Fluid antenna (FA) technology has recently emerged as an effective means of exploiting spatial diversity through position-domain reconfigurability. This paper investigates the integration of FA into over-the-air federated learning (OTA-FL) systems with the aim of improving aggregation reliability and user participation under realistic channel conditions. By dynamically selecting antenna positions, FA-equipped users can exploit additional spatial degrees of freedom to realize more favorable channel conditions, thereby increasing the probability of successful contribution to the OTA aggregation process in each communication round. We consider an uplink OTA-FL framework consisting of a single fixed-antenna access point and multiple FAenabled users operating over spatially correlated fading channels. Unlike existing studies that primarily rely on optimization-based designs or numerical evaluations, we develop a tractable analytical framework that enables a rigorous performance characterization of FA-assisted OTA-FL. In particular, closed-form expressions are derived for the aggregation error outage probability and the expected number of participating users per round. Spatial channel correlation across FA ports is modeled using a copula-based approach, where the Clayton copula is adopted to capture lower-tail dependence relevant to worst-case fading conditions. Numerical results validate the analytical findings and demonstrate that FA-assisted OTA-FL significantly outperforms conventional fixed-antenna schemes in terms of aggregation reliability and participation efficiency, while providing insights under practical system considerations.

A model-driven deep learning framework is proposed for channel estimation in Rydberg atomic quantum receivers (RAQRs) based on the measurement of holographic snapshots. Specifically, we develop a Transformer-based unrolling architecture, termed URformer, to solve the non-linear biased phase retrieval problem, which is derived by unrolling a stabilized variant of the expectation-maximization Gerchberg-Saxton (EM-GS) algorithm. Each layer of the proposed URformer incorporates three trainable modules: 1) a learnable filter network that replaces the fixed Bessel kernel in the classic EM-GS algorithm; 2) a trainable gating mechanism that adaptively combines classic updates to ensure training stability; and 3) an efficient channel Transformer module that learns to correct residual errors by capturing non-local channel dependencies. Numerical results demonstrate that the proposed URformer significantly outperforms classic iterative algorithms and conventional black-box neural networks with less pilot overhead.

In this paper, we investigate a wireless powered communication network (WPCN) in which a multi-antenna hybrid access point (HAP) communicates with multiple Internet-of-Things (IoT) devices, assisted by an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS). In the energy transfer (ET) phase, the IoT devices harvest energy from the HAP with a nonlinear energy harvesting (EH) model, and subsequently transmit information signals to the HAP during the information transmission (IT) phase. To explore its full potential, the aSTAR-RIS employs energy splitting (ES), mode switching (MS), and time switching (TS) protocols. A sum rate maximization problem is formulated for each protocol, which jointly optimize the beamforming at the HAP, allocation of time slots and transmitting power for the IoT devices, and the adaptation of the aSTAR-RIS coefficients. To address the optimization problem with multiple coupled variables and complex non-convex constraints, we firstly decompose it into several subproblems. Specifically, to optimize the coefficients of the aSTAR-RIS in the IT phase, we develop a fractional programming-based successive convex approximation algorithm to handle the fractional objective function and the minimum rate constraints. Moreover, to obtain the coefficients of the aSTAR-RIS during the ET phase, we design a penalty-based SCA algorithm to address the binary constraints in the MS protocol and the rank-one constraints. Numerical results demonstrate that 1) employing the aSTAR-RIS in WPCNs can realize the extraordinary sum rate gain in comparison with the benchmarks of the active RIS and the passive STAR-RIS; 2) among the three operation protocols, the ES demonstrates the best performance, with the MS following closely behind, while the TS is the least effective; 3) as the minimum required data rate for each IoT device decreases, the performance gap among the three protocols becomes narrower.

The rapid development and integration of wireless sensor networks (WSNs) in the consumer electronics industry signify an important shift toward more intelligent and interconnected technologies. The localization of network nodes is essential for promoting intelligent automation and effective decision-making processes across a wide range of consumer applications. To achieve more precise intelligent localization, this article proposes an algorithm based on Triple-Anchor Centroid and expected hop progress (EHP) weighting to estimate the distance between sensing devices (regular nodes) and gateway devices (anchor nodes). The algorithm is a range-free positioning scheme that combines the geometric constraints between two devices and the advantages of EHP. And it uses the geometric centroid of the shadow part formed by the triple-anchor point to estimate devices distance. Then, the devices distance is weighted based on the EHP estimation, and the experimental results show that the proposed method improves the accuracy of estimating distance. Finally, an improved quantum-adaptive gray wolf optimizer (QAGWO) is proposed to estimate the coordinates of sensing devices. The step factor is proposed to balance the global and local search capabilities to improve the search efficiency and performance of the algorithm. The simulation results show that the scheme is superior to the other algorithms in terms of positioning accuracy, and the application scenarios are more extensive.

