By integrating the Internet of Things and sensing technologies, transportation systems can achieve higher management accuracy and efficiency. This paper explores a Reconfigurable Intelligent Surface (RIS) assisted integrated wireless sensing and power transfer (IWSPT) system in traffic scenarios with channel estimation errors and obstacles. Specifically, a transmitter deployed within transportation infrastructure optimizes the beamforming vector and RIS phase shifts cooperatively. The objective is to maximize the energy received by multiple energy harvesting devices (EHDs) under the constraint of beampattern thresholds for sensing in multiple directions. The coupled optimization variables in the proposed problem yield a non-convex result, so we propose a semi-infinite relaxation-based method for solving this optimization problem. We then introduce a low-complexity optimization algorithm to address the high computational complexity of the semi-infinite relaxation approach. The proposed algorithm significantly reduces the computational burden by leveraging Taylor expansion and successive convex approximation (SCA) techniques. Simulation results validate the effectiveness and robustness of the algorithm, highlighting its practical applicability in intelligent transportation systems.

Rate-splitting multiple access (RSMA) has been studied for multiuser multiple-input multiple-output (MU-MIMO) systems especially in the presence of imperfect channel state information (CSI) at the transmitter. However, its precoding designs that maximize the sum rate normally have high computational complexity. To implement an efficient RSMA scheme for the MU-MIMO system, in this work, we propose a novel robust precoding design, which can handle imperfect CSI. Specifically, we first adopt the generalized mutual information to construct a lower bound of the objective function in the sum rate maximization problem. Then, we apply a smooth lower bound of the non-smooth sum rate objective function to construct a new optimization problem. By revealing the relationship between the generalized signal-to-interference-plus-noise ratio and the minimum mean square error matrices, we transform the constructed problem into a tractable one. After decomposing the transformed problem into three subproblems, we investigate a new alternating precoding design based on sequential solutions. Simulation results demonstrate that the proposed precoding scheme achieves comparable performance to conventional methods, while significantly reducing the computational complexity.

Emerging wireless applications pursue a paradigm shift towards the integrated system that is capable of secure data transmission and high-resolution sensing in near-field environments. Conventional far-field use-cases suffer from the fundamental limitations in security, spatial precision, and spectral coexistence. Against this backdrop, this paper investigates an integrated covert communications and sensing (ICCS) system operating in the near-field environment. Specifically, the transmitter (Alice) aims to covertly convey messages to legitimate receivers (Bobs), while circumventing the detection by the eavesdropper (Willie) as well as improving the sensing performance at the target. To elevate communication performance, we aim to maximize the achievable sum rate to jointly optimize the communication and sensing beamforming matrices at Alice. The optimization problem is subject to multiple constraints with coupled variables: the transmit power budget at Alice, the minimum communication rate requirements for Bob, the Cram$\acute {e}$r-Rao bound (CRB) constraint to ensure accurate parameter estimation in sensing, and the covertness constraint against Willie’s detection. Given the non-convex nature of the formulated problem, an efficient successive convex approximation and semidefinite relaxation algorithms are proposed. In addition, we provide a theoretical analysis to confirm the convergence behaviour of the proposed algorithm, which can achieve the near-optimal solution. Finally, the numerical results are presented to highlight the superiority of the proposed ICCS system over existing counterparts. These results numerically verify the effectiveness of the proposed approach in enhancing communication rates while maintaining sensing performance and covertness in the near-field regime.

This paper studies a new multi-agent deep reinforcement learning (MADRL) approach for unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where UAV-mounted servers provide offloading services to mobile users (MUs). We aim to minimize the total energy consumption of MUs by optimizing UAV mobility, UAV-MU association, resource allocation, and task offloading ratios. In the multi-UAV scenario, we model the MEC network as a multi-agent partially observable Markov decision process (POMDP), where each UAV agent operates with limited information for decentralized decision-making. Conventional MADRL methods manually design such UAV interaction messages, thereby incurring performance degradation. To address this issue, we propose a new neural network (NN)-based UAV interaction mechanism that generates autonomously task-oriented messages to minimize energy consumption. Such message-generating NNs are developed under the MADRL framework, which allows for joint optimization of UAV interactions and decentralized decisions in an end-to-end manner. Numerical results demonstrate that our approach outperforms traditional MADRL methods and achieves performance close to ideal centralized schemes while maintaining scalability with varying UAV numbers.

This paper investigates a near-field integrated sensing and symbiotic radio (SR) communication system supported by a reconfigurable intelligent surface (RIS). In the near-field region, the base station (BS) leverages the RIS to realize symbiotic communication performance while simultaneously performing target sensing by analyzing echo signals. The BS antenna architecture encompasses both fully-digital and dynamic metasurface antenna (DMA) configurations. An optimization problem is developed to maximize the symbiotic transmission rate for the IoT devices, subject to constraints imposed by the Cram4er-Rao bound (CRB), the signal-to-noise ratio (SNR), the RIS phase shifts, the antenna parameters and system power. An alternating optimization (AO) framework with a semidefinite relaxation (SDR) is proposed to solve the problem, while for the Lorentz-constrained phase matrix of the frequency response of DMA surface elements, we propose to apply the Riemannian conjugate gradient (RCG) algorithm to solve it. Numerical results validate the efficiency of the proposed framework, demonstrating that the near-field approach enables accurate target localization. Furthermore, where the DMA configuration achieves higher symbiotic transmission rates with lower power consumption compared to fully-digital antennas.

Future wireless networks are expected to incorporate diverse services that often lack general mathematical models. To address such black-box network management tasks, the large language model (LLM) optimizer framework, which leverages pretrained LLMs as optimization agents, has recently been promoted as a promising solution. This framework utilizes natural language prompts describing the given optimization problems along with past solutions generated by LLMs themselves. As a result, LLMs can obtain efficient solutions autonomously without knowing the mathematical models of the objective functions. Although the viability of the LLM optimizer (LLMO) framework has been studied in various black-box scenarios, it has so far been limited to numerical simulations. For the first time, this paper establishes a theoretical foundation for the LLMO framework. With careful investigations of LLM inference steps, we can interpret the LLMO procedure as a finite-state Markov chain, and prove the convergence of the framework. Our results are extended to a more advanced multiple LLM architecture, where the impact of multiple LLMs is rigorously verified in terms of the convergence rate. Comprehensive numerical simulations validate our theoretical results and provide a deeper understanding of the underlying mechanisms of the LLMO framework.

In this paper, we consider the physical layer security (PLS) problem for integrated sensing and communication (ISAC) systems in the presence of hybrid-colluding eavesdroppers, where an active eavesdropper (AE) and a passive eavesdropper (PE) collude to intercept the confidential information. To ensure the accuracy of sensing while preventing the eavesdropping, a base station transmits a signal consisting of information symbols and sensing waveform, in which the sensing waveform can be also used as artificial noise to interfere with eavesdroppers. Under this setup, we propose an alternating optimization-based two stage scheme (AO-TSS) for improving the sensing and communication performance. In the first stage, based on the assumptions that the perfect channel state information (CSI) of the AE and statistical CSI of the PE are known, the communication and sensing beamforming problem is formulated with the objective of minimizing the weighted sum of the beampattern matching mean squared error (MSE) and cross-correlation, subject to the secure transmission constraint. To tackle the non-convexity, we propose a semi-definite relaxation (SDR) algorithm and a reduced-complexity zero-forcing (ZF) algorithm. Then, the scenarios are further extended to more general cases with imperfect AE CSI and unknown PE CSI. To further improve the communication performance, the second-stage problem is developed to optimize the secrecy rate threshold under the radar performance constraint. Finally, numerical results demonstrate the superiority of the proposed scheme in terms of sensing and secure communication.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution to support wireless devices' computation-intensive services in the absence of terrestrial infrastructures. Nevertheless, the heterogeneous nature of MEC services and the security vulnerability of wireless channels present significant challenges to achieving efficient and secure computation offloading. In this paper, we investigate a multi-UAV-assisted MEC network in which wireless devices need to process diverse computation tasks. The devices can perform local computing or offload their computation tasks to UAV servers that have pre-cached relevant service programs in the presence of eavesdroppers. To facilitate secure service provisioning, we propose a cooperative jamming-based scheme in which a UAV jammer transmits jamming signals to interfere with eavesdroppers during devices' computation offloading processes. Taking into account UAV servers' constrained caching spaces and secure offloading requirements, we minimize the total task completion delay of devices by jointly optimizing multi-UAV deployment, task offloading decisions, service placement, UAV jammer's transmit power, and devices' transmit power. To tackle the formulated mixed-integer nonlinear programming problem, we design an optimization-embedding multi-agent twin delayed deep deterministic policy gradient (OE-MATD3) algorithm. Specifically, the MATD3 approach is leveraged to deal with optimization variables concerning UAVs, while a closed-form solution for devices' transmit power is derived and guides MATD3-based decision-making. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of devices' task completion delay.

In (Meng et al., 2023), an algorithm was proposed to jointly optimize user association, sensing time selection and unmanned aerial vehicle (UAV) trajectory. While deriving a solution, it was claimed that two sub-problems are convex. However, we will show that they are non-convex.

Limited feedback is a key technique for conveying channel state information (CSI) back to the base station (BS). However, its reliance on quantization to select the optimum code-word from a predefined codebook results in severe degradation in achievable rate due to quantization error. To address this issue, robust techniques should be developed. In this paper, we first examine an approximation for the second-order statistics of quantized CSI. Based on the proposed approximation, we then propose a novel robust precoding design that minimizes the conditional expectation based mean square error (MSE). Numerical results show that the proposed design significantly improves the achievable rate compared to conventional precoding schemes.

This article presents a cooperative multiagent deep reinforcement learning (MADRL) approach for unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks. An UAV with computing capability can provide task offlaoding services to ground Internet of Things devices (IDs). With partial observation of the entire network state, the UAV and the IDs individually determine their MEC strategies, i.e., UAV trajectory, resource allocation, and task offloading policy. This requires joint optimization of decision-making process and coordination strategies among the UAV and the IDs. To address this difficulty, the proposed cooperative MADRL approach computes two types of action variables, namely, message action and solution action, each of which is generated by dedicated actor neural networks (NNs). As a result, each agent can automatically encapsulate its coordination messages to enhance the MEC performance in the decentralized manner. The proposed actor structure is designed based on graph attention networks such that operations are possible regardless of the number of IDs. A scalable training algorithm is also proposed to train a group of NNs for arbitrary network configurations. Numerical results demonstrate the superiority of the proposed cooperative MADRL approach over conventional methods.

This paper presents a flexible deep learning strategy that tackles decentralized optimization tasks in multi-tier networks where wireless nodes are deployed in a hierarchical structure. Practical multi-tier networks have arbitrary node populations as well as their backhaul connections. Thus, node operations in the multi-tier network request versatile inference rules for arbitrary network configurations. To this end, we present a tree-based learning strategy which transforms the multi-tier network optimization into a collaborative inference process over random trees. For the decentralized structure, each node in a tree is equipped with dedicated deep neural network (DNN) modules. A group of these component DNNs builds a tree deep neural network (TNN) where forward pass calculations define the node interaction policy. The TNN is carefully designed such that it can be universally applied to random trees. The training mechanism is developed to involve a number of random tree instances so that the TNN can be generalized to arbitrary network configurations. As a consequence, the TNN can scale up with a large number of nodes which requires only a single training process. The scalability of the proposed framework is validated for various multi-tier network optimization problems. Numerical results demonstrate the effectiveness of the TNN over existing approaches.

Mobile edge computing (MEC) offloads computation intensive tasks of individual users to computing clouds to alleviate the computing loads. Virtual machines (VMs), in practice, are often adopted to realize the parallel computing feature of MEC clouds. A careful local interaction among VMs further reduces the overall computing latency. However, their management turns out quite challenging in practical wireless MEC networks. This paper aims at minimizing the latency of the overall MEC task with the min-max criterion. To this end, a novel distributed strategy is developed for the joint management of the task allocation and the offloading balance among VMs. This task offloading protocol is carried out through a message-passing framework that enables a simultaneous consideration of the min-max criterion about multiple MEC tasks. The numerical results demonstrate that the proposed scheduling for distributed MEC operations achieves a 40% improvement in network utility performance over existing optimization techniques.

Wireless powered internet of thing (IoT) systems allow small IoT devices to operate without accompanying dedicated power sources. A well-known protocol for such networks utilizes the harvest and then transmit concept which involves wireless energy transfer (WET) followed by wireless information transfer (WIT). We formulate two optimization problems for wireless powered IoT systems to maximize weighted sum-rate for time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) by optimizing the harvesting time and transmission time variables. First, we derive a semi-closed form solution,which achieves the global optimum, for both problems. The proposed approach is highly computationally efficient for large IoT networks. We prove that the scalar equations in both TDMA and NOMA maintain a unique solution which can be found via bisection. It is revealed that when the device’s circuit power consumption is negligible, NOMA outperforms TDMA. However, when devices consume large circuit power, TDMA is more efficient than NOMA. Numerical results determine a critical point where NOMA surpasses TDMA in weighted sum-rate if plotted versus WET transmit power. The critical point depends on the WET power, device’s circuit power consumption, conversion efficiency and saturation level of the (non-)linear energy harvester, and finally the number of devices and their associated weights.

Mobile edge computing (MEC) pushes computing and caching resources close to the network edge, which allows devices to offload computation-intensive tasks to MEC servers. Considering that wireless signals may be easily blocked by obstacles, reconfigurable intelligent surface (RIS) has emerged as a promising technique to improve the efficiency of computation offloading. In this paper, we consider a RIS-assisted MEC network, where a MEC server caches service programs required for task execution and a RIS helps computation offloading in the presence of eavesdropping. Due to the diversity of services and the broadcast nature of wireless channels, it is challenging to achieve efficient and secure computation offloading in this network. Therefore, we first formulate a task completion delay minimization problem by jointly optimizing service caching, computation offloading decisions, RIS passive beamforming, and transmit power subject to the constraints of secure offloading rate and limited storage space. To address the highly non-convex nature of the problem, we then develop a dual-layer optimization algorithm via a vertical decomposition on its layered structure. The outer-layer problem, which deals with service caching and computation offloading decisions, is solved by a cross-entropy-based caching and offloading learning algorithm. For the inner-layer problem that optimizes RIS passive beamforming and transmit power, we utilize a horizontal decomposition by invoking the block coordinate descent method. Finally, simulation results demonstrate that the proposed scheme exhibits performance improvements compared to several baseline schemes.

This article proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating in the millimeter-wave band. Specifically, the ISAC system consists of a radar subsystem and a communication subsystem to detect multiple targets and communicate with the users simultaneously. The IRS is used to configure the radio propagation environment by changing the phase of the radio signal to enhance the communication transmission rate. In the proposed scheme, we first derive a closed-form solution for the radar signal covariance matrix to generate a radar beampattern in the angle of interest. Then, we jointly optimize the beamforming vector of the communication subsystem and the IRS phase shifts to enhance the communication transmission rate. To decouple the multiple variables to be optimized, the alternating optimization and quadratic transformation methods are applied to determine the communication beamforming vector and the IRS phase shifts. Specifically, we utilize the majorization minimization and the complex circle manifold methods to compute the IRS phase shifts. Simulation results verify the effectiveness of the proposed algorithm and demonstrate that an IRS can improve the performance of ISAC systems.

It has been well known that the achievable rate of multiuser multiple-input multiple-output systems with limited feedback is severely degraded by quantization errors when the number of feedback bits is not sufficient. To overcome such a rate degradation, we propose new robust precoding designs which can compensate for the quantization errors. In this paper, we first analyze the achievable rate of traditional precoding designs for limited feedback systems. Then, we obtain an approximation of the second-order statistics of quantized channel state information. With the aid of the derived approximation, we propose robust precoding designs in terms of the mean square error (MSE) with conditional expectation in non-iterative and iterative fashions. For the non-iterative precoding design, we study a robust minimum MSE (MMSE) precoding algorithm by extending a new channel decomposition. Also, in the case of iterative precoding, we investigate a robust weighted MMSE (WMMSE) precoding to further improve the achievable rate. Simulation results show that the proposed precoding schemes yield significant improvements over traditional precoding designs.

This article studies secure communication in an Internet of Things (IoT) network, where the confidential signal is sent by an active refracting reconfigurable intelligent surface (RIS)-based transmitter, and a passive reflective RIS is utilized to improve the secrecy performance of users in the presence of multiple eavesdroppers. Specifically, we aim to maximize the weighted sum secrecy rate by jointly designing the power allocation, transmit beamforming (BF) of the refracting RIS, and the phase shifts of the reflective RIS. To solve the nonconvex optimization problem, we propose a linearization method to approximate the objective function into a linear form. Then, an alternating optimization (AO) scheme is proposed to jointly optimize the power allocation factors, BF vector, and phase shifts, where the first one is found using the Lagrange dual method, while the latter two are obtained by utilizing the penalty dual decomposition method. Moreover, considering the demands of green and secure communications, by applying Dinkelbach’s method, we extend our proposed scheme to solving a secrecy energy maximization problem. Finally, simulation results demonstrate the effectiveness of the proposed design.

Intelligent reflecting surface (IRS) is a promising technique to extend the network coverage and improve spectral efficiency. This paper investigates an IRS-assisted terahertz (THz) multiple-input multiple-output (MIMO)-nonorthogonal multiple access (NOMA) system based on hybrid precoding with the presence of eavesdropper. Two types of sparse RF chain antenna structures are adopted, i.e., sub-connected structure and fully connected structure. First, cluster heads are selected for each beam, and analog precoding based on discrete phase is designed. Then, users are clustered based on channel correlation, and NOMA technology is employed to serve the users. In addition, a low-complexity forced-zero method is utilized to design digital precoding in order to eliminate inter-cluster interference. On this basis, we propose a secure transmission scheme to maximize the sum secrecy rate by jointly optimizing the power allocation and phase shifts of IRS subject to the total transmit power budget, minimal achievable rate requirement of each user, and IRS reflection coefficients. Due to multiple coupled variables, the formulated problem leads to a non-convex issue. We apply the Taylor series expansion and semidefinite programming to convert the original non-convex problem into a convex one. Then, an alternating optimization algorithm is developed to obtain a feasible solution of the original problem. Simulation results verify the convergence of the proposed algorithm, and deploying IRS can bring significant beamforming gains to suppress the eavesdropping.

This work presents a joint design of user association and resource allocation in a heterogeneous network, which is comprised of a single macro base station and a group of small base stations interconnected through wireless backhaul. In such a configuration, we optimize user association and resource allocation so that the total sum of generalized utilities is maximized. This problem is cast as a combinatorial formulation with a fractional objective. To handle this design challenge, we develop a novel message-passing framework to obtain an efficient joint autonomous solution for user association and resource allocation. The simulation results show that the proposed algorithm outperforms existing techniques with various network utility functions.

In this paper, we introduce an intelligent reflecting surface (IRS)-assisted wireless powered heterogeneous network (WPHN) consisting of two heterogeneous groups of devices. Specifically, one group of devices, i.e., energy-harvesting devices (EHDs), are charged by external energy supplies, while the other group of devices, i.e., non-energy-harvesting devices (NEHDs), are powered by internal energy supplies. An IRS aims to participate in the wireless energy transfer (WET) in downlink and the wireless information transfer (WIT) in the uplink. A sum throughput maximization problem is formulated subject to the constraints of individual energy consumption, transmission time scheduling, and IRS phase shifts. To cope with the non-convexity of the problem, we first derive the optimal IRS phase shifts of the uplink WIT independently. Next, the semi-definite programming (SDP) relaxation is adopted to recast this non-convex problem into the convex one, which can be numerically solved. Then, a novel low-complexity scheme is developed to gain more insights and mitigate the computational complexity induced by the SDP relaxation. In particular, the dual problem and Karush-Kuhn-Tucker conditions are first utilized to obtain the optimal transmission time scheduling. Then, we propose a method based on Riemannian manifold optimization to compute the optimal IRS phase shifts of the downlink WET in closed-form. Finally, simulation results are presented to verify the optimality of our proposed scheme, and highlight the benefits induced by the IRS to coordinate these heterogeneous devices.

This paper studies a deep reinforcement learning (DRL) approach for the unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks where a UAV-mounted server offloads computation tasks of mobile users (MUs). We aim at minimizing the energy consumption of the MUs by adjusting UAV mobility, UAV-MU association, computation resource allocation, and task offloading rules. This requires an online and joint optimization of different types of variables constructing heterogeneous solution spaces. To realize real-time optimization strategies, we propose an online DRL method based on the twin-delayed deep deterministic policy gradient (TD3) framework. The joint optimization of heterogeneous action variables is tackled by a novel actor neural network that partitions the high-dimensional action set into several solution spaces. In addition, the proposed TD3 framework achieves adaptability to new task offloading requests through our proposed training and execution strategy. Numerical results verify the effectiveness of the proposed DRL architecture over benchmark schemes.

This paper proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating at the millimeter-wave (mmWave) band. Specifically, the ISAC system combines communication and radar operations and performs on the same hardware platform, detecting and communicating simultaneously with multiple targets and users. The IRS dynamically controls the amplitude or phase of the radio signal via the reflecting elements to reconfigure the radio propagation environment and enhance the transmission rate of the ISAC system in the mmWave band. By jointly designing the radar signal covariance (RSC) matrix, the beamforming vector of the communication system, and the IRS phase shift, the ISAC system transmission rate can be improved while matching the desired waveform for radar. The problem is non-convex due to multivariate coupling, and thus we decompose it into two separate subproblems. First, a closed-form solution of the RSC matrix is derived from the radar desired waveform. Next, the quadratic transformation (QT) technique is applied to the subproblem, and then alternating optimization (AO) is applied to determine the communication beamforming vector and the IRS phase shift. Also, we derive a closed-form solution for the formulated problem, effectively decreasing computational complexity. Finally, the simulations verify the effectiveness of the algorithm and demonstrate that the IRS can improve the performance of the ISAC system.

In this article, we study a data-driven real-time machine learning method for end-to-end wireless systems for the Internet of Things (IoT). For a two-way communication link between two IoT devices, we propose an efficient learning algorithm that can train the autoencoder-based transmitter and receiver in each device without needing to know the channel between two devices. To this end, we adopt the conditional generative adversarial network (cGAN) that can learn an output distribution of the channel for a given conditioning signal. Our proposed training method consists of the link update stage and the self-update stage. In the link update stage, two devices transmit the training data and update their own receiver and the cGAN simultaneously. Subsequently, in the self-update stage, the two devices train their transmitters at the same time for the given receivers and cGANs. Our proposed real-time training method is updated without the knowledge of the channel models nor information feedback for training. Finally, we demonstrate that the proposed training method achieves significant performance gains over conventional schemes in various practical communication scenarios.

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes.

This work develops a joint design of user association and time allocation for wireless powered communication networks. A harvest-then-transmit protocol is applied with base stations (BSs) transfer wireless energy to users in the first downlink phase. Then, the users utilize the harvested energy for their information transmission to the BS in the subsequent uplink phase. In this configuration, we employ a general$\alpha $–fair utility to maximize the network throughput. In particular, the maximization of sum-rate, proportional fairness and minimum rate are investigated individually. We introduce a new message-passing based framework to provide an efficient distributed solution for the user association and optimize the time allocation between the downlink and uplink phase. To achieve this joint goal, each user selects a BS in a distributed manner to maximize the$\alpha $–fair utility. After identifying the user association, the time allocation is determined by an efficient line search technique. Simulation results show that the proposed algorithm outperforms existing approaches.

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

Bistatic backscatter communication is emerged as a promising technique to significantly enlarge the lifetime of Internet of Things (IoT) network due to its inherently low-power passive component. However, the effective communication range is limited to only several meters. This article studies the tag circuit shunt network, and propose three modes, namely series mode, parallel mode, and mixed mode, to adjust circuit load impedance of the tag to extend the communication range as well as address the integrated circuit (IC) power supply problem. Specifically, we formulate the bit error rate (BER) minimization problems for the three modes by changing the reflection coefficients, subject to power supply constraint. The resulting problems are shown to be nonconvex fractional optimization problems, which are hard to be solved optimally in general. We first obtain a globally optimal solution to the series mode problem by exploiting the hidden monotonic structure based on monotonic optimization theory. Subsequently, we propose a low-complexity iterative suboptimal algorithm for the three modes based on the successive convex approximation (SCA) techniques. Numerical results show that when the direct link is available, the mixed mode outperforms the parallel mode and series mode, and can adaptively adjust the reflection coefficient to satisfy the requirement of IC power supply. In contrast, when the direct link is unavailable, the series mode is the best choice in terms of IC power supply. In addition, traditional on-off keying modulation is shown to be suitable for a low IC power supply, whereas a shunt network is necessary for high of power supply. Furthermore, the performance of SCA-based method closely approaches the optimal solution while with much lower complexity.

In this article, we study a robust beamforming design for multiuser multiple-input–multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet-of-Things (IoT) devices under the nonlinear energy harvesting (EH) model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve EH fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, the original robust problem is transformed into a two-layer optimization problem, where the inner layer problem is reformulated as semidefinite programming (SDP) and the outer layer problem is solved by a one-dimensional (1-D) line search algorithm. In addition, in order to reduce computational complexity, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method achieves the same performance as the two-layer algorithm with much lower complexity.

Terahertz (THz) communication is a promising technique for chip-to-chip communication and wireless personal area networks. In this paper, we present an experimental study and design to realize such THz communication systems. We develop two different chip sets for on-off-keying (OOK) modulation based THz transceivers which include carrier generators, modulators, THz amplifiers, and baseband amplifiers. Specifically, the first chip set integrates the circuit blocks for the OOK modulation without the THz amplifier for short-range communication. In addition, the second chip set design includes the THz amplifier modules to extend the coverage of transmission. For these two chip sets, we experimentally demonstrate the feasibility of the wireless communication at THz frequency bands and assess performance using the bit error rate (BER) analysis. We estimate the BER by calculating the signal-to-noise ratio (SNR) based on the eye diagram and compare with actual BER measurements and Monte Carlo simulations. We also address the impact of the distance, the transmit power, and the data rate for the proposed THz transceivers based on the link budget analysis, and confirm the accuracy of the derived BER expression.

In this article, we study an energy efficient management of two-tier heterogeneous cellular networks (HetNets) which consist of one macro base station (BS) and multiple micro base stations. This article presents a distributed user association algorithm that maximizes the network-wide energy efficiency (EE) in HetNets. A subset of BSs that support only a small number of users can be turned off to save the energy consumption. By turning off BSs in the HetNet and offloading serving users to adjacent active BSs, the network-wide energy consumption is minimized, while the sum throughput is maximized. To solve the problem efficiently, we introduce a new approach based on a message-passing framework and derive a distributed load balancing algorithm. The proposed method provides a very efficient solution with reduced computational complexity compared to existing schemes. Simulation results verify that the proposed algorithm outperforms other conventional load balancing strategies.

This work develops ajoint design of user association and wireless backhaul bandwidth allocation with unequal user priorities in two-tier cellular heterogeneous networks (HetNets). A large number of transmit antennas is installed at a macro base station (MBS), while small base stations (SBSs) are equip with a single antenna and are connected with the MBS through wireless backhaul. Reverse time-division duplex (RTDD) scheme and dynamic frequency reuse are applied to manage inter-cell and intra-cell interference between users. We jointly determine user association and bandwidth allocation in order to maximize the sum throughput weighted by different user priorities. Based on a message-passing framework, we propose a distributed algorithm which efficiently determines user association and bandwidth allocation. Simulation results show that the proposed algorithm outperforms existing approaches consistently.

In this work, we study a scenario where a distant central monitor covertly wiretaps the communication between a pair of suspicious users via several single-antenna full-duplex spoofing relays and a cooperative jammer for wireless surveillance. Under an adaptive transmission policy where the data rate of the suspicious users is determined based on the channel condition at the receiver, the spoofing relays intercept and forward manipulated information to control the data rate of the suspicious users for effective eavesdropping. We first propose an inter-relay interference suppression method to eliminate undesirable signals at the spoofing relays. Then, we provide a joint optimization technique for relay weights and jamming power to maximize the eavesdropping rate by a two-layer semi-definite relaxation approach. Simulation results show that the proposed solution outperforms other practical baseline schemes.

