Millimeter-wave rate-splitting multiple access (mmWave-RSMA) technology combines the wideband characteristics of millimeter waves with the rate-splitting mechanism of RSMA, significantly improving the communication efficiency of the system. However, this feature also poses greater challenges to its secure transmission, especially in networks with dual-identity nodes which act as legitimate users while also being able to eavesdrop on information from other users. This behavior will simultaneously weaken the security of both public and private streams. In this regard, the active reconfigurable intelligent surface (RIS) offers a promising method by suppressing eavesdropper channels and enhancing the quality of legitimate links. Focusing on the physical layer security (PLS) challenge posed by dual-identity users in mmWave-RSMA networks, this paper proposes an active RIS assisted secure beamforming scheme integrated with resource allocation optimization. Specifically, while ensuring the quality of service (QoS) for dual-identity user and power amplification constraints of the active RIS, the sum secrecy rate is maximized by jointly optimizing the transmit beamforming vector, reflection coefficient matrix, common rate allocation, and power allocation coefficients. To solve this non convex problem, we divide it into three subproblems. Then, successive convex approximation (SCA) and semidefinite relaxation (SDR) are used to solve these subproblems. Simulation results validated the critical role of active RIS in reducing eavesdropping, and emphasized the importance of joint resource allocation for secure beamforming in mmWave-RSMA.

In the future generation of mobile communication systems, many scenarios will require high-resolution range-based positioning. However, the accuracy of time-of-arrival (TOA) estimation algorithms for single wideband systems is generally limited due to the constraints of available bandwidth. Currently, leveraging multiple available frequency bands and carrier frequency switching to obtain multi-band channel state information (CSI) for TOA estimation is gaining popularity, as it effectively constructs an equivalent wideband signal. In this paper, we proposed a subspace-based TOA estimation algorithm using multi-band CSI by constructing a correlation matrix and then exploiting its mathematical properties. The proposed algorithm eliminates the need for grid search and thus has lower computational complexity. We analyze the Cramér-Rao Bound (CRB) for the multi-band data model and derive a tighter lower bound for our algorithm. Simulation results show that our algorithm converges to the CRB at high SNR and closely follows the proposed lower bound across all SNR levels. Additionally, our algorithm demonstrates superior performance compared to single-band algorithms and offers advantages in either accuracy or complexity when compared to other multi-band algorithms.

The extremely large-scale array is considered to be one of the key technologies for 6G, which can significantly improve spectral efficiency. However, the extremely-large number of antennas results in a larger range of the near field (e.g., hundreds of meters), leading to the electromagnetic wave propagation modeling changing from plane wave to spherical wave. This makes conventional channel estimation methods suffer from inevitable performance degradation due to the additional distance information in the spherical wavefront. To address this problem, this paper proposes a deep reinforcement learning based near-field channel estimation, in which the multi-agent deep deterministic policy gradient (MADDPG) is employed. Specifically, the near-field channel estimation task is first formulated as a compressed sensing problem by using a sparse spatial grid-based dictionary. Then, the Actor Critic (AC) network based MADDPG algorithm is employed to jointly optimize the angular and distance information by an approximate global search. In addition, an advantage Actor Critic network-based channel estimation algorithm is proposed, which improves both the stability and efficiency of the AC-based algorithm. Finally, the numerical results show that the proposed algorithms outperform the benchmarks in terms of normalized mean square error.

This paper introduces a novel system architecture that integrates an active simultaneous transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS) with rate-splitting multiple access (RSMA) to achieve wireless covert communication in the presence of a multi-antenna warden. Departing from conventional approaches that deploy STAR-RIS at fixed positions, we treat the aSTAR-RIS location as a design variable to be optimized for maximizing the covert transmission rate. Our objective is to jointly optimize the aSTAR-RIS placement, transmit beamforming, transmission/reflection coefficients (TRCs), and common rate allocation so as to maximize both the common and private rates for the covert user. The resulting multi-variable optimization problem is addressed via an alternating optimization (AO) framework, which decomposes the problem into four tractable subproblems. For the multi-ratio fractional programming subproblem in the context of transmit beamforming and TRCs optimization, we adopt a Lagrangian dual formulation and quadratic transformation to reformulate the objective into a difference of convex (DC) form. The rank-one constrained beamforming design is then resolved using a penalized successive convex approximation (SCA) method combined with Gaussian randomization. A closed-form solution is derived for the common rate allocation subproblem, while the aSTAR-RIS location optimization is efficiently tackled via the SCA technique. Simulation results validate that the proposed scheme substantially enhances covert transmission rates while preserving communication covertness and guaranteeing reliable common signal reception. Importantly, the results highlight that optimizing the aSTAR-RIS location plays a critical role in improving the overall covert performance of the system.