This article proposes a novel covert communication framework utilizing movable antennas (MAs) to enable covert communications in which the evading detection eavesdropper aided by a reconfigurable intelligent surface (RIS). The trajectories of the MAs over the entire time slot, transmit beamforming, and the phase shift of the RIS in each time slot are jointly optimized to improve the covert rate. Specifically, the movement trajectories of the MAs are modeled as a Markov decision process (MDP), optimized by developing a novel deep reinforcement learning (DRL) approach. Furthermore, an alternating optimization (AO) algorithm is designed to jointly optimize the beamforming and phase. In particular, penalty-based two-layer iterative algorithm is proposed to guarantee that the solution satisfies the rank-one constraints. Numerical results show that the proposed MA-assisted covert communications system significantly outperforms conventional fixed-position antenna (FPA) schemes in terms of covert rate.

This article explores the benefits of deploying simultaneously transmitting and reflecting reconfigurable intelligence surfaces (STAR-RIS) in autonomous aerial vehicle (AAV) networks with simultaneous wireless information and power transfer. In the proposed system, the AAV utilizes STAR-RIS to radiate energy-carrying information signals (ECISs) to multiple outdoor energy receivers and multiple indoor information receivers without flying over indoor no-fly zone. Based on the AAV propulsion power formula, we successively introduce fly-hover-broadcast (FHB) and path discretization (PD) protocols to minimize the total AAV energy consumption by jointly using the extended penalty function and optimization algorithm based on the expected value of channel status information, where the AAV flight constraints, no-fly constraint, minimized energy or information threshold constraints, and STAR-RIS phase-shift constraints are met. The FHB protocol, which requires the AAV to radiate the ECISs to the users at only a few hovering positions, provides a lower bound performance of AAV energy consumption, while the PD protocol is used to discuss the general situation of the ECISs during AAV flight. Simulation results demonstrate that the utilization of the STAR-RIS in AAV networks outperforms the traditional RIS in improving energy efficiency and extending AAV flight time.

This article explores the impact of source data compression on the performance of task execution at the receiver side of a communication system, and investigates the interference and impact of channel environment variations on semantic coding features. In order to further optimize the performance of the semantic communication model, a semantic communication framework (DIB-DeepSC) based on disentangled information bottleneck is proposed, which improves the inference accuracy of the model by separating and decoupling irrelevant information in the source data, thus compressing valid information related to downstream task execution to a greater extent, reduced communication overhead. Meanwhile, the influence of the Lagrange multiplier$\beta $in the classical information bottleneck (IB) framework is eliminated, which avoids the need to manually optimize$\beta $several times in the semantic communication model. And the dynamic coding method (DIB-DE) is further designed based on adaptive weights, which can dynamically adjust the coding features according to the channel conditions and enhance the robustness of the model. Numerous experiments show that the proposed DIB-DeepSC framework combined with the DIB-DE dynamic encoding communication scheme possesses better semantic extraction and task inference performance relative to the benchmark methods. This scheme is expected to realize more efficient and reliable semantic transmission in practical communication systems and provides new ideas for developing practical semantic communication systems.

This paper investigates how to improve the energy efficiency of unmanned aerial vehicle (UAV)-enabled simultane-ous wireless information and power transfer (SWIPT) systems with multiple outdoor energy receivers (ERs) and multiple indoor wired-charging information receivers (IRs) by utilizing simul-taneously transmitting and reflecting reconfigurable intelligence (STAR-RIS), in which the UAV avoids flying over the indoor no-fly zone. Specifically, the total UAV energy consumption is minimized, while ensuring that the energy harvesting require-ment (EHR) of each ER and the communication throughput requirement (CTR) of each IR are met. To achieve this, the total UAV energy consumption is minimized by optimizing the STAR-RIS phase-shifts, the UAV trajectory, and hovering time using an iterative technique based on the fly-hover-broadcast (FHB) protocol. The technique allows the UAV to radiate energy-carrying information signals for the ERs and IRs at a limited number of hover positions. Simulation results demonstrate that the proposed design significantly outperforms other benchmark schemes, demonstrating its potential for improving the energy efficiency of UAV-enabled SWIPT systems while meeting the EHRs of each ER and the CTR of each IR.