Magnetic induction (MI) communication uses the mutual inductance between coil antennas to achieve the communication process. As MI communication is not affected by most factors of the propagation environment, we can achieve the detection and monitoring tasks through a stealth operation. In the MI system, the path loss is the most important parameter when estimating the channel and the communication range. The pipeline is used to transport the liquid, and thus it is a special scenario of the underwater communication. Since the index of refraction of the boundaries is different, there are three possible scenarios at the boundaries, i.e. semi-reflection, total reflection, and no reflection. Hence, the distribution of the magnetic field is changed and the path loss is difficult to be estimated. In this paper, we build an MI-based software-defined radio (SDR) system testbed in a water tank to simulate the underwater pipeline. Then, we adopt a deep neural network (DNN) with supervised learning to estimate the path loss of the MI communication. Also we discuss the communication range in the theoretical path loss model and our proposed model.

In this paper, we investigate simultaneous wireless information and power transfer (SWIPT) where a multi-antenna transmitter conveys information and energy simultaneously to a multi-antenna receiver equipped with time switching (TS) circuits for an energy harvesting (EH) mode and an information decoding (ID) mode. In contrast with conventional uniform TS (UTS) structure where all the receive antennas at the receiver apply a single TS circuit, to improve the SWIPT performance, we suggest a general dynamic TS (DTS) receiver architecture which consists of an individual TS circuit for each antenna. We aim to analyze the achievable rate-energy (R-E) tradeoff of the DTS system by jointly optimizing the covariance matrices at the transmitter and the time durations for the EH and the ID modes of the receive antennas. To determine the boundary points of the R-E region, we suggest the globally optimal algorithm for the rate maximization problem via convex optimization techniques. Numerical examples verify the efficacy of the proposed DTS over conventional UTS methods.

In this work, we investigate a proactive eavesdropping system where a central monitor covertly wiretaps the communications between a pair of suspicious users via multiple intermediate nodes. For successful eavesdropping, it is required that the eavesdropping channel capacity is higher than the data rate of the suspicious users so that the central monitor can reliably decode the intercepted information. Hence, the intermediate nodes operate in two different modes, namely eavesdropping mode and jamming mode, to facilitate eavesdropping. Specifically, the eavesdropping nodes forward the intercepted data from the suspicious users to the central monitor, while the jamming nodes transmit jamming signals to proactively control the data rate of the suspicious users. We propose an efficient deep learning-based approach to identify the optimal mode selection for the intermediate nodes and the optimal transmit power for the jamming nodes. Numerical results confirm the significant performance gain of our proposed method both in terms of performance and time complexity over conventional schemes.

In this letter, we propose an online power allocation (PA) method to maximize energy efficiency (EE) in energy harvesting systems with realistic battery constraints for two receivers. The optimization problem in this system configuration is challenging since it has a non-convex fractional form and the information of channel quality and the harvested energy can only be obtained causally in practice. To overcome these issues, we design time-average EE maximization through Lyapunov optimization techniques where the transmission power is computed with current battery information and channel fading. Moreover, based on random matrix theory, we present a simplified online EE algorithm. Numerical experiments verify that the proposed PA outperforms conventional online approaches with much reduced computational complexity.

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this letter proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL.

This paper studies simultaneous wireless information and power transfer (SWIPT) techniques for point-to-point multiple-input multiple-output channels, where a multi-antenna transmitter conveys information and energy at the same time to a multi-antenna receiver equipped with time switching (TS) circuits for an energy harvesting (EH) mode and an information decoding (ID) mode. Unlike conventional uniform TS (UTS) structure where all the receive antennas at the receiver employ a single TS circuit, in this paper, we propose a general dynamic TS (DTS) receiver architecture which has an individual TS circuit for each antenna. In the proposed DTS, the operation modes of the antennas can be dynamically changed to improve SWIPT performance. We aim to identify the achievable rate-energy (R-E) tradeoff of the DTS protocol for both linear and non-linear EH models by maximizing the information rate subject to the EH constraint. This results in joint optimization of the transmit covariance matrices and the time durations for the EH and the ID modes of the receive antennas, which is jointly non-convex in general. To tackle the non-convexity of the original problem, the successive convex approximation technique is adopted by addressing a series of approximated convex problems. As a result, efficient optimization algorithms are proposed for determining the boundary points of the achievable R-E region. We also provide a low-complexity algorithm which achieves near-optimal performance with much reduced complexity. Numerical results demonstrate that the proposed DTS presents significant performance gains over conventional UTS approaches.

Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This paper studies the optimization of the analog and digital components of the hybrid beamforming solution for remote radio heads (RRHs) in a downlink cloud radio access network architecture. Digital processing is carried out at a baseband processing unit (BBU) in the “cloud,” and the precoded baseband signals are quantized prior to transmission to the RRHs via finite-capacity fronthaul links. In this system, we consider two different channel state information (CSI) scenarios: 1) ideal CSI at the BBU and 2) imperfect effective CSI. The optimization of digital beamforming and fronthaul quantization strategies at the BBU as well as analog radio-frequency (RF) beamforming at the RRHs is a coupled problem since the effect of the quantization noise at the receiver depends on the precoding matrices. The resulting joint optimization problem is examined with the goal of maximizing the weighted downlink sum-rate and the network energy efficiency. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraint on the RF beamforming matrices. For the case of perfect CSI, a block coordinate descent scheme is proposed based on the weighted minimum-mean-square-error approach by relaxing the constant modulus constraint of the analog beamformer. Also, we present the impact of imperfect CSI on the weighted sum-rate and network energy efficiency performance, and the algorithm is extended by applying the sample average approximation. The numerical results confirm the effectiveness of the proposed scheme and show that the proposed algorithm is robust to estimation errors.

In this paper, we study a legitimate proactive eavesdropping scenario where a central monitor covertly wiretaps the communications between a pair of suspicious users via multiple intermediate nodes. For this system, it is necessary to ensure the eavesdropping channel capacity higher than the data rate of the suspicious users so that the central monitor can reliably decode the intercepted information. To this end, the intermediate nodes operate in either eavesdropping or jamming modes. The eavesdropping nodes forward the intercepted data from the suspicious users to the central monitor, while the jamming nodes transmit jamming signals to control the data rate of the suspicious users if necessary. We optimize the mode selection and transmit power of each intermediate node to achieve the maximum eavesdropping rate. Two different scenarios are investigated, in which the intermediate nodes communicate with the central monitor through wired links or wireless channels. For both configurations, globally optimal solutions are developed for joint mode selection and transmit power optimization problems. We also propose low-complexity methods which achieve near-optimal performance with reduced computational complexity. The numerical results validate the efficiency of our proposed algorithms.

This paper studies unmanned aerial vehicle (UAV) aided wireless communication systems where a UAV serves uplink communications of multiple ground nodes by flying the area of the interest. We aim to maximize the minimum average rate of the UAV by jointly optimizing the UAV trajectory and the ground nodes' uplink transmit power. However, this problem is shown to be non-convex in general, and thus existing convex optimization techniques and algorithms cannot be directly applied. By employing the successive convex approximation (SCA) techniques, we present an efficient algorithm which is guaranteed to converge to at least a local optimal point for the non-convex problems. To this end, proper convex approximations are derived for the non-convex constraints. Numerical results demonstrate the proposed algorithm performs better than baseline scheme.

In this paper, we investigate simultaneous wireless information and power transfer (SWIPT) where a multi- antenna transmitter sends data and energy to single antenna receivers with a time switching (TS) circuit. In this system, a general joint TS protocol is introduced which includes conventional TS schemes as special cases. We aim to analyze the achievable rate region of the joint TS under energy harvesting constraint at the receivers by jointly optimizing the TS ratios and the transmit covariance matrices. To tackle non-convex rate region characterization problems, we first decouple the original problems into several subproblems with fixed auxiliary variables. Then, the globally optimal TS ratios and the transmit covariance matrices are computed via convex optimization techniques. Numerical examples verify the efficacy of the proposed joint TS over conventional methods.

In this paper, we consider a legitimate proactive eavesdropping scenario where a central monitor tries to intercept the information exchanged between a pair of suspicious entities through amplify-and-forward full- duplex relays and a cooperative jammer. Specifically, the eavesdropping relays simultaneously listen to the suspicious transmitter and forward the eavesdropped information to the central monitor. At the same time, the jammer broadcasts the jamming signal to maintain the suspicious data rate below the channel capacity of the central monitor so that the eavesdropped information can be successfully decoded by the central monitor. In this system, we jointly design the relay precoders at the eavesdropping relays and the transmit covariance matrix at the jammer to maximize the eavesdropping rate by a two-layer semi-definite relaxation approach. Simulation results verify the efficiency of our proposed solution and show considerable performance gains over conventional schemes.

In this paper, we study a multi-user multiple-inputmultiple- output secrecy simultaneous wireless information and power transfer (SWIPT) channel which consists of one transmitter, one cooperative jammer (CJ), multiple energy receivers (potential eavesdroppers, ERs), and multiple co-located receivers (CRs). We exploit the dual of artificial noise (AN) generation for facilitating efficient wireless energy transfer and secure transmission. Our aim is to maximize the minimum harvested energy among ERs and CRs subject to secrecy rate constraints for each CR and total transmit power constraint. By incorporating norm-bounded channel uncertainty model, we propose a iterative algorithm based on sequential parametric convex approximation to find a near-optimal solution. Finally, simulation results are presented to validate the performance of the proposed algorithm outperforms that of the conventional AN-aided scheme and CJaided scheme.

Wireless energy transfer over radio frequency has been recognized as a promising alternative solution to powering the low power low complexity wireless equipments in future cellular networks. In this work, simultaneous wireless information and power transfer (SWIPT) operation for secure multi-user multipleinput multiple-output (MIMO) broadcast system is investigated with imperfect channel state information at the transmitter. The corresponding robust secure beamforming problem is studied, where the transmit power is to be minimized subject to the secrecy rate outage probability constraint for legitimate information users, and the harvested energy outage probability constraint for energy harvesting receivers. The original problem is shown to be non-convex due to the presence of the probabilistic constraints. These outage constraints are then transformed into deterministic forms by using the Bernstein-type inequalities. Based on successive convex approximation (SCA), a low-complexity approach, which reformulates the original problem as second order cone programming (SOCP), is proposed. Simulation results show that the proposed scheme outperforms the conventional method with lower complexity.

This work deals with a joint downlink and uplink design for wireless powered cloud radio access network, where a baseband processing unit (BBU) communicates with downlink and uplink users through multiple remote radio heads (RRHs) connected to the BBU via finite-capacity fronthaul links. In the downlink, the RRHs send information to the downlink users and transfer energy to the uplink users (ULUs). By using the harvested energy, each ULU transmits information to the BBU through the uplink channels. In this work, we maximize the uplink sum-rate of the ULUs subject to the minimum downlink rate constraint as well as the per-node transmit power and the fronthaul capacity constraints. Numerical results confirm the advantages of the proposed algorithm compared to baseline schemes.

This paper studies beamforming design problems for multi-cell multi-user downlink networks with simultaneous wireless information and power transfer. In this system, base stations (BSs) concurrently transfer information and energy to multiple single-antenna information decoding (ID) and energy harvesting (EH) users. We aim to maximize energy harvesting efficiency (EHE), which is defined as the ratio of the harvested energy at the EH users to the amount of energy consumption at the BSs, while guaranteeing quality-of-service constraint for each ID user. First, for the centralized case where global channel state information (CSI) is available at all BSs, we propose a centralized beamforming method based on the semi-definite relaxation techniques. Next, in order to reduce the backhaul signaling overhead, a decentralized algorithm is presented where each BS computes its beamforming vector by only using local CSI. Simulation results show that the proposed algorithm offers a significant EHE performance gain over conventional schemes.

In this work, we investigate a wireless powered communication network (WPCN) where multiple eavesdroppers attempt to intercept the information between a hybrid access-point (H-AP) and an energy harvesting (EH) user. During the first energy transfer (ET) phase, the EH user and an EH cooperative jammer harvest energy from the transmitted signals of the H- AP. Then, in the next information transfer (IT) phase, the user sends confidential information to the H-AP while the jammer broadcasts artificial noises to the eavesdroppers by utilizing their previously harvested energy. We particularly consider optimization of the time allocation between the ET and the IT phases by which the secrecy rate is maximized. To cut down a computational burden, a low-complexity closed-form solution of the time allocation factor with some interesting behaviors will be proposed by a worst-case approximation. Through simulation results, we evaluate the performance of our proposed scheme and show that a performance gain compared to conventional schemes becomes clearer with the increased number of eavesdroppers.

This paper investigates an unmanned aerial vehicle (UAV)-aided wireless powered communication network where a mobile hybrid access point serves multiple energy-constrained ground terminals (GTs) in terms of wireless energy transfer and data collection. Specifically, to support information transmission of the GTs, the mobile UAV first transfers wireless energy in the downlink. Then, by harvesting this wireless energy, the GTs transmit their uplink information signals to the UAV in a time division multiple access manner. In this system, we jointly optimize the trajectory of the UAV and the uplink power control policy in order to maximize the minimum throughput of the GTs. By applying the concave-convex procedure, we propose an iterative algorithm which efficiently identifies a locally optimal solution. Simulation results verify the efficiency of the proposed algorithm compared to conventional schemes.

In this paper, we study full-dimension multiple-input multiple-output (FD-MIMO) systems where a base station is equipped with multiple panel array (MPA) antennas. As the MPA is regarded as a promising means of practical implementation for the FD-MIMO, it is important to characterize a channel modeling and a codebook design. Thus, we first examine the exponential correlation model and the three-dimensional correlation model for the FD-MIMO with MPA. In addition, since antenna elements are not uniformly spaced in the MPA systems, a discrete Fourier transform (DFT) codebook may not be suitable due to phase ambiguity (PA). Thus, we investigation three new codebook design methods which reflect the PA. In numerical results, we confirm that PA compensation and per-panel quantization are effective for MPA systems.

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output (MIMO) secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem is shown to be a single-variable optimization that can be solved by one-dimensional (1-D) line search. To reduce computational complexity, a sequential parametric convex approximation (SPCA) method is proposed to find a near-optimal solution. Furthermore, tightness of the relaxation for the 1-D search method is validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D search method but with much lower complexity.

This paper studies device-to-device wireless communications, where two energy-constrained Internet-of-Things (IoT) nodes, which do not have constant power supplies, wish to exchange their information with each other. Because of small form factor, the IoT nodes are normally equipped with simple energy storages, which might suffer from a high self-discharging effect. Therefore, the energy stored in each node would not be available after a few time duration. In this system, we investigate power splitting (PS)-based energy exchange methods by exploiting radio frequency (RF) wireless energy transfer techniques, and propose a new concept called wireless information and power exchange (WIPE). In this WIPE protocol, each node operates either in a transmit mode and a receive mode at each time slot. First, a transmit node sends the information signal to a receive node which utilizes a PS circuit for information decoding and energy harvesting. Then, the harvested energy of the receive node is stored in the energy storage. At the consecutive time slot, two nodes switch their operations, i.e., the receive node in the previous time slot now operates in a transmit mode which transfers RF signals by using the harvested energy. This procedure continues by changing the operations of two nodes at each time slot. For the proposed WIPE protocol, we provide two different PS ratio optimization schemes which maximize the weighted sum throughput performance according to the level of channel state information (CSI) knowledge. For the ideal full CSI case where the CSI for all time slots is known in advance, the globally optimal PS algorithm is presented by applying convex optimization techniques. Also, for a practical scenario where only the causal CSI is available, we propose an efficient PS optimization method which achieves performance almost identical to the ideal full CSI case. Simulation results verify that the WIPE protocol with the proposed PS optimization techniques performs better than conventional schemes.

This paper studies beamforming design problems for multi-cell multi-user downlink networks with simultaneous wireless information and power transfer (SWIPT). In this system, multi-antenna base stations (BSs) concurrently transfer information and energy to multiple single-antenna information decoding (ID) and energy-harvesting (EH) users. We aim to maximize EH efficiency (EHE) that is defined as the ratio of the harvested energy at the EH users to the amount of energy consumption at the BSs while guaranteeing the quality-of-service constraint for each ID user. The EHE metric quantifies the efficiency of the power transfer capability of the SWIPT network. For the EH operation, both an ideal linear model and a practical non-linear model are individually investigated. We optimally solve this non-convex problem in two different scenarios according to the cooperation level among the BSs. First, for the centralized case, where global channel state information (CSI) is available at all BSs, we propose a centralized beamforming method based on the semi-definite relaxation and the successive convex approximation techniques. Next, in order to reduce the backhaul signaling overhead, decentralized algorithms are presented where each BS computes its beamforming vector by only using local CSI. The simulation results show that the proposed SWIPT beamforming algorithms offer a significant EHE performance gain over conventional schemes.

In this paper, we study a wireless powered communication network (WPCN) in a generalN-user interference channel (IFC), where a hybrid access-point (H-AP) in each cell supports its corresponding user. In this multi-cell environment, the H-AP first sends the energy signal to charge users in the downlink (DL) phase, while in the subsequent uplink (UL) phase, each user transmits its information signal to the corresponding H-AP utilizing the previously harvested energy. For the WPCN in this IFC scenario, cross-link interference occurs due to asynchronous time allocation of the DL and the UL amongNcells which significantly affects the overall performance. To handle the interference issue efficiently, we jointly optimize the DL and UL time allocation of each cell as well as the transmit power allocation at the H-APs and the users so that the weighted sum-rate of UL information transmission is maximized. To tackle non-convexity of the weighted sum-rate maximization problem, we propose an iterative algorithm where the time allocation and the transmit power are updated based on the weighted minimum mean square error criteria and the gradient projection method, respectively. Furthermore, we consider two simple protocols where the DL time allocation of each cell is synchronized and present resource allocation method, respectively. In simulation results, we verify that the proposed algorithm for the asynchronous protocol outperforms conventional schemes.

This paper considers multiple-input single-output simultaneous wireless information and power transfer (SWIPT) broadcast channels (BCs) where a multi-antenna transmitter serves single antenna receivers each equipped with a time switching (TS) circuit for information decoding (ID) and energy harvesting (EH). To be specific, we study a scheme which jointly determines the time durations allocated for the ID and the EH modes at each receiver and the transmit covariance matrices at the transmitter. Then, we present a general joint TS protocol for the SWIPT BC which includes conventional TS schemes as special cases. In order to fully characterize the performance of the proposed joint TS systems, the achievable rate region is analyzed under EH constraint at the receivers. By applying the rate profile methods, we identify the optimal TS ratios and the optimal transmit covariance matrices which achieve the boundary points of the rate region. Then, the boundary points are obtained by solving the average transmit power minimization problems with individual rate constraints at the receivers. To solve these non-convex problems, the original problems are decoupled into subproblems with fixed auxiliary variables. Then, the globally optimal TS ratios and the transmit covariance matrices are computed by finding the optimal auxiliary variables via convex optimization techniques. Numerical results demonstrate that the proposed joint TS scheme outperforms conventional TS methods.

In this paper, we consider a legitimate proactive eavesdropping scenario where a distant central monitor covertly wiretaps the communication between a pair of suspicious users via several multi-antenna full-duplex spoofing relays and a multi-antenna cooperative jammer. Assuming an adaptive transmission policy at the suspicious users, the spoofing relays not only intercept but also forward the manipulated information to control the data rate of the suspicious users in collaboration with the jammer. We provide a technique which jointly optimizes the receive combining vector at the central monitor, the precoders at the relays, and the transmit covariance matrix at the jammer for maximizing the eavesdropping rate. To reveal some fundamental properties of the optimal operation, we first study a single-relay system. It is shown that when the central monitor experiences a poor eavesdropping channel link, the spoofing relay and the jammer should transmit destructive signals and jamming signals, respectively. In this case, the suspicious users are forced to decrease their data rate, while the intercepted information can be successfully decoded by the central monitor. On the other hand, when the eavesdropping channel condition is favorable, the spoofing relay forwards constructive signals to further increase the data rate at the suspicious users in a way that more information can be intercepted from the suspicious users. We then formulate a general eavesdropping rate maximization problem for multiple relays and present a semi-definite relaxation approach. A low-complexity design is also proposed based on our analysis for the single-relay system. Simulation results verify the efficiency of the proposed solutions compared to other baseline schemes in various practical setups.

In this paper, we consider a proactive eavesdropping scenario, where a central monitor tries to intercept the information exchanged between a pair of suspicious entities through amplify-and-forward full-duplex relays and a cooperative jammer. Specifically, the eavesdropping relays simultaneously listen to the suspicious transmitter and forward the eavesdropped information to the central monitor. At the same time, the jammer broadcasts jamming signals to maintain the data rate of the suspicious users below the channel capacity of the central monitor so that the eavesdropped information can be successfully decoded by the central monitor. In this system, we jointly design the receive combining vector at the central monitor, the relay precoders at the eavesdropping relays and the transmit covariance matrix at the jammer to maximize the eavesdropping rate. First, we examine the case of a single eavesdropping relay equipped with a single antenna and provide some useful insights. Also, an effective two-layer optimization method is proposed to obtain the globally optimal solution. Then, we study a general case of multiple eavesdropping relays with multiple antennas, and solve a semi-definite relaxation problem. Numerical results verify the efficiency of our proposed solutions for both single and multiple relay cases and show considerable performance gains over conventional schemes.

In this paper, we study vector parameter estimation in multiple-input multiple-output wireless-powered sensor networks (WPSNs) where sensor nodes operate by harvesting the radio frequency signals transmitted from energy access points (E-APs). We investigate a joint design of sensor data precoders, a fusion rule, and energy covariance matrices to minimize the mean square error (MSE) of the parameter estimate based on a non-linear energy harvesting model. First, we propose a centralized algorithm to solve the MSE minimization problem. Next, to reduce the computational complexity at the fusion center (FC) and feedback overhead from the sensors to the FC, we present a distributed algorithm to locally compute the precoders and the energy covariance matrices. We employ the alternating direction method of multipliers technique to minimize the MSE in a distributed manner without any coordination from the FC. In the proposed distributed algorithm, each sensor node calculates its own precoders and determines the local information of the fusion rule, and then messages are broadcast to other sensor nodes and E-APs. Simulation results demonstrate that the distributed algorithm performs close to the centralized algorithm with reduced complexity. Moreover, the proposed methods exhibit superior estimation performance over conventional techniques in WPSNs.

This paper considers point-to-point wireless communications where an energy-constrained node, which has insufficient energy for data transmission, wants to exchange messages with a node with enough energy. In this system, we study power splitting (PS) based energy cooperation methods by exploiting wireless energy transfer techniques and propose a new concept called sustainable wireless information and power exchange (SWIPE). In this SWIPE protocol, the node which has sufficient energy first transmits the information signal to the energy-constrained node. Then, the received signal at the energy-constrained node is utilized for both information decoding and energy harvesting via a PS circuit. At the consecutive time slot, by using the harvested energy, the energy- constrained node is now able to send a signal to the other node which employs a similar PS technique. This procedure continues by switching the operations of two nodes at each time slot. For the proposed SWIPE protocol, we present the optimal PS ratio computation algorithm in order to maximize the weighted sum throughput performance. Simulation results confirm the efficacy of the proposed SWIPE protocol over conventional schemes.

In this paper, we study self energy recycling techniques for point-to-point multiple-input multiple-output systems where a full-duplex transmitter with multiple antennas communicates with a multi-antenna receiver. Due to the full-duplex nature, the transmitter receives a signal transmitted by itself through a loop-back channel. Then, the energy of the signal is harvested and stored in an energy storage. Assuming time-slotted systems, we propose a new communication protocol in which the harvested energy at the transmitter is recycled for future data transmissions to the receiver. Under this setup, we present a transmit covariance matrix optimization method in order to maximize the sum rate performance for two different cases. First, for a perfect channel state information (CSI) case, the globally optimal algorithm for the sum rate maximization problem is proposed. Next, for an imperfect CSI case, we provide a robust covariance matrix optimization approach where the worst-case sum rate performance can be maximized. Numerical results demonstrate that the proposed methods offer a significant performance gain over conventional schemes.

In this paper, we study a wireless powered communication network (WPCN) in a two-user interference channel, where two hybrid access-points (H-APs) support a user in each cell. In this two cell scenario, the H-APs first transmit the energy signal to charge both users in the downlink (DL) phase. Then, in the subsequent uplink (UL) phase, each user sends its information signal to the corresponding H-AP utilizing the harvested energy. Due to asynchronous time allocation of the DL and the UL between two cells, cross-link interference affects the overall performance. In this system, we aim to maximize the sum-rate by jointly optimizing the time durations for the DL and the UL phases of each cell, and the UL transmit power of all users. As the sum-rate maximization problem becomes non-convex, it is difficult to obtain an optimal solution. To solve this problem, we propose a new algorithm where the time allocation and the transmit power are alternatively updated based on the weighted sum-minimum mean square error criteria and the projected gradient method. In simulation results, we verify that the proposed algorithm for the asynchronous protocol outperforms conventional schemes.

When implementing single radio frequency (RF) chain multi-user (MU) millimeter-wave (mmWave) systems, an RF beamforming algorithm with a short beam training overhead is essential. In this paper, we propose a new MU RF beamforming algorithm based on the conventional RF beam training method in IEEE 802.11ad. Then, we investigate its asymptotic behavior for a large number of users scenario. We show that the proposed scheme approaches the optimal full search scheme with much reduced beam training latency as the number of users grows. Our simulation results demonstrate that a performance gain of the proposed method over the full search method is about 35% in practical MU mmWave environments in terms of the effective data rate.

In this paper, we study parameter estimation in multiple-input multiple-output (MIMO) wireless powered sensor networks (WPSN). The sensor nodes are powered exclusively by harvesting the radio frequency signals transmitted from the energy access points. We propose a joint design of the sensor data precoders and energy covariance matrices to minimize the mean square error (MSE) of the parameter estimate. This design also incorporates optimal allocation of the harvested power for data acquisition and data transmission. We employ a zero-forcing precoding based estimation framework and the alternating minimization technique to compute the precoders, power allocation, and energy covariance matrices. Simulation results demonstrate that the proposed method achieves a superior estimation performance in comparison to the conventional energy transfer techniques for estimation in WPSNs.

In this paper, we consider a wireless powered communication network with an energy harvesting (EH) jammer where eavesdroppers try to wiretap the communication between users and a hybrid access-point (H-AP). In our system, the H-AP first transmits an energy signal to recharge the batteries of the EH users and the EH jammer in the energy transfer (ET) phase. Then, in the subsequent information transfer (IT) phase, each user sends information to the H-AP in a time division multiple access manner, while the jammer generates jamming signals to interfere the eavesdroppers. We adopt two different secrecy performance measurements according to the level of channel state information (CSI) of the eavesdroppers. First, with a single user, we maximize the secrecy rate by optimizing the time allocation between the ET and the IT phase when perfect CSI of the eavesdroppers is available at all nodes. In contrast, when the instantaneous CSI of the eavesdroppers is not available at legitimate nodes, we analyze and minimize the secrecy outage probability. We also extend the single user analysis to a more general multi-user situation with an additional consideration of the transmit power allocation at the jammer. Finally, we evaluate the performance of our proposed solutions through simulations and demonstrate that a performance gain compared to conventional schemes becomes more pronounced with the increased number of eavesdroppers and users.

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem, on the other hand, is shown to be a single-variable optimization that can be solved by 1-D line search. To reduce computational complexity, a sequential parametric convex approximation method is proposed to find a near-optimal solution. This paper is then extended to the imperfect channel state information case with norm-bounded channel errors. Furthermore, tightness of the relaxation for the proposed schemes is validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D but with much lower complexity.