In this letter, we introduce a novel carrier-phase-based frequency syntonization method for multi-band systems. Different from other methods, by leveraging the wide bandwidth characteristics of multi-band signals, our approach shows superior clock-skew estimation performance compared to single-band signals. We proposed a bidirectional communication protocol to collect multi-band phase difference of arrival (PDOA) measurements. Then, we derive an approximate maximum-likelihood estimator for clock-skew estimation. Finally, link-level simulations demonstrate that the proposed method's estimation results closely approach the Cramér-Rao bound (CRB) for high signal-to-noise ratio (SNR), validating the effectiveness of our estimator. The comparison with other methods further highlights the superiority of our algorithm.

The high-resolution TOA estimation of multipath channel is essential for many areas while the resolution of conventional algorithms is constrained by the system bandwidth. With the advancement of communication technology, more applications demand higher positioning accuracy, yet single-band systems are restricted by their limited bandwidth. To address this, we consider utilizing decentralized multi-band signals to obtain a larger available bandwidth for high-resolution TOA estimation. Taking dual-band signals as an example, we exploit the dual-band coherent subspace and propose a novel subspace-based high-resolution TOA estimation algorithm. The proposed algorithm does not require parameter matching with high complexity and is suitable for the existing dual-band communication systems. We also derive the Cramér-Rao Bound (CRB) for the dual-band signal model. Simulation results show that the performance of the proposed algorithm is better than existing subspace-based algorithms available for dual-band signals and converges to the derived CRB.

In this paper, we redesign the hybrid analog/digital precoding scheme to form focused beams at specific spatial locations for near-fieldintegrated sensing and communications(ISAC) systems. Specifically, we derive the near-fieldCramér-Rao bound(CRB) for joint distance and angle sensing with arbitrary signal coherence matrices. Unlike previous optimization strategies that either maximize the communication rate or maximize the sensing performance, we aim to maximize the achievable communication rate under unit sensing error. The optimization problem is modeled to maximize the ratio of the sum rate to the CRB by jointly optimizing the analog and digital precoders. Since it is difficult to solve the problem with complicated non-convex fractional program, we first perform an equivalent reformulation to remove the fractional constraint. Then, the fully-digital precoder is obtained bysuccessive convex approximation(SCA). Finally, a low-complexity hybrid precoding algorithm based on alternate optimization is proposed to divide the fullydigital precoder into analog and digital precoders. Simulation results show the effectiveness of the proposed algorithm and the performance of the proposed beam focusing is superior to beam steering in the near-field.

By leveraging geometric and probabilistic information contained in multipath components (MPCs), multipath-based simultaneous localization and mapping (SLAM) enables the localization of both mobile agents and a varying number of map features (MFs). Traditional solutions assume that each MPC is associated with a single MF, while focusing only on MFs’ positions. However, advancements in communication technologies provide higher-resolution multipath parameters (MPPs), resulting in large MFs generating multiple MPCs. This challenges the existing association assumptions and provides opportunities to estimate the extents and shapes of MFs. In this paper, we first integrate the many-for-one association relationship and random matrix-based extent modeling into the existing Bayesian SLAM framework. We then categorize MFs by shape, developing multiple shape and measurement models for each category. By exploring these models, we derive the joint posterior distribution and represent it using a factor graph, which serves as the foundation for our proposed message passing algorithm. Numerical results demonstrate that the proposed algorithm achieves superior localization and mapping performance, successfully classifying different types of MFs while estimating their orientations and sizes.