In this article, we consider an active reconfigurable intelligent surface (ARIS)-aided nonorthogonal multiple access (NOMA) and rate-splitting (RS)-enabled communication system, in which a base station applies RS and NOMA to the downlink transmission of a hidden user and a public user with the assistance of an ARIS in the presence of an illegal user. More specifically, the closed expressions for the outage probability (OP), covert rate, and detection error probability (DEP) are derived. Also, we present the analysis for the secrecy OP (SOP), and analyse the effects of detection threshold, total power consumption, reconfigurable intelligent surface (RIS) deployment distance, and power allocation factors on the system performance. The numerical results show that under the same total power consumption, the ARIS has a lower OP value than the passive RIS (PRIS), which can effectively overcome the “multiplicative fading” effect. Moreover, applying the ARIS can result in a larger covert rate and a higher minimum DEP value at the optimal detection threshold, and can reduce the SOP values of hidden and public users by reducing the power allocation factors. In addition, at the same minimum DEP value, the RS-NOMA scheme has a higher covert rate than the NOMA scheme.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multiuser near-field wideband communication system is investigated, in which a robust deep reinforcement learning (DRL) based algorithm is proposed to enhance the users’ achievable rate by jointly optimizing the active beamforming at the base station (BS) and passive beamforming at the STAR-RIS. To mitigate the beam split issue, the delay-phase hybrid precoding structure is introduced to facilitate wideband beamforming. Considering the coupled nature of the STAR-RIS phase-shift model, the passive beamforming design is formulated as a problem of hybrid continuous and discrete phase-shift control, and the proposed algorithm controls the high-dimensional continuous action through hybrid action mapping. Additionally, to address the issue of biased estimation encountered by existing DRL algorithms, a softmax operator is introduced into the algorithm to mitigate this bias. Simulation results illustrate that the proposed algorithm outperforms existing algorithms and overcomes the issues of overestimation and underestimation.

A joint cascaded channel estimation scheme is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems with hardware imperfections. In particular, the practical hybrid near- and far-field electromagnetic radiation with spatial non-stationarity is investigated. By exploiting the cascaded channel correlations between different users and between different STAR-RIS elements, a multi-task learning (MTL)-based channel estimation framework is proposed. This framework is capable of estimating the cascaded channels for transmission and reflection simultaneously based on noisy observations of the mixture channel. Following the design guideline of the proposed MTL framework, an efficient multi-task network (MTN) is developed to reconstruct the high-dimensional channels with limited pilot overhead. In the proposed MTN architecture, a mixed convolution and multilayer perception module is exploited to capture the effective hybrid-field channel features. This module integrates the locality bias modeling of the channel-wise convolution and the long-range dependency modeling of MLP, which finely learns both local spatial correlations and specific spatial non-stationarity of the hybrid-field cascaded channels. Numerical results show that the proposed MTN achieves superior channel estimation accuracy with less training overhead compared with the existing state-of-the-art benchmarks, in terms of required pilots, computations, and network parameters.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhanced simultaneous wireless information and power transfer (SWIPT) system is investigated, in which a multi-antenna access point (AP) provides communication service and power supply for multiple single-antenna information decoding receivers (IDRs) and energy harvesting receivers (EHRs), respectively. To fully investigate the potential of STAR-RIS, three types of STAR-RIS protocols are employed in the SWIPT systems, namely the energy splitting (ES), the mode switching (MS), and the time switching (TS) protocols. The weighted sum power maximization problem is formulated for each STAR-RIS protocol to maximize the sum power received at the EHRs by jointly optimizing the beamforming at the AP and STAR-RIS. The non-convex problem for each STAR-RIS protocol is handled by the proposed low-complexity Gaussian randomization-based and high-precision penalty-based joint optimization algorithms to provide a more comprehensive range of algorithmic choices. Furthermore, we demonstrated that the AP only needs to send the information beam to achieve the best power supply and communication service in adopting any STAR-RIS protocol. Finally, numerical results demonstrate that: 1) the proposed schemes can achieve higher sum received power compared to the benchmarks; 2) the sum power received at the EHRs of the three STAR-RIS protocols can be ranked as ES > TS > MS; and 3) the performance gap in sum power received between penalty-based and Gaussian random algorithms depends on the number of STAR-RIS elements.