In this paper, we study a joint spatial division multiplexing (JSDM) beamforming scheme, which enables large-scale spatial multiplexing gains for massive multi-input multi-output downlink systems. In contrast to the conventional JSDM, which employs a block diagonalization method as a pre-beamformer, we aim to maximize sum-rate by applying minimum-mean-squared error (MMSE) approaches when designing a pre-beamformer and a multi-user precoder sequentially. First, to suppress inter-group interference, we design the pre-beamformer, which minimizes an upper bound of the sum mean-squared-error in the large-scale array regime. Then, to mitigate same-group interference, we present the multi-user precoder based on the weighted MMSE (WMMSE) optimization method, which requires the same channel state information overhead as the conventional JSDM. Also, in order to reduce the computational complexity, we compute deterministic equivalents of the WMMSE beamforming parameters to generate the beamformers by employing asymptotic results of large system analysis. Through simulation results, we confirm that the proposed two-step beamforming methods bring substantial performance gains in terms of sum-rate over the conventional JSDM schemes especially in a low and medium signal-to-noise ratio regime with comparable complexity.

In this paper, we investigate an energy efficiency (EE) maximization problem in multiple input single output broadcast channels. The optimization problem in this system model is difficult to solve in general, since it is in non-convex fractional form. Hence, conventional algorithms have addressed the problem in an iterative manner for each channel realization, which leads to high computational complexity. To tackle this complexity issue, we propose a new simple method by utilizing the fact that EE maximization becomes identical to spectral efficiency (SE) maximization for the region of the power below a certain transmit power termed as saturation power. In order to calculate the saturation power, we first introduce upper and lower bounds of the EE performance by adopting a maximal ratio transmission beamforming strategy. Then, we propose an efficient way to compute the saturation power for the EE maximization problem. Once we determine the saturation power in advance, we can transform the EE maximization problem into a simplified sub-optimal EE problem, which can be solved by the SE maximization schemes with low complexity. The derived saturation power is parameterized by employing random matrix theory, which relies only on the second-order channel statistics. Hence, this approach needs much lower computational complexity compared with a conventional scheme, which requires instantaneous channel state information. Numerical results validate that the proposed algorithm achieves near optimal EE performance with significantly reduced complexity.

In this paper, we study multi-user multi-input multi-output cognitive wireless powered communication networks (WPCN), in which a secondary WPCN shares spectrum with a primary wireless information transfer system. A typical WPCN consists of two different phases. In the first downlink phase, a hybrid access point (H-AP) transfers energy to charge users, and then in the subsequent uplink phase, the users send information by using the harvested energy to the H-AP. We consider two different cognitive WPCN protocols depending on the cooperation level between the primary transmitter and the secondary H-AP. For both cases, we formulate sum throughput maximization problems by taking the interference leakage to the primary network into consideration. The problems are generally non-convex due to coupled variables in the WPCN. To tackle this issue, we first convert the problems into equivalent convex forms, and then identify the global optimal solutions by applying the proposed iterative optimization algorithms. Finally, the simulation results demonstrate that the proposed algorithms outperform conventional schemes.

In this paper, we provide minimum mean-squared error-based source-relay-destination transceiver designs for multiple-input multiple-output amplify-and-forward relaying systems, where direct link between the source and the destination is non-negligible. In an earlier work, a local optimal technique was introduced which employs a projected gradient method and an interior point method. Since these methods may have quite high computational complexity, we investigate a new local optimal solution for the source-relay-destination transceiver which has low complexity. To this end, we first introduce the optimal closed-form solution for the relay transceiver for given source and destination filters. Then, for given relay and destination transceivers, the optimal source precoder design is derived, which requires only 1-D bisection search. Based on these solutions, we propose a joint optimization algorithm which iteratively finds a local optimal solution. Also, we introduce a simple non-iterative algorithm which computes filters in closed-forms with low complexity. Furthermore, since perfect channel knowledge may not be feasible in practical systems, a joint transceiver technique which is robust to channel uncertainties is provided. It is confirmed by simulation results that the proposed schemes outperform conventional techniques with significantly reduced complexity.

This paper considers the maximization of total throughput and device lifetime for point-to-point multiple-input multiple-output communication systems, where a transmission node has a battery which exhibits non-linear battery discharge behaviors. We adopt a battery model called Peukert's law to render the non-linear battery characteristics and formulate the battery constraint from Peukert's law. Then, we prove that the total throughput is a strictly concave function in terms of the battery lifetime under the battery constraint. Also, we derive the optimality conditions for the total throughput maximization and device lifetime maximization problems from the strict concavity, and compute the optimal solutions by a bi-section method. Furthermore, we provide a solution based on asymptotic analysis for the case, where the eigenvalue distribution of the channel matrix is not available. In the simulation section, we conduct accurate battery discharge simulations to validate this paper. We confirm that the analysis matches well with the battery simulations, and the derived optimal scheme outperforms the baseline schemes, which neglect the non-linear battery discharge properties. Also, the proposed solution based on asymptotic analysis is shown to have almost the same performance compared with the optimal solution.

This paper investigates multi-user multi-input multi- output cognitive wireless powered communication networks (WPCN), in which a secondary WPCN shares spectrum with a primary wireless information transfer system. A typical WPCN consists of two different phases. In the first downlink phase, a hybrid access point (H-AP) transfers energy to charge users, and then in the subsequent uplink phase, the users send information by using the harvested energy to the H-AP. We consider underlay cognitive WPCN without cooperation between the primary transmitter and the secondary H-AP. In this case, we formulate sum throughput maximization problem by taking the interference leakage to the primary network into consideration. The problem is generally non-convex due to coupled variables in the WPCN. To tackle this issue, we first convert the problem into equivalent convex form, and then identify the global optimal solution by applying the proposed iterative optimization algorithm. Finally, simulation results demonstrate that the proposed algorithm outperforms conventional schemes.

In this work, we consider a wireless powered communication network (WPCN) with an energy harvesting (EH) jammer where an eavesdropper tries to wiretap the communication between a user and a hybrid access-point (H-AP). In our system, the H-AP first transmits an energy signal to recharge the batteries of the EH user and the EH jammer in the energy transfer (ET) phase. Then, in the subsequent information transfer (IT) phase, the user sends its information signal to the H-AP, while the jammer generates the jamming signal to interfere the eavesdropper utilizing the harvested energy in the ET phase. Assuming only the channel distribution information (CDI) of the eavesdropper is available at the legitimate nodes, we analyze and minimize the secrecy outage probability by optimizing the time allocation between the two phases. To reduce the complexity, we also provide a simple closed-form solution, and the simulation results verify that its performance approaches the optimum.

In this paper, we propose a new beamforming design to maximize energy efficiency (EE) for multiple input single output interfering broadcast channels (IFBC). Under this model, the EE problem is non-convex due to the coupled interference and its fractional form, and thus it is difficult to solve the problem. Conventional algorithms which address this problem have adopted an iterative method for each channel realization, which requires high computational complexity. In order to reduce the computational complexity, we parameterize the beamforming vector by scalar parameters related to beam direction and power. Then, by employing asymptotic results of random matrix theory, we identify the optimal parameters to maximize the EE in the large system limit. Based on the asymptotic results, the proposed scheme can provide insights on the average EE performance, and a simple yet efficient beamforming strategy is introduced for the finite system case. Numerical results confirm that the proposed scheme shows a negligible performance loss compared to the best result achieved by the conventional approaches even with small system dimensions, with much reduced system complexity.

This paper studies multi-user wireless powered communication networks, where energy constrained users scavenge energy of the radio frequency signals radiated from a hybrid access point (H-AP). The energy is then utilized for the users' uplink information transmission to the H-AP in time division multiple access mode. In this system, we aim to maximize the uplink sum rate performance by jointly optimizing energy and time resource allocation for multiple users. To this end, we first derive the optimal downlink energy transmission policy at the HAP. Based on this result, analytical resource allocation solutions are obtained. Simulation results confirm that the proposed algorithms offer significant sum rate performance gain over conventional schemes.

In this paper, we present a new beamforming design at the relay in AF two-way relaying systems where a relay node with Nr antennas serves two source nodes equipped with a single antenna. In this system, we design low complexity relay beamforming which minimizes the mean squared error (MSE). To this end, unlike conventional designs based on perfect self-interference cancellation at each source whose computational effort grows with an order of O(N6r), we determine the level of SIC in an MSE optimization problem, thereby obtaining a new insightful closed-form solution with complexity of O(N2r). It is also shown that the channel estimation overhead can be reduced by our methods. Finally, simulation results demonstrate that the proposed design method achieves the minimum MSE with reduced complexity.

In this paper, we study an energy harvesting scheme for a multiple-input-single-output secrecy channel under imperfect channel state information case with either deterministic and statistical channel uncertainties. The system consists of one multi-antenna transmitter, several multi-antenna energy receivers (ERs) and one single-antenna co-located receiver (CR) who adopts a power splitter to decode information and harvest power simultaneously. We consider the artificial noise (AN) embedded information-bearing signal to interfere potential eavesdroppers (i.e., ERs) and capture the harvested power. We perform joint optimization for the masked beamforming matrix, the AN covariance matrix and the power splitting ratio, such that the transmit power is minimized to satisfy the target secrecy rate of the CR, the total transmit power and the energy harvesting constraints for the CR and the ERs. By incorporating norm-bounded channel uncertainty model, we propose a robust joint design method to obtain the optimal solution. Also, a suboptimal algorithm for the outage constrained robust optimization problem is proposed by adopting the Bernstein-type inequality. Furthermore, the tightness of the relaxation for the proposed schemes are verified by showing that the optimal solution of the relaxed problem is rank-one. Finally, simulation results are presented to validate the performance of our proposed schemes.

In this paper, we study a joint spatial division multiplexing (JSDM) beamforming scheme which enables large-scale spatial multiplexing gains for massive MIMO downlink systems. In contrast to the conventional JSDM which employs a block diagonalization (BD) method as a pre-beamformer, we aim to maximize sum-rate by applying minimum-mean-squared error (MMSE) approaches when designing a pre-beamformer and a multi-user precoder sequentially. First, to suppress inter-group interference, we design the pre-beamformer which minimizes an upper bound of the sum mean-squared-error in the large-scale array regime. Then, to mitigate same-group interference, we present the multi-user precoder based on the weighted MMSE (WMMSE) optimization method, which requires the same channel state information overhead as the conventional JSDM. Through simulation results, we confirm that the proposed two-step beamforming method brings substantial performance gains in terms of sum-rate over the conventional JSDM schemes especially in low to medium signal-to-noise ratio (SNR) regime.

In this paper, we propose a new user selection algorithm for full-duplex (FD) multiuser multiple-input single-output (MU-MISO) systems where a FD base station (BS) communicates with multiple half-duplex (HD) users in both downlink and uplink channels simultaneously. Due to self-interference at the BS and co-channel interference among users, a joint downlink and uplink user selection to maximize system performance incurs high search complexity. To reduce the complexity, we introduce a two step user selection algorithm which successively chooses downlink users followed by uplink users based on the decomposed sum rate of the FD systems. From the numerical results, we confirm that the proposed user selection algorithm for the FD MU systems exhibits a small performance loss compared to the optimal user selection algorithm with much reduced complexity.

In simultaneous wireless information and power transfer (SWIPT) systems, dedicated energy signals only convey wireless energy, but not information. For this reason, the energy-carring signals in the SWIPT can be pre- determined in advance and is shared among communication nodes. By exploiting this nature, this paper designs the optimal transmit beamforming vectors for the multiple-input single-output SWIPT interference channel with signal cooperation (IFC-SC), where the energy- carrying signal waveforms are known to transmitters and receivers. Specifically, we aim to identify the optimal tradeoff between the information rate and the harvested energy. To this end, an information rate maximization problem is formulated under minimum required harvested energy constraint, which is non-convex in general. To solve the problem, a new parameterization technique is introduced, and we can decouple the original problem into two subproblems, which yields closed-form beamforming solutions by addressing the line search method for the parameter. Simulation results confirms that the proposed optimal IFC-SC beamforming vectors outperform conventional SWIPT IFC systems.

In this paper, we study simultaneous wireless information and power transfer (SWIPT) for multiuser multipleinput- single-output (MISO) secrecy multicasting channels with imperfect channel state information. First, a robust secure beamfoming design is considered, where the transmit power is minimized subject to the secrecy rate outage probability constraint for legitimate users and the harvested energy outage probability constraint for energy harvesting receivers. The original problem is non-convex due to the presence of the probabilistic constraints. By utilizing Bernstein-type inequalities, we transform the outage constraints into the deterministic forms. In order to identify a local optimal rank-one solution, we propose an efficient approach based on a constrained concave convex procedure method to convert the original problem into a sequence of convex programming problems. Finally, simulation results are provided to validate the performance of our proposed design methods.

This letter studies a joint design of fronthaul and radio access links for a cloud radio access network (C-RAN) with wireless fronthauling, where a baseband unit (BBU) controls a number of remote radio heads (RRHs) via wireless fronthaul links to communicate with user equipments. We first review a basic approach based on a single cell concept, whereby the RRHs operate as decode-and-forward (DF) relays. Then, a cooperative transmission from the RRHs based on decompress-and-forward (DCF) relaying is proposed, which is a standard concept in C-RAN. For both strategies, a problem of jointly optimizing the BBU and RRH operations is tackled with the goal of maximizing the weighted sum-rate subject to the BBU and per-RRH power constraints. For each formulated problem, an iterative algorithm is derived that achieves a sequence of monotonically nondecreasing objective values at each iteration. It is confirmed via numerical results that the DCF-based cooperative scheme significantly outperforms the DF-based single-cell approach.

In this paper, we propose a new user selection algorithm for full-duplex (FD) multiuser multiple-input single-output (MU-MISO) systems, where an FD base station (BS) communicates with multiple half-duplex users in both downlink and uplink channels simultaneously. Due to self-interference at the BS and co-channel interference among users, a joint downlink and uplink user selection to maximize system performance incurs high search complexity. To reduce the complexity, we introduce a two-step user selection algorithm, which successively chooses downlink users followed by uplink users based on the decomposed sum rate of the FD systems. In addition, we analyze the average sum rate performance of our proposed user selection algorithm for FD MU-MISO systems and derive a tight approximation of the performance. From the numerical results, we confirm that our analysis matches well with simulation results, and the proposed user selection algorithm for the FD MU-MISO systems exhibits a small performance loss compared with the optimal user selection algorithm with much reduced complexity.

In this paper, we investigate wireless powered communication network for OFDM systems, where a hybrid access point (H-AP) broadcasts energy signals to users in the downlink, and the users transmit information signals to the H-AP in the uplink based on orthogonal frequency division multiple access. We consider a full-duplex H-AP which simultaneously transmits energy signals and receives information signals. In this scenario, we address a joint subcarrier scheduling and power allocation problem to maximize the sum-rate under two cases: perfect self-interference cancellation (SIC) where the H-AP fully eliminates its self-interference (SI) and imperfect SIC where residual SI exists. In general, the problems for both cases are nonconvex due to the subcarrier scheduling, and thus it requires an exhaustive search method, which is prohibitively complicated to obtain an optimal solution. In order to reduce the complexity, for the perfect SIC scenario, we jointly optimize subcarrier scheduling and power allocation by applying the Lagrange duality method. Next, for the imperfect SIC case, the problem becomes more complicated due to the SI at the H-AP. To solve this problem, we propose an iterative algorithm based on the projected gradient method. Simulation results show that the proposed algorithm for the case of perfect SIC exhibits almost the same sum-rate performance compared to the optimal algorithm, and the proposed iterative algorithm for the imperfect SIC case offers a significant performance gain over conventional schemes.

In this paper, we propose a new beamforming design to maximize energy efficiency (EE) for multiple input single output interfering broadcast channels (IFBCs). Under this model, the EE problem is nonconvex in general due to the coupled interference and its fractional form, and thus it is difficult to solve the problem. Conventional algorithms which address this problem have adopted an iterative method for each channel realization, which requires high computational complexity. In order to reduce the computational complexity, we parameterize the beamforming vector by scalar parameters related to beam direction and power. Then, by employing asymptotic results of random matrix theory with this parametrization, we identify the optimal parameters to maximize the EE in the large system limit assuming that the number of transmit antennas and users are large with a fixed ratio. In the asymptotic regime, our solutions depend only on the second order channel statistics, which yields significantly reduced computational complexity and system overhead compared to the conventional approaches. Hence, the beamforming vector to maximize the EE performance can be determined with local channel state information and the optimized parameters. Based on the asymptotic results, the proposed scheme can provide insights on the average EE performance, and a simple yet efficient beamforming strategy is introduced for the finite system case. Numerical results confirm that the proposed scheme shows a negligible performance loss compared to the best result achieved by the conventional approaches even with small system dimensions, with much reduced system complexity.

This paper studies multiuser wireless powered communication networks, where energy constrained users charge their energy storages by scavenging energy of the radio frequency signals radiated from a hybrid access point (H-AP). The energy is then utilized for the users' uplink information transmission to the H-AP in time division multiple access mode. In this system, we aim to maximize the uplink sum rate performance by jointly optimizing energy and time resource allocation for multiple users in both infinite capacity and finite capacity energy storage cases. First, when the users are equipped with the infinite capacity energy storages, we derive the optimal downlink energy transmission policy at the H-AP. Based on this result, analytical resource allocation solutions are obtained. Next, we propose the optimal energy and time allocation algorithm for the case where each user has finite capacity energy storage. Simulation results confirm that the proposed algorithms offer about 30% average sum rate performance gain over conventional schemes.

This paper studies precoder designs for simultaneous wireless information and power transfer (SWIPT) in multi-input multi-output (MIMO) channels, where a transmitter sends information to information decoding (ID) users while satisfying the minimum energy requirement of energy harvesting users. In contrast to the previous designs focused only on maximum information rate (MIR), we propose a more general and simpler solution using the weighted minimum mean squared error (WMMSE) criterion. To solve the SWIPT-WMMSE problem which is generally non-convex, we suggest two different design schemes, separate and joint designs. Interestingly, it is shown that the joint design achieves optimal performance with a single initial point and a few iterations, while the separate design needs a large number of iterations and initial points to approach the optimum. Based on the observation, we propose a simple closed-form solution, which is shown to achieve near optimal performance with reduced complexity. The derived solution can be adopted in various pragmatic applications of MIMO communications, such as the MMSE, quality-of-service, equal error designs, as well as the MIR by adjusting the weight matrix. We also confirm that our design strategies are a great use for managing co-channel interference in multiple ID-user scenarios. Finally, simulation results demonstrate the efficiency of the proposed MIMO-SWIPT framework.

In this paper, we investigate simultaneous wireless information and power transfer systems for multiuser multiple-input single-output secure broadcasting channels. Considering imperfect channel state information, we introduce a robust secure beamforming design, where the transmit power is minimized subject to the secrecy rate outage probability constraint for legitimate users and the harvested energy outage probability constraint for energy harvesting receivers. The original problem is non-convex due to the presence of the probabilistic constraints. With the aid of Bernstein-type inequalities, we transform the outage constraints into the deterministic forms. Based on a successive convex approximation (SCA) method, we propose a low-complexity approach, which reformulates the original problem as a second-order cone programming problem. Also, we prove the convergence of the SCA-based iterative algorithm. Simulation shows that the proposed scheme outperforms the conventional method with lower complexity.

In this paper, we investigate wireless powered communication network for OFDM systems, where a hybrid access point (H-AP) broadcasts energy signals to users in the downlink, and the users transmit information signals to the H-AP in the uplink based on an orthogonal frequency division multiple access scheme. We consider a full-duplex H-AP which simultaneously transmits energy signals and receives information signals, and a perfect self-interference cancellation where the H-AP fully eliminates its self interference. In this scenario, we address a joint subcarrier scheduling and power allocation problem to maximize the sum-rate. In general, the problem is on-convex due to subcarrier scheduling, and thus it requires an exhaustive search method, which is prohibitively complicated to obtain the globally optimal solution. In order to reduce the complexity, we jointly optimize subcarrier scheduling and power allocation by applying the Lagrange duality method. Simulation results show that the proposed algorithm exhibits only negligible sum-rate performance loss compared to the optimal exhaustive search algorithm and a significant performance gain over conventional scheme.

This paper investigates simultaneous wireless information and power transfer in multiple-input single-output interference channels, and designs transmit beamforming vectors which achieves the optimal tradeoff between the information rate and the harvested energy. To this end, the problem for maximizing the information rate is formulated with minimum required harvested energy constraint. In order to solve this nonconvex problem, we introduce parameterization techniques for characterizing the achievable rate- energy (R-E) region. As a result, the original problem is separated into two subproblems, for which closed- form solutions are obtained by addressing the line search method. Finally, we provide numerical examples for the achievable R-E region through simulations.

In this paper, we consider K-user multiple-input single-output interference channels with a cognitive relay. Assuming that data of all transmitters and channel state information are known at the cognitive relay, we design a linear precoder for the cognitive relay with the aim of maximizing the sum-rate without changing the transmitter operations at all transmitters. We first define the receiver set as a set which contains a part of receivers, and then present a performance metric called "partial signal-to- interference-plus-noise ratio" (PSINR) based on the receiver set. Then, we can obtain a precoder at the cognitive relay by solving the PSINR maximization problem. The optimal receiver set which yields the maximum sum-rate can be identified by checking all possible receiver sets. Since this exhaustive search has prohibitive complexity, we introduce a greedy set search method and finally propose a precoder design scheme by combining the PSINR maximization problem and the greedy set search method. Numerical simulation results confirm that the proposed scheme shows performance close to the projected gradient method with reduced complexity.

In this paper, we focus on maximizing weighted sum energy efficiency (EE) for a multi-cell multi-user channel. In order to solve this non-convex problem, we first decompose the original problem into a sequence of parallel subproblems which can be optimized separately. For each subproblem, a base station employs dirty paper coding to maximize the EE for users within the cell while regulating interference induced to other cells. Since each subproblem can be transformed to a convex multiple-access channel problem, the proposed method provide a closed-form power allocation. Then, based on the optimal covariance matrix, a locally optimal solution is obtained to maximize the sum EE. Finally, simulation results show that our algorithm based on the non-linear precoding achieves close to 20 percent gain than the conventional linear precoding method.

In this paper, we investigate an energy efficiency (EE) maximization problem in multi-user multiple input single output downlink channels. In this system model, the optimization problem is difficult to solve since it is in a non-convex fractional form. Hence, conventional algorithms have addressed the problem in an iterative manner for each channel realization that leads to high computational complexity. To tackle this complexity issue, we propose a new simple method based on the fact that the EE maximization is identical to the spectral efficiency maximization for the region of power below the certain transmit power referred to as saturation power. In order to determine the saturation power, we introduce upper and lower bounds of the EE performance by adopting maximal ratio transmission beamforming strategy. Then, we propose an efficient way to compute the saturation power for the maximization problem in closed form. Based on the derived saturation power, we suggest a simplified scheme to calculate EE with low complexity. The saturation power is parameterized by employing random matrix theory, which relies only on the second order channel statistics. Numerical results validate that the proposed algorithm achieves near optimal EE performance with much reduced complexity.

This paper studies the simultaneous wireless information and power transfer (SWIPT) in a multiuser broadcast (BC) multiple-input multiple-output (MIMO) system, in which a base station sends messages to several information decoding (ID) users, and transmits wireless power to multiple energy harvesting (EH) user at the same time. We aim to maximize the sum-rate of the ID users while maintaining a minimum EH constraint for each EH user. Firstly, an optimal rate-energy (R-E) boundary is characterized by using a BC-multiple access channel (MAC) duality derived from dirty paper coding (DPC). Since the complexity of the DPC is quite high due to continuously encoding and decoding at the transceivers, we then propose a sub- optimal algorithm using a weighted minimum mean square error (WMMSE) approach, which has lower complexity and iteratively converges a local optimal point. Finally, the performance comparisons and convergence properties are illustrated by simulation results.

In this paper, we study a transmit beamforming technique for multiple input single output downlink multi-user systems with directional antennas where a transmit antenna gain is determined in three dimensional coordinates. For a multiuser active antenna system, beamforming designs to maximize the weighted sum rate are proposed by optimizing the transmit antenna gain and power allocation. Since finding joint optimal parameters requires prohibitively high computational complexity, we separate the optimization problem into two sub-problems of the vertical beamforming and the power allocation. Then a simple vertical beamforming algorithm based on a high signalto- noise ratio assumption is presented. Also, for a multi-user passive antenna system, we provide a beamforming scheme which employs a multi-sector concept. Simulation results show that the proposed beamforming schemes outperform the conventional beamforming schemes.

We address a new robust optimization problem in a multiuser multiple-input single-output broadcasting system with simultaneous wireless information and power transmission. Assuming that perfect channel- state information (CSI) for all channels is not available at the BS, the uncertainty of the CSI is modeled by an norm-bounded uncertainty set. To optimally design transmit beamforming weights and receive power splitting, an average total transmit power minimization problem is investigated subject to the individual harvested power constraint and the received signal-to-interference-plus-noise ratio constraint at each user. The original design problem is reformulated to a relaxed semidefinite program, and then two different approaches based on convex programming are proposed, which can be solved efficiently by the interior point algorithm. Interestingly, we show that the semidefinite relaxation (SDR) is tight. Numerical results are provided to validate the robustness of the proposed algorithms.

In this paper, we investigate a multiuser downlink distributed antenna system with simultaneous wireless information and power transmission under the assumption of imperfect channel state information at the distributed antenna (DA) port. To optimally design robust transmit beamforming vectors and receive power splitting factors, our design objective is to maximize the average worst-case signal-to-interference-plus-noise ratio while simultaneously achieving the individual energy harvesting (EH) constraint for each user and the per-DA port power constraint. We solve this non- convex problem by reformulating it into a two-stage problem. Simulation results are shown to validate the robustness and effectiveness of the proposed algorithms.

In this paper, we present a power allocation method for a distributed antenna system (DAS) to maximize energy efficiency (EE), which is defined as the ratio of the transmission rate to the total consumed power. Different from conventional EE maximization schemes that require iterative numerical methods, we derive the optimal solution as a closed form by solving Karush-Kuhn-Tucker conditions. The obtained closed-form expression is applicable to DAS with an arbitrary number of distributed antenna (DA) ports and general per-DA port power constraints and is also guaranteed to be globally optimum. Then, we provide several interesting observations on the proposed EE maximizing power allocation scheme. Based on these results, we propose a simplified practical power allocation method that employs the DA port selection and computes the power level in a distributed manner. Through Monte Carlo simulations, we show that the proposed optimal power allocation method produces the EE identical to exhaustive search with significantly reduced computational complexity. In addition, it is shown that the proposed simplified power allocation method based on the DA port selection exhibits little performance loss compared to the optimal algorithm with a remarkable reduction in the system overhead.

In this paper, we provide new precoding schemes which jointly optimize the source and relay precoders in multiple-input multiple-output amplify-and-forward relaying systems with minimum mean-squared error decision feedback equalizer (DFE) at the destination node. Instead of conventional schemes which resort to an iterative method, we propose simple precoding schemes based on a closed-form solution. To this end, we first extend the decomposable property of the error covariance matrix for a linear receiver to relaying systems with DFE receivers. Then, we suggest two closed-form solutions which successively identify the source and the relay precoders. To improve the performance, a mode selection which adaptively chooses one of the two closed-form precoding schemes according to the channel conditions is introduced. Then, we propose a simple eigenvalue based mode selection algorithm, and analyze its selection probability behavior for Rayleigh fading channels. Simulation results demonstrate that the performance of the proposed method is almost identical to the iterative solution with much reduced complexity.

In this paper, we propose new relay transceiver designs based on the minimum mean square error (MMSE) criterion for amplify-and-forward multiple-input-multiple-output (MIMO) relaying systems with direct link. Since each antenna element is equipped with its own power amplifier, a norm power constraint, which restricts the transmit power with the expected norm of the transmit signal vector, is not suitable for practical systems. Therefore, we consider a shaping constraint (SC), which imposes a limit on the shape of the transmit covariance matrix. The SC includes several power constraints such as the peak power constraint and the per-antenna power constraint as special cases. To this end, we first derive the optimal structure of the MMSE relay transceiver under the SC. Then, by introducing an upper bound of the mean square error, we provide closed-form relay transceiver solutions. Due to limited bandwidth of the feedback channel, perfect channel knowledge at the transmitter may not be feasible. Thus, we also propose a quantized relay transceiver design based on Grassmannian codebooks for a limited-feedback scenario. From simulation results, it is confirmed that the proposed relay transceiver techniques demonstrate a significant performance improvement compared with conventional schemes.

