tilt angles and parallel distribution. Due to its arbitrary randomness in tilt angles and lengths, oblique stripe noise increases the difficulty of detection compared to vertical or horizontal stripe noise. For the first time, we propose a group of oblique stripe noise synthesis and detection algorithms combining imaging mechanisms and deep learning. To get controllable synthetic oblique stripe noise data for training detection model, two sample augmentation methods are presented by the image correction’s imaging mechanisms with new linear transformation and the generative adversarial network algorithm with Cycle-GAN, respectively. A large-scale simulated stripe noise dataset (SOSD, simulated oblique stripe noise dataset) is simulated using these two methods. A new deep learning detection algorithm (RDOS, Robust detection of oblique stripe Noise) is presented considering the presence of oblique stripe noise. RDOS is trained using both SOSD and a real stripe noise dataset, and it obtains the optimal detection model for testing. The experimental results show that the accuracy reaches 82.93%, the recall rate reaches 85.17%, the F1 score reaches 84.04%, the average precision (AP) reaches 82.34%, and the frames per second (FPS) reaches 33.33. Compared with the general line detection models, our model exceeds ~300% in accuracy and ~60% in speed. In the future, the proposed algorithms have great potential for application in various areas such as quality evaluation, image preprocessing, and engineering problems related to multi-angle linear object augmentation and detection.

Integrated sensing and massive connectivity (MC) can potentially provide not only low-cost full-coverage sensing by utilizing the transmit signals of the huge number of devices in the wide areas, but also efficient user activity detection and channel estimation by utilizing the sensed environment/channel information. However, due to the huge number of users, burst traffic, and short packets, random and non-orthogonal multiple access is a natural choice for MC. In this case, efficient receiver design for integrated sensing and massive connectivity is clearly a challenging problem. In this paper, by introducing auxiliary variables properly, we reconstruct the probability model of the system, and then propose an iterative Bayesian receiver structure, consisting of four modules, namely multi-user decomposition (MUD), multipath decomposition (MPD), parameter fusion (PF), and communications and sensing output (CSO). These modules are designed based on message passing, variational inference, sum-product rules, and Bayesian inference, respectively, to compose a novel variational message passing receiver. Numerical results show that the proposed design can obtain high-accuracy positioning and sensing performance as well as improved performance of channel estimation and data communication.

In this paper, we consider asynchronous massive connectivity, where massive low-power and low-rate devices with sporadic activity patterns connect to a multi-antenna access point (AP) in an asynchronous manner. Asynchronous transmission minimizes the amount of coordination between the devices and the AP, and thereby simplifies the transmitter design, yet at the cost of a more challenging receiver design. Specifically, asynchronous transmission results in inter-symbol interference (ISI) since the sampling at AP generally cannot match with the symbol intervals of uncoordinated devices. To enable reliable reception, we develop a turbo approximate message passing (TAMP) algorithm that consists of a channel-signal decomposition (CSD) module and a delay learning (DL) module. The CSD carries out sparse matrix factorization to estimate the channels and the ISI corrupted signals of active devices, and the DL is designed to estimate the delay of each active user and resolve the corresponding ISI based on the Bayesian principle. To refine the delay estimation, we further divide the DL module into symbol-level delay learning (SDL) and sub-symbol-level delay learning (sub-SDL) submodules. In particular, the sub-SDL estimates the residue delays (obtained by taking modulo of the symbol interval) and then finely compensates the ISI. Due to the continuity and randomness of time delay, the receive signal constellation consists of lines and curves instead of discrete points, even if the transmit signal constellation is discrete. To reduce the complexity of soft demodulation, we introduce a truncation and projection based approximation method to simplify the related message calculation. Numerical results demonstrate the superior performance of the proposed TAMP algorithm. Particularly, the TAMP algorithm is able to approach the single-user bound with known user delay.

In this paper, we develop an asynchronous massive access framework. Besides the multiple-access interference issue, asynchronous transmission induces inter-symbol interference effect of the same user that is determined by the unknown user delay. To solve this problem, we propose an Bayesian receiver where the whole receiver is divided into two modules: the channel-signal decomposition (CSD) module and the delay learning (DL) module. The CSD module demixes the transmit signals of different users by leveraging the bilinear generalized approximate message passing (BiGAMP) algorithm, and the DL module is designed to estimate the time delay of each user based on the Bayesian principle. Additionally, due to the continuity of time delay, the constellation of all possible received user signals consists of lines and curves instead of discrete points, even if the original transmit signals are discrete. To reduce complexity, we introduce a truncation and projection based approximation method to simplify the related message calculation. Numerical results demonstrate the superior performance of the proposed scheme. Particularly, the proposed scheme is able to approach the single-user interference-free bound with known user delay.