Precoding technology is very promising in edge computing-assisted cell-free massive multiple-input multiple-output (ECF-mMIMO) systems because it can eliminate interference and thus improve performance. However, it is challenging to design a computationally efficient hybrid precoding scheme to maximize the achievable rate when only the local channel state information (CSI) is known. To maximize the achievable rate and minimize the computational energy consumption within the tolerable computational latency, we propose a novel optimization framework for joint design of distributed hybrid precoding and computational offloading decision in ECF-mMIMO systems. The joint optimization problem is modeled as maximizing the sum of the ratio of the achievable rate to the computational energy consumption. Since it is difficult to directly solve the joint optimization problem with non-convex and fractional constraints, we first use the quadratic transform method to remove the fractional constraint and perform an equivalent transformation of the original problem. Next, we decompose the equivalent optimization problem into three subproblems and propose an alternate optimization algorithm. Specifically, we successively adopt a local block diagonalization hybrid precoding scheme and a game-based power allocation algorithm to maximize the total rate, and a computational resource allocation scheme to minimize computational offloading energy consumption under the constraint of computational latency. Numerical simulation results illustrate that the performance obtained by our proposed scheme with only local CSI is 93% of that as compared to the full CSI case. Besides, we also prove the convergence of the alternate optimization algorithm theoretically.

Loop closure detection is an important aspect of simultaneous localization and mapping (SLAM) to correct long-term drift by finding overlapping trajectories. However, in multipath SLAM using radio signals, due to the limitation of low-resolution sensors and few features, most of the existing works have not fully explored loop closure. In this paper, we propose a loop closure detection algorithm for multipath SLAM, Wi-Loop SLAM, that can reduce cumulative errors. To recognize previously visited places, we use the multipath parameters extracted from received signals as matching features without introducing new information. We use a delayed selection strategy to ensure that the most appropriate pairing is selected, and a confirmation of location drift is required to minimize unnecessary computation. Finally, we derive a Bayesian model to perform loop optimization with the particle-based sum-product algorithm (SPA). Simulation results show that the proposed Wi-Loop SLAM can correct the state estimate drift in the environment where propagation paths are often obscured.

Unmanned aerial vehicles (UAVs) have emerged as a promising technology for facilitating communication in the Internet of Things (IoT), owing to their ability to provide enhanced security. However, the communication performance of UAVs can be significantly impacted by the jittering characteristics resulting from random airflow and fuselage vibration. To address this issue, a novel active reconfigurable intelligent surface (RIS) has been developed, which enables a secure and energy-efficient beamforming design. This design takes into account the effect of UAV jittering and involves joint optimization of the reflecting coefficient of the active RIS, beamforming at the UAV-borne base station, and UAV trajectory, subject to worst-case secrecy rate constraints. Unfortunately, the joint optimization problem is nonconvex, making it intractable to solve. To overcome this challenge, the nonconvex problem is reformulated using linear approximation and treated with linear matrix inequality using S-procedure and Schur’s complement. The problem is then decoupled into three subproblems, including UAV trajectory, passive beamforming, and active RIS’s reflecting coefficient optimization, which are solved via alternate optimization. The results of numerical simulations demonstrate that active RIS significantly enhances the power efficiency of secure UAV communication, even under the influence of UAV jitter.

In this article, we investigate the energy efficiency (EE) maximization problem of the multihop intelligent reflecting surfaces (IRSs) assisted millimeter-wave (mmWave) Internet of Things (IoT) system. In this system, the base station (BS) provides services to IoT devices through the visible link established by the cascaded reflection of multihop IRSs. We aim to maximize the EE by jointly optimizing the active beamforming vector (ABV) at the BS, the number of active reflect elements (REs), the BS’s transmit power, and the phase shift (PS) at the multihop IRSs. To achieve this target, we formulate a nonlinear fractional programming problem. However, the formulated problem is nonconvex and all the variables are deeply coupled. To solve this problem, by using the sparse scattering property of the mmWave channels and the maximum ratio transmission method, we first transform the optimization of ABV at the BS into the optimization of the BS’s transmit power. We then propose an alternating optimization-based method to decouple all variables iteratively. Specifically, for the PS at multihop IRSs, we employ the sparse scattering property of the mmWave channels to divide it into multiple convex problems and obtain the optimal solution. For the number of active REs, we use the successive convex approximation method to obtain a suboptimal solution. For the BS’s transmit power, we employ the Dinkelbach method iteratively solving it and obtaining an optimal solution. Finally, we obtain an iterative suboptimal solution. The simulation results show that the EE of the proposed scheme outperforms the conventional schemes.