This paper investigates the optimal transmit beamforming designs for simultaneous wireless information and power transfer (SWIPT) in multiple-input single-output interference channels (IFC). Based on cooperation level among transmitters and receivsers, we classify the SWIPT IFC systems into two categories. First, we consider the IFC with partial cooperation, where only channel state information (CSI) is available at transmitters and receivers, but not the signal waveform. Second, we examine the IFC with signal cooperation, where both the CSI and the signal waveforms are known to transmitters and receivers. Then, for the both scenarios, we identify the Pareto boundary of the achievable rate-energy (R-E) region which characterizes the optimal tradeoff between the information rate and the harvested energy. To this end, the problems for maximizing the information rate are formulated with minimum required harvested energy constraint. To solve these non-convex problems, we introduce parameterization techniques for characterizing the R-E region. As a result, the original problem is separated into two subproblems, for which closed-form solutions are obtained by addressing the line search method. Finally, we provide numerical examples for the Pareto boundary of the R-E region through simulations.

In this paper, we consider K-user multiple-input single-output interference channels with a cognitive relay. Assuming that data of all transmitters and channel state information are known at the cognitive relay, we design a linear precoder for the cognitive relay with the aim of maximizing the sum-rate without changing the transmitter operations at all transmitters. We first define the receiver set as a set which contains a part of the receivers, and then introduce a performance metric called “partial signal-to-interference-plus-noise ratio” (PSINR) based on the receiver set. Then, we can obtain a precoder at the cognitive relay by solving the PSINR maximization problem. The optimal receiver set which yields the maximum sum-rate can be identified by checking all possible receiver sets. Since this exhaustive search has prohibitive complexity, we develop a low complexity set search method by utilizing the properties of the optimal receiver set. Combining the PSINR maximization problem and the low complexity search method, we finally propose a precoder design scheme for the sum-rate maximization. Numerical simulation results confirm that the proposed scheme shows performance close to the projected gradient method with much reduced complexity.

In this paper, we propose a new relay transceiver design based on the minimum mean-squared error criterion for non-regenerative multiple-input multiple-output relaying systems with direct link. Since norm power constraint at the relay node does not restrict the output power at each antenna element in designing the relay transceiver, we consider maximum eigenvalue constraint (MVC) which imposes a limit on the peak power of the output. To this end, we first derive the optimal structure of the relay transceiver under the MVC. Then, by introducing an upper bound of the mean-squared error which makes the problem tractable, we provide a closed-form relay transceiver design with the MVC. From simulation results, it is confirmed that the proposed relay transceiver technique demonstrates a significant performance improvement compared to conventional schemes.

Most of the beamforming designs that have been proposed in literature for relay networks often assume flat fading channels with perfect channel state information. However, in practice, the channel is likely to be frequency selective. On the other hand, in many cases, having the perfect channel state information is impossible. In this paper, we investigate the robust filter and forward beamforming for a relay network consisting a transmitter, a receiver and R relay nodes. All channels between the transmitter, relays and the receiver are assumed to be frequency selective with imperfect channel state information and it is also assumed that the channel uncertainty is bounded by a spherical region. Our aim in this paper is to minimize the total transmit power of the relays subject to a constraint on the worst signal-to-interference-plus-noise ratio (SINR). To solve the resulting non convex problem, we use a two-stage approach that considers the channel error. The robust beamforming can be obtained by KKT conditions, an auxiliary variable and S-procedure. Simulation results demonstrate the efficiency of the proposed method.

In this paper, we analyze the ergodic capacity performance for diversity reception schemes over composite fading channels using a mixture gamma (MG) distribution. By adopting the MG distribution, various composite fading channel models can be approximated with mathematically tractable and highly accurate properties. In other words, signal- to-noise ratio statistics which contain complicated functions for each diversity reception scheme are formulated as a weighted sum of gamma distributions. With an aid of properties of the gamma distribution, we can derive closed-form expressions of an ergodic capacity for important diversity reception schemes such as maximal ratio combining and selection combining. We observe that our analysis can be expressed with the general number of receiver branches over various composite fading conditions. Simulation results verify that the derived ergodic capacity expressions match well with the empirical results.

In this paper, we study bit allocation and pairing methods based on zero forcing beamforming for downlink multiuser distributed antenna systems with limited feedback. Before assigning the feedback bit for each distributed antenna (DA) port, we need to solve the pairing issue which determines the set of DA ports to support a user. To this end, we first analyze an upper bound of a mean rate loss between perfect channel state information systems and limited feedback systems. Since minimizing the obtained bound is a joint optimization problem with respect to the pairing and the bit allocation, it is difficult to identify a solution analytically. Instead, we propose a two-step algorithm which derives the pairing based on the bound of the rate loss, and then obtain the feedback bit allocation method independently. From simulation results, we confirm that the proposed algorithms offer about 135% performance gains over a conventional equal bit allocation scheme for 5 DA ports systems.

In this paper, we study a feedback bit allocation algorithm with signal-to-leakage plus noise ratio maximizing beamforming (FA-SMB) for distributed antenna systems (DAS) presented in work [1]. We first investigate a scaling law of feedback bits for both the FA-SMB scheme and equal bit allocation. Through this analysis, we confirm that the required number of feedback bits to satisfy the maximum allowable rate gap between DAS with perfect channel state information (CSI) and limited feedback linearly increases with signal-to-noise ratio. Also, it is verified that the FA-SMB scheme saves the feedback bits by up to 30% over the equal bit allocation with the same rate gap at SNR = 40 dB for three-user DAS. Moreover, we show that the FA-SMB scheme substantially reduces the computational complexity compared to exhaustive search. Finally, we provide simulation results to demonstrate the efficacy of the FA-SMB scheme.

In this paper, we study the design of Distributed Space-Time Coding (DSTC) for two-way relay networks, where two users have bidirectional communication in the presence of two relay nodes and a direct link. Two new DSTC methods based on Amplify-and-Forward (AF) and Decode-and-Forward (DF) relaying are proposed that provide full transmission rate in the network. We evaluate the performance of the proposed methods and compare them by computer simulations. Our simulations show that both schemes achieve the maximum diversity and in addition, the proposed DF-based method outperforms the AF-based one.

In this paper, we propose a new beamforming technique that maximizes the end-to-end signal-to-noise ratio (SNR) for amplify-and-forward multiple-input-multiple-output cooperative relaying systems with direct link between thesource and the destination. Instead of conventional schemes resorting to an iterative method, such as a gradient ascentalgorithm, our scheme provides a simple closed-form solution for source-relay joint beamformer designs. To this end, wefirst derive a new expression of the end-to-end SNR for the cooperative relaying systems and its lower bound, which isgiven as the harmonic mean of two individual SNRs. Then, a new beamforming scheme, which adaptively optimizes one of the two SNRs depending on the channel condition, is proposed. In addition, we perform a diversity order analysis of the proposed scheme and show that our scheme achieves a full diversity order of relaying systems with direct link. It is confirmed by simulation results that the proposed technique obtains almost identical performance to the gradient ascent algorithm with much reduced complexity, and our analytical work accurately predicts the numerical results.

In this paper, we study a vertical beamforming technique for multiple-input multiple-output downlink multi-user systems. In general, the transmit antenna gain is controlled by adjusting the boresight of antennas in directional antennas, and thus the cell average rate varies according to the angle of the boresight. First, we compute the tilting angles for directional antenna systems which maximize the cell average rate. To this end, the probability density function of a three-dimensional user distribution is derived. Based on the result, we analyze the average rate gain of active antenna systems over passive antenna systems for a single user case. Furthermore, for a multi-user active antenna system, beamforming designs to maximize the weighted sum rate are proposed by optimizing the transmit antenna gain and power allocation. Since finding joint optimal parameters requires prohibitively high computational complexity, we separate the optimization problem into two sub-problems of the vertical beamforming and the power allocation. Then a simple vertical beamforming algorithm based on a high signal-to-noise ratio assumption is presented. Also, for a multi-user passive antenna system, we provide a beamforming scheme based on a multi-sector concept. Simulation results show that the proposed beamforming schemes outperform the conventional beamforming schemes.

In this paper, we study bit allocation and pairing methods based on distributed zero forcing beamforming for downlink multi-user distributed antenna (DA) systems with limited feedback. Before assigning the feedback bit for each DA port, we need to solve the pairing issue that determines the set of DA ports to support a user. To this end, we first analyze an upper bound of a mean rate loss between perfect channel state information systems and limited feedback systems. Since minimizing the obtained bound is a joint optimization problem with respect to the pairing and the bit allocation, it is difficult to identify a solution analytically. Instead, we propose a two-step algorithm that derives the pairing based on the bound of the rate loss and then obtain the non-iterative bit allocation method independently. To further improve the performance, an enhanced feedback bit allocation algorithm is also proposed by applying an iterative optimization technique. In addition, we investigate a scaling law of limited feedback systems to maintain a constant rate loss as signal-to-noise ratio increases. From simulation results, we confirm that the proposed algorithms offer about 135% performance gains over a conventional scheme for five DA port systems and verify that our analysis is well matched with the numerical results.

In this paper, we consider multipoint-to-multipoint multi-input multi-output relay systems where multiple source-destination pairs simultaneously communicate through a single multiple antenna relay. In this system configuration, we optimize two metrics when designing the source, relay and destination filters. The first one is sum mean-squared-error (MSE) and the second one is maximum pairwise MSE. These two problems are shown to be non-convex, and thus it is quite complex to find an analytical solution. To address this issue, we first introduce the sum MSE minimization scheme based on global channel state information (CSI) at all nodes. Then, a method which reduces complexity and CSI requirements is proposed by utilizing error covariance decomposition and MSE duality between broadcast channel and multiple access channel. Second, the pairwise MSE balancing algorithms are developed to guarantee fairness for all pairs. Similar to the case of the sum MSE minimization problem, we simplify the original pairwise MSE balancing problem to a second-order cone programming problem which can be solved using standard optimization tools. Through numerical simulations, we confirm the effectiveness of our proposed schemes.

In this paper, we analyze the error probability and ergodic capacity performance for diversity reception schemes over generalized-K fading channels using a mixture gamma (MG) distribution. With high accuracy, the MG distribution can approximate a variety of composite fading channel models and provide mathematically tractable properties. In contrast to previous analysis approaches that require complicated signal-to-noise ratio (SNR) statistics, it is shown that a distribution of the received SNR for diversity reception schemes is composed of a weighted sum of gamma distributions by exploiting the properties of the MG distribution. Then, based on this result, we can derive the exact average symbol error probability and simple closed-form expressions of diversity and array gains for maximal ratio combining and selection combining. In addition, an expression of the ergodic capacity for these schemes is obtained in independent and identically distributed fading channels. Our results lead to meaningful insights for determining the system performance with parameters of the MG distribution. We show that our analysis can be expressed with any number of receiver branches over various fading conditions. Numerical results confirm that the derived error probability and ergodic capacity expressions match well with the empirical results.

In this paper, we consider multipoint-to-multipoint multi-input multi-output relay systems where multiple source-destination pairs simultaneously communicate through a single multiple antenna relay. In this system configuration, we optimize sum mean-squared-error (MSE). This problem is shown to be non-convex, and thus it is quite complex to find an analytical solution. To address this issue, we first introduce the sum MSE minimization scheme based on global channel state information (CSI) at all nodes. Then, a method which reduces complexity and CSI requirements is proposed by utilizing error covariance decomposition and MSE duality between broadcast channel and multiple access channel. Through numerical simulations, we confirm the effectiveness of our proposed schemes.

In this paper, we propose a new distributed approach for designing the beamforming vectors based on virtual signal-to-interference-plus-noise ratio (VSINR) for weighted sum-rate (WSR) maximization in multiple-input single-output interference channels. Recently, it was shown that by adaptively adjusting parameters which control the leakage interference according to channel realizations and the signal-to-noise ratio (SNR) level, the WSR performance can be improved compared to conventional methods with fixed parameters. However, due to an iterative procedure for each channel realization, this approach requires high computational complexity. To overcome this problem, by utilizing asymptotic results from random matrix theory, we propose a new low-complexity beamforming scheme with constant parameters which depend only on the channel statistics and SNR. Numerical results confirm that the proposed scheme provides the near-optimal WSR performance with much reduced system complexity.

In this paper, we study a transmit beamforming technique for multiple input single output downlink single-user systems with three dimensional antennas where a transmit antenna gain is determined in three dimensional coordinates. In general, the transmit antenna gain is controlled by adjusting the boresight of antennas in directional antennas. To derive the optimal tilting angles for the directional antenna systems, we provide the probability density functions (PDF) of the three dimensional user distribution. Furthermore, based on the PDF, the analysis for the average rates of passive and active antenna systems is presented. Simulation results verify the accuracy of the performance analysis.

In this paper, we investigate the performance for diversity reception schemes over composite fading channels using a mixture gamma (MG) distribution. With high accuracy, the MG distribution can approximate a variety of composite fading channel models and provide mathematically tractable properties. In contrast to previous analysis approaches which require complicated signal-to-noise ratio statistics, we derive simple closed-form expressions of a diversity and array gain for important diversity reception schemes such as maximum ratio combining and selection combining by adopting the MG distribution. Our results lead to meaningful insights for determining the system performance with parameters of the MG distribution. We observe that for various transmitted signal modulations, our proposed analysis can be expressed with the general number of receiver branches over independent and non-identically distributed composite fading cases. Simulation results confirm that the derived diversity and array gain match well with the empirical results.

In this paper, we present a power allocation method for a distributed antenna system (DAS) to maximize energy efficiency (EE) which is defined as the ratio of the transmission rate to the total consumed power. Different from conventional EE maximization schemes which require iterative numerical methods, we derive the optimal solution as closed form by solving Karush-Kuhn-Tucker conditions. The obtained closed form expression is applicable to DAS with an arbitrary number of distributed antenna (DA) ports and general per-DA port power constraints, and is also guaranteed to be globally optimum. Through Monte Carlo simulations, we show that the proposed power allocation method produces the optimal average EE identical to exhaustive search with significantly reduced computational complexity. In addition, we demonstrate that DAS is more beneficial in terms of EE compared to conventional antenna systems.

In this paper, we provide a new design scheme which jointly optimizes the source and relay precoders in multiple-input multiple-output amplify-and-forward relaying systems with minimum mean-squared error (MMSE) decision feedback equalizer (DFE) at the destination node. Instead of conventional optimal designs which require an iterative method, we propose a simple and near optimal closed-form scheme. First, we extend the decomposable property of the error covariance matrix based on linear MMSE receiver to our case. Next, we suggest a new scheme which jointly finds the source and relay precoders with a closed-form solution. Simulation results demonstrate that the performance of the proposed method is less than 1 dB away from the optimal solution with much reduced complexity.

In this paper, we propose a new beamforming technique for signal-to-noise ratio maximization in nonregenerative multiple-input multiple-output cooperative relaying systems where a non-negligible direct link between the source and the destination exists. Instead of conventional optimal schemes resorting to an iterative method such as a gradient ascent algorithm, we provide a simple closed form solution for source-relay joint beamformer designs. To this end, we apply the error decomposition property which allows us to separate the problem into two phases. Then, we propose a new beamforming scheme which adaptively aligns the transmit signal to each phase depending on the channel condition. From simulation results, we confirm that the proposed technique obtains the near-optimal performance.

A signature identification algorithm is a method to obtain the cell identification information for wireless cellular systems or determine the intended user for wireless local area network. In this paper, we propose a simple and efficient signature identification algorithm using Zadoff-Chu sequence in orthogonal frequency division multiplexing systems. To demonstrate efficacy of the proposed algorithm, we derive the probability of signature identification failures for different power delay profiles. From the derived probability, it is shown that the proposed algorithm fully exploits frequency selective fadings. Especially, we reveal that the slope of the failure probability curves is determined by the channel length for equal power fading channels. Simulation results show that the proposed algorithm outperforms conventional signature algorithms in frequency selective fading channels. Also, we confirm that our analysis matches well with the empirical results of the proposed signature identification algorithm.

In this paper, we study an inter-cell interference cancellation (ICIC) scheme for multicell multiple-input single-output downlink systems where each base station (BS) simultaneously supports multiple active users and only partial channel state information (CSI) is shared among BSs. Each BS jointly selects one of following beamforming strategies: an egoistic zero-forcing beamforming (ZFBF) which applies the ZFBF for only home users and an altruistic strategy which performs the ZFBF for both home users and neighboring users for ICIC. We first derive closed-form expressions for the average rate of an individual user in 2-cell and 3-cell networks when BSs choose one of the aforementioned beamforming strategies. With the derived closed-form expressions, we propose a multiuser adaptive ICIC scheme in order to maximize the average sum rate of the system based on user locations. Numerical results show that the proposed scheme outperforms conventional methods in terms of the average sum rate for both 2-cell and 3-cell networks.

In this paper, we study multiple-input single-output downlink cellular systems which jointly design adaptive inter-cell interference cancellation and user scheduling assuming that partial channel state information (CSI) is shared among base stations (BSs). Since the optimal solution requires high complexity, we propose a new low complexity algorithm which selects the best users and their beamforming (BF) strategies in terms of maximizing the weighted sum rate. To this end, we first develop a simple threshold criterion for each user to decide the preferred BF strategy based on the derivation of the expected signal-to-interference-plus-noise ratio. Then, according to users' feedback about their decisions, a successive user and BF selection algorithm is performed at the BSs. From simulation results, we show that combined with proportional fair scheduling, the proposed scheme provides excellent sum rate performance with very low computational complexity.

In this paper, we consider a user scheduling problem which maximizes the weighted sum-rate (WSR) in multicell multiple input single output (MISO) downlink systems. Since an exhaustive search algorithm requires high computational complexity, we propose a low complexity algorithm which finds a user set in terms of WSR maximization. We first consider a greedy user selection algorithm which almost achieves the system performance of the exhaustive search algorithm in high SNR regime. Next, a distributed user scheduling algorithm is proposed by employing the maximized signal-to-leakage-and-noise ratio (SLNR) in the selection criteria. This leads to a reduction of the system overhead of exchanging channel state information and computational complexity. From simulation results, we show that the performance of our proposed scheme is very close to the conventional greedy user scheme with lower overhead.

In this paper, we study a filter design which maximizes the weighted sum rate (WSR) in multi-user multi-relay systems equipped with multiple antennas at each node and account for the direct links between the base station and the mobile users. Since this problem is generally non-convex, it is quite complicated to analytically find a solution. Hence, we transform the WSR maximization problem to an equivalent weighted sum mean-squared-error (WSMSE) minimization problem which is more amenable. Then, we identify the filters at the base station and the relays for minimizing the WSMSE with a proper weight and propose an alternating computation algorithm which guarantees a local optimum solution. Through simulations, we confirm the effectiveness of our proposed scheme.

In this paper, we consider multipoint-to-multipoint multi-input multi-output relay systems where multiple sourcedestination pairs simultaneously communicate through a single multiple antenna relay. In this system configuration, we minimize the maximum pairwise mean-squared-error (MSE) to guarantee fairness for all pairs. This problem is non-convex, and thus it is quite complex to find an analytical solution. In order to simplify this problem, we apply error covariance decomposition approach and broadcast channel to multiple access channel MSE duality, and then a pairwise MSE balancing scheme which utilizes global channel state information (CSI) is proposed. In order to reduce the complexity and CSI requirements, a new method is developed. Through numerical simulations, we confirm the effectiveness of our proposed schemes.

In this paper, we study single cell multi-user downlink distributed antenna systems (DAS) where antenna ports are geographically separated in a cell. First, we derive an expression of the ergodic sum rate for the DAS in the presence of pathloss. Then, we propose a transmission selection scheme based on the derived expressions which does not require channel state information at the transmitter. Utilizing the knowledge of distance information from a user to each distributed antenna (DA) port, we consider the optimization of pairings of DA ports and users to maximize the system performance. Based on the ergodic sum rate expressions, the proposed scheme chooses the best mode maximizing the ergodic sum rate among mode candidates. In our proposed scheme, the number of mode candidates are greatly reduced compared to that of ideal mode selection. In addition, we analyze the signal to noise ratio cross-over point for different modes using the sum rate expressions. Through Monte Carlo simulations, we show the accuracy of our derivations for the ergodic sum rate. Moreover, simulation results confirm that the proposed scheme produces the average sum rate identical to the ideal mode selection with significantly reduced candidates.

In this paper, we propose new precoding methods for two-way multiple input multiple output physical network coding (PNC) systems which employ the modulo operation. In our work, the transmit and receive filters are determined to maximize the minimum distance of the received constellations assuming global channel state information. The precoding operations are separately optimized for the multiple access (MA) and the broadcast stages, and the optimal precoding is obtained by applying a semidefinite relaxation method. Especially, we prove that for the system with linear detection the modulo operation for the PNC achieves optimality with the derived precoding for the MA stage in terms of the minimum distance. Also, we present a closed-form solution for the optimal filter designs for two special cases. For computing solutions, we transform our max min problem into a simple maximization problem by imposing additional constraints. Also, we propose a suboptimal non-iterative precoding scheme whose performance is within 1 dB at a bit error rate (BER) of 10-4compared to the optimum iterative method with much reduced complexity. Finally, the simulation results show that the proposed systems achieve 2-3 dB gains at a BER of 10-4compared to the optimal amplify-and-forward systems.

A signature identification algorithm is a method to obtain the cell identification information for wireless cellular systems or determine the intended user for wireless local area network. In this paper, we propose a simple and efficient signature identification algorithm on the basis of Zadoff-Chu sequence in orthogonal frequency division multiplexing systems. In addition, we prove that the proposed algorithm achieves a maximum likelihood solution if the receiver knows the channel length. Also, the exact probabilities of signature identification failures of the proposed algorithm are provided for different power delay profiles. To demonstrate efficacy of the proposed algorithm in fading channels, we derive the failure probability at high signal-to-noise ratio (SNR). Through a high SNR expression, it is shown that the proposed algorithm fully exploits frequency selective fadings. Especially, we reveal that the slope of the failure probability curves at high SNR is determined by the channel length regardless of power delay profiles. Simulation results show that the proposed algorithm outperforms conventional signature algorithms in frequency selective fading channels. Also, we confirm that our analysis matches well with the empirical results of the proposed signature identification algorithm.

In this paper, we analyze the average sum rate of downlink multi-antenna systems with zero-forcing beamforming (ZFBF). In practical implementations, each antenna is equipped with its own power amplifier and is limited individually by linearity of the amplifier. Thus, this paper adopts a more realistic per-antenna power constraint instead of conventional sum-power constraint on transmit antennas. To this end, we first show that a distribution of the received signal-to-noise ratio (SNR) of the ZFBF scheme with per-antenna power constraint and equal-rate metric can be approximated as a minimum of chi-square random variables. Based on this result, we present an accurate formula of the average sum rate in a closed form. Furthermore, employing extreme value theory, an expression of the asymptotic average sum rate with large numbers of transmit antennas and users is derived from the limiting distribution of the received SNR. Simulation results verify the validity of our analysis even with not so large numbers of transmit antennas and users.

In this paper, we consider weighted sum-rate (WSR) maximization problems in multiple-input single-output (MISO) interference channels (IFC) and interfering broadcast channels (IFBC). Most of existing techniques have tried to improve the WSR performance by utilizing instantaneous channel state information. However, since these methods in general should be carried out for each channel realization, they require high computational complexity, which may not be suitable for practical systems. To overcome this issue, we propose a new low complexity beamforming scheme for IFC based on virtual signal-to-interference-plus-noise ratio with constant parameters which depend only on the long-term channel statistics. In our approach, to obtain the constant parameters, the asymptotic values of the leakage coefficients which control the interference signal power are derived by employing asymptotic results from random matrix theory. Moreover, based on the results in MISO IFC, we extend the algorithm to the MISO IFBC case by applying a power allocation algorithm. Numerical results confirm that the proposed schemes provide the near-optimal WSR performance with much reduced system complexity.

In this paper, we study new network information flow called multiple-input multiple-output (MIMO) two-way relay interference channels where two links of relay systems are interfering with each other. In this system, we characterize the achievable total degrees of freedom (DOF) when all user nodes and relays have M and N antennas, respectively. We provide three different methods, namely, time-division multiple access, signal space alignment for network coding (SSANC), and a new interference neutralization (IN) scheme. In the SSA-NC scheme, one relay is selected to fully exploit the dimension of the chosen relay for network coding. For the IN, we propose a new relay transmission scheme where two relays cooperatively design the beamforming vectors so that the interference signals are neutralized at each receiver. By adopting three different relaying strategies, we show that the DOF of max {min(4N, 2M), min(2N, 2⌊4/3M⌋), min(2N - 1, 4M)} is achieved for MIMO two-way relay interference channels.

In this paper, we investigate the outage and ergodic capacity of downlink distributed antenna systems (DAS) where each distributed antenna unit (DAU) has multiple antennas with per-DAU power constraint. We first derive the optimal beamforming vector in a closed form by applying a matrix minor condition to relax the positive semi-definite constraint. We observe that our derived solution has a form of maximum ratio transmission per each DAU with full power. Based on the derived optimal beamforming, the outage and ergodic capacity under Rayleigh fading channels are analyzed. To this end, we show that a distribution of the received signal-to-noise ratio is characterized as a Gamma distribution by approximating a sum of non-identical independent Nakagami-m random variables as a single Nakagami-m random variable based on the moment matching method. Then, we present an accurate formula of the outage and ergodic capacity in a closed form which matches well with the simulation results. Furthermore, we derive an upper bound of an achievable average rate of DAS with limited feedback. We then propose a new feedback bit allocation algorithm to maximize the derived metric. Simulation results confirm the accuracy of the derived outage and ergodic capacity expressions and the efficiency of the proposed bit allocation method.

In this paper, we study coordinated multi-cell downlink systems where multiple base stations jointly design a transmission strategy by sharing channel state information. Particularly, we tackle the signal-to-interference-plus-noise ratio (SINR) balancing problem to maximize the worst-user rate. We consider single-input single-output (SISO) interference channels (IFC) where all nodes are equipped with a single antenna, and there is one active user in each cell. First, achievable rate regions with symmetric complex (SC) and asymmetric complex (AC) signaling techniques are investigated. Then, we present the optimal and near-optimal SINR balancing algorithms with the SC signaling for two and three user SISO IFC. Due to residual interference, the worst-user rate of the SC signaling is saturated at high signal-to-noise-ratio region. To alleviate this problem, we also propose efficient balancing schemes based on the AC signaling for both two and three-user cases. Simulation results confirm effectiveness of the proposed SINR balancing algorithms and show that a substantial gain of the AC signaling is achieved over the SC signaling in terms of the maximum worst-user rate.

In this paper, we study K-user multiple-input multiple-output (MIMO) interference channels (IC) in the presence of antenna correlations. Although interference alignment (IA) achieves the maximum multiplexing gain, i.e., degrees of freedom (DOF), the actual performance is degraded due to ill-conditioned channels caused by the antenna correlations. To enhance the overall system performance, we thus consider a transmission mode selection scheme which adaptively determines the number of data streams at each transmitter-receiver pair in a distributed fashion using local channel state information (CSI). A filter update process is exploited in order to estimate the performance regarding each transmission mode successively by considering the actual channel conditions as well as signal-to-noise ratio (SNR). Simulation results show that our mode selection scheme enhances the sum rate performance compared to the conventional full data stream transmissions in spatially correlated MIMO IC.