A non-orthogonal multiple access (NOMA)-based spatial modulation system operating over multiple subchannels is investigated. For scheduled users of each subchannel, a mixed multicast and unicast transmission is delivered. The multicast content is transmitted via the transmit antenna domain, while unicast contents are transmitted through the amplitude-phase modulated symbols using NOMA via the active antenna. Firstly, the unicast rate for each user and an upper bound for the multicast rate are derived. Secondly, a joint subchannel and power allocation problem for weighted sum rate maximization is formulated. To solve this challenging mixed-integer non-linear problem, we decompose it into three subproblems, namely the decoding order design, the subchannel assignment, and the power allocation. A heuristic scheme is developed to solve the first one by investigating the characteristics of the decoding order constraint. To avoid the high complexity caused by exhaustive search, the subchannel assignment is reformulated as a many-to-one matching with peer effect, and the Gale-Shapley method and swap operation are designed to solve it. The power allocation is solved by employing the successive convex approximation. Moreover, a joint subchannel and power allocation algorithm is proposed to further boost the performance, and a robust power allocation algorithm is proposed under channel uncertainties.

Considering the charging needs of the Internet of Things, we introduce the simultaneous wireless information and power transfer (SWIPT) technology into the ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul. However, SWIPT can bring potential eavesdropping issues. In this paper, we study the secure communication caused by SWIPT in the UD-CRAN network. Specifically, the transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense networks, such as base station diversity and high probability of line-of-sight transmission. Aiming at maximizing the security energy efficiency, we jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of remote radio heads (RRHs), and user-RRH association. We propose an iterative algorithm based on the Dinkelbach’s transform to deal with the fractional objective function. To solve the mix-integer non-convex inner problem, we design: (1) a successive convex approximation (SCA) based method in which the problem at each iteration is a mixed-integer second-order cone program; (2) and an alternating optimization algorithm based on semidefinite relaxation (SDR) to further balance the complexity and performance. Finally, numerical results are presented to demonstrate the efficiency of the proposed schemes. Moreover, the proposed SCA method can achieve excellent performance while preserving integer variables, which inevitably increases algorithm complexity. Furthermore, the proposed SDR method can avoid the iteration process of SCA and further reducing the algorithm complexity.

A near-field wideband beamforming scheme is investigated for reconfigurable intelligent surface (RIS) assisted multiple-input multiple-output (MIMO) systems, in which a deep learning-based end-to-end (E2E) optimization framework is proposed to maximize the system spectral efficiency. To deal with the near-field double beam split effect, the base station is equipped with frequency-dependent hybrid precoding architecture by introducing sub-connected true time delay (TTD) units, while two specific RIS architectures, namely true time delay-based RIS (TTD-RIS) and virtual subarray-based RIS (SA-RIS), are exploited to realize the frequency-dependent passive beamforming at the RIS. Furthermore, the efficient E2E beamforming models without explicit channel state information are proposed, which jointly exploits the uplink channel training module and the downlink wideband beamforming module. In the proposed network architecture of the E2E models, the classical communication signal processing methods, i.e., polarized filtering and sparsity transform, are leveraged to develop a signal-guided beamforming network. Numerical results show that the proposed E2E models have superior beamforming performance and robustness to conventional beamforming benchmarks. Furthermore, the tradeoff between the beamforming gain and the hardware complexity is investigated for different frequency-dependent RIS architectures, in which the TTD-RIS can achieve better spectral efficiency than the SA-RIS while requiring additional energy consumption and hardware cost.

A multi-scale attention based channel estimation framework is proposed for reconfigurable intelligent surface (RIS) aided massive multiple-input multiple-output systems, in which hardware imperfections and time-varying characteristics of the cascaded channel are investigated. By exploiting the spatial correlations of different scales in the RIS reflection element domain, we construct a Laplacian pyramid attention network (LPAN) to realize the high-dimensional cascaded channel reconstruction with limited pilot overhead. In LPAN, we leverage the multi-scale supervision learning to progressively capture the spatial correlations of the cascaded channel, where the attention mechanism based dual-branch architecture is designed. To balance network performance and complexity of LPAN, we further propose a lightweight LPAN-L architecture. In LPAN-L, the partial standard convolutional layers are decomposed into the group convolution, dilated convolution and point-wise convolution, which forms a sparse convolutional filter set to extract the channel feature with less computation cost. Furthermore, we leverage parameter sharing and recursion strategy to reduce the space complexity. Moreover, a selective fine-tuning strategy is developed to realize the domain adaption. Simulation results show that the proposed LPAN can achieve higher estimation accuracy than the existing estimation schemes, while the LPAN-L architecture with a close performance to LPAN efficiently reduces the network complexity1.