Capturing complex high-order interactions among data is an important task in many scenarios. A common way to model high-order interactions is to use hypergraphs whose topology can be mathematically represented by tensors. Existing methods use a fixed-order tensor to describe the topology of the whole hypergraph, which ignores the divergence of different-order interactions. In this work, we take this divergence into consideration, and propose a multi-order hypergraph Laplacian and the corresponding total variation. Taking this total variation as a regularization term, we can utilize the topology information contained by it to smooth the hypergraph signal. This can help distinguish different-order interactions and represent high-order interactions accurately.

In this paper, we present several theoretic results on density evolution (DE) for nonbinary spatially-coupled low-density parity-check (SC-LDPC) ensembles when the transmission takes place on the binary erasure channel (BEC). In specific, we establish the duality rule for entropy for the nonbinary variable-node (VN) and check-node (CN) operators in such a scenario. We define the partial order between densities and show that the VN and CN operators exhibit the property of partial order preservation. More importantly, we explicitly construct the potential functions for uncoupled and coupled DE recursions, the forms of which almost coincide with those for binary LDPC and SC-LDPC ensembles over general binary memoryless symmetric (BMS) channels. These theoretic findings greatly facilitate the proof of threshold saturation for nonbinary SC-LDPC ensembles on the BEC. Finally, we develop the threshold saturation theorem and its converse, following the lines established by S. Kumar et al.

For massive machine-type communications, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides possible solutions, yet poses new challenges for efficient receiver design. In this paper, we develop a joint user identification, channel estimation, and signal detection (JUICESD) algorithm. We divide the detection scheme into two modules: slot-wise multi-user detection (SMD) and combined signal and channel estimation (CSCE). SMD is designed to decouple the transmissions of different users by leveraging the approximate message passing (AMP) algorithms, and CSCE is designed to deal with the nonlinear coupling of activity state, channel coefficient and transmit signal of each user separately. To address the problem that the exact calculation of the messages exchanged within CSCE and between the two modules is complicated due to phase ambiguity issues, this paper proposes a rotationally invariant Gaussian mixture (RIGM) model, and develops an efficient JUICESD-RIGM algorithm. JUICESD-RIGM achieves a performance close to JUICESD with a much lower complexity. Capitalizing on the feature of RIGM, we further analyze the performance of JUICESD-RIGM with state evolution techniques. Numerical results demonstrate that the proposed algorithms achieve a significant performance improvement over the existing alternatives, and the derived state evolution method predicts the system performance accurately.

Employing low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays at the receivers has drawn considerable interests in the millimeter wave (mm-wave) system. Since mm-wave channels are sparse in angular dimensions, exploiting the structure could reduce the number of measurements while achieving acceptable performance at the same time. Motivated by the variational Bayesian line spectral estimation (VALSE) algorithm which treats the angles as random parameters, in contrast to previous works which confine the estimate to the set of grid angle points and induce grid mismatch, this paper proposes the grid-less quantized variational Bayesian channel estimation (GL-QVBCE) algorithm for antenna array systems with low resolution ADCs. Numerical results show the near optimal performance of GL-QVBCE by comparing with the Cramèr Rao bound (CRB) and the state-of-art methods.

Massive machine-type communication is one of the most important scenarios for future wireless communications. Due to its huge number of potential users and its bursty packet arrivals, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides a possible solution, and at the same time poses a challenge for efficient receiver design. In this paper, we propose a joint user identification, channel estimation, and signal detection (JUICESD) scheme based on message passing principles to solve the problem. By introducing two types of auxiliary variables, we divide the whole iterative receiver into two modules: one is a linear module leveraging the existing approximate message passing (AMP) algorithm for its low complexity and asymptotic optimality; the other is a non-linear module decoupled for different users. We further discuss the message passing in the non-linear module and between the two modules. Instead of using the conventional Gaussian approximation, we propose to use a structured Gaussian mixture approximation in message updates. With these ideas, an efficient iterative algorithm is developed and analyzed. Numerical results show that the proposed scheme achieves a significant performance improvement over the existing alternatives. Moreover, the complexity of our scheme is linear with the number of users, which is especially suitable for machine type communication with massive devices.