In this paper, we propose a limited feedback system for two-way wireless relaying channels with physical network coding (PNC). The optimal system with full feedback has already been studied when a modulo operation is employed for the PNC. In this case, phase and power of channels of two end nodes are adjusted to maximize the minimum distance. Based on this full feedback system, we design new quantization methods for the phase and the power. From the observation of the minimum distance, we present a codebook design. Especially, for the quantization of power in the system with 16-QAM, a novel power quantization scheme is proposed to maximize the worst minimum distance. Finally, the simulation results confirm that our proposed scheme outperforms conventional limited systems with reduced complexity.

When perfect channel state information (CSI) is available at each transmitter in K-user multiple-input multiple-output (MIMO) interference channels, it was shown that interference alignment (IA) achieves a full spatial multiplexing gain. However, a significant performance loss is inevitable in the IA when the CSI is fed back from receivers using the limited number of feedback bits. In this paper, we propose a new channel quantization strategy to optimize the performance of the IA with limited feedback. In our proposed scheme, we introduce an additional receive filter to minimize the chordal distance which accounts for the quantization error on Grassmann manifold. Simulation results verify that the proposed scheme provides substantially better performance than the conventional method as the number of feedback bits is increased. We show that our scheme exhibits 30% and 40% sum rate gains compared to the conventional scheme when the numbers of the feedback bits are 10 and 15, respectively, with two antennas per node.

In this paper, we investigate the ergodic capacity for distributed antenna systems over Rayleigh fadings, including a distance-dependent pathloss model. Based on the proof of asymptotic normality, the mean and the variance of the instantaneous capacity were recently presented as a closed form solution, which includes a root of polynomials. However, this solution is too complicated to capture quantitative performance measures such as multiplexing gain and power offset. In this work, we derive a simple and accurate expression for the ergodic capacity by utilizing the high signal-to-noise ratio (SNR) analysis. Our simple solution provides meaningful insights on how the ergodic capacity is affected as SNR, pathloss and antenna configurations change. Numerical results confirm the validity of our analytical results under a realistic pathloss model.

In this paper, we investigate the outage and ergodic capacity of downlink distributed antenna systems (DAS) where each distributed antenna unit (DAU) has multiple antennas with per-DAU power constraint. We first derive the optimal beamforming vector in a closed form by applying a matrix minor condition to relax the positive semi-definite constraint. We observe that our derived solution has a form of maximum ratio transmission per each DAU with full power according to each DAU power constraint. Based on the derived optimal beamforming, the outage and ergodic capacity under Rayleigh fading channels are analyzed. To this end, we show that a distribution of the received signal-to-noise ratio is characterized as a Gamma distribution by approximating a sum of non-identical independent Nakagami-m random variables as a Nakagami-m random variable based on the moment matching method. Then, a formula of the outage and ergodic capacity is presented in a closed form. Simulations confirm that our analysis is accurate and matches well with the simulation results.

In this paper, we propose an accurate link performance abstraction technique for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing systems with maximum likelihood (ML) receiver. The performance of ML detection (MLD) is estimated by using capacity bounds of two simple linear receivers. To this end, we give a simple parametrization to compute the desired per-stream signal-to-noise ratio (SNR) values, which can be applied for both vertically and horizontally coded MIMO systems. Based on the per-stream SNR estimates, the block error rate performance for each encoding block is finally obtained using the received-bit information rate metrics. From extensive simulations, we verify that the proposed method is accurate in the MIMO-MLD link evaluation with very low computational complexity.

In this paper, we consider coordinated beamforming techniques where multiple base stations jointly design a transmission strategy by sharing channel state information. Particularly, we tackle the signal-to-interference-plus-noise ratio (SINR) balancing problem to maximize the worst-user rate for multi-cell downlink systems. To solve this problem, both symmetric complex (SC) and asymmetric complex (AC) signaling methods are investigated. First, we present the SINR balancing algorithm with the SC signaling. Due to residual interference, the worst-user rate in the SC signaling is saturated at high signal-to-noise ratio (SNR). To alleviate this issue, we also propose the SINR balancing technique based on the AC signaling which combines both interference alignment and power control methods. Simulation results confirm that the AC signaling outperforms the SC signaling scheme over all SNR range.

In this paper, we present the diversity-multiplexing tradeoff (DMT) analysis for minimum mean squared error (MMSE) based amplify-and-forward cooperative multiple antenna half-duplex relaying systems where a non-negligible direct link exists between the source and the destination. First, we evaluate an upperbound of the DMT which offers a theoretical limit of the system and show that the upperbound is actually achievable by existing optimal and suboptimal designs for the relay amplifying matrix. Thereby, we establish the optimal DMT for the MMSE-based cooperative relaying system. Our analysis also illustrate the optimality of the conventional relay matrix designs in terms of the DMT and leads to several interesting observations. Finally, numerical simulations demonstrate the accuracy of our analysis.

In this paper, we propose a linear precoding technique for weighted sum rate (WSR) maximization in multiple-input multiple-output interfering broadcast channels. In multicell environments, the WSR can be jointly maximized through centralized processing which causes a large amount of channel state information (CSI) exchange. In order to reduce the overhead associated with CSI, we focus on a distributed precoding scheme utilizing local CSI at each base station (BS). First, applying a high signal-to-interference-plus-noise ratio assumption, we decouple the WSR maximization problem into distributed problems. Then we solve this distributed WSR maximization problem for each BS by using a zero-gradient based algorithm which converges to a local maximum point. Unlike conventional distributed schemes which require additional information, our proposed scheme at each base station utilizes only the local CSI to compute its precoding matrices. Through the Monte-Carlo simulation, we show that our proposed algorithm exhibits the performance almost identical to the centralized scheme requiring the global CSI.

A relay selection method is one of popular approaches to overcome fading effects in wireless relay channel due to its simplicity. However, previous works were concentrated on the case of analog network coding systems. In this paper, we consider a relay selection scheme in two-way relaying systems with physical network coding. Since conventional schemes based on analog network coding does not provide effective performance in physical network coded systems, we propose new selection criteria which maximize the minimum distance at the multiple access phase to achieve full diversity. Moreover, through asymptotic error rate analysis, it is confirmed that the proposed scheme is able to attain full diversity. Simulations results are also provided to verify the analysis of the proposed scheme.

In this paper, we propose a low-complexity semi-blind joint carrier frequency offset (CFO) and data estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems. Given channel information, we first provide a new iterative algorithm which jointly estimates the CFO and data based on pilots by minimizing the mean square error between the received OFDM symbol and its regenerated signal. By using the matrix inversion lemma, the joint CFO and data estimator is divided into a CFO estimator and a data detector without loss of optimality, which significantly reduces the computational complexity. Also, we present a decision feedback strategy to select reliable data from previously detected data by adopting the probability metric which evaluates the reliability. Then, the simplified CFO estimator can utilize the selected reliable data as pilots in the next iteration step. Simulation results show that the simplified CFO estimator can achieve the average Cramer Rao bound in moderate and high signal to noise ratio (SNR) regions within a few iterations even for a small number of pilots with the help of the proposed decision feedback strategy.

In this paper, we propose a pilot aided carrier frequency offset (CFO) estimation algorithm for orthogonal frequency division multiplexing access (OFDMA) uplink systems based on two consecutive received OFDMA symbols. Assuming that the channels and the CFOs are static over the two consecutive symbols, we express the second received OFDMA symbol in terms of the CFOs and the first OFDMA symbol. Based on this signal model, a new estimation algorithm which obtains the CFOs by minimizing the mean square distance between the received OFDMA symbol and its regenerated signal is provided. The simulation results show that the proposed algorithm approaches the average Cramer Rao bound for moderate and high signal to noise ratio regions. Moreover, the algorithm can be applied for any carrier assignment schemes.

In this paper, we optimize antenna locations for a distributed antenna system (DAS) with distributed antenna (DA) ports equipped with multiple antennas under per-DA port power constraint. Maximum ratio transmission and scaled zero-forcing beamforming are employed for single-user and multi-user DAS, respectively. Instead of maximizing the cell average ergodic sum rate, we focus on a lower bound of the expected signal-to-noise ratio (SNR) for the single-cell scenario and the expected signal-to-leakage ratio (SLR) for the two-cell scenario to determine antenna locations. For the single-cell case, optimization of the SNR criterion generates a closed form solution in comparison to conventional iterative algorithms. Also, a gradient ascent algorithm is proposed to solve the SLR criterion for the two-cell scenario. Simulation results show that DAS with antenna locations obtained from the proposed algorithms achieves capacity gains over traditional centralized antenna systems.

In this paper, we study a power allocation technique for Tomlinson-Harashima precoding (THP) in multi- user multiple input single output (MISO) downlink systems. In contrast to previous approaches, a mutual information based method is exploited for maximizing the sum rate of zero-forcing THP systems. Then, we propose a simple power allocation algorithm which assigns proper power level for modulo operated users. Simulation results show that the proposed scheme outperforms a conventional water-filling method, and achieves near optimal performance with much reduced complexity.

In this paper, we investigate minimum mean squared error (MMSE) based amplify-and-forward cooperative multiple antenna relaying systems where a non-negligible direct link exists between the source and the destination. First, we provide a new design strategy for optimizing the relay amplifying matrix. Instead of conventional optimal design approaches resorting to an iterative gradient method, we propose a near optimal closed-form solution which provides an insight. As relay systems with a direct link incur a non-convex problem in general, we exploit the decomposable property of the error covariance matrix and a relaxation technique imposing a structural constraint on the problem. Next, we study the error performance limit of the proposed scheme using diversity-multiplexing tradeoff analysis, which leads to several interesting observations on MMSE-based cooperative relaying systems. Finally, through numerical simulations, we confirm that the proposed solution shows the performance very close to the optimum with much reduced complexity and the analysis closely matches with simulation results.

For multi-user interference channels (IC), an altruistic approach based on the zero-forcing (ZF) criterion shows the near-optimal performance at high signal-to-noise ratio (SNR), whereas its performance at low SNR becomes poor compared to a simple egoistic algorithm (selfish beamforming). Thus, balancing between the egoism and the altruism has been an important issue to achieve good sum-rate performance at overall SNR regime. In this paper, we propose a new approach for enhancing the performance by regularizing the ZF based transceivers. To this end, we start with investigating efficient ZF transceivers for 2-user and 3-user ICs. First, coordinated spatial multiplexing (CSM) is proposed for 2-user IC. For the 3-user case, it is shown that the enhanced interference alignment (E-IA) introduced in our previous work is the optimal ZF transceivers in terms of the sum-rate performance. Next, to improve the performance of the CSM and E-IA schemes at low SNR, we propose a non-iterative regularization method under the high SNR approximation. The distributed implementation of the proposed regularization method is also presented where each node is able to compute its own precoding or decoding matrix using local channel state information. From simulations, it is observed that the proposed regularized design outperforms the conventional schemes in overall SNR regime. Also, we confirm that our distributed approach provides a substantial performance gain over the conventional distributed scheme with reduced computational complexity.

In this paper, we study spatial degree-of-freedom (DOF) for two mutually interfering broadcast channels (IBC). As the demand for space-division multiple access (SDMA) increases, the IBC where each link has a single transmit node and multiple receive nodes becomes important. This paper presents the results of the lower and upper bounds on the DOF of the IBC. From the derived results, it is shown that for most cases, zero-forcing (ZF) beamforming can achieve the optimal DOF of the IBC except for some special cases. Also, we identify a condition that disabling receive cooperation in the multiple-input multiple-output interference channels causes no DOF loss. It is confirmed that we cannot expect a DOF improvement by enabling in-cell receive cooperation if in at least one of two BSs, the number of antennas is greater than or equal to that of users per cell. Furthermore, we observe a positive result that as the number of users goes to infinity, the total DOF of the IBC converges to the interference-free DOF, which is the maximum achievable DOF in the absence of the inter-cell interference.

In this letter, we study a linear beamforming technique for weighted sum-rate (WSR) maximization in multiple-input single-output interfering broadcast channels. We focus on a distributed beamforming scheme which utilizes local channel state information (CSI) to mitigate inter-cell interference. In order to decouple the WSR maximization problem which involves the beamforming vectors of all base stations (BSs) into a distributed WSR problem as a function of local CSI, we apply high signal-to-interference-plus-noise ratio approximation. After defining the distributed WSR function, we solve the decoupled problems by using a zero-gradient based algorithm which converges to a local optimal point. Unlike conventional distributed schemes where additional information should be exchanged at each iteration, each BS of the proposed scheme utilizes only the local CSI to compute its beamforming vectors. Also we prove the convergence of the proposed algorithm. Simulation results show that our proposed algorithm exhibits the WSR performance almost identical to the centralized scheme with substantially reduced overhead.

This letter considers a multi-user wireless communication system employing physical network coding (PNC), where a pair of users communicates with each other via a relay. To attain reliable communication over time varying channels, we apply an opportunistic scheduling scheme for the PNC to both the broadcast channel phase and the multiple access channel phase. We propose two criteria for selecting users based on the channel norm and the minimum distance. Also, an efficient method to compute the minimum distance for superposed QPSK signals is introduced. Simulation results show that the proposed scheduling for PNC provides a significant improvement over conventional schemes.

In this paper, we study single cell multi-user downlink distributed antenna systems (DAS) where antenna ports are geographically separated in a cell. First, we derive an expression of the ergodic sum rate for the DAS in the presence of pathloss. Then, we propose a transmission selection scheme based on the derived expressions which does not require channel state information at the transmitter. Utilizing the knowledge of distance information from a user to each distributed antenna (DA) port, we consider the optimization of pairings of DA ports and users to maximize the system performance. Based on the ergodic sum rate expressions, the proposed scheme chooses the best mode maximizing the ergodic sum rate among mode candidates. In our proposed scheme, the number of mode candidates are greatly reduced compared to that of ideal mode selection. In addition, we analyze the signal to noise ratio cross-over point for different modes using the sum rate expressions. Through Monte Carlo simulations, we show the accuracy of our derivations for the ergodic sum rate. Moreover, simulation results with the pathloss modeling confirm that the proposed scheme produces the average sum rate identical to the ideal mode selection with significantly reduced candidates.

In this paper, we propose a new coordinated spatial multiplexing algorithm for multiuser multiple-input multiple-output (MIMO) downlink systems. Unlike other conventional coordination methods, our scheme exploits orthogonalized effective channels to yield the transmit precoding and receive combining matrices without requiring an iterative operation. In order to determine each row of the effective channels, a greedy type successive process is utilized. Simulation results show that the performance of our proposed scheme is very close to the conventional iterative scheme with significantly reduced complexity. Also, our method achieves a 3 dB performance gain over the conventional non-iterative scheme.

In K-user multiple-input multiple-output (MIMO) interference channels, it was shown that interference alignment (IA) achieves a full spatial multiplexing gain when perfect channel state information (CSI) is available at each transmitter in the network. When the CSI is fed back from receivers using the limited number of feedback bits, a significant performance loss is inevitable in the IA due to quantized channel knowledge. In this paper, we propose a new channel quantization strategy to optimize the performance of the IA with limited feedback. In our proposed scheme, we introduce an additional receive filter to minimize the chordal distance which accounts for the quantization error on Grassmann manifold. Besides, we analyze a reduction in terms of the chordal distance in our scheme compared to conventional methods. Simulation results verify that the proposed scheme provides substantially better performance than the conventional method as the number of feedback bits is increased. We show that our scheme exhibits 30% and 40% sum rate gains compared to the conventional scheme when the numbers of the feedback bits are 10 and 15, respectively, with two antennas per node.

In this paper, we consider K-user Y channels where K users simultaneously exchange messages with each other via an intermediate relay. Degrees of freedom (DOF) of Y channels with multiple antennas is not known in general. Investigation of the feasibility conditions of signal space alignment for network coding is an initial step for addressing this open problem. We verify that when user i with M-i antennas sends K-1 independent messages to the other users through a relay with N antennas and each message achieves the DOF of d, the total DOF of dK(K-1) is attained if M-i >= d(K - 1), N >= dK(K-1)/2 and N < min {M-i + M-j - d vertical bar for all i not equal j}. It is accomplished by adopting the signal space alignment for the network coding during both the multiple access phase and the broadcast phase. It is shown that the proposed scheme obtains not only a network coding gain but also an alignment gain in terms of the normalized DOF, as K ->infinity. Also for Y channels where all nodes have a single antenna, we show that the DOF of 2 is achieved regardless of the number of users by using the rational dimension framework.

In this paper, we present a low-complexity carrier frequency offset (CFO) compensation algorithm based on the minimum mean square error (MMSE) criterion for uplink orthogonal frequency division multiple access systems. CFO compensation with an MMSE filter generally requires an inverse operation on an interference matrix whose size equals the number of subcarriers. Thus, the computational complexity becomes prohibitively high when the number of subcarriers is large. To reduce the complexity, we employ the conjugate gradient (CG) method which iteratively finds the MMSE solution without the inverse operation. To demonstrate the efficacy of the CG method for our problem, we analyze the interference matrix and present several observations which provide insight on the iteration number required for convergence. The analysis indicates that for an interleaved carrier assignment scheme, the maximum iteration number for computing an exact solution is at most the same as the number of users. Moreover, for a general carrier assignment scheme, we show that the CG method can find a solution with far fewer iterations than the number of subcarriers. In addition, we propose a preconditioning technique which speeds up the convergence of the CG method at the expense of slightly increased complexity for each iteration. As a result, we show that the CFO can be compensated with substantially reduced computational complexity by applying the CG method.

In this paper, we investigate a behavior of the cell average ergodic capacity for distributed antenna systems (DAS) in a composite fading channel model which contains small-scale and large-scale fadings. For small-scale Rayleigh fadings, based on the proof of asymptotic normality, the mean and the variance of the instantaneous capacity were recently presented as a closed form solution. However, this solution is too complicated to be applied directly for obtaining the average ergodic capacity over a cell. In this work, we derive a simple and accurate expression for the ergodic capacity by utilizing the high signal to noise ratio (SNR) analysis. Our simple solution provides meaningful insight on how the ergodic capacity is affected as SNR, pathloss and antenna configurations change. Also it is useful for capturing quantitative performance measures such as the multiplexing gain and the power offset. In addition, we analyze the cell average ergodic capacity by taking into account the shadowing effect and the pathloss on the basis of our results on the small-scale fadings. These expressions lead to some insights on the performance of DAS under practical environments. Finally, numerical results confirm the validity of our analytical results.

In this letter, we propose a low complexity pilot aided carrier frequency offset (CFO) estimation algorithm for orthogonal frequency division multiplexing access (OFDMA) uplink systems based on two consecutive received OFDMA symbols. Assuming that the channels and the CFOs are static over the two consecutive symbols, we express the second received OFDMA symbol in terms of the CFOs and the first OFDMA symbol. Based on this signal model, a new estimation algorithm which obtains the CFOs by minimizing the mean square distance between the received OFDMA symbol and its regenerated signal is provided. Also, we implement the proposed algorithm via fast Fourier transform (FFT) operations by utilizing the block matrix inversion lemma and the conjugate gradient method. Simulation results show that the proposed algorithm approaches the average Cramer Rao bound for moderate and high signal to noise ratio (SNR) regions. Moreover, the algorithm can be applied for any carrier assignment schemes with low complexity.

Link adaptation allows a communication system to adapt its transmission modes according to channel conditions. Although a maximum likelihood (ML) receiver for multiple-input multiple-output (MIMO) systems provides optimal performance, estimating its link performance has been a difficult problem. In this paper, we propose a new link performance abstraction technique for MIMO orthogonal frequency-division multiplexing systems with the ML receiver. The performance of ML detection (MLD) is estimated by employing capacity bounds of two simple linear receivers. Then, we give a simple parametrization to compute the desired per-stream signal-to-noise ratio (SNR) values, which can be applied for both vertically and horizontally coded MIMO systems. Based on the derived per-stream SNR estimates, the block error rate is obtained using the received-bit information rate metrics. We also examine the effect of imperfect channel estimation as well as spatial correlations among antennas. Finally, extensive simulation results show that the proposed method provides superior estimation accuracy in the MIMO-MLD link evaluation with very low computational complexity.

In this paper, we propose new algorithms to determine the antenna location for downlink distributed antenna systems (DASs) in single-cell and two-cell environments. We consider the composite fading channel which includes small and large scale fadings. First, for the single-cell DAS, we formulate the optimization problem of distributed antenna (DA) port locations by maximizing the lower bound of the expected signal to noise ratio (SNR). In comparison to the conventional algorithm based on the squared distance criterion which requires an iterative method, our problem generates a closed form solution. Next, for the two-cell DAS, we propose a gradient ascent algorithm which determines the optimum DA locations by maximizing the lower bound of the expected signal to leakage ratio (SLR). In our work, we consider selection transmission, maximal ratio transmission and zero-forcing beamforming (ZFBF) under sum power constraint and study equal gain transmission and scaled ZFBF under per-antenna power constraint. Simulation results show that our proposed algorithms based on both the SNR and the SLR criteria offer a capacity gain over the conventional centralized antenna systems.

In this paper, we propose new beamforming techniques based on virtual signal-to-interference-plus-noise ratio (VSINR) for weighted sum-rate (WSR) maximization in coordinated multi-cell transmission. In earlier works based on the VSINR maximization, the parameters which control the interference power and the noise variance were set to fixed values regardless of channel realizations and the signal-to-noise ratio level. In order to obtain an improved WSR performance, we propose a method which adaptively adjusts the parameters after establishing a connection between the WSR and VSINR. Our proposed method can be applied to the cases of various coordination levels among base stations. To address practical implementation issues, a decentralized implementation of the beamforming techniques is also proposed based on local channel state information. Numerical results confirm that the proposed centralized schemes provide near-optimal WSR performance and the proposed decentralized methods show a negligible performance loss compared to the centralized algorithms with reduced system complexity.

In this letter, we consider a new interference alignment (IA) strategy for single-input single-output interference broadcast channels with constant channel coefficients. First, we show that 1.5 degrees of freedom (DOF) is achievable for 3-cell case by utilizing asymmetric complex signaling (AC) and multiuser diversity without symbol extension. It is also investigated that the achievable DOF varies with the user scaling condition and ω(√(SNR)) is required for guaranteeing the DOF of 1.5. To improve the sum-rate performance, user scheduling algorithms combined with the beamforming techniques are suggested which outperform the conventional IA schemes. After introducing an exhaustive scheduling algorithm which shows optimal sum-rate, a simplified scheduling method is also proposed which reduces both scheduling metric computations and the search size.

In this letter, we study multiple-input single-output joint processing (JP) systems with limited feedback where base stations exchange both channel state information and their data via ideal backhaul links. In order to optimize the sum-rate performance of the JP system, we propose a new feedback bit allocation scheme which maximizes quantization accuracy in the presence of pathloss. The quantization accuracy is formulated by the expectation of the inner product between the actual channel vector and the quantized channel vector. First, we derive the quantization accuracy as a closed form, which compensates the phase difference of two channels. Then, the maximum quantization accuracy is achieved by searching possible bit combinations. Simulation results show that the sum rate of our proposed feedback bit allocation strategy is more than twice compared to the conventional equal bit allocation method in the three cell case.

In this paper, we propose precoding methods for multiple input multiple output physical network coding systems, which employ the modulo operation. In our work, the transmit and receive filters are determined to maximize the minimum distance on the received constellations. Especially, to address non-concavity of the given problems, we change our problem into a concave one which can be optimized by an iterative method. Also, we propose a suboptimal non-iterative scheme whose performance is very close to the optimum iterative method. In our derivations, we prove that the modulo operation achieves the optimality in terms of the minimum distance. Finally, it is confirmed from the simulation results that the proposed systems outperform conventional schemes in terms of the error performance.

In this paper, we investigate the minimum mean squared error based relay transceiver design in amplify-and-forward multiple antenna relay systems in the presence of direct link. Instead of the conventional optimal design which requires iterative methods, we propose a simple and near optimal closed-form solution. The proposed method exploits the decomposable property of the error covariance matrix to simplify the problem. Then, we impose a structural constraint on the non-convex problem to attain a simple closed-form solution. Through numerical simulations, we confirm that the proposed solution has almost no performance loss compared to the optimal iterative design with much reduced complexity.

In order to employ adaptive modulation and coding, it is important to determine the post-detected signal-to-noise ratio (SNR) of the system. In this paper, we propose a new method to predict the link performance for multiple-input multiple-output (MIMO) systems with maximum likelihood detector (MLD) which adopt a single channel encoder. By using the relation between the upper and the lower bound systems of the MIMO system with MLD and defining the dynamic SNR penalty, we show that it is possible to represent the link performance of MIMO systems with MLD as a single scalar value with high accuracy. By adopting the proposed method to adaptive modulation and coding systems, we confirm the efficiency of the proposed method for frequency selective channels through simulations.

In this paper, we propose an iterative low-complexity carrier frequency offset (CFO) compensation algorithm based on the conjugate gradient (CG) method for uplink orthogonal frequency division multiple access systems. In general, a linear minimum mean square error (MMSE) CFO compensation algorithm is simple and efficient, but it requires an inverse operation to compute the MMSE solution of the CFO-induced inter-carrier interference (ICI) matrix whose size equals the number of subcarriers. Thus, the computational complexity becomes prohibitively high when the number of subcarriers is large. To address this issue, we employ the iterative CG method to solve linear equations whose matrix is Hermitian positive-definite. First, we derive the eigenvalue distribution of the CFO-induced ICI matrix. Based on the distribution, we present several observations which lead to the iteration number required for the convergence. Especially, the analysis shows that for an interleaved carrier assignment scheme, the maximum iteration number to compute an exact solution is at most the same as the number of mobile units. Simulation results exhibit that the proposed scheme can yield almost the same bit error rate performance of the MMSE compensation algorithm with substantially reduced computational complexity and memory requirements.

In this paper, we consider K-user MIMO Y channels where K users simultaneously exchange messages with each other via an intermediate relay. Degrees of freedom (DOF) of this channel are not known in general. The investigation of the feasibility conditions of signal space alignment for network coding is an initial step for addressing this open problem. We verify that when user i has Miantennas (i = 1, ⋯, K) and a relay has N antennas, the DOF of K(K - 1) is achieved if Mi≥ K - 1, N ≥ (K(K-1)/2) and Ni+ Mj|∀ i ≠ j}. It is accomplished by adopting the signal space alignment for network coding during both multiple access channel and broadcasting channel phase. The achievability is shown by an amplify-and forward (AF) relaying strategy combined with the signal space alignment for the network coding scheme, which provides a lower bound of the capacity.

In this paper, we develop linear precoding methods for cognitive radio (CR) multi-user multiple-input multiple-output (MU-MIMO) broadcast systems where unlicensed secondary users (SUs) simultaneously use the same spectrum of the licensed primary user (PU). When the zero-forcing block diagonalization (ZF-BD) precoder is extended to the CR network, a noise enhancement problem occurs. Therefore, we propose a regularized BD precoder based on the minimum mean-square error (MMSE) criteria subject to zero interference constraint for the PU. As a result, the proposed MMSE-BD scheme improves the signal-to-interference-plus-noise ratio at each SU's receiver compared to the ZF-BD based method. Simulation results demonstrate that the proposed algorithm outperforms the ZF based technique by more than 5 dB at the sum-rate 10 bps/Hz for CR MU-MIMO downlink systems.

In this paper, we propose beamforming techniques based on virtual signal-to-interference-plus-noise ratio (VSINR) maximization for weighted sum-rate (WSR) maximization in multiple-input single-output (MISO) interference channels. In the earlier work by Zakhour and Gesbert, it was shown that all Pareto-optimal beamformers can be expressed as a solution to the VSINR problem. However, how to choose the weight coefficients in the VSINR expression is not addressed when solving the WSR maximization problem. Thus, we provide a method of computing the weight terms to achieve a certain desired WSR maximizing point. Since the beamforming vectors in the proposed scheme should be computed as a function of global channel state information (CSI), we also propose a decentralized approach which shows a performance close to the centralized scheme with a significant reduction in the CSI exchange overhead.