Future autonomous vehicles will incorporate high date rate communications and high-accuracy radar sensing capabilities operating in the millimeter-wave (mmWave) and higher frequencies, which results in Integrated sensing and communication (ISAC). Hybrid beamforming (HBF) is an attractive technology for practical vehicular ISAC systems. The HBF with the partially-connected structure (PCS) can effectively reduce the hardware cost and power consumption compared to fully-connected structure (FCS). But the constant-modulus constraint caused by PCS makes the HBF design problem non-convex, which poses a greater challenge. In this paper, we consider the HBF design with PCS as a weighted minimization problem of the communication and radar beamforming errors under the constant-modulus constraints and power constraints. Dual functions of communication and radar are expressed as a tradeoff in this question. Despite the optimization problem being non-convex and hard to obtain the global minimizer, we reduce the problem into a two-step subproblem including the analog precoder design and digital precoder design. Then, a variable projection-based alternating minimization algorithm is proposed to solve these problems. Unlike previous works, which focused on the relationship between variables to iteratively solve, our method exploits the intrinsic geometric features of the mmWave channel and the variable projection to simplify the solution of the beamformers. Simulation results demonstrate that the proposed algorithm achieves significantly improved performance in terms of the system spectral efficiency over the existing solutions and greatly reduces the computational complexity.

To achieve the theoretical performance gains of massive multiple-input multiple-output (MIMO) systems, the base station (BS) must acquire the downlink channel state information (CSI). In frequency division duplexing (FDD) massive MIMO systems, downlink CSI is estimated at user terminals with the pilot symbols transmitted by the BS at the first step and then user terminals feed it back to the BS. However, the huge number of antennas at the BS will result in heavy feedback overhead. Meanwhile, CSI acquisition is very challenging because of the high mobility of user terminals which causes the priori channel knowledge of the channels to change from one slot to another. In order to solve these problems, we propose a joint channel training and feedback scheme based on compressive sensing (CS) and deep learning (DL). Specifically, with the CS-based algorithm, named AS-JOMP, the sparse channel in time-delay domain can be adaptively reconstructed firstly. Then the DL-based network, named DnLSTM, is utilized to estimate the CSI. Simulation results demonstrate that the proposed method can reduce the training and feedback overhead and outperforms the existing classical algorithms at time-varying channel estimation.

This letter proposes a multipath subspace projection andextreme learning machine(ELM)-based indoor fingerprint localization algorithm called MuSpel-Fi, where thechannel state information(CSI) is utilized as the raw data to establish fingerprints. In this algorithm, the CSI is firstly organized into a time-domain matrix and then is projected into a subspace. This processing not only preserves the channel multipath information as much as possible, but also reduces the data dimension. Based on the reduced dimension projected data, the ELM network is exploited to implement the fingerprint localization. Considering the limited performance of a single ELM network, multiple ELM networks are jointly optimized to improve the localization performance. The experimental results demonstrate that the proposed MuSpel-Fi algorithm has higher positioning accuracy than traditional ones.

This paper investigates a full-duplex (FD) secure communication system with the assistance of an intelligent reflecting surface (IRS). Compared with the traditional FD system, the IRS-assisted FD communication not only greatly improves the spectrum efficiency but also provides a new way to enhance physical layer security due to the overlapping of multiple signals at the eavesdropper. Furthermore, we consider a more practical scenario without perfect channel state information (CSI) because it is very difficult to obtain the perfect CSI especially for cascaded channels via IRS. In addition, the eavesdropper is usually passive and hidden which will not actively exchange CSI with the user, which leads to an obstacle for obtaining the perfect CSI of eavesdropping channels. To this end, a worst-case achievable security rate (ASR) optimization problem is formulated under the bounded CSI error model. Due to the existence of non-convexity and highly coupled variables, this problem is extremely challenging. To directly tackle the nonconvexity of the considered optimization problem, similar to successive convex approximation (SCA), we first transform the original problem into its equivalent convex optimization problem directly, and finally obtain the optimal solution of the original non-convex problem by iteratively calculating the convex optimization problem. On this basis, we iteratively solve the transmission beamforming and IRS phase shift through Alternate Optimization (AO). In particular, when optimizing the phase shift coefficient, a penalty convex-concave procedure solution is proposed. Simulation results demonstrate that our proposed robust secure beamforming scheme can effectively improve ASR, and also outperforms the nonrobust one.