In this paper, we study a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) supported simultaneous wireless information and power transfer (SWIPT) system, in which a multi-antenna access point (AP) provides power supply and communication services for multiple energy harvesting receivers (EHRs) and information decoding receivers (IDRs) respectively. To maximize the performance of the STAR-RIS, we utilize the energy splitting (ES) operation protocol in this paper. By optimizing the AP beamforming and the STAR-RIS transmission and reflection coefficients, we create a weighted sum-power maximization problem. We demonstrate that the AP can service both the IDRs and the EHRs by merely delivering information signals, which simplifies the initial optimization challenge. Then, the initial problem is split into two subproblems, and an alternative optimization method is utilized to produce high-precision suboptimal solutions to the optimization problem. Finally, we testify that the sum power received by EHRs is remarkably enhanced by the STAR-RIS-aided SWIPT systems compared with benchmark schemes in numerical results.

A joint cascaded channel estimation framework is proposed for simultaneously transmitting and reflecting recon-figurable intelligent surfaces (STAR-RIS) systems with hardware imperfection, in which practical the hybrid-field electromagnetic wave radiation with spatial non-stationarity is investigated. By exploiting the cascaded channel correlations in user domain and STAR-RIS element domain, we propose a multitask network (MTN) with multi-expert branches to simultaneously reconstruct the high-dimensional transmitting and reflecting channels from the observed mixture channel with noise. In the proposed MTN architecture, a learnable shrinkage module is exploited to constrict the communication noise, and self-attention mechanism-based Transformer layers are utilized to extract the nonlocal feature of the non-stationary cascaded channel. Numerical results show that the proposed MTN achieves superior channel estimation accuracy with less training overhead compared with existing state-of-the-art benchmarks, in terms of required pilots, computations, and network parameters.

This paper studies the secrecy wireless information and power transfer problem in ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul, which is a promising framework for future Internet of Things (IoT). The transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense network such as base station diversity and high probability of line-of-sight transmission. Specifically, we employ the idea of block diagonalization to deal with the fronthaul interference, which support multi-stream fronthaul transmission for each remote radio head (RRH). We then jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of RRHs, and user-RRH association. In order to solve the formulated mixed integer non-convex optimization problem, we leverage the sparsity of beamforming vectors brought by the ultra-dense RRHs. We then solve the reformulated problem by employing the successive convex approximation approach. Finally, numerical results are presented to demonstrate the effectiveness of the proposed scheme.

Motivated by the development of single image super-resolution (SR) reconstruction in computer version, classic SR networks have been widely applied to the channel estimation of wireless communication system. To capture the spatial correlations in the reflection element-domain of reconfigurable intelligent surface (RIS), we propose a multi-scale supervised learning-based Laplacian pyramid wide residual network (LapWRes) to achieve the progressive reconstruction of cascaded channel in a coarse-to-fine fashion. The LapWRes can be divided vertically into feature extraction branch (FEB) and channel reconstruction branch (CRB), while it can also be viewed horizontally as multiple channel reconstruction modules (RMs) at different scales. In the FEB, the wide activation residual blocks are stacked to extract the high-frequency information of cascaded channel. In the CRB, the high-frequency and low-frequency information of cascaded channel is fused by utilizing the residual learning. Simulation results show that the LapWRes can achieve better estimation accuracy than other channel estimation schemes and faster convergence than existing SR network-based channel estimation models.

Interferences due to non-orthogonality of signals usually exist in wireless networks when the number of users is larger than the sequence length, such as non-orthogonality of the pilots in multi-cell systems and non-orthogonality of the signature sequences in overloaded code-division-multiple-access (CDMA) systems. We address this effect from the perspective of non-orthogonal sequence design in a two-cell multiple-antenna network. Specifically, we aim at designing pilot sequences to minimize the sum mean-squared-error (MSE) of channel estimation with a given sequence length$\tau $where$\tau \in [K,2K]$and$K$is the number of users per cell. Considering the strength disparity between channels originating from the home cell and the neighbor cell, this problem boils down to minimizing the sum of squares of weighted correlations among sequences, whose lower bound is obtained inclosed formand can be regarded as a generalization of the well-known Welch bound (Welch, 1974). We prove this extended Welch bound is achievable, and design an algorithm based on the Davies-Higham method to generate the interference-minimizing sequences. Three fundamental properties of the proposed sequences are presented. Finally, we derive closed-form expressions of the average signal-to-interference-plus-noise-ratio (SINR) and rate for data transmission, based on which the optimal training duration can be found.