In this paper, we study multiple-input single-output joint processing (JP) systems with limited feedback where two adjacent base stations exchange both channel state information and their data. To optimize the sum-rate performance of the JP system, we propose a new feedback bit allocation method which maximizes quantization accuracy in the presence of pathloss. The quantization accuracy is formulated by the expectation of the inner product between the actual channel vector and the quantized channel vector. In order to maximize the quantization accuracy, we employ a new method which compensates the phase difference of the two channels. Through numerical evaluations, we show that our proposed feedback bit allocation strategies provide about 50% performance gain in terms of the sum rate performance compared to the conventional method with the equal bit allocation scheme.

This paper considers a three-way wireless communication system employing physical layer network coding (PNC), where each user desires to transmit independent data to the other users via relay. However, one difficulty with this system is that the performance is limited by worst channel. To overcome this problem, we adopt a scheduling system. Since channel characteristics vary over time in wireless communications, a scheduling technique employing network coding where users are selected based on the instantaneous signal-to-noise ratio was proposed for the broadcast channel (BC) phase. In this paper, we extend the scheduling technique to the multiple access channel phase as well as the BC phase. We propose two criteria for selecting users based on the channel norm and the minimum distance criterion. Also, an efficient method to compute the minimum distance is introduced. The proposed scheduling for PNC provides a significant improvement over the conventional scheme.

This paper investigates the achievable rate region of two-way amplify-and-forward (AF) relaying systems with multiple relays, where all nodes are equipped with multiple antennas. First, we find linear processing filters to optimize the weighted sum-rate for the two-way channels with sum power constraints. The proposed algorithm achieves the maximum weighted sum-rate by deriving the gradient expressions and iteratively applying the gradient descent method. Consequently, the proposed scheme outperforms the conventional methods in terms of the weighted sum-rate. Also, the achievable rate region is obtained by adjusting the weights in the proposed algorithm. It is observed from the ergodic rate region that channel reciprocity can improve the achievable sum-rate in two-way relay systems unlike one-way channels.

Random beamforming (RBF) is a simple yet effective technique for multiuser multiple-input multiple-output systems with limited feedback. In this paper, we analyze the performance of the RBF in the presence of other cell interference (OCI), where a base station with M antennas supports Msusers (Ms≤ M) selected by scheduling among K single antenna users. Employing extreme value theory, we derive a closed-form approximation on the asymptotic ergodic sum rate by examining the limiting distribution of the largest signal-to-interference-plus-noise ratio (SINR) among K independent users. Also, we prove that even if the OCI exists, we have the same sum rate scaling law of Ms\log2\log2K as the system without OCI. Simulation results verify that our analysis provides an accurate estimation for the average sum rate performance even when K is not so large.

Physical layer network coding (PNC) was first introduced by Zhang et al. for two-way relay channels (TWRCs). By utilizing the PNC, we can complete two-way communications within two time slots, instead of three time slots required in non- PNC systems. Recently, the upper and lower bounds for a bit error rate (BER) of PNC have been analyzed for fading channels. In this paper, we derive an exact BER of the PNC for the TWRC over fading channels. We determine decision regions based on the nearest neighbor rule and partition them into several wedge areas to apply the Craig's polar coordinate form for computing the BER. We confirm that our derived analysis accurately matches with the simulation results.

In this paper we propose new algorithms to determine the antenna location for downlink distributed antenna systems (DAS) with selection transmission (ST). ST has some advantages for DAS since the feedback overhead is quite small and other-cell interference can be reduced compared to other transmission schemes. For the single-cell case, we consider a circular antenna layout with or without a center antenna and divide a cell into regions with the same physical area. Then, we formulate the optimization problem of distributed antenna (DA) port locations which maximizes the lower bound of the expected signal to noise ratio in each region. Also, for the two-cell DAS, we maximize the lower bound of the expected signal to leakage ratio to identify the optimum DA positions. In order to solve the problem, we propose an iterative method by deriving the gradient of the cost function for a gradient ascent algorithm. The DA locations obtained from our proposed method are compared with conventional solutions. Simulation results show that the proposed algorithms offer a large capacity gain over the centralized antenna systems in single-cell and two-cell environments.

In multi-cell environments, interference alignment (IA) introduced by Cadambe and Jafar is an effective strategy for managing interference. However, this requires a large number of symbol extension in time/frequency domain to gurantee the optimal number of degrees of freedom (DOF). Recently, in the single-input single-output (SISO) case, a new idea of the IA scheme based on asymmetric complex signaling and symbol extension was proposed where at least the DOF of 1:2 is achievable for all complex channel values. In this paper, we prove that at least the DOF of 1:5 is achievable in multi-cell and multi-user interfering broadcast channels under the assumption of constant channel coefficients with no symbol extension. We also show that the achievable DOF varies with the condition of user scale and ω(√(SNR)) users are required to guarantee the DOF of 1:5. Furthermore, a simple IA scheme in conjunction with user selection which groups semi-aligned users is proposed to realize the achievable DOF.

Orthogonalized spatial multiplexing (OSM) systems with optimal precoding, denoted by POSM, was recently proposed which allows a single-symbol decodable maximum likelihood receiver in closed-loop multiple-input multiple-output (MIMO) systems. It was shown that the performance of the POSM is identical to that of the optimal closed-loop MIMO system in terms of the minimum distance. In this letter, we derive an exact symbol error rate (SER) expression of the POSM for 4-QAM and 16-QAM in Rayleigh fading channels. By using polar coordinates and the singular value distributions, the final expression of the SER can be evaluated with a single-integral form. Also, in the high SNR regime, we obtain a simple average SER expression of the POSM which shows that the POSM with two transmitting antennas achieves full diversity. Simulation results confirm the accuracy of our derived analysis.

In this paper, we provide lower and upper bounds for the number of degree of freedom (DOF) of B multiple-input single-output (MISO) broadcast channels (BC) where each base station (BS) equipped with M antennas supports its corresponding K single antenna users suffering from inter-cell interference. The sufficient and necessary condition for tightness of two bounds is presented. From the derived result, it can be observed that in-cell receiver cooperation does not help in most of the cases in a multiple-input multiple-output (MIMO) interference channel (IFC) except for one special case. Even for that special case, the DOFs with and without in-cell receive cooperation approach the same value for large K. Also, in a MIMO IFC with symmetric antenna settings (i.e., M = K), if both transmit and receive cooperations are removed to make it a single-input single-output (SISO) IFC, we show that the DOF is not affected. In addition, the DOF is studied for two mutually interfering broadcast channels in the presence of a cognitive BS. We obtain an interesting result that disabling in-cell receive cooperation of the MIMO IFC causes no DOF loss if at least one of two transmitters is a cognitive BS.

In this paper, we propose a general framework to quantify the average error probability of the minimum mean squared error based precoding schemes in amplify-and-forward relay networks where all nodes are equipped with multiple antennas. Especially, we investigate spatial multiplexing schemes which transmit multiple data streams simultaneously. Due to difficulty in finding an exact expression of the average error rate, we exploit the high signal-to-noise-ratio (SNR) based approach which allows a simple and accurate characterization of the performance. Then, we derive new closed form expressions for bit error rate performance of both the optimal source-relay joint precoding schemes and the optimal relay only precoding schemes in terms of a coding gain as well as a diversity gain. Taking a different pathloss in each hop into consideration, we evaluate the performance in a generalized environment. Through our analysis, we discuss several interesting observations and provide a helpful guideline for designing MMSE-based relaying systems. Monte-Carlo simulations show that our analytical work accurately predicts numerical results.

We consider the downlink of a cellular network with multiple cells and multi-antenna base stations. Our model includes distance-dependent pathloss, arbitrary clusters of cooperating cells, and general “fairness” requirements. Beyond Monte Carlo simulation, no efficient computation method to evaluate the ergodic throughput of such systems has been presented, yet. Furthermore, for systems of practical size with tens of cells and hundreds of users per cell, even simulation becomes challenging. We develop an analytic framework based on the combination of results from large random matrix theory and convex optimization. This allows computationally efficient calculation of the system performance in the so-called “large system limit”, i.e., in the limit of a large number of antennas per base station and a large number of users per cell, while the ratio of antennas per user is kept constant. In particular, the system ergodic throughput, subject to per-base station power constraints and to general fairness criteria, is obtained via the iterative solution of a system of fixed-point equations. Comparisons with finite-dimensional simulation results show that the large-system analysis provides remarkably accurate approximations for the actual finite-dimensional systems, even for a small number of users and base station antennas.

In this letter, we consider a multipoint-to-multipoint transmission system which employs a single relay in wireless networks where all source, destination and relay nodes are equipped with multiple antennas. For amplify-and-forward relay systems, we propose new linear processing strategies for maximizing the sum rate performance by applying a blockwise relaying method combined with convex optimization techniques. To reduce a computational complexity, we first consider a zero-forcing based relay only optimization scheme, which generate a closed-form solution in a non-iterative fashion. In order to further improve the sum rate at low signal-to-noise ratio regimes, we additionally design an enhanced relay filter by exploiting the blockwise relaying method based on a minimum mean-square error criterion. Simulation results show that the proposed relay design strategies outperform the existing conventional schemes in terms of the sum rate.

In this paper, we present a linear precoder design for cognitive radio (CR) multi-user multiple-input multiple-output (MU-MIMO) downlink systems where unlicensed secondary users (SUs) can simultaneously utilize the same spectrum used by a licensed primary user (PU). Although a zero-forcing block diagonalization (ZF-BD) precoder is extended to the CR network with the PU, a transmit power boost problem occurs. Therefore, we propose a regularized BD precoder method based on the minimum mean-squared error (MMSE) criteria subject to the interference power constraint under a predetermined threshold for the PU. As a result, the proposed CR-MMSE-BD scheme improves the signal-to-interference-plus-noise ratio at each SU's receiver, compared to the ZF-BD based method. The simulation results demonstrate that the proposed algorithm outperforms the ZF based technique for CR MU-MIMO downlink systems.

In this letter, we analyze the sum rate of random beamforming (RBF) for downlink multi-antenna systems in the presence of other cell interference (OCI). Employing extreme value theory, an expression of the asymptotic ergodic sum rate with a large number of users is derived from the limiting distribution of the sample maximum of the received signal-to-interference-plus-noise ratio. Based on our result, the scaling law of multiuser diversity gain is also exhibited in the context of RBF systems with the OCI, which is shown to coincide with the previous result without the other cell interferers. Simulation results verify the validity of our analysis even with not so large number of users.

In this letter, we propose an efficient algorithm which reduces the complexity of conventional vector perturbation schemes by searching the real and imaginary components of a perturbation vector individually. To minimize a performance loss induced from the decoupled joint search, we apply diagonal precoding at the transmitter whose parameters are iteratively optimized to maximize the chordal distance between subspaces spanned by the real and imaginary components. We also propose a simple non-iterative method with a slight performance loss which can achieve a significant complexity reduction compared to the conventional vector perturbation schemes.

In this letter, we investigate the diversity performance of coded beamforming schemes in AF multiple antenna relay systems for frequency selective channels. To extract available multipath diversity, we utilize orthogonal frequency division multiplexing combined with bit-interleaved coded modulation. The pairwise error probability is analyzed based on the correlated fading assumption, and the theoretical evaluation of the maximum achievable diversity order is presented. From the analysis, a proper code construction criterion is provided which achieves the full diversity with the minimum code memory. Our analysis also demonstrates that the subcarrier mapping (or pairing) operations at the relay have no impact on the diversity order. Simulations confirm that our analysis is accurate and matches well with the simulation results.

In multiuser multiple-input multiple-output (MIMO) downlink systems, orthogonal space division multiplexing (OSDM) techniques have been studied to approach the optimum performance of dirty paper coding with low complexity. Assuming a large number of users in the multiuser system, a proper user scheduling is needed to utilize the OSDM. In addition, the performance of the chosen users can be maximized by properly configuring the numbers of data streams, or the transmission modes. In this paper, we propose a joint user and mode selection algorithm which approaches the performance of the exhaustive search with significantly lower complexity. To this end we first decompose the sum rate into the sum of the individual rates, and derive an upper bound of each individual rate. By utilizing the obtained upper bound, the user and mode subset is determined to maximize the sum rate. Comparing with other conventional low-complexity schemes, the proposed scheme requires much lower complexity especially when the number of users is large. For example, our method achieves a complexity reduction of 92% compared to the conventional scheme when the number of users is 25. Simulation results show that our method achieves more than 95% of the sum rate of the exhaustive search.

In this paper, we suggest novel criteria for pilot designs which minimize outliers for consistent carrier frequency offset (CFO) estimation in the orthogonal frequency division multiplexing (OFDM) systems. In order to derive the criteria, we first analyze the average pairwise error probability (PEP) of integer CFO (iCFO) estimation with consistent pilots in the OFDM systems where fadings among subcarriers are correlated, and address several issues based on the PEP analysis. Especially, the PEP reveals the relation between the channel and the minimum Hamming distance of the training sequence which is composed of consistent pilots. Based on the observations made from the PEP, we revise the conventional criteria to make them suitable for systems for correlated fading channels. Simulation results show that the sequence developed from the proposed criteria yields much reduced outliers compared to the conventional sequences for consistent CFO estimation.

In this paper, we propose an efficient carrier frequency offset (CFO) estimation technique based on the space alternating generalized expectation-maximization (SAGE) for uplink orthogonal frequency division multiple access (OFDMA) systems. In general, the SAGE method transforms a multidimensional search problem into a sequence of one-dimensional searches, which greatly simplifies the estimation procedure. However, the conventional algorithms based on the SAGE method require a large amount of computations to estimate the CFO due to exhaustive grid search. To reduce the computational burden, we exploit the leakage on the fast Fourier transform (FFT) output of the received signal after the multiple access interference is removed by the SAGE method. Then, this leakage-based approach reduces the complexity of the conventional SAGE algorithm regardless of an employed carrier assignment scheme by avoiding grid search. Simulation results show that our modified SAGE algorithm approaches the Cramer Rao bound at all signal to noise ratio (SNR) region with greatly reduced complexity compared to the conventional SAGE algorithms.

In this paper, we introduce multi-link two-way relay channels where multiple two-way relay systems are interfering with each other. We study the capacity of this system by investigating the degree of freedom (DOF) with various message settings. Specifically, we consider two cases of two-way relay interference channels and two-way relay X channels. We show that the two-way relay interference channel where all nodes have M antennas obtains the DOF of 2M and compare with multipair two-way relay channels. Next, we introduce general message settings for two-way relay X channels. For the case where each user is equipped with 3 antennas and relays have 4 antennas, we prove that the DOF of 8 is achieved by employing network coding.

Block diagonalization (BD) is an attractive method for approaching sum capacity in multiuser multiple-input multiple-output (MIMO) broadcast channels. However, when channel state information (CSI) is limited, the performance of the BD is degraded because inter-user interference cannot be completely eliminated. In this paper, we study limited feedback systems for the BD where a large number of users are supported per cell. Based on an estimate of the received signal-to-interference-plus-noise ratio (SINR), we present an efficient feedback scheme which reflects the quality of each user''s channel in addition to the use of a quantization codebook. In simulation, we compare the sum rate of the BD scheme under practical scenarios and verify that the proposed SINR feedback is an effective way to exploit multiuser diversity.

In this paper, we investigate an interference alignment (IA) technique introduced by Cadambe and Jafar in mutually interfering broadcast channels (IFBCs). First, we study the spatial multiplexing gain (SMG) for the 3-cell IFBC where all base stations and mobile users are equipped with multiple antennas. To achieve the derived optimal SMG, we extend the IA algorithm designed for K-user multi-input multi-output (MIMO) interference channels (IFCs) to the IFBC. In this paper, we present the IA scheme in conjunction with user selection which outperforms the time division multiple access (TDMA) technique in the IFBC environment. The optimal scheduling method capitalizes on multiuser diversity to achieve a significant fraction of sum capacity by using an exhaustive search algorithm. Since the computational complexity of the optimal scheduling method is prohibitive, a reduced complexity suboptimal scheduling method is proposed based on a coordinate ascent approach. Simulation results confirm that the reduced complexity scheduling algorithm achieves the sum rate close to the optimal algorithm with much reduced complexity.

In this paper, we consider a downlink transmission technique for multi-cell and multi-user interfering broadcast channels (IFBCs). In this IFBC model, the simplest way to manage inter-cell interference is an orthogonal access method, such as time-division multiple-access (TDMA). Recently, a novel idea of an interference management scheme named interference alignment (IA) has been introduced by Cadambe and Jafar, which requires symbol extension to guarantee a degree-of-freedom (DOF) gain. In this paper, we propose a new IA scheme without symbol extension which outperforms the TDMA and the conventional IA by exploiting multi-user diversity and asymmetric complex signaling. We first introduce an exhaustive scheduling algorithm which shows the optimum performance. Then we propose a simplified suboptimum method to reduce the computational complexity and the search size.

In this paper, we provide a precise expression of the spatial multiplexing gain (SMG) for two mutually interfering multiple-input multiple-output (MIMO) broadcast channels using linear transceiver, referred to as MIMO-IBC. The MIMOIBC has two base stations and K1, K2users, each equipped with multiple antennas, where independent messages are transmitted over fixed channels. We observe the variation of the SMG with respect to user antenna distribution, and compare the derived result to the SMG of the interference channel with full cooperation among users. Additionally, we propose a linear preceding and decoding scheme for the MIMO-IBC in terms of maximizing the total sum rate, by extending one designed for single-cell multiple-input single-output broadcast channels. Simulation results confirm the accuracy of our theoretical SMG analysis for the MIMO-IBC.

In this paper, we analyze asymptotic ergodic capacity of multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems with channel state information (CSI) at the relay. By exploiting the asymptotic results for eigenvalue distributions, we derive the ergodic capacity in various asymptotic antenna regimes as a closed-form expression with arbitrary system parameters. The analyzed results demonstrate that increasing the number of source antennas causes the capacity shrink phenomenon which is analogous to the channel hardening effect in multi-user MIMO systems. Although we assume asymptotically large antennas to obtain the closed-form expressions, simulation results show that our derived expressions are surprisingly accurate even with the moderate number of antennas, and thus can serve for analyzing practical MIMO relay networks.

The optimization of the weighted ergodic sum rate is considered for the downlink of a cellular networks with multiple cells and multi-antenna base stations. We focus on the large system limit where the number of base station antennas and the number of users per cell go to infinity with a fixed ratio. We consider two extreme cases of full inter-cell cooperation (network MIMO) and no inter-cell cooperation (single-cell multiuser MIMO). Using the large random matrix theory and Lagrangian optimization, we obtain a numerical algorithm that exactly computes the maximum weighted sum rate in this asymptotic regime. Numerical results are presented for a simple case of two interfering cells in a linear arrangement.

In this paper, we propose a new vector perturbation scheme which reduces the complexity associated with finding perturbation vectors by searching for the real and imaginary components individually. To minimize a performance loss induced from decoupling joint search, we apply diagonal preceding at the transmitter whose phase angles are iteratively optimized according to the criterion of maximizing the chordal distance between subspaces spanned by the real and imaginary components. Since our main objective is to reduce the system cost, we also propose a simple non-iterative method of finding phase angles with slight performance loss. Simulation results show that the proposed decoupled vector perturbation reduces the average number of search candidate by 56% in comparison to the original vector perturbation in 4-by-4 system. It is also noted that the simple choice of phase angles provides the performance almost identical to that of the iteratively optimized angles. Moreover, we develope a receive antenna combining method for the vector perturbation systems with multiple-antenna receivers. The proposed algorithm designs receive combiners based on the criterion of optimizing the performance metric via a block-coordinate ascent approach.

The original interference alignment (IA) scheme provides poor sum-rate performance compared to simple orthogonal access schemes such as time-division multiple access (TDMA) in low-to-medium SNR under total power constraint. In this paper, we address this problem by proposing a method of regularizing the IA scheme with a criterion of minimizing the weighted sum of the mean square error (WMSE) function. To perform the regularization process efficiently, the weight terms in the WMSE metric should be computed from the optimal zero-forcing (ZF) schemes. Thus, we first prove the optimality of the enhanced IA algorithm introduced in our previous work in the ZF sense. From simulation results, it is shown that the proposed scheme outperforms the TDMA in overall SNR regime. We can further improve the performance by repeating the proposed regularization process iteratively. Moreover, we propose a modified design that provides robustness in the presence of channel uncertainty.

In this paper, we propose a general framework to investigate the average bit error rate (BER) performance of minimum mean square error (MMSE) based transceiver designs in amplify-and-forward relaying systems where all nodes are equipped with multiple antennas. Especially, we consider spatial multiplexing relaying schemes which transmit independent data streams simultaneously, which include the single stream beamforming design as a special case. Due to difficulty in finding a closed form expression of the average BER, we focus on the high signal-to-noise-ratio (SNR) analysis which generates simple analytical expressions. Then we derive new closed form expressions for the high-SNR performance of relay schemes under different design criteria, quantifying the performance in terms of a diversity gain and a coding gain. Monte-Carlo simulations show that our analytic work predicts accurately the diversity and coding gain.

We consider the downlink of a cellular network with multiple cells and multi-antenna base stations under arbitrary inter-cell cooperation, realistic distance-dependent pathloss, and general “fairness” requirements. Beyond Monte Carlo simulation, no efficient computation method to evaluate the ergodic throughput of such systems has been provided so far. We propose an analytic method based on the combination of the large random matrix theory with Lagrangian optimization. The proposed method is computationally much more efficient than Monte Carlo simulation and provides a very accurate approximation (almost indistinguishable) for the actual finite-dimensional case, even for of a small number of users and base station antennas. Numerical examples include linear 2-cell and planar three-sectored 7-cell layouts, with no inter-cell cooperation, sector cooperation, and full cooperation.

It has been shown that the performance of multiple-input multiple-output (MIMO) spatial multiplexing systems is significantly degraded when spatial correlation exists between transmit and receive antenna pairs. In this paper, we investigate designs of a new statistical precoder for spatial multiplexing systems with maximum likelihood (ML) receiver which requires only correlation statistics at the transmitter. Two kinds of closed-form solution precoders based on rotation and power allocation are proposed by means of maximizing the minimum Euclidean distance of joint symbol constellations. In addition, we extend our results to linear receivers for correlated channels. We provide a method which yields the same profits from the proposed precoders based on a simple zero-forcing (ZF) receiver. The simulation shows that 2dB and 8dB gains are achieved for ML and ZF systems with two transmit antennas, respectively, compared to the conventional systems.

In this paper, we consider the network multipleinput multiple-output (MIMO) where the base stations (BS) exchange only channel state information (CSI) to jointly design their transmission strategy. We particularly focus on a signal-tointerference-plus-noise ratio (SINR) balancing problem. First, the achievable rate regions with symmetric complex (SC) and asymmetric complex (AC) signaling techniques are invetigated. It is observed that the AC signaling shows a substantial gain over the SC signaling in terms of maximizing the worst-user rate as the system signal-to-noise-ratio (SNR) increases. After establishing the optimal SINR balancing algorithm with the SC signaling, we confirm the effectiveness of the AC signaling by proposing an efficient balancing scheme which outperforms the SC signaling scheme over all SNR regime.

In this letter, we present an improved precoding technique which reduces a modulo loss in vector perturbation with low complexity. Instead of searching perturbation vectors in the infinite lattice, the proposed scheme restricts the search range by utilizing the distribution of the perturbation vector depending on transmitted data. As a result, we can achieve significant complexity savings at the transmitter while providing better performance compared to the original vector perturbation.

In this letter, we implement a simplified version of the Cover van der Meulen Hajek Pursley (CMHP) coding originally characterized by Wajcer, Wiesel, and Shamai. The vector Gaussian broadcast channel with imperfect channel state information at the transmitter (CSIT) is considered where the transmitter only knows the channel mean and variance. Our focus is on the implementation and performance analysis of CMHP under the imperfect CSIT model using practical codes. Turbo codes described in IEEE 802.20 draft specification and quadrature amplitude modulation are used to implement CMHP. In order to find the optimal power allocation and beamforming vectors which maximize the sum rate with practical codes, we introduce the SINR penalty factor. The SNRs that achieve various target spectral efficiency are presented and analyzed.

For multiple-input multiple-output (MIMO) systems, the optimum maximum likelihood (ML) detection requires tremendous complexity as the number of antennas or modulation level increases. This paper proposes a new algorithm which attains the ML performance with significantly reduced complexity. Based on the minimum mean square error (MMSE) criterion, the proposed scheme reduces the search space by excluding unreliable candidate symbols in data streams. Utilizing the probability metric which evaluates the reliability with the normalized likelihood functions of each symbol candidate, near optimal ML detection is made possible. Also we derive the performance analysis which supports the validity of our proposed method. A threshold parameter is introduced to balance a tradeoff between complexity and performance. Besides, we propose an efficient way of generating the log likelihood ratio (LLR) values which can be used for coded systems. Simulation results show that the proposed scheme achieves almost the same performance as the ML detection at a bit error rate (BER) of 10-4with 28% and 15% of real multiplications compared to the conventional QR decomposition with M-algorithm (QRD-M) in 4-QAM and 16- QAM, respectively. Also we confirm that the proposed scheme achieves the near-optimal performance for all ranges of code rates with much reduced complexity. For instance, our scheme exhibits 74% and 46% multiplication reduction in 4-QAM and 16-QAM, respectively, compared to the sphere decoding based soft-output scheme with rate-1/2 convolutional code.

In this letter, we propose a new groupwise receive combiner design for multiple-input multiple-output spatial multiplexing systems. The conventional group detection (GD) suffers from a considerable performance loss since the noise components are not taken into account. The output signal-to-interference-plus-noise ratio (SINR) is defined in each subgroup in order to consider both the desired signal and noise statistics. Adopting the real-valued representation, we provide an optimal receive combiner which maximizes the SINR with a general group size. The simulation results show that the proposed scheme achieves a large performance gain over the conventional GD in coded systems. Also, when combining with near-optimal detection algorithms such as sphere decoder, the proposed GD scheme offers a comparable performance with significant reduced complexity.

In this letter, we provide an expression of spatial multiplexing gain (SMG) for two mutually interfering multiple-input multiple-output (MIMO) broadcast channels, referred to as MIMO-IBC, with linear transceiver. We derive the SMG with respect to user antenna distribution, and compare the systems with and without cooperation among receive antennas in each cell. Additionally, we propose a linear precoding and decoding algorithm for the MIMO-IBC which maximizes the sum rate by extending a solution for single-cell multiple-input single-output broadcast channels. Simulation results confirm the accuracy of our theoretical SMG analysis for the MIMO-IBC.

In this letter, we analyze asymptotic ergodic capacity of MIMO amplify-and-forward (AF) relaying systems which employ linear processing at the relay. By exploiting the asymptotic results for eigenvalue distributions, we derive the ergodic capacity in various asymptotic antenna regimes as a closed-form expression with arbitrary system parameters. The analyzed results demonstrate that increasing the number of source antennas causes the capacity shrink phenomenon which is analogous to the channel hardening effect in multi-user MIMO systems. Although we assume asymptotically large antennas to obtain the closed-form expressions, simulation results show that our derived expressions are surprisingly accurate even with the moderate number of antennas, and thus can serve for analyzing practical MIMO relay networks.

This paper considers both one-way and two-way relaying systems with multiple relays between two terminal nodes where all nodes have multiple-input multiple-output (MIMO) antennas. We propose a unified algorithm which computes the optimal linear transceivers jointly at the source node and the relay nodes for amplify-and-forward (AF) protocols. First, optimization designs based on the sum-rate and the mean-square error (MSE) criteria are formulated for the two-way AF relaying channel. Due to non-convexity of the given problems, the proposed schemes iteratively identify local-optimal source and relay filters by deriving the gradients of the cost functions for a gradient descent algorithm. Then, the proposed algorithm can optimize a one-way multiple relay system as a special case of the two-way channel. Finally, we prove the global optimality of the maximum sum-rate scheme under an asymptotically large antenna assumption. From simulation results, it is confirmed that the proposed methods yield the near optimum result for the MIMO multiple relay channel even with a moderate number of antennas. Consequently, we show that the proposed algorithm outperforms conventional schemes in terms of the sum-rate and the error performance for both one-way and two-way protocols.