In this paper, we investigate antenna selection based on user velocity in frequency division duplex (FDD) massive MIMO system. In order to get this scheme, we first derive the system spectrum efficiency (SE) based on the estimated downlink channel and then analyze the relationship between the optimal number of antennas and the user velocity. Simulation results show that the proposed antenna selection scheme can improve SE.

In this paper, we analyze the performance of the fractional pilot reuse (FPR) scheme with pilot contamination mitigation in massive multiple-input multiple-output (MIMO) system. In FPR, users are classified into the cell- center and the cell-edge ones according to their signal-to- interference-plus-noise ratios (SINRs) at the receiver side. Then, the cell- edge users with low SINRs in different cells use different pilots to avoid strong interference from adjacent cells while the cell- center users with high SINRs in different cells reuse the same pilots to improve the pilot efficiency. In order to identify the cell- center users and the cell-edge ones, we first express the SINR as the function of the distance between the user and the base station (BS) and then find the relationship between the network throughput and the distance threshold. The optimal distance threshold is found to maximize the network throughput. The analytical and simulation results demonstrate advantages of the proposed scheme over conventional ones.

In this paper, we analyze the performance of the fractional pilot reuse (FPR) scheme with vertical sectorization in massive multiple-input multiple-output (MIMO) system. In this scheme, users are classified into the cell-center ones in inner vertical sector and the cell-edge ones in outer vertical sector according to their signal-to-interference-plus- noise ratios (SINRs) at the receiver side. Then, the cell-edge users with low SINRs in different cells use different pilots to avoid strong interference from adjacent sector while the cell-center users with high SINRs in different cells reuse the same pilots to improve the pilot efficiency. In order to identify the cell-center users and the cell-edge ones, we first express the SINR to the function of the distance between the user and the base station (BS) and then find the relationship between the network throughput and the distance threshold. By using a proposed heuristic iterative algorithm, the optimal distance threshold is found to maximize the network throughput. The analytical and simulation results demonstrate advantages of the proposed scheme compared with the conventional FPR scheme.

Active antenna system (AAS) is a promising technology to boost the capacity of next generation wireless communication systems. As a key feature of AAS, vertical sectorization can help form new sub-sectors vertically in a conventional macro cell, facilitates reusing the frequency resources for multiple users, and thus has the potential to improve the system peroformance. In this paper, we investigate the performance of vertical sectorization by optimizing the antenna downtilt and transmit power in LTE-A downlink systems. We first derive the achievable data rate of a downlink wireless communication system considering vertical sectorization and then formulate the problem based on the derived data rate. Finally, antenna downtilt and transmit power are optimized to improve the performance of vertical sectorization. The simulation results demonstrate the effectiveness of the proposed algorithm.

In this paper, we consider a multiple dimension resources allocation problem, including user grouping in the spatial domain and resource blocks (RBs) allocation in the time-frequency domain, in a frequency-division-duplexing (FDD) massive MIMO system. We formulate an optimization problem on joint user grouping and resource allocation to maximize the system capacity. Then, we propose two user grouping methods with load balance to fully use the resources in each user group and a corresponding greedy resource allocation algorithm to verify the effectiveness of the user grouping methods. The simulation results demonstrate that the proposed schemes can obtain better performance over the existing ones and the optimal number of scheduled users can be obtained.

Millimeter Wave (mmW) communication has received more and more attention since it can use the massive available bandwidths to improve the rate of wireless networks. In this paper, we will analyze and optimize the performance of mmW communication system with directional cell discovery based on beam scanning. We first model the mmW communication system with beam scanning and then analyze the effect of the number of the scanning beams and the beam gains on its performance. Since changing the number of scanning beams, K, can not only adjust the beam gains but also control the cell searching delay equivalently, K can be optimized to maximum the system capacity and reduce pilot signal overhead. The simulation and analysis results show that there exists an optimal K which not only makes the system capacity maximum but also trade off the cell searching delay and the beam gain.

