In study, we developed a positron emission tomography (PET) insert for simultaneous brain imaging within 7-Tesla (7T) magnetic resonance (MR) imaging scanners. The PET insert has 18 sectors, and each sector is assembled with two-layer depth-of-interaction (DOI)-capable high-resolution block detectors. The PET scanner features a 16.7-cm-long axial field-of-view (FOV) to provide entire human brain images without bed movement. The PET scanner early digitizes a large number of block detector signals at a front-end data acquisition (DAQ) board using a novel field-programmable gate array (FPGA)-only signal digitization method. All the digitized PET data from the front-end DAQ boards are transferred using gigabit transceivers via non-magnetic high-definition multimedia interface (HDMI) cables. A back-end DAQ system provides a common clock and synchronization signal for FPGAs over the HDMI cables. An active cooling system using copper heat pipes is applied for thermal regulation. All the 2.17-mm-pitch crystals with two-layer DOI information were clearly identified in the block detectors, exhibiting a system-level energy resolution of 12.6%. The PET scanner yielded clear hot-rod and Hoffman brain phantom images and demonstrated 3D PET imaging capability without bed movement. We also performed a pilot simultaneous PET/MR imaging study of a brain phantom. The PET scanner achieved a spatial resolution of 2.5 mm at the center FOV (NU 4) and a sensitivity of 18.9 kcps/MBq (NU 2) and 6.19% (NU 4) in accordance with the National Electrical Manufacturers Association (NEMA) standards.

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 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 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.

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.

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.

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 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 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 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 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.

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 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 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 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.

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 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 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 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.

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.

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 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.