In this paper, we propose a new design strategy based on the minimum mean-squared error (MMSE) in closed-loop non-regenerative multiple-input multiple-output relaying systems. Instead of conventional singular value decomposition based methods, we address the problem for joint MMSE design in a different approach using the Wiener filter solution which leads to simple derivations of the optimal MMSE designs. First, allowing the channel state information (CSI) at the source, we provide a new closed form solution for a source-relay-destination joint MMSE design by extending existing relay-destination joint MMSE designs. Second, for the limited feedback scenario, we address a codebook design criteria for the multiple streams preceding design with respect to the MMSE criterion. From our design strategy, we observe that compared to conventional non-regenerative relaying systems, the source or the destination only needs to know the CSI corresponding to its own link such as the source-to-relay or the relay-to-destination in view of the MMSE. Simulation results show that the proposed design gives about 7.5 dB gains at a bit error rate of 10-4over existing relay-destination joint MMSE schemes and we can get close to the optimal unquantized schemes with only a few feedback bits.

This paper studies linear precoding and decoding schemes for K-user interference channel systems. It was shown by Cadambe and Jafar that the interference alignment (IA) algorithm achieves a theoretical bound on degrees of freedom (DOF) for interference channel systems. Based on this, we first introduce a non-iterative solution for the precoding and decoding scheme. To this end, we determine the orthonormal basis vectors of each user's precoding matrix to achieve the maximum DOF, then we optimize precoding matrices in the IA method according to two different decoding schemes with respect to individual rate. Second, an iterative processing algorithm is proposed which maximizes the weighted sum rate. Deriving the gradient of the weighted sum rate and applying the gradient descent method, the proposed scheme identifies a local-optimal solution iteratively. Simulation results show that the proposed iterative algorithm outperforms other existing methods in terms of sum rate. Also, we exhibit that the proposed non-iterative method approaches a local optimal solution at high signal-to-noise ratio with reduced complexity.

This paper proposes a new preprocessing scheme for rate-one quasi-orthogonal space-time block code (QO-STBC), which requires no channel state information at the transmitter. We consider QO-STBCs in real domain so as to utilize the real-valued multidimensional constellation. First, we derive a preceding matrix which makes the effective channel matrix of QO-STBC column-wise orthogonal. Surprisingly, the proposed scheme provides the channel gains identical to the singular values of the channel matrix of STBCs. Since singular value decomposition based preceding method provides better performance than other conventional maximum-likelihood decoding method in coded systems, the proposed scheme outperforms other STBC schemes in coded systems without feedback information, while reducing the decoding complexity dramatically. Second, we employ the rotated multidimensional constellation in order to achieve full diversity in uncoded systems. Unlike the previous works where heuristic approaches should be needed to obtain solutions in QO-STBC systems with more than four transmit antennas, the proposed precoding combined with the rotated multidimensional constellation provides an optimal solution based on analysis regardless of the number of transmit antennas.

Recently linear processing strategies have been developed to approach the optimum performance of dirty paper coding with low complexity in multiuser multiple-input multiple-output (MIMO) downlink systems. In this paper, we propose a new linear coordinated beamforming algorithm which is applicable to the systems with an arbitrary number of antennas and users. Unlike other conventional methods, our scheme exploits the orthogonalized effective channels to yield the transmit beamforming and receive combining vectors. As the proposed scheme generates a solution in a non-iterative fashion, it is more feasible than other iterative schemes. Simulation results show that the performance of our proposed scheme is very close to the conventional iterative scheme with significantly reduced complexity. For a system equipped with four transmit and four receive antennas with two users, the proposed scheme exhibits the bit error rate (BER) comparable to the conventional scheme with a complexity reduction of 79%.

In this paper, we provide a lower and upper bound for the number of degrees of freedom (DOF) of B multiple-input single-output (MISO) broadcast channels (BC) where each base station (BS) equipped with M antennas supports its corresponding K single antenna users suffering from inter-cell interference. We show that two bounds meet with each other when M = 1 or M ¿ K or B > max(M,K)/min(M,K). From the derived result, we learn that the available DOF of the B-user multiple-input multiple-output (MIMO) interference channels is degraded by disabling receive cooperation if and only if M < K and B ¿ max(M,K)/min(M,K). We observe that even for that case, the multiple BCs where interference mitigation should be fully performed at the transmitters due to the distributed receive antennas can achieve the DOF more than 2/3 of the DOF of the MIMO interference channels. In addition, the exact number of DOF is derived for two mutually interfering and deterministic broadcast channels in the presence of cognitive BSs. Our result shows that in two BCs interfering with each other, whether receive cooperation exists or not, it does not affect the DOF if one of two transmitters is a cognitive BS.

In this paper, as a extended structure of the existing relay-destination joint minimum mean square error (MMSE) design, we propose a new source-relay-destination joint MMSE design strategy allowing the channel state information (CSI) additionally at the source node. Instead of conventional singular value decomposition based methods, we address the problem for joint MMSE design in a different approach using the Wiener filter solution which leads to the simple derivation of the optimal MMSE design. From our design strategy, we also observe that compared to conventional non-regenerative relaying systems, the source or the destination only needs to know the CSI corresponding to its own link such as the source-to-relay or the relay-to-destination for minimizing the mean square error. Furthermore, numerical results show that the proposed design gives about 7.5 dB gains at a bit error rate (BER) of 10-4over existing relay-destination joint MMSE schemes.

In this paper, we propose a linear preceding and decoding scheme maximizing the sum rate of abuser interference channel systems where each node has multiple antennas. With an iterative approach, the preceding matrices are identified by deriving the gradient of the sum rate and applying the gradient descent method. Due to non-convexity of the formulated problem, the proposed precoder cannot guarantee the global optimal solution, and a locally maximized sum rate can be found by the proposed precoding scheme. Then, we obtain the single-symbol decodable receiver from the modified minimum mean-squared error filter. From simulation results, we exhibit a local optimal sum rate of the interference channel systems with the proposed method. Also, we demonstrate that the proposed algorithm outperforms other existing methods in terms of the sum rate.

It was shown by Cadambe and Jafar that the interference alignment (IA) algorithm achieves the theoretical bound on degrees of freedom (DOF) for interference channel systems. However, since this method addresses the precoder design problem from the DOF point of view, some optimization processes are additionally needed in order to improve the sum rate performance. In this paper, based on the IA method, we propose a two-stage optimization of the precoding and decoding matrices in the interference channels. Simulation results show that the proposed method significantly improves the sum rate of the conventional IA scheme while maintaining the optimality of the DOF.

In this paper, we present an improved precoding technique which reduces a modulo loss in vector perturbation (VP) with low complexity for the downlink of a multiuser multiple-input multiple-output (MIMO) system. At low SNR regime, the VP suffers from the modulo loss due to the increased number of nearest neighbors. For the original VP, the sphere encoder searches perturbation vectors in the infinite lattice. In contrast, the proposed scheme restricts the search range utilizing the distribution of the perturbation vector depending on transmitted data. As a result, we can achieve significant complexity savings at the transmitter and the receiver while providing better performance compared to the conventional sphere encoder. Simulation results show that the proposed scheme provides a 0.2 dB gain over the conventional VP at a bit error rate (BER) of 10-3for the case of four transmit antennas and four users with 4QAM. Also, the proposed scheme reduces the maximum number of candidate search by 95% in comparison to the original VP.

For multiple-input multiple-output (MIMO) systems, the optimum maximum likelihood (ML) detection requires tremendous complexity as the number of antennas or modulation level increases. This paper proposes a new algorithm which attains the ML performance with significantly reduced complexity. Based on the minimum mean square error (MMSE) criterion, the proposed scheme reduces the search space by excluding unreliable candidate symbols in data streams. Utilizing the probability metric which evaluates the reliability with the normalized likelihood functions of each symbol candidate, near optimal ML detection is made possible. A threshold parameter is introduced to balance a tradeoff between complexity and performance. Besides, we propose an efficient way of generating the log likelihood ratio (LLR) values which can be used for coded systems.

In this paper, we derive a precise expression of spatial degrees of freedom (DOF) for two mutually interfering broadcast channels (IFBC) as a function of arbitrary numbers of transmit antennas and users. The lower bound on the DOF is obtained by showing that the zero-forcing solution suffices to achieve all the DOF. Also, the upper bound which coincides with the lower bound is shown using Jafar's earlier work. From the derived result, we observe that disabling receive cooperation of the MIMO interference channel causes the DOF loss. Additionally, we propose a linear precoding scheme for the IFBC by extending one designed for broadcast channels with an aim of maximizing the sum rate performance. We utilize the fact that the precoding matrices in stationary point always satisfy the zero-gradient condition. Our result is confirmed through numerical simulations on the sum rate performance of the proposed precoding technique.

In this paper, we consider a relay system in multiple-input multiple-output (MIMO) multi-user channels where a single MIMO relay is engaged in communication between multiple source-destination terminal pairs. We propose two amplify-and-forward (AF) relaying schemes which maximize the sum-rate for the interference channel with multiple source-destinations. First, we introduce an iterative scheme which provides a local optimal solution. The proposed scheme iteratively searches an optimum relay matrix by deriving the gradient of the sum-rate and applying the gradient descent algorithm. Next, in order to reduce the computational complexity of the iterative scheme, we propose a block diagonalization (BD) method which utilizes the minimum mean-square error (MMSE) criterion with singular value decomposition (SVD) based rate maximization. Simulation results show that the proposed iterative scheme achieves a near-optimal sum-rate for the given channel model, and the MMSE based BD with the rate maximization approaches the local optimal sum-rate at high signal-to-noise ratio (SNR) regime.

Recently an orthogonalized spatial multiplexing scheme with optimal precoding, referred to as POSM, has been proposed. The POSM achieves optimal performance in terms of maximizing the received minimum Euclidean distance dmin,Rin closed-loop multiple-input multiple-output (MIMO) systems. In this paper, we derive exact closed-form expressions of the symbol error rate (SER) for the POSM with 4-QAM and 16-QAM in Rayleigh fading channels. We first introduce a new system model for the given channel. Based on this system model and a unique feature of the POSM effective constellations, the final expression of the SER using polar coordinates can be evaluated with a single-integral form. Simulation results confirm the accuracy of our derived analysis.

Adaptive modulation and coding (AMC) is a powerful technique to enhance the link performance by adjusting the transmission power, channel coding rates and modulation levels according to channel state information. In order to efficiently utilize the AMC scheme, an accurate signal-to-noise ratio (SNR) value is normally required for determining the AMC level. In this paper, we propose a simple method to represent the SNR values for maximum likelihood (ML) detector in multi-input multi-output (MIMO) systems. By analyzing the relation between the upper bound and the lower bound of the ML detector performance, we introduce an efficient way to determine the SNR for the ML receiver. Based on the proposed SNR representation, an AMC scheme for single antenna systems can be extended to MIMO systems with ML detector. From computer simulations, we confirm that the proposed SNR representation allows us to achieve almost the same system throughput as the optimum AMC systems in frequency selective channels with reduced complexity.

This paper considers two-way relaying systems with a multiple-input multiple-output (MIMO) relay between two MIMO terminal nodes. The two-way relaying protocol can enhance the spectral efficiency compared with the one-way protocol by compensating the loss from half-duplex signaling. In this paper, we propose an iterative scheme to find a relay weighting matrix maximizing the sum-rate for two-way relay channels. Due to the non-convexity of the given problem, the proposed scheme iteratively identifies a local optimal solution by deriving the gradient of the sum-rate and applying the gradient descent algorithm. Simulation results show that the proposed iterative scheme with provable convergence achieves a near-optimal sum- rate for the two-way MIMO relay channels. Also, we show that the proposed scheme with a few iterations still outperforms the conventional schemes.

This paper considers amplify-and-forward (AF) relaying systems with multiple relay nodes between the source and the destination node, where all nodes are equipped with multiple antennas. Using multiple relay nodes to transmit a message, a distributed diversity gain can be obtained. In this paper, we address the optimization problem of relay weighting matrices which minimizes the mean-square-error (MSE). For general relaying systems which have an arbitrary number of relay nodes, the given problem has a non-convex cost function. Thus, we propose an iterative scheme which identifies a local optimal relay precoder by deriving the gradient of the MSE and applying the gradient descent algorithm. Simulation results show that the proposed iterative scheme outperforms the conventional schemes for multiple MIMO relay systems with a small number of iterations in terms of both the MSE and the bit error rate.

In this paper, we develop a minimum mean-squared error (MMSE) based block diagonalization (BD) algorithm for multiuser multi-input multi-output (MIMO) broadcast systems where each user has more than one antenna in the presence of other cell interference (OCI). Unlike the conventional BD based multiuser MIMO transmission schemes which suffer from the noise enhancement problem as eliminating all multi-user interference (MUI) completely, the proposed scheme attempts to suppress the MUI with a consideration of the OCI plus noise and employs an additional residual interference suppression process based on an MMSE criterion. As a result, the proposed scheme improves the signal-to-interference-plus-noise ratio (SINR) at each user's receiver compared to conventional BD based schemes. Simulation results demonstrate that the sum rate performance of the proposed algorithm is always better than that of the conventional BD based algorithms for various OCI configurations.

A signal space diversity (SSD) scheme is one of techniques to achieve diversity gain in fading channels. This method consists of two key operations: constellation rotation and component-wise interleaving. Because of these operations, the decision boundaries for the SSD are no longer perpendicular, and thus, different coordination approaches are required for the analysis of error rates compared to conventional rectangular coordinates. In this letter, we derive an exact expression of the symbol error rate for the SSD scheme in Rician fading channels with M-QAM and M-PSK. By defining the ratio of the standard deviation of the inphase and quadrature components, we introduce a new signal model for the SSD. Based on this signal model, we can compute the exact symbol error rate using polar coordinates. The computer simulation results confirm the accuracy of our analysis for fading channels.

Adaptive modulation and coding (AMC) is a powerful technique to enhance the link performance by adjusting the transmission power, channel coding rates and modulation levels according to channel state information. In order to efficiently utilize the AMC scheme, an accurate signal-to-noise ratio (SNR) value is normally required for determining the AMC level. In this paper, we propose a simple method to represent the SNR values for maximum likelihood (ML) detector in multi-input multi-output (MIMO) systems. By analyzing the relation between the upper bound and the lower bound of the ML detector performance, we introduce an efficient way to determine the SNR for the ML receiver. Based on the proposed SNR representation, an AMC scheme for single antenna systems can be extended to MIMO systems with ML detector. From computer simulations, we confirm that the proposed SNR representation allows us to achieve almost the same system throughput as the optimum AMC systems in frequency selective channels with reduced complexity.

In this letter, we present a new maximum likelihood (ML) decoding algorithm for space time block codes (STBCs) that employ multidimensional constellations. We start with a lattice representation for STBCs which transforms complex channel models into real matrix equations. Based on the lattice representation, we propose a new decoding algorithm for quasiorthogonal STBCs (QO-STBC) which allows simpleML decoding with performance identical to the conventional ML decoder. Multidimensional rotated constellations are constructed for the QO-STBCs to achieve full diversity. As a consequence, for quasi-orthogonal designs with an arbitrary number of transmit antennas N (N ≤ 4), the proposed decoding scheme achieves full rate and full diversity while reducing the decoding complexity from 𝒪(McN/2) to 𝒪(McN/4) in a Mc-QAM constellation.

This letter proposes a new optimal ordering method which minimizes error propagation in the vertical Bell-lab layered space-time (V-BLAST) by exploiting the whole filter output. A suboptimal ordering metric is also proposed which requires much reduced complexity compared to the optimal ordering metric. We also derive a simplified version of the suboptimal ordering metric which achieves a significant performance gain over the conventional ordering with minor additional complexity.

In this paper, we present a new precoding technique using rotation transformations for closed loop multiple-input multiple-output (MIMO) wireless systems, which does not require the singular value decomposition (SVD) operation of the channel transfer matrix and allows a simple maximum-likelihood (ML) decoding at the receiver. We divide the precoding process into two steps: orthogonalization transformation which induces orthogonality between transmitted signals and beamforming transformation which achieves diversity gain. In the proposed method, we utilize a design criterion based on the minimum Euclidean distance between the received signals and then the vector orthogonalization is connected to the vector-norm maximization. In this paper, we focus on spatial multiplexing systems transmitting two independent data streams. Compared with the SVD based schemes, the proposed approach maintains a low complexity by relying only on three different kinds of rotation matrices for both the orthogonalization and beamforming transformation. Simulation results confirm that the proposed two step precoding achieves the better performance than the conventional SVD based MIMO precodings with reduced complexity.

This paper proposes an efficient beamforming scheme which attains optimality as singular value decomposition (SVD) based systems with low complexity utilizing transmit maximum-ratio combining (TMRC) techniques. The TMRC scheme is the optimum structure for single beamforming systems in terms of received signal-to-noise ratio (SNR) in multiple-input single-output (MISO) channels. In this paper, we generalize the TMRC scheme to multiple beamforming multiple-input multiple-output (MIMO) systems which support more than one data stream in coded systems. We express each beamforming vector as a linear combination of TMRC vectors whose coefficients are optimized in a successive manner. Optimization of the beamforming vector is followed by the decorrelation process. All TMRC vectors used as a basis of the remaining beamforming vectors are made orthogonal to previously computed beamforming vectors. Exploiting the concept of the gradient ascent algorithm, we propose a simple non-iterative method of computing the precoder which obtains the near optimal performance. Also we derive a closed form expression of the output SNR distribution for the proposed scheme. Simulation results demonstrate that the proposed scheme achieves the almost identical link performance as the SVD-based system for arbitrary configurations with reduced complexity.

Block diagonalization (BD) is a well-known precoding method in multiuser multi-input multi-output (MIMO) broadcast channels. This scheme can be considered as a extension of the zero-forcing (ZF) channel inversion to the case where each receiver is equipped with multiple antennas. One of the limitation of the BD is that the sum rate does not grow linearly with the number of users and transmit antennas at low and medium signal-to-noise ratio regime, since the complete suppression of multi-user interference is achieved at the expense of noise enhancement. Also it performs poorly under imperfect channel state information. In this paper, we propose a generalized minimum mean-squared error (MMSE) channel inversion algorithm for users with multiple antennas to overcome the drawbacks of the BD for multiuser MIMO systems. We first introduce a generalized ZF channel inversion algorithm as a new approach of the conventional BD. Applying this idea to the MMSE channel inversion for identifying orthonormal basis vectors of the precoder, and employing the MMSE criterion for finding its combining matrix, the proposed scheme increases the signal-to-interference-plus-noise ratio at each user's receiver. Simulation results confirm that the proposed scheme exhibits a linear growth of the sum rate, as opposed to the BD scheme. For block fading channels with four transmit antennas, the proposed scheme provides a 3 dB gain over the conventional BD scheme at 1% frame error rate. Also, we present a modified precoding method for systems with channel estimation errors and show that the proposed algorithm is robust to channel estimation errors.

In this paper, we investigate the transceiver design for coded multiple-input multiple-output (MIMO) systems assuming channel knowledge at both transmitter and receiver. First we derive an expression of the diversity order of singular value decomposition (SVD) based systems with arbitrary channel coding configurations. Motivated by this analysis, we propose a blockwise design based on uniform channel decomposition (UCD) which utilizes a successive interference cancellation (SIC) receiver. To eliminate error propagation inherent in the SIC structure, the proposed scheme applies the UCD precoder for a pair of subchannels to achieve single-symbol decodable maximum likelihood detection (MLD) instead of the SIC receiver. From the analysis, we demonstrate that the proposed scheme has an enhanced diversity order compared to the SVD scheme by exploiting the feature of the UCD. Also, in the presence of imperfect channel knowledge at the transmitter, we describe an appropriate receive filter design for the proposed scheme. The simulation results show that the proposed transceiver technique outperforms both the SVD scheme and the conventional UCD by about 6 dB in 4 by 4 MIMO systems at the spectral efficiency of 12 bps/Hz.

In this paper, we present a low-complexity method to generate a transmit beamforming vector for multiple-input-multiple-output (MIMO) systems. We begin by introducing new definitions regarding orthogonality between two complex valued vectors and then present new expressions of complex rotation matrices for the complex vector orthogonalization. The rotation matrices are utilized to derive the weight vector for the maximum-norm combining (MNC) process of two complex vectors, which provides a constructive basis for a new beamforming method. The proposed transmit beamforming method uses successive column combining of MIMO channel matrices based on MNC, and as a result, an approximate solution to the optimum beamforming vector is obtained. The proposed method offers a good tradeoff between complexity and performance. Simulation results demonstrate that the proposed beamforming method achieves the near-optimal performance with much reduced computational complexity, compared to the optimal beamforming scheme using singular-value decomposition (SVD) of the channel matrix.

A signal space diversity (SSD) scheme is one of techniques to achieve diversity gain in fading channels. This method consists of two key operations: constellation rotation and component-wise interleaving. Because of these operations, the decision boundaries for the SSD are no longer perpendicular, and thus, different coordination approaches are required for the analysis of error rates compared to conventional rectangular coordinates. In this paper, we derive an exact expression of the symbol error rate for the SSD scheme in Rayleigh fading channels for 4 QAM and 16 QAM. By defining the ratio of the standard deviation of the inphase and quadrature components, we intruduce a new signal model for the SSD. Based on this signal model, we can compute the exact symbol error rate using polar coordinates. The computer simulation results confirm the accuracy of our analysis for Rayleigh fading channels.

This paper proposes a new precoding algorithm for orthogonalized spatial multiplexing (OSM) systems over flat-fading multiple-input multiple-output (MIMO) channels. The OSM scheme was recently introduced for closed-loop MIMO systems which allows single symbol decodability for maximum likelihood detection. To further improve the performance in OSM systems, we propose a new precoding method. For identifying the parameters of a precoder, we introduce a partitioning approach on the minimum Euclidean distance between constellation points in the effective channel. Also, it is shown that two real value parameters and one bit are required for feedback information in 4-QAM systems. Simulation results demonstrate that our precoding algorithm allows us to significantly improve the system performance with small increase of feedback values. We also confirm through simulations that the performance of the proposed scheme is the same as the optimum closed-loop MIMO systems.

When channel state information (CSI) is available at both transmit and receive sides, singular value decomposition (SVD) converts the MIMO channel into parallel subchannels. It is well-known that the diversity gain of the SVD scheme is limited by the subchannel gain with the smallest singular value. The SVD scheme can be combined with error correcting codes to compensate for the performance loss due to the smallest subchannel gain. In this paper, we provide the analysis of the diversity order for coded SVD schemes with arbitrary system configurations. When utilizing channel coding of code rate Rcfor systems which transmit N streams with Nttransmit and Nrreceive antennas, the maximum diversity order of the coded SVD schemes is derived as (Nt- lceilN . Rcrceil +1)(Nr- lceilN . Rcrceil +1). This analysis result shows that there is a tradeoff of the code rate and the diversity order in the coded SVD scheme and provides an insight for code design.

Channel inversion is one of the simplest techniques for multiuser downlink systems with single-antenna users. In this paper, we extend the regularized channel inversion technique developed for the single-antenna user case to multiuser multiple- input multiple-output (MIMO) channels with multiple-antenna users. We first employ the multiuser preprocessing to project the multiuser signals near the null space of the unintended users based on the MMSE criterion, and then the single-user preprocessing is applied to the decomposed MIMO interference channels. In order to reduce the complexity, we focus on non- iterative solutions for the multiuser transmit beamforming and use a linear receiver based on an MMSE criterion. Simulation results show that the proposed scheme outperforms existing joint iterative algorithms in most multiuser configurations.

In this paper, we present a new beamforming scheme for a downlink of multiuser multiple-input multiple- output (MIMO) communication systems. Recently, a block- diagonalization (BD) algorithm has been proposed for the multiuser MIMO downlink where both a base station and each user have multiple antennas. However, the BD algorithm is not efficient when the number of supported streams per user is smaller than that of receive antennas. Since the BD method utilizes the nullspace based on the channel matrix without considering the receive combining, the degree of freedom for beamforming cannot be fully exploited at the transmitter. In this paper, we optimize the receive beamforming vector under a zero forcing (ZF) constraint, where all inter-user interference is driven to zero. We propose an efficient algorithm to find the optimum receive vector by an iterative procedure. The proposed algorithm requires two phase values feedforward information for the receive combining vector. Also, we present another algorithm which needs only one phase value by using a decomposition of the complex general unitary matrix. Simulation results show that the proposed beamforming scheme outperforms the conventional BD algorithm in terms of error probability and obtains the diversity enhancement by utilizing the degree of freedom at the base station.

This paper introduces a new efficient design scheme for spatial multiplexing (SM) systems over closed loop multiple- input multiple-output (MIMO) wireless channels. Extending the orthogonalized spatial multiplexing (OSM) scheme which was developed recently for transmitting two data streams, we propose a new SM scheme where a larger number of data streams can be supported. To achieve this goal, we partition the data streams into several subblocks and execute the block-diagonalization process at the receiver. The proposed scheme still guarantees single- symbol maximum likelihood (ML) detection with small feedback information. Simulation results verify that the proposed scheme achieves a huge performance gain at a bit error rate (BER) of 10-4over conventional closed-loop schemes based on minimum mean-square error (MSE) or bit error rate (BER) criterion. We also show that an additional 2.5 dB gain can be obtained by optimizing the group selection with extra feedback information.

This paper proposes a low complexity unitary precoding scheme for multiple-input multiple-output (MIMO) systems. The singular-value decomposition (SVD) based transmission is capable of maximizing the system throughput when combined with power allocation and bit loading, and is known to be optimum in terms of capacity. This paper focuses on a system which attains the same optimality as the SVD-based system with low complexity utilizing transmit maximum-ratio combining (TMRC) techniques. The TMRC scheme is the optimum structure for single beamforming systems in terms of received signal-to-noise ratio (SNR) in multiple-input single-output (MISO) channels. In this paper, we generalize the TMRC scheme to multiple beamforming MIMO systems which supports more than one data stream in coded systems. Simulation results demonstrate that the proposed scheme achieves the almost identical link performance as the SVD precoding system for arbitrary configurations with reduced complexity.

Recently a number of transmission schemes have been introduced to achieve sum capacity for multiuser multi- input multi-output (MIMO) broadcast channels (BC). A block diagonalization (BD) is an attractive method which operates only a few dB away from the sum capacity. This scheme is a generalization of the zero-forcing channel inversion to the case where each receiver is equipped with multiple antennas. One of the limitation of the BD is that the sum rate does not grow linearly with the number of users due to the noise enhancement. In this paper, we propose a generalized minimum mean-squared error (MMSE) channel inversion algorithm for users with multiple antennas to overcome the drawbacks of the BD for multiuser MIMO systems. Simulation results confirm that the proposed scheme achieves performance improvement over the conventional BD scheme. Also, we present a preceding method for systems with channel estimation errors and show that the proposed algorithm is robust to the channel estimation errors.

In this paper, we propose a new groupwise receiver combiner design method for spatial multiplexing multiple- input multiple-output (MIMO) systems. The conventional group detection (GD) suffers from a considerable performance loss since the noise components are not taken into account. The proposed method further enhances the detection performance than the conventional GD by considering the noise correlation. To prevent the noise enhancement problem, the proposed combiner design utilizes a signal-to-interference-plus-noise ratio (SINR) maximization criterion for each desired subgroup. In addition, we investigate an effect of group selection based on the received SINR. The simulation results show that the proposed scheme achieves a performance gain of 4 dB at a frame error rate (FER) of 10-2over the conventional GD with four transmit and four receive antennas.

In this paper, we propose a two-step precoding approach for multiple-input multiple-output (MIMO) systems by dividing the precoding process into two steps: spatial multiplexing transformation which establishes orthogonalized subchannels with the same quality and beamforming transformation which achieves diversity gain. In the proposed precoding method, we improve the system performance by maximizing the minimum Euclidean distance between the received signals. Compared with the optimal singular value decomposition based precoding methods, the proposed approach significantly reduces the processing complexity as well as the feedback overhead since precoding techniques are based on simple rotation transformations.

