Search

This paper considers a two-cell uplink cochannel multiple-input–multiple-output (MIMO) network with users sequentially arriving at the network. We study the problem of sequential base-station (BS) selection for the users, with the selection criterion based on the degrees of freedom (DoFs) available for the new arriving user. We find that different sequential BS selections affect individual and network performance in terms of the individual and network sum DoF, as well as the number of admissible users in the network. We propose a method to build the tree structure for sequential BS selection, which carries trellis information for individual and system-wide selections. The properties of the tree are analytically studied. It turns out that by adopting an interference coordination strategy based on the concept of interference alignment (IA), better individual and network performance can be achieved. Simulation compares the proposed DoF-based BS selection and traditional BS selection schemes and highlights the advantages of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we study the codebook size design strategies in the multiple-input–multiple-output (MIMO) broadcast channel (BC) systems. We adopt the nonlinear Tomlinson–Harashima precoder (THP), which is designed by the imperfect channel direction information (CDI) acquired from the random vector quantization (RVQ) feedback mechanism. Under the limitation of the total feedback rate, we flexibly allocate the size of each user's codebook so that the overall channel capacity is maximized. Since the design problem is not convex, directly finding out the optimum allocation of codebook size is not attainable. However, by sophisticatedly using the discrete majorization theory, we can derive the codebook size allocation strategies for the high- and the low-signal-to-noise-ratio (SNR) regions. Interestingly, the results show that the equal size codebook is preferable in the low-SNR region, whereas the codebook allocates the whole available codebook size to a single user in the high-SNR region. Simulations certify the theoretical result of our design. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel low-complexity beamforming algorithm for multiuser successive maximum ratio transmission (MS-MRT) toward space-division multiplexing to achieve video quality maximization in the downlink of a multiuser (MU) multiple-input–multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA)-based fourth-generation (4G) wireless network. We compare the performance of this algorithm in terms of system throughput and video quality with the two popular precoding techniques, namely, block diagonalization (BD) and successive optimization (SO). We also compare its performance with the generalized versions of these two algorithms termed coordinated transmit–receive processing (CTR)-BD and CTR-SO algorithms, respectively. Further, we propose an extension of the MS-MRT scheme to broadcast scenarios, termed broadcast successive maximum ratio transmission (BS-MRT), which computes the optimal broadcast beamforming vector to maximize the video quality at each of the broadcast group members while maintaining orthogonality to the previously scheduled user groups. We also demonstrate that the proposed MS-MRT and BS-MRT schemes can be naturally adapted to the context of various MU OFDMA scheduling algorithms such as proportional fairness (PF) and round robin (RR), to name a few, to maximize video quality through optimal scheduling in the presence of a large number of users. We discuss implementations of the proposed MS-MRT scheme in association with these scheduling algorithms for MU unicast and broadcast video transmission scenarios. The simulation results presented in this work rely on the rate models derived using the Joint Scalable Video Model (JSVM) software developed by the Joint Video Team (JVT) and are, thus, readily applicable in practice. The presented results clearly demonstrate the ability of the proposed algorithm to maximize the video quality in comparison with the other competing MU MIMO precoding techniques. Further, employing the proportional fair scheduling algorithm in conjunction with the MS-MRT results in an overall enhancement in the received video quality compared with the RR and max-rate algorithms. [ABSTRACT FROM PUBLISHER]

The cellular systems with network coordination are known for increasing the cell edge user throughput by processing signals captured by geographically distributed base stations coherently. This concept is also known as macrodiversity or coordinated multipoint (CoMP) in the literature. It is now well known that large-scale fading has a significant influence on the link-level performance of users in multiuser (MU) multicell systems. In this paper, we investigate a channel distribution information (CDI)-based MU multiple-input–multiple-output (MIMO) power control problem in the uplink with joint processing to overcome the large-scale fading in macrodiversity systems. There are many power control algorithms for different variants of this problem, but the CDI-based problem in MU scenario has not been well addressed in the literature. Perhaps, the lack of understanding of the performance of macrodiversity MU systems, in terms of CDI, may have prohibited advanced power control solutions. Despite such analytical difficulties, we propose a simple algorithm for this uplink power control problem, and its accuracy is proved using Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

In this paper, we study feedback optimization problems that maximize the users' signal to interference plus noise ratio (SINR) in a two-cell multiple-input multiple-output broadcast channel. Assuming the users learn their direct and interfering channels perfectly, they can feed back this information to the base stations (BSs) over the uplink channels. The BSs then use the channel information to design their transmission scheme. Two types of feedback are considered: 1) analog and 2) digital. In the analog feedback case, the users send their unquantized and uncoded channel state information (CSI) over the uplink channels. In this context, given a user's fixed transmit power, we investigate how he/she should optimally allocate it to feed back the direct and interfering (or cross) CSI for two types of BS cooperation schemes, namely, multicell processing (MCP) and coordinated beamforming. In the digital feedback case, the direct and cross link channel vectors of each user are quantized separately, each using the random vector quantization scheme, with different size codebooks. The users then send the index of the quantization vector in the corresponding codebook to the BSs. Similar to the feedback optimization problem for the analog feedback, we investigate the optimal bit partitioning for the direct and interfering link for both types of cooperation. We focus on regularized channel inversion precoding structures and perform our analysis in the large