We investigate multi‐user (MU) multiple‐input multiple‐output (MIMO) scheduling under the practical constraints in long‐term evolution advanced (LTE‐A) downlink cellular networks. The authors first derive the received signal model in MU‐MIMO systems when there exist both inter‐stream interference (ISI) and inter‐user interference (IUI) at each user device. Based on this, they formulate the optimisation problem as joint user pairing, precoding matrix indicator (PMI) selection, and resource block (RB) allocation to maximise the total system throughput. The authors then develop a codebook grouping technique for user pairing and PMI selection in MU‐MIMO. With the help of codebook grouping, the authors propose some suboptimal and low‐complexity scheduling algorithms to improve system throughput. From system‐level simulation, the proposed algorithms can improve the network throughput significantly when exploiting only limited feedback designed for single‐user (SU) MIMO in the LTE‐A specification.

In this paper, we analyze the spectral efficiency (SE) and energy efficiency (EE) of multi-user (MU) multiple-input and multiple-output (MIMO) in multi-cell downlink networks. We first analyze the achievable sum-rate, i.e., the SE, of MU-MIMO systems with maximal ratio transmission (MRT) and zero-forcing (ZF) precoders in downlink cellular networks. Different from the conventional analysis, we derive the achievable sum-rate under the assumption that the number of the BS antennas is huge but limited. Based on the analytical results, we obtain the system EE and further analyze the effect of the number of BS antennas and scheduled users on system EE. The computer simulation results show that the analytical results is accurate and there exists an optimal relationship between the BS antennas and the scheduled users.

In this paper, we investigate three-dimensional (3D) multi-user (MU) multiple-input multiple-output (MIMO) transmission for long term evolution advanced (LTE-A) downlink systems. We investigate some key techniques for 3D MU-MIMO to improve the performance of LTE-A systems, including rank and precoding matrix (PM) determination and user pairing. To reduce the complexity caused by a large number of co-scheduled users in 3D MU-MIMO, we develop a simplified and high efficient 3D MU-MIMO scheduling algorithm. The performance improvement of the proposed algorithm is demonstrated by system level simulation.

In this paper, we investigate vertical beamforming with antenna downtilt optimization in downlink cellular networks. We first use Gamma distribution to approximate the achievable sum-rates with respect to the antenna downtilts. Then, based on this approximation, we first formulate an optimization to maximize system throughput and then propose a simple heuristic algorithm to find its solution. Simulation result shows that the proposed vertical beamforming with antenna downtilt optimization can significantly improve system sum-rates.

In this paper, we study base station (BS) antenna configuration for downlink multi-cell multi-user distributed multiple input multiple output (MIMO) system to maximize its energy efficiency (EE). In order to obtain the optimal configuration of antennas and users, we first derive the EE expression by considering a realistic power consumption model (PCM). Then an energy-efficient optimization model with respect to the number N of radio access unit (RAU) antennas and the number K of users is formulated to obtain the optimal number Nopt of RAU antennas and the optimal number Kopt of users for maximizing the EE. The EE is concave with respect to the number of RAU antennas and users, so an iteration algorithm is proposed to obtain the optimization solution. The simulation results suggest that the proposed algorithm can obtain the optimal configuration of RAU antennas and supportable users and each user needs only about 2 BS antennas to maximize the EE with zero-forcing (ZF) precoding. Moreover, the proposed antenna configuration scheme can obviously improve the EE of distributed MIMO system.

In this paper, we investigate three-dimensional (3D) multiple-input multiple-output (MIMO) techniques with dynamic rank selection for long term evolution advanced (LTE-A) downlink cellular networks. To facilitate users to transmit different numbers of data streams, we develop a new structure for designing 3D precoding matrix (PM). Then based on the designed PM, 3D MIMO transmission with rank adaptation is proposed. The performance improvement of the proposed algorithms is demonstrated by system level simulation.