In this paper, we propose a new precoding algorithm for orthogonalized spatial multiplexing (OSM) systems over flat-fading multiple-input multiple-output (MIMO) channels. The OSM scheme was recently introduced for closed-loop MIMO systems which allows single symbol decodable maximum likelihood detection. To further improve the performance of the OSM system, we propose a new precoding method by maximizing the minimum Euclidean distance between constellation points in the effective channel. In order to efficiently identify the parameters of a precoder which maximizes the minimum distance, we introduce a partitioning approach. Through analysis, it is shown that one real value parameter and two bits are required for feedback information for precoding in 16-QAM systems. Simulation results demonstrate that our algorithm provides 9 dB and 7.5 dB gains at a bit error rate (BER) of 10-4over the conventional OSM systems for 4-QAM and 16-QAM, respectively. We also confirm that the performance of the proposed scheme is the same as that of the optimum closed-loop MIMO systems in terms of the minimum distance. Consequently, our precoding algorithm significantly improves the system performance with a small increase of feedback amount.

In this paper, we propose a new power allocation algorithm for orthogonalized spatial multiplexing (OSM) systems over flat-fading multiple-input multiple-output (MIMO) channels. Compared to SVD-based transmission scheme, the OSM scheme exhibits a good system performance with lower complexity and feedback overhead. To further improve the performance in OSM systems with power allocation, we introduce a geometric approach on the Euclidean distance between the constellation points in the effective channel. Using this approach, we show that the optimal power allocation parameters in terms of the minimum distance can be obtained. Simulation results demonstrate that our algorithm provides a 5 dB gain at a bit error rate (BER) of 10-4over that of no power allocation case with both QPSK and 16-QAM.

In this paper, we investigate spatial multiplexing schemes for closed-loop multiple-input multiple-output (MIMO) systems. The performance of the singular value decomposition (SVD) scheme is limited by the smallest singular value. When all the subchannels are utilized, uniform channel decomposition (UCD) was recently proposed to obtain a performance gain by making subchannels have equal gains. The UCD requires a successive interference cancellation (SIC) receiver, and thus it suffers from the error propagation inherent in the SIC receiver. We propose the blockwise UCD (BL-UCD) scheme which increases the minimum subchannel gain by pairing two singular values. The proposed scheme allows single-symbol decodable maximum-likelihood detection (MLD) instead of the SIC receiver. The simulation results demonstrate that the proposed BL-UCD scheme outperforms the SVD scheme and the conventional UCD at full spatial multiplexing for four transmit antennas and four receive antennas by 8 dB and 5 dB, respectively.

In this paper, we present a new transmission technique for the downlink of a multiuser multiple-input multiple- output (MIMO) system. Recently, the block-diagonalization (BD) algorithm has been proposed for the multiuser MIMO downlink where each user has multiple antennas. The key idea of the BD scheme is to eliminate all multiuser interference by transmitting each user's data along the nullspace of the other users' channel matrix. However, the BD algorithm based on the zero- interference condition is not a proper choice when the interference is known at the transmitter noncausally. In this paper, we propose a block-echelonization (BE) algorithm for utilizing the noncausally known interference at the transmitter. The sum rate analysis and simulation results show the effectiveness of the proposed multiuser scheme.

We present a new hybrid scheme which combines space-time block codes (STBC) and spatial multiplexing (SM) to achieve both diversity and spatial multiplexing gain. We design a new precoding scheme based on the givens rotation by utilizing the phase feedback information from the receiver. Exploiting the STBC structure embedded in the hybrid scheme, the proposed scheme enables a simple maximum likelihood (ML) detection such that the receiver complexity is reduced by a factor of 1/16 for the case of 4bps/Hz. Practical precoder designs are also provided to minimize the feedback information, while maintaining near optimal performance. Also, unlike conventional schemes which require at least two receive antennas, the proposed hybrid scheme works well with a single receive antenna. Simulation results show that the proposed hybrid scheme achieves a performance gain of 7dB at a bit error rate (BER) of 10-4in 2 bps/Hz spectral efficiency over other closed- loop hybrid schemes.

In this paper, we propose an optimal metric for detection ordering in terms of symbol error rate (SER) in vertical Bell-lab Layered Space-Time (V-BLAST) systems. Motivated by the log-likelihood ratio based ordering scheme introduced in [1], a new ordering method is presented which can further enhance the V-BLAST performance. The proposed ordering based on the new metric minimizes the SER by exploiting the instantaneous noise and considering its correlation at the linear filter output. A simpler suboptimal ordering metric is also proposed which requires much less computational complexity than that of the optimal ordering metric. We further show that there exists a simplified version of the suboptimal ordering metric for BPSK and 4QAM which achieves a significant performance gain over the conventional ordering scheme with no additional complexity. Simulation results show that the optimal ordering scheme achieves a performance gain of 13 dB at a SER of 10 over conventional signal-to-noise ratio (SNR) ordering schemes with four transmit and four receive antennas. The performance of the suboptimal ordering scheme is shown to be less than 2 dB away from that of the optimal ordering.

We propose a new transmit beam- forming technique for multiple-input-multiple-output (MIMO) systems to improve the link level performance. We present a method for orthogonalization of two complex-valued vectors by introducing a variation of the Jacobi rotations. We will show that based on the proposed rotation transformations, the orthogonality can be established among different complex-valued column vectors in the channel response matrix. Utilizing the orthogonality, we can achieve the channel gain comparable to the maximum singular value of the channel matrix. Simulation results demonstrate that the proposed beamforming scheme achieves the near-optimum performance with much reduced complexity and feedback overhead. Especially, for the two transmit antenna case, we show that the proposed beamforming scheme provides the optimal beamforming vector for MIMO systems.

In this paper, we propose a new transmit diversity technique for multiple-input multiple-output (MIMO) systems to improve the link level performance of open-loop systems over flat fading channels. By cyclically applying a predetermined set of precoding weight vectors, artificially induced fluctuation is created to achieve additional diversity gain in flat fading channels. To design the set of the precoding vectors, we exploit the knowledge on the distribution of near optimum precoding vectors observed in a beamforming scheme based on the rotation transformations. Simulation results demonstrate that the proposed open-loop diversity scheme with an arbitrary number of transmit antennas achieves a full diversity gain with computational complexity comparable to a single-input single-output (SISO) system.

This paper proposes a simple downlink transmit beamforming scheme for multiple-input multiple-output systems with low complexity. We first introduce the beamforming system where the base station (BS) utilizes only a fraction of the full channel response. The mobile station (MS) of the proposed system selects a single antenna at the receive side based on which the BS adjusts beamforming weights without any overhead to the structure of the BS. Without requiring the singular value decomposition operation, the proposed scheme achieves a performance very close to that of the optimum beamforming system. An exact analysis of the received signal-to-noise ratio for the proposed scheme with 2 receive antennas is also presented. Simulation results show that with much reduced complexity the performance of the proposed beamforming technique is only a few tenth of a dB away from the optimal beamforming system regardless of antenna configuration.

In this paper, we consider multiuser multi-input/multi-output antenna systems with zero-forcing receivers in downlink. In this case, to exploit multiuser diversity, spatial-division multiple access (SDMA) system allows to assign different users to a part of transmit antennas at the base station whereas spatial-division multiplexing (SDM) system assigns all antennas to single user's data stream. In this paper, we present analytical frameworks to evaluate the performance of these systems. We first analyze the performance of these two systems by deriving closed-form expressions of achievable throughput. Numerical results show that the derived expressions are very tight. In addition, we approximate the capacity expression of SDM and SDMA systems and compare the SDM with the optimal case.

For space-time bit-interleaved coded modulation (ST-BICM) systems with iterative decoding, the overall performance is affected by the chosen mapping. In bit-error rate (BER) curves, one mapping reaches an error floor (EF) at a low signal-to-noise ratio (SNR), while other mappings result in a lower EF at a higher SNR. The constellation mappings are divided into groups where each group exhibits a distinctive BER curve. We show that the convergence abscissa of the system depends on the average total bit errors and the harmonic mean of the minimum squared Euclidean distance. In this letter, we characterize all mapping groups for ST-BICM with 8-phase-shift keying and present the optimal selection for each mapping group over independent fading channels

In this paper, we investigate coded layered space-time architectures for frequency-selective fading multiple-input multiple-output orthogonal frequency-division multiplexing (OFDM) channels. By computing outage capacity formulas, we will show that the capacity of the vertical Bell Labs layered space-time (V-BLAST) architecture can closely approach the Shannon capacity in the frequency-selective OFDM environment. Motivated by the capacity analysis, we propose pragmatic approaches which preserve the optimality of the layered space-time concept. We present methods to prevent the error propagation from catastrophically affecting the signal detection in subsequent layers. First, we start with a comprehensive signal modeling which includes error propagation. We derive an improved signal detector and describe the optimal soft-bit log-likelihood ratio value-computation method by taking decision errors into account for soft-input channel decoding. Then, to further enhance the V-BLAST performance, we show that cancellation using decoded decisions from previous layers makes the decision errors almost completely disappear, so that the layered space-time architecture can approach the attainable channel capacity. Finally, simulations confirm that the proposed schemes show a significant performance improvement over the conventional methods

Transmission efficiency in radio channels can be considerably improved by using multiple transmit and receive antennas and employing a family of schemes called space-time (ST) coding. Both extended range and/or improved bandwidth efficiency can be achieved, compared with a radio link with a single transmit and receive antenna. Bit-interleaved coded modulation schemes give diversity gains on fading channels with higher order modulation constellations combined with conventional binary convolutional codes also for the case of a single transmit and receive antenna radio link. In this paper, we study a family of flexible bandwidth-efficient ST coding schemes which combine these two ideas in a narrowband flat-fading channel and single-carrier modems. We address receiver complexity for the case of a large number of transmit antennas and higher order modulation constellations. Especially, we focus on practical configurations, where the number of transmit antennas is greater than that of receive antennas. Simplified receivers using tentative decisions are proposed and evaluated by means of simulations. Tradeoffs between complexity reduction and performance loss are presented. We emphasize systems that are of particular interest in applications where the number of transmit antennas exceeds the number of receive antennas. A system with four transmit antennas with an eight-fold complexity reduction and a performance loss of about 1 dB is demonstrated

If the channel is static and is perfectly known to both the transmitter and the receiver, the water-filling technique with adaptive modulation is known to be optimal (Gallager, 1968). However, for orthogonal frequency-division multiplexing (OFDM) systems, this requires intensive traffic overheads for reporting channel state information on all subcarriers to the transmitter. In this paper, we consider an adaptive modulation and coding scheme for bit-interleaved coded OFDM with reduced feedback information satisfying a specified quality of service level. We propose a rate adaptation scheme, which utilizes the estimated bit error rate for supportable transmission rates. In this scheme, a user equipment chooses a modulation and coding scheme (MCS) level, which can provide the maximum spectral efficiency based on one OFDM symbol rather than on all subchannels. Then the user needs to send back only the selected MCS level index. The proposed scheme does not require the water-filling procedure, and the amount of the feedback information reduces to a single integer value irrespective of the number of subcarriers. Simulation results show that the proposed scheme can significantly reduce the system complexity while minimizing the performance loss compared to the optimum water-filling scheme.

In this paper, we present a novel maximum likelihood (ML) decoding algorithm for space-time block codes (STBC) over fading channels. Using a lattice representation for space-time codes by transforming complex channel models into real matrix equations, we propose a new efficient ML decoding algorithm with performance identical to the conventional ML decoder. We show that the complex orthogonal space-time codes, in fact, allow a separate ML decoding for each inphase and quadrature-phase component. For rate one quasiorthogonal designs with N transmit antennas (N > 4), the proposed decoding scheme reduces the decoding complexity from O(McN/2) to O(McN/4) in a Mc-QAM constellation. Moreover, multidimensional rotated constellations are constructed by which the quasi-orthogonal codes can achieve full diversity, while the proposed ML decoding method offers significant computational savings as compared with previous approaches.

This paper proposes an improved vertical Bell Labs Layered Space-Time (V-BLAST) with iterative detection and decoding (IDD) scheme for coded layered space-time architectures in MIMO-OFDM systems. For the iterative process, a low-complexity demapper is developed by making use of both nonlinear interference cancellation and linear filtering. Also a simple cancellation method based on hard decision is presented to reduce the overall complexity. Simulation results demonstrate that the proposed V-BLAST with IDD scheme offers the performance close to the optimal turbo-MIMO approach, while providing tremendous savings in computational complexity.

In wireless access systems, there has been much interest in enhancing the performance of orthogonal frequency division multiplexing (OFDM) in a frequency selective fading channel. If the channel is static and is perfectly known to both the transmitter and the receiver, the water-filling technique with adaptive modulation is known to be optimal [1]. However, this requires intensive traffic overheads for reporting channel state information on all subcarriers. In this paper, we consider an adaptive modulation and coding scheme for bit-interleaved coded OFDM with reduced feedback information satisfying a specified quality of service level. We propose a rate adaptation scheme which utilizes the estimated bit error rate for supportable transmission rates. In this scheme, a user equipment chooses the best modulation and coding scheme (MCS) level based on one OFDM symbol rather than on the whole subcarriers, then the user needs to send back only the selected MCS level index. The proposed scheme does not require the water-filling procedure and the amount of the feedback reduces to a single integer value irrespective of the number of subcarriers. Simulation results show that the proposed scheme can significantly reduce the system complexity while minimizing the performance loss compared to the optimum water-filling scheme.

When channel state information (CSI) is available at the transmitter, the system throughput can be enhanced by adaptive transmissions and opportunistic multiuser scheduling. In this paper, we consider multi-input multi-output (MIMO) systems employing bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM). We first propose a bit- loading algorithm based on the Levin-Campello algorithm for the BIC-OFDM. Then we will apply this algorithm to the MIMO system with a finite set of constellations, by reassigning residual power on each stream. Simulation results show that proposed bit-loading scheme which takes the residual power into account improves the system performance especially at high signal-to- noise ratio (SNR) range.

When adapting the transmitter to the channel state information (CSI), improved transmission is possible compared to the open loop system where no CSI is provided at the transmitter. However, since the perfect channel information is rarely available at the transmitter, the system design based on the partial CSI becomes an important factor. Especially, in mobile environments, the consideration for the outdated CSI should be applied for mitigating the performance degradation. In this paper, we propose a robust adaptive modulation and coding scheme for bit-interleaved coded orthogonal frequency division multiplexing over time-varying channels. With reasonable feedback overhead, the proposed scheme shows the enhanced performance by compensating for the outdated CSI due to Doppler spread. Simulation results confirm that the performance gain is achieved by applying an accurate BER estimation method

In this paper, we present an asymptotical analysis of channel capacity of Bell labs layered space-time (BLAST) architectures based on a zero-forcing(ZF) criterion in the sense of signal-to-noise ratio (SNR). We begin by introducing a new relationship related to multi-input multi-output (MIMO) channel capacity. We prove that Diagonal Bell Labs Space-Time (D-BLAST) attains the lower bound for MIMO channels when interference nulling is carried out based on the ZF-criterion. An exact closed-form expression for the probability density function of the channel capacity is analyzed. Based on the asymptotic behavior of the channel capacity of each layer, closed-form expressions for the asymptotic ergodic capacity are derived for BLAST. Based on the analysis presented in this paper, we gain an insight on the channel capacity behavior for a MIMO channel. Computer simulation results have verified the validity and accuracy of the proposed analysis for a wide range of antenna array sizes.

In this paper, we propose a new spatial multiplexing scheme for transmission over flat-fading multiple-input multiple-output (MIMO) channels, which allows a simple maximum-likelihood decoding at the receiver with small feedback information. We begin with a real-valued representation of the complex-valued system model and show that we can achieve orthogonality between transmitted signals by applying a proper rotation to transmitted symbols. Based on the minimum Euclidean distance between received vectors, we also present a simple antenna selection metric for the proposed spatial multiplexing systems. Simulation results demonstrate that our spatial multiplexing system performs close to the optimum closed loop system with much reduced complexity and feedback overhead.

Multiple-input-multiple-output (MIMO) systems provide a very promising means to increase the spectral efficiency for wireless systems. By using orthogonal frequency-division multiplexing (OFDM), wideband transmission can be achieved over frequency-selective fading radio channels. First, in this paper, we introduce an improved vertical Bell Labs layered space-time (V-BLAST) receiver which takes the decision errors into account. Second, we propose an iterative detection and decoding (IDD) scheme for coded layered space-time architectures in MIMO-OFDM systems. For the iterative process, a low-complexity demapper is developed by making use of both nonlinear interference cancellation and linear minimum mean-square error filtering. Also, a simple cancellation method based on hard decision is presented to reduce the overall complexity. Simulation results demonstrate that the proposed IDD scheme combined with the improved V-BLAST performs almost as well as the optimal turbo-MIMO approach, while providing tremendous savings in computational complexity.

Transmission efficiency in radio channels can be considerably improved by using multiple transmit and receive antennas, and employing a family of schemes called space–time coding. Both extended range and/or improved bandwidth efficiency can be achieved, compared with a radio link with a single transmit and receive antenna. Bit-interleaved coded modulation schemes give diversity gains on fading channels with higher order modulation constellations combined with conventional binary convolutional codes, also for the case of a single transmit and receive antenna radio link. In this paper, we study a family of flexible bandwidth-efficient space–time coding schemes which combine these two ideas in a narrowband flat-fading channel and single-carrier modems. We address receiver complexity for the case of a large number of transmit antennas and higher order modulation constellations. Especially, we focus on practical configurations where the number of transmit antennas is greater than that of receive antennas. Simplified receivers using tentative decisions are proposed and evaluated by means of simulations. Tradeoffs between complexity reduction and performance loss are presented. We emphasize systems that are of particular interest in applications where the number of transmit antennas exceeds the number of receive antennas. A system with four transmit antennas with an eight-fold complexity reduction and a performance loss of about 1 dB is demonstrated.

In this paper, we evaluate and enhance the performance of a Forward Error Correction (FEC) scheme for IEEE 802.11 Medium Access Control (MAC). A novel retransmission combining technique is proposed to enhance the performance of the MAC-level FEC scheme.,We also identify the problem with the IEEE 802.11a physical (PHY) layer when it is used with the MAC-level FEC. A new PHY frame format, backward compatible with the original format, is proposed to resolve the problem. Finally, we analytically evaluate the error performance of the MAC-level FEC, and its enhanced performance via retransmission combining and new 802.11a PHY frame format in AWGN environment. Additionally, we present and discuss the results from simulations using TCP/UDP traffic in more realistic channel environments.

Efficient transmission methods for fading radio channels often require an iterative decoder. This is for example the case for systems using turbo codes. Receiver decoder iterations could potentially lead to a latency problem which impacts the performance of the medium access control protocol. In this paper, we present modifications based on the carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol to accommodate the increased latency in the iterative processing. One area of applications is wireless local area networks (WLANs) with high data rate. The simulation results performed in the IEEE 802.11a WLAN environment by replacing the 802:11a's convolutional coding with turbo coding demonstrate that the proposed algorithm provides a throughput gain over the conventional method.

In multiple antenna systems, vertical Bell Labs Layered Space-Time (V-BLAST) systems enable very high throughput by nulling and cancelling at each layer detection. In this paper, we propose a V-BLAST system which combines with signal space diversity technique. The benefit of the signal space diversity is that we can obtain an additional gain without extra bandwidth and power expansion by applying inphase/quadrature interleaving and the constellation rotation. Through simulation results, it is shown that the performance of the proposed system is less than 0.5 dB away from the ideal upper bound

In this paper, we investigate coded layered space-time architectures for frequency-selective fading MIMO-OFDM channels. We start with a comprehensive signal model including error propagations. Based on the signal model, we derive an improved signal detector in the mean-square-error criterion. We also describe the optimal soft bit log-likelihood ratio (LLR) value computation method by taking the decision errors into account for soft-input Viterbi decoder. We compare the proposed scheme with the conventional V-BLAST system and show that significant performance improvement at high data rates can be attained.

Bit-interleaved coded modulation schemes give diversity gains on fading channels with higher order modulation constellations combined with conventional binary convolutional codes. By employing multiple transmit and receive antennas, both extended range and/or improved bandwidth efficiency can be achieved in space-time bit-interleaved coded modulation schemes. In this paper, we analyze the performance of this space-time coding scheme in a narrow band flat fading channel and single carrier modems. We give constructive binary codes that yield maximum diversity. We also show that the puncturing mechanism in general fails to achieve the full diversity in such a scheme.

Space-time coding techniques are methods to improve transmission efficiency in radio channels by using multiple transmit and/or receive antennas. Bit-interleaved coded modulation gives good diversity gains with higher order modulation schemes using well known binary convolutional codes. By using orthogonal frequency division multiplexing (OFDM), wideband transmission can be achieved over frequency selective fading radio channels. In this paper, we combine these three ideas into a family of flexible space-time coding methods where modulation order and coding rates as well as the number of transmit and receive antennas can be changed without drastic redesign of the space-time codes. Near optimum iterative decoders are evaluated in terms of simulation for slowly varying wireless LAN type channels. Significant performance gains over the 802.11a standard system are reported.

A novel architecture design to speed up the Viterbi algorithm is proposed. By increasing the number of states in the trellis, the serial operation of a traditional add-compare-select unit is transformed into a parallel operation, thus achieving a substantial speed increase. The proposed architecture would increase the speed by 33% at the expense of a fairly modest increase in area, thus becoming an attractive approach in high-speed applications. A simple example is shown to illustrate the proposed algorithm in maximum-likelihood sequence detector. A comparative synthesis is made to compare the proposed architecture with other approaches, and synthesis simulations confirm the projection of the throughput gain. Also, the proposed algorithm is extended to the block-processing architecture, and we show that an additional 50% speedup is achieved.

Sphere decoding for multiple antenna systems has been shown to achieve near-ML performance with low complexity. However, the achievement of such an excellent performance-complexity tradeoff is highly dependent on the initial choice of sphere radius. We present a new sphere decoding algorithm which is even less computationally complex than the original sphere decoder. Moreover, the complexity of the new sphere decoder is relatively insensitive to the initial choice of sphere radius. Thus, by making the choice of radius sufficiently large, the ML solution is guaranteed with low complexity, even for large constellations. In our simulations, we show that with 4 transmit and 4 receive antennas and 64-QAM, our new sphere decoding algorithm achieves the exact ML solution with approximately a factor of 3.5 reduction in complexity when compared to the original sphere decoder, and a factor of 10/sup 5/ reduction when compared to brute-force ML decoding.

We apply a block processing technique to the MAP algorithm used in turbo decoding. This new technique leads to a power-efficient way to access memory and to a reduced memory size. We introduce the "very long data word" (VLDW) memory architecture, which leads to a reduction in power consumption for memory access operations. The proposed architecture provides a low power implementation of the turbo decoder.

The performance of a serially concatenated system which includes a channel with memory preceded by a precoder as a rate-1 inner coder is presented. The effect of different precoders on the maximum-likelihood bit-error performance is analyzed. The precoder weight gain, which explains the good bit-error rate (BER) performance, is identified through a union bound analysis. Precoders are divided into two groups based on an analysis of the Euclidean distance and its multiplicity, and each precoder group shows a distinct BER curve behavior. It is shown that the BER curves for two precoder groups cross over each other. Convolutional codes are considered as outer codes in simulations on various intersymbol interference channels. Several important design considerations for the choice of precoders are derived based on the analysis and these are confirmed through simulations with an iterative decoding algorithm.

A novel architecture design to speed up the Viterbi algorithm is proposed. By doubling the number of states in the trellis, the serial operation of a traditional add-compare-select (ACS) unit is transformed into a parallel operation, thus achieving a substantial speed increase. The use of the proposed architecture would increase the speed by 33% at the expense of a fairy modest increase in area, thus removing the Viterbi detector/decoder from the worst case speed bottleneck path in most high-speed applications. A simple example is shown to illustrate the proposed algorithm in a maximum likelihood sequence detector.

The performance of a serially concatenated system which includes a channel with memory precoded by a precoder as a rate one inner coder is presented. The effect of different precoders on the maximum likelihood bit error performance is analyzed. The precoder weight gain is identified through a union bound analysis. It is shown that the choice of precoders is critical for a given channel to achieve a good bit error rate performance. Several important design considerations for the choice of precoders are derived based on analysis and these are confirmed through simulations with iterative decoding algorithm.

Ariyavisitakul et al. (IEEE Trans. Commun., vol.47, p.1073-83, 1999 July) provided a unified analysis of optimum space-time processors based on the following two analytical diversity receiver models: 1) a general model-with a linear filter on each diversity branch, and 2) a "matched filter" model-with a bank of matched filters on each branch, followed by common filter. Closed-form results were given for each receiver model, in terms of the minimum mean-square-error or maximum signal-to-noise ratio solutions for different types of equalizers, including a linear equalizer (LE), a decision-feedback equalizer (DFE), and a maximum-likelihood sequence estimator (MLSE). Although we implied that the two receiver models lead to the same optimum solutions, this requires some proof that is not directly obtainable from the results we presented. We therefore provide such a proof in the present paper for the completeness of the overall unified analysis.

We consider optimum space-time equalizers with unknown dispersive interference, consisting of a linear equalizer that both spatially and temporally whitens the interference and noise, followed by a decision-feedback equalizer (DFE) or maximum-likelihood sequence estimator (MLSE). We first present a unified analysis of the optimum space-time equalizer, and then show that, for typical fading channels with a given signal-to-noise ratio (SNR), near-optimum performance can be achieved with a finite-length equalizer. Expressions are given for the required filter span as a function of the dispersion length, number of cochannel interferers, number of antennas, and SNR, which are useful in the design of practical, near-optimum space-time equalizers.

We consider optimum space-time equalizers with unknown dispersive interference, consisting of a linear equalizer that both spatially and temporally whitens the interference and noise, followed by a decision-feedback equalizer or maximum-likelihood sequence estimator. We first present a unified analysis of the optimum space-time equalizer, and then show that, for typical fading channels with a given signal-to-noise ratio (SNR), near-optimum performance can be achieved with a finite-length equalizer. Expressions are given for the required filter span as a function of the dispersion length, number of cochannel interferers, number of antennas, and SNR, which are useful in the design of practical near-optimum space-time equalizers.

Tap spacings in a tapped delay line equalizer are optimized. We derive a set of nonlinear equations for the optimum tap spacings and tap weights for the decision feedback equalizer. A recursive numerical technique is used to obtain the optimum settings. The nonuniformly spaced tapped delay line equalizer with the optimized tap spacings outperforms the conventional uniformly spaced equalizer with the same number of taps.

In this correspondence we derive the finite-length, minimum mean-squared error decision feedback equalizer (MMSE-DFE). We include decision delay as an explicit parameter. Our derivation yields an algebraic interpretation of the effect of decision delay on DFE performance (measured by mean-squared error). It also allows the fast computation of the MMSE-DFE for several different values of both decision delay and the number of feedback taps. Our approach is especially useful for short filter lengths, when the decision delay can significantly affect DFE performance.

The discrete multitone (DMT) modulation is considered to be a viable transmission scheme for high-speed subscriber loop. In this paper, the fast algorithm for computing the equalizer settings derived in [1] is extended and applied for the DMT in high-speed subscriber loop. The channel pulse response is assumed to be given by the channel identification method, and then the equalizer filter settings are computed. In simulations, a fast algorithm for the symbol spaced equalizer in a colored noise channel is used. Simulation results performed in various CSA loops indicate that the fast algorithm yields the near-optimum settings for the DMT system

A novel fast algorithm for computing the minimum MSE decision feedback equalizer settings is proposed. The equalizer filters are computed indirectly, first by estimating the channel, and then by computing the coefficients in the frequency domain with the discrete Fourier transform (DFT). Approximating the correlation matrices by circulant matrices facilitates the whole computation with very small performance loss. The fractionally spaced equalizer settings are derived. The performance of the fast algorithm is evaluated through simulation. The effects of the channel estimation error and finite precision arithmetic are briefly analyzed. Results of simulation show the superiority of the proposed scheme.