Time synchronization is crucial for the safe and reliable operation of the fifth generation (5G) network, especially for applications requiring ultra-reliable low-latency data transmissions. The time synchronization problem, however, becomes increasingly challenging in high-mobility scenarios because the channel conditions, e.g., the multipath delay spread may vary rapidly. While there exist numerous works on the design of efficient channel decoding algorithms, decoding linear codes such as polar codes in the presence of synchronization errors is a less-explored topic. In this paper, we aim to fill this void and develop a systemic approach to decode general binary linear codes over binary symmetric channels with synchronization errors in which the lack of synchronization is modeled as the deletion channel model. The maximum likelihood (ML) decoding problem for binary linear codes over deletion channels is first formulated as a nonlinear optimization problem, in which a set of linear constraints are employed to characterize the input-output relationship of a deletion channel. It turns out that both the objective function and the constraints of this optimization problem are nonlinear, which poses significant challenges against the design of efficient decoding algorithms. As a remedy, we first replace the nonlinear objective function of this optimization problem via a lower bound. And we prove this lower bound is a linear function in the special case that the input is binary. We then apply the linear programming (LP) relaxation approach to obtain an approximate solution to the proposed nonlinear optimization problem. An adaptive branch-and-cut decoding algorithm has also been developed by making use of the ML-certificate property of the LP decoder for deletion channel. It is seen through simulation studies that the proposed decoding algorithm can achieve close-to-optimal bit error rate (BER) decoding performance at moderate computational complexity.

This paper studies the non-orthogonal multiple access (NOMA)-based spatial modulation (SM) systems with multiple subchannels. A mixed multicast and unicast transmission is considered in each channel, in which a common content is multicasted in the transmit antenna (TA) domain to all the users, and the unicast contents are transmitted as amplitude- phase modulated (APM) symbols in the classical signal domain using NOMA via the active antenna. First, we obtain the achievable unicast rate for each user and an upper bound for the achievable multicast rate in the TA domain. Then, the subchannel assignment and power allocation schemes are designed to maximize the system sum rate. Specifically, the subchannel assignment is formulated as a many-to-one matching with peer effect, and we propose a suboptimal but efficient algorithm incorporating the swap operation to solve it. We then optimize the power allocation subproblem by employing the successive convex approximation approach, which iteratively approximates the original nonconvex problem to a convex one. Finally, numerical results are presented to demonstrate the effectiveness of the proposed schemes.

This paper studies the multicast precoding problem in frame-based multigateway multibeam satellite communications with feeder link interference. We formulate a sum rate maximization problem that incorporates the minimum signal-to-interference-and-noise-ratio (SINR) requirement for each user, the sum power constraint at each gateway as well as the per feed power constraints at the satellite. We propose two algorithms to solve the formulated problem. In the first algorithm, by employing the successive convex approximation (SCA) approach, we iteratively approximate the original nonconvex problem to a second-order cone program (SOCP) which can be solved by modern solvers efficiently. For the second, we propose a modified joint power control and beamforming algorithm which computes QoS beamforming and geometric programming (GP) based power allocation iteratively. Compared to the traditional method [5] which uses a subgradient and projection based approach for the power control, the GP based solution is more implementation friendly and practical appealing which also achieves a slightly better sum rate performance. Finally, numerical results are presented to validate the efficiency of the proposed schemes.

This paper studies the multigroup multicast precoding problem in frame-based multigateway multibeam satellite communications with feeder link interference. We formulate a sum rate maximization problem that incorporates the minimum signal-to-interference-and-noise-ratio requirement for each user, the sum power constraint at each gateway, as well as the per feed power constraints at the satellite. Both transparent payload and payload with on-board processing are considered. In the former case, we propose a centralized algorithm by employing the successive convex approximation (SCA) approach, which iteratively approximates the original nonconvex problem to a second-order cone program. Moreover, in order, for each gateway, to compute its precoding vector locally with local channel state information, we devise a decentralized algorithm by incorporating consensus alternating direction method of multipliers (ADMM) into the SCA framework. For the latter case, we devise a two-stage precoding scheme where, in the first stage, a leakage-based minimum mean-square-error scheme is employed to control the feeder link interference efficiently. In the subsequent second stage, we use the SCA-ADMM approach to deal with the user link interference while maximizing the sum rate. Finally, numerical results are presented to demonstrate the performance of the proposed schemes.