In this paper, we investigate the joint impact of both the number of small BSs and the transmit powers on the energy efficiency (EE) of Heterogeneous cellular networks (HetNets). Based on a typical HetNet structure, we propose a feasible focusing searching algorithm to find the optimal number of small BSs and the corresponding transmit power under coverage and spectral efficiency constraints that maximize the network EE. Simulation results show that there exists an optimal number of small BSs and the transmit power which can not only maximize network EE but also satisfy the constraints.

In this paper, we investigate multiuser MIMO scheduling in LTE downlink cellular networks. We formulate the downlink LTE-A MIMO scheduling as a weighted sum rate maximization problem by allocating the RBs to users or user pairs subject to some constraints in LTE-A. We develop a low-complexity MU-MIMO scheduling and resource allocation algorithm and propose an adaptive switching approach between SU-MIMO and MU-MIMO to improve network performance. System-level simulation results demonstrate significantly performance improvement of the proposed MU-MIMO scheduling algorithm and the adaptive switching algorithm.

In this paper, we investigate multi-user (MU) multiple-input multiple-output (MIMO) scheduling for long term evolution advanced (LTE-A) downlink cellular networks, where only limited feedback designed for single-user (SU) MIMO is available. To help the base station (BS) allocate resource blocks (RBs), we develop codebook grouping and MU-SINR estimation techniques. Two adaptive SU/MU-MIMO scheduling methods are proposed to improve system throughput. Although the proposed adaptive SU/MU- MIMO scheduling algorithms exploit the only limited feedback in the LTE-A specification, they can improve the network throughput by 13% from system level simulation.

In this paper linear precoders for non-cooperative transmitters and iterative multiuser detectors (MUD) for the receiver are jointly designed to achieve the individual quality-of-service (QoS). An evolutional analysis framework is first developed for predicting and tracking the performance evolution for each user along iterations. This semi-analytical tool then leads to a joint QoS and quality-of-convergence (QoC) constrained precoder design problem formulation that is complicated and non-convex. Due to the competitive nature of a multiaccess system, a game-theoretic reformulation is adopted to reveal the underlying partial convexity structure of the problem, which further facilitates the development of an efficient iterative solver for the problem. Extensive simulation results are given to support the mathematical development of the problem.

In this paper, we investigate multiuser pairing and resource allocation with interference avoidance in LTE uplink cellular networks. We first derive the received signal-to-interference-plus-noise ratio (SINR) in spatial multiuser SC-FDMA systems with frequency-domain minimum mean-square error (MMSE) equalization. Based on it, we formulate an optimization multiuser pairing and resource allocation problem to maximize system weighted throughput. With the help of the exchange of high interference indicators among multiple base stations, we develop a distributed joint optimal algorithm and a distributed low-complexity algorithm. Simulation results show that the proposed algorithm with interference avoidance significantly outperforms the algorithm without interference avoidance in [12].

In this paper, we investigate joint user pairing and resource allocation under the practical constraints in single-carrier frequency-division multiple access (SC-FDMA) LTE uplink systems. We first introduce a joint optimal algorithm based on branch-and-bound search as a benchmark. To reduce complexity, we divide the joint optimization problem into two subproblems: user pairing and resource allocation. For both these subproblems, we develop several low-complexity algorithms by exploiting the properties of the application of the optimization problem itself. It is shown by the simulation results that the proposed algorithms have better throughput and fairness than the conventional algorithms in and in for LTE uplink no matter whether power control is perfect or not.

In this paper, we investigate joint user pairing and resource allocation under the practical constraints in single carrier frequency-division multiple access (SC-FDMA) LTE uplink systems. We first introduce a joint optimal algorithm based on branch-and-bound search as a benchmark. To reduce complexity, we divide the joint optimization problem into two subproblems: user pairing and resource allocation. For these subproblems, we develop several suboptimal but low-complexity algorithms. The simulation results show that the proposed algorithms outperform the conventional one.

In multiple-input multiple-output (MIMO) systems, transmit antenna selection is a very promising technique for reducing implementation cost and computational complexity. In this paper, we propose two novel fast near-optimal antenna selection algorithms based on channel capacity maximization, whose core idea is to use an effective iterative process to reduce computational complexity and retain near-optimal performance. Compared with the conventional algorithm, the proposed algorithms have lower computational complexity and achieve almost the same capacity and bit-error rate (BER) performance as the optimal one. In particular, the BER performance gain of the proposed algorithms is much larger than the conventional algorithms in correlated fading channels. Finally, computer simulations are presented to validate and demonstrate performance of our algorithms.