Quantum wireless communications (QWC) is gaining much attention for its incomparable advantages of secure key transmission in free-space scenarios. In this paper, a novel iterative multi-user quantum key distribution (MQKD) non-orthogonal multiple-access (NOMA) system over Poisson atmospheric channels is proposed. Considering the atmospheric attenuation and quantum direct product operation, a MQKD parallel interference cancellation (PIC) approach is designed at the receiver. In particular, a quantum nonlinear Poisson shot noise limited sum-product interference cancellation algorithm is derived. Simulation results show that the proposed scheme is capable of mitigating the multi-user interference effectively, even considering the polarization mismatch. Quantitatively, for the 4-user scenario, it can achieve the classical Bit Error Rate (BER) of 3 x 10-6 at 2.5 km distance with polarization mismatch factor of 0.05. Moreover, the proposed NOMA-MQKD scheme can have obviously higher secure key rate than the conventional WDMA-MQKD approach.

Quantum wireless communications (QWC) is emerging as a promising candidate for future terrestrial information networks. In this paper, over noisy quantum atmospheric turbulence channels, a new non-orthogonal coherentstate massive-MIMO uplink scheme is proposed. At the receiver, a composite quantum iterative multi-stage measuring instrument and MIMO detector is developed. In particular, a non-additive Poisson shot noise limited quantum iterative massive-MIMO interference cancellation algorithm is derived. Simulation results show that the proposed scheme is capable of obtaining the substantial spatial diversity gain in severely noisy quantum turbulence channels, while effectively mitigating the quantum multi-user (MU) interference. Quantitatively, for the 8 × 64 MIMO scenario, it can achieve the BER of 10-6 at -179.3dBJ per bit over a strong Gamma-Gamma turbulence fading channel (α = 2.1, βc = 2.4). Furthermore, the proposed scheme can provide significant performance improvements over a recent reported photon-counting MIMO receiver [18].

Multiuser MIMO (MU-MIMO) empowers access points (APs) with multiple antennas to transmit multiple data streams concurrently to users by exploiting spatial multiplexing. In MU-MIMO, users need to estimate channel state information (CSI) and report it to APs, thus opening a backdoor to attackers who may forge CSI to eavesdrop the content of victims. In this paper, we explore the eavesdropping attack in a novel and practical context in which CSI forgery entangles MU-MIMO user selection in a many-users regime. The attacker hopes to optimize both the eavesdropping opportunity of being selected with the victim and the corresponding decoding quality. We propose new attack and defense mechanisms: (1) USE Attack that enables attackers to achieve near optimal eavesdropping opportunity and high decoding quality through constructing orthogonal CSI against victims followed by stepwise refinements; (2) AngleSec that exploits channel reciprocity for attacker detection without any modification to legacy CSI feedback in which CSI forgery induces a mismatching of downlink and uplink angular spectra at the AP. We implement and evaluate USE Attack and AngleSec in a software defined radio platform WARPv3. Extensive experiments manifest that USE Attack significantly improves the overall eaves-dropping quality compared with state-of-the-art counterparts and AngleSec is able to detect CSI forgery attackers almost for sure.

Non-orthogonal multiple access (NOMA) comprises a prospective technology in the 5th generation (5G) and future wireless communications. In this paper, due to intrinsic nonorthogonal properties of coherent state multi-user communications, a new iterative quantum spatial coupling NOMA (q-SC-NOMA) scheme is proposed. The simulation results confirm that, compared to the recent reported multi-stage displacement based receiver (MDR), the q-SC-NOMA scheme is capable of achieving obviously better performance, saving about 3dB mean signal power in 3-user scenario. Moreover, the q-SC-NOMA scheme can obtain the lower bit error rate (BER) than the non-spatial-coupled quantum NOMA (q-NOMA) approach with reduced computational complexity. In particular, the q-SC-NOMA also offers high robustness against many practical quantum domain imperfections, e.g. mode mismatch and dark counts, etc.