Interferences due to non-orthogonality of training sequences usually exist in cellular networks when the number of all users is relatively large compared to the coherence time, such as the case in massive MIMO systems. In this paper, we address this effect from the perspective of non-orthogonal training sequence design in two-cell interference networks with K users per cell. We relax the general assumption in which the cross-correlations of sequences are restricted to be 0 or 1, and target at designing the training sequences to minimize training phase interference with a given pilot length τ, which is no larger than the total number of users, i.e., τ ∈ [K, 2K]. We note that when large scale fading between different cells β ≠ 1, the strengths of interferences arising from non-orthogonal training sequences within a cell or from the adjacent cell become asymmetric, and optimal design needs to treat the intra-cell sequence correlation and inter-cell correlation differently. To this end, by incorporating β into the design, we extend the Welch bound (Welch 1974 [1]) to the two-cell scenario with asymmetric intra-cell and inter-cell interference, and characterize the lower bound of the interference precisely. Specifically, we obtain the result that the sum of the squares of β -weighted cross-correlations of the training sequences is lower-bounded by [(2K2(1+β2))/(K+(τ-K)β2)], which can be achieved by the proposed training sequence design in closed-form. Particularly, when β = 1, this bound reduces to [((2K)2)/(τ)] which is exactly the Welch bound. This result is applicable for the uplink design of general interference networks such as the pilot design in massive MIMO and the signature sequence design in multicell CDMA systems.

In this paper, we aim to maximize the total energy efficiency for a multi-cell MIMO broadcast channel with dirty paper coding, where both the base stations and users employ multiple antennas. The EE metric is defined as the ratio of the total sum- rate to the total power consumption. Because the original problem is non-convex and difficult to tackle directly, we employ the fractional programming and iterative linear approximation methods to transform it into a set of sub-problems. After using Lagrange dual decomposition, each sub- problem essentially becomes a precoding design problem in MIMO broadcast channel (BC) and is still non-convex, which is then dealt with via BC- multiple access channel (MAC) duality property. Specifically, we propose a gradient descent (GD) method to compute the uplink precoder in each MAC problem which has low complexity. Thus, the dual BC problem can be solved and each BS can iteratively update its precoding matrices with small amount of information exchange among the base stations. Numerical results validate the better performance of our proposed algorithm over conventional linear precoding method.

In this paper, we evaluate the existing transmission schemes in multi-cell environment, such as single-cell transmission with Inter-cell interference (ICI) as well as Collaborative Multi-Point transmission/reception (CoMP) technique in LTE-A, and propose an more effective solution to deal with the inter-cell interference. The proposed transmission structure takes advantage of the cooperation between base stations (BS), and combines both closed-loop and open-loop techniques. With the concept of Effective Channel, the classic Space Frequency Block Coding (SFBC) decode scheme can be used directly in our receiver for single layer transmission. Thus, we prove that the scenario of multiple base stations can employ SFBC conveniently while the performance is guaranteed. Analysis and simulation results show that our method outperforms conventional single cell transmission. Moreover, the proposed transmission scheme can obtain elegant performance enhancement compared with local precoding and global precoding of CoMP in terms of capacity, and doubly reduce the feedback overhead at the same time.

This paper focuses on transmit precoding for multiuser MIMO downlink systems. In multiuser MIMO systems, multiuser interference (MUI) and noise are two well-known factors with respect to the system's performance. Block diagonalization (BD) method is proposed to completely eliminate MUI by placing the intended users in the nullspace of all the unintended users. But the BD method imposes a condition on the relation between the number of transmit and receive antennas. In addition, BD method causes the noise enhancement due to not considering noise's influence. Thus, at low and medium signal-to-noise ratio (SNR) regime, the performance of the BD scheme is poor. In this paper, we propose a novel precoding approach for users with multiple antennas to overcome the above mentioned drawbacks of the BD method for multiuser MIMO precoding systems. Simulation results confirm that the proposed algorithm achieves performance improvement over the conventional BD scheme with low complexity. The effect of channel estimate errors on system performance is also studied.