In this paper, we investigate adaptive resource allocation in downlink transmission of orthogonal frequency division multiple access (OFDMA) networks. A block-level resource allocation scheme is developed to maximize the overall throughput of the networks. We focus on application in long term evolution (LTE) systems where all resource blocks (RB's) allocated to the same user must use the same modulation and coding scheme (MCS). The complexity of such optimization problem is usually high. In order to reduce the complexity, we divide the original optimization problem into two suboptimal ones. We first allocate appropriate RB's to users with the best channel quality and then perform power allocation. Finally, a more effective MCS is selected, which not only ensures block-error rate (BLER) performance of RB with the poor channel condition but also makes full use of the RB's with good channel condition. Simulation results show that the proposed resource allocation scheme can improve the overall throughput of the network by 20% compared with the existing scheme when the number of users is 4 and SINR = 10 dB.

In this paper, we investigate resource block (RB) assignment and modulation-and-coding scheme (MCS) selection to maximize downlink throughput of long-term evolution (LTE) systems, where all RB's assigned to the same user in any given transmission time interval (TTI) must use the same MCS. We develop several effective MCS selection schemes by using the effective packet-level SINR based on exponential effective SINR mapping (EESM), arithmetic mean, geometric mean, and harmonic mean. From both analysis and simulation results, we show that the system throughput of all the proposed schemes are better than that of the scheme in. Furthermore, the MCS selection scheme using harmonic mean based effective packet-level SINR almost reaches the optimal performance and significantly outperforms the other proposed schemes.

In this paper, we investigate adaptive resource allocation in downlink transmission of orthogonal frequency division multiple access (OFDMA) networks. A block-level resource allocation scheme is developed to maximize the overall throughput of the networks by appropriately allocating resource blocks (RB's) and power to different users. We focus on application in long term evolution (LTE) systems where all RB's allocated to the same user must use the same modulation and coding scheme (MCS). Unfortunately, the complexity of solving such an optimization problem is prohibitively high in general. In order to reduce the complexity, we divide the original joint optimization problem into two subproblems. We first allocate appropriate RB's to users with the best channel condition and then perform power allocation. Then, a more effective MCS is selected in our scheme, which not only ensures the block-error rate (BLER) of the RB with the worst channel condition but also makes full use of the RB's with better channel conditions. Simulation results show that the proposed resource allocation scheme can improve the overall throughput of the network by 20% compared with the existing scheme when the number of users is 4 and the signal-to-interference-plus-noise ratio (SINR) is 10 dB.

In this paper, we propose an oblique projection-based robust linear receiver for spatial multiplexing systems in multi-cell environments. In our receiver design, we first obtain an oblique projection operator by using the received data, and then use such operator to extract the desired signal subspace and cancel the interference-plus-noise subspace from the received signals. After suppressing the unknown cochannel interference (CCI) included in the interference-plus-noise subspace, we further use zero-forcing (ZF) receiver to suppress the coantenna interference (CAI) and detect the desired transmitted symbols in the desired signal subspace. Finally, simulation results show that the proposed linear receiver obtains significant performance improvement over ZF receiver without considering the unknown CCI in the practical environments.

Cyclic delay diversity (CDD) is a low-complexity transmit diversity technique for coded orthogonal frequency division multiplexing (OFDM), which transforms a mutiple-input channel into an equivalent single-input channel with increased frequency selectivity and provides additional frequency diversity. However, it makes channel estimation more difficulty due to the increased frequency selectivity. In this paper, we focus on the direct estimation of the equivalent single-input channel for CDD-OFDM systems. We first propose a cyclic cross-correlation channel estimation scheme based on the equispaced constant-modulus pilot structure. Using the statistical parameters of the channel and noise derived by the result of the cyclic cross-correlation channel estimation, we propose an improved channel estimation scheme based on the Wiener filter to reduce the estimation error further. Computation simulations are given to demonstrate the effectiveness of two proposed channel estimation schemes for CDD-OFDM systems.