We show that non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) have similar performance in systems with centralized control and perfect channel state information (CSI). Otherwise, NOMA has distinct advantages over OMA. We outline a randomized power control (RPC) strategy for NOMA. RPC can provide significantly enhanced throughput in random access systems with successive interference cancelation (SIC) or iterative detection. We also show that interleave division multiple access (IDMA) is a simple implementation technique for NOMA to facilitate RPC.

This paper develops a generalized channel-aware power control scheme to enhance the multi-packet reception (MPR) capability for random access. In the proposed scheme, each user randomly selects its transmit power with a probability in accordance with a distribution function based on its own channel state. For two-user systems, we show that the optimal power distribution function has a simple discrete structure, based on which the optimal random access strategy can be efficiently computed. Leveraging the relevant insights, we further propose a decentralized binary power control scheme for general K-user systems. It is established that such a simple scheme can asymptotically approach the performance of the optimal centralized scheme, as the system load grows large. Numerical results show that the proposed schemes obtain noticeable performance improvement over the existing alternatives.

Time allocation optimisation for a multi‐antenna wireless energy and information transfer system is studied in this study. The terminal first harvests the wireless energy from the base station (BS) during phase I, and then uses the harvested energy to power its information transmission during phase II. Realistic channel state information (CSI) assumptions are made where the CSI at the BS is obtained via training and feedback and is subject to channel estimation error and quantisation error. Therefore, in addition to the tradeoff between information and power transfer, there is also a tradeoff in determining the overheads of training and feedback. To gain insight into this problem, the authors characterise the asymptotic behaviour of the parameters in the large system limit where the number of transmit antennas N t and transmission block length L tot tend to infinity with fixed ratio L tot / N t . The optimal overheads of training and feedback within each phase are first derived to maximise the harvested power and the information transmission rate, respectively. The optimised time allocation between phase I and phase II is then obtained by maximising the bounds of the effective average information transmission rate. Finally, numerical results are presented to verify the proposed theorems.

This paper is concerned with pilot transmission schemes in a large antenna system with non-orthogonal multiple-access (NOMA). We investigate two pilot structures-orthogonal pilot (OP) and superimposed pilot (SP). In OP, pilots occupy dedicated time (or frequency) slots, while in SP, pilots are superimposed with data. We study an iterative data-aided channel estimation (IDACE) receiver, where partially decoded data are used to refine channel estimation. We analyze the achievable rates for systems with IDACE receivers for both OP and SP. We show that the optimal portion of pilot power tends to zero for SP with Gaussian signaling. This result is consistent with existing findings obtained via the replica method in statistical physics. The latter involves multiple codes, which is convenient for theoretical analysis but difficult to implement. As a comparison, IDACE is potentially implementable in practice. We demonstrate that, with code optimization, SP can outperform OP in a high mobility environment with a large number of users. We provide numerical examples to verify our analysis.

This paper considers the beamforming design for a multiuser multiple-input single-output (MU-MISO) downlink with an arbitrary number of (context-specific) shaping constraints. In this setup, the state-of-the- art beamforming schemes cannot attain the well-known performance bound promised by the semidefinite program (SDP) relaxation technique. To close the gap, we propose a linear space-time beamforming (LiST-BF) scheme, consisting of a circulant space-time symbol mapper followed by the beamforming design with orthogonality constraints. It is shown that the proposed LiST-BF scheme can perform general rank-$K$ beamforming for user symbols in a low-complexity and structured manner. Sufficient conditions are derived to guarantee that the LiST-BF scheme always achieves the SDP bound for linear beamforming schemes. Based on such conditions, an efficient algorithm is then developed to obtain the optimal LiST-BF solution in polynomial time. Numerical results demonstrate that the proposed scheme enjoys substantial performance gains over the existing alternatives.

Recently massive multi-input multi-output (MIMO) techniques have attracted growing research interest. However, most existing works assume centralized control, which may involve a heavy overhead as the number of users also becomes massive. In this paper, we focus on random-access massive MIMO systems. We first analyze the performances of two conventional schemes widely used in massive MIMO, and derive their closed-form expressions in the asymptotic case. Building on the latter, we maximize the system throughput through transmission control. To further exploit the potential benefit of massive MIMO techniques, we propose a novel multi-level transmission and grouped inter-ference cancellation (MLT-GIC) scheme, which can obtain a higher system throughput and provide a flexible tradeoff between throughput and complexity. Numerical results demonstrate the merits of the proposed scheme.

