The core technology to construct large-scale quantum networks is to efficiently distribute remote entanglement among quantum nodes. Entanglement routing deals with the problem of discovering and selecting paths to perform the multi-hop entanglement swapping between two distant nodes. In order to succeed in end-to-end entanglement creation for a given path, entanglement distribution of all direct quantum links and Bell state measurement of all intermediate nodes along the path should be successful. Existing studies have addressed the entanglement routing problem based on path discovery algorithms considering the above characteristics and selecting the optimal path from the individual performance perspective of each path. In this article, we newly define the entanglement routing problem considering the joint-utilization between multiple paths to maximize the network resource efficiency, and present an optimal link-set configuration algorithm, named RELiC, as a solution for this. The performance of the proposed entanglement routing protocol is evaluated in small- and large-scale quantum networks through mathematical analysis and simulation study.

Recently, the demand for communication systems that utilize a high-frequency band and massive antennas has increased. These systems can generate very small beams, resulting in high communication capacity; however, beam search for initial access is difficult due to the numerous beams in a given region. In this paper, we propose fast beam scanning and tracking techniques based on frequency-gradient array antennas. The proposed system provides a faster beam search technique than the phase-gradient array antenna systems considered in conventional communication systems. In numerical analysis and simulation studies, the performance of fast beam scanning and tracking techniques is evaluated and compared.

This paper investigates an end-to-end multiuser precoding in massive multiple-input multiple-output (MIMO) system. We propose a novel neural network architecture which exploits an appropriate human expert knowledge in wireless communication domain. The employed domain knowledge addresses the inter-user interference. In particular, the concept of a leakage is adopted to decouple the sum-rate maximization problem by transforming the input to the decoder neural network (NN) at base station (BS). In contrast with the existing NN architecture, the proposed architecture is proven to be scalable to the number of user equipments (UEs) as the common NN model (parameter set) can be employed for the different numbers of UEs. Numerical results demonstrate that the proposed scalable decoder NN architecture can learn the distribution of the inter-user interference effectively.

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 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.

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 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 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 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.

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.

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.

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 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 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 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 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 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 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 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 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 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.

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 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.

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 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.

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 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 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.

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.

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.

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.

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.

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 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 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 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.

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.