Heat Pump Systems with energy efficiency greater than 1 are mainly used in electric vehicles to increase vehicle mileage. However, the lower the ambient air temperature, the lower the saturation pressure of the low-pressure side refrigerant, making it impossible to use the Heat Pump System. This paper suggests a method of increasing the flow rate by recirculating the high-pressure side refrigerant to the low-pressure side using the Hot Gas Recycle System. Experiments are conducted to investigate the hot gas recycle mode in a EV6 car at various EXV steps, ambient air temperatures, and waste heats. The heating performance is increased by 63.2% when the refrigerant is recycled at 0C ambient air temperature. Because the saturation pressure of low-pressure side refrigerant increased, resulting in the higher mass flowrate

The next-generation electric vehicle will be applied with a next generation battery and autonomous driving system that generates high heat. However, since the proper operating temperature is low, it is essential to combine it with a refrigeration cycle for vehicle air conditioning. However, the existing cooling system is difficult to satisfy air conditioning and battery load at the same time, so to solve this problem, we developed a cascade cooling system using two vapor compression refrigeration systems connected by PIHX for the first time in the world. In this paper, the existing system and the cascade system were compared. Compared to the existing system, it was found that the cooling performance of the battery was improved while the cooling performance of the air conditioning was at the same level.

The rapid advancements in Artificial Intelligence (AI) increasingly underscore the critical importance of multi-modal datasets for training robust and versatile models. However, the dominance of English-centric resources and the scarcity of datasets capturing diverse languages and cultural nuances limit AI's inclusivity and global applicability, posing a key challenge. Critically, language's intrinsic link to cultural context demands models possess profound cultural insight beyond mere translation to genuinely grasp societal norms and unique cultural expressions. Culturally rich broadcast content offers a solution; its systematic curation can mitigate linguistic/cultural imbalances, fostering culturally deep multi-modal datasets. This paper introduces the HAN (Korean Heritage Augmented Narrative Visual-Language Description) dataset, a new resource for multilingual image captioning and retrieval. HAN comprises 41,000 images captured from Korean broadcast video clips with 410,000 Korean/English narrative-style captions offering multifaceted perspectives on each visual instance. By incorporating Korean heritage, HAN reflects cultural diversity, addressing limitations of existing datasets focused on simple descriptions. Furthermore, this work analyzes HAN's caption diversity impact on retrieval, proposing strategies to enhance efficacy. These findings underscore HAN's potential to significantly advance multi-modal and multilingual processing, supported by its value as a rich resource for learning approaches that integrate diverse data types (like vision and text), natural language processing across various languages, and Korean heritage studies.

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 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 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 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 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 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 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 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.

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.

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 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 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.

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.

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.

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.

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.

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%.

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 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.

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 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.

This paper discusses the limitations of implementing low power listening (LPL) on the contention-based IEEE 802.15.4 MAC protocol, and proposes an implementation technique called virtual preamble cross-checking (VPCC), which enables LPL. The proposed technique uses the concept of virtual preamble and a cross-checking method to meet the requirements of the relationship between preamble length and listening interval. These methods lead to reliable data communication, as well as the reduction of the idle listening. We implemented the mechanism on IEEE 802.15.4-compatible RF hardware and validated the performance. The experiment results show that increasing the listening interval within a tolerable latency can optimize the duty cycle of the RF transceiver for low power consumption.

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