We study the uplink transmission in multiple-input multiple-output (MIMO) systems with antenna correlation. We focus on schemes that require only channel covariance information at the transmitter (CCIT), which involves lower cost than full channel state information at the transmitter (CSIT). We start from mutual information analysis and show that a simple CCIT-based scheme, referred to as statistical water-filling (SWF), can perform close to the optimal full CSIT-based one in MIMO systems with more receive antennas than transmit ones. We then focus on the implementation of SWF in practically coded systems. An iterative linear minimum mean squared error (LMMSE) receiver is assumed and an extrinsic information transfer (EXIT) chart type curve matching technique is developed based on Hadamard precoding techniques. Simulation results show that the proposed scheme can obtain significant performance improvement compared to the conventional equal power transmission. Finally, we show that the proposed scheme is also very efficient in multi-user uplink MIMO systems with distributed channel information.

We establish area theorems for iterative detection and decoding (or simply, iterative detection) over coded linear systems, including multiple-input multiple-output channels, intersymbol interference channels, and orthogonal frequency-division multiplexing systems. We propose a linear precoding technique that asymptotically ensures the Gaussianness of the messages passed in iterative detection, as the transmission block length tends to infinity. Area theorems are established to characterize the behavior of the iterative receiver. We show that, for unconstrained signaling, the proposed single-code scheme with linear precoding and iterative linear minimum mean-square error (LMMSE) detection is potentially information lossless, under various assumptions on the availability of the channel state information at the transmitter. We further show that, for constrained signaling, our proposed single-code scheme considerably outperforms the conventional multicode parallel transmission scheme based on singular value decomposition and water-filling power allocation. Numerical results are provided to verify our analysis.

This paper is concerned with the decentralized power allocation problem in random access systems. We propose a scheme that is especially suitable for systems requiring high throughput but with difficulty in establishing centralized control, such as cognitive radio environments. Specifically, we assume successive interference cancellation (SIC) at the receiver for multi-packet reception (MPR). We consider a decentralized random power transmission strategy where each user selects its transmitted power level randomly according to a power distribution conditioned on its own channel state. Our focus is on the design of this distribution such that the system packet throughput is maximized under rate and power constraints. We start from a two-user system. A main finding of this paper is that the supports of the optimal power distributions are of discrete nature. This finding greatly simplifies the distribution optimization problem. We also discuss a sub-optimal solution to systems with more than two users. Numerical results demonstrate that the proposed scheme can achieve noticeable performance improvement compared with conventional single-user detection (SUD) based ones and offer a flexible tradeoff between the system throughput and power consumption.

In this paper, we propose an analytical model for evaluating the MAC throughput in an unsaturated IEEE 802.11 wireless local area network (WLAN) where multiple packets reception (MPR) is possible using multiuser detection techniques. In particular, a recently proposed successive interference cancellation (SIC) scheme for MPR is considered where users can randomly choose the transmission power from a set of discrete power levels. We derive an explicit expression for throughput of the WLAN based on such an SIC scheme and validate the accuracy of the model via ns-2 simulation results. We show that the throughput is significantly improved compared to the conventional 802.11 MAC protocol just by resolving collisions between two packets with different transmission power levels. In addition, we provide the optimal power distribution to maximize the throughput achievable in an SIC-enabled WLAN.

In this paper, we present a joint forward-error-correction (FEC) coding and linear precoding scheme for multiple-input multiple-output (MIMO) and inter-symbol interference (ISI) channels with imperfect channel state information at the transmitter (CSIT). We first study the performance of ideally coded systems. We focus on an average power gain (APG) method that can achieve capacity in the two extreme cases of no CSIT and perfect CSIT. In the more general case, the performance of the APG method improves progressively with the CSIT quality. We then consider the implementation of this APG method in a practically coded system. We propose a unified scheme involving beamforming, water-filling, and diversity coding. The core of the new scheme is a joint FEC coding and linear precoding strategy at the transmitter and an iterative detection process at the receiver. Simulation results demonstrate that the proposed scheme can achieve significant performance gain by efficiently utilizing the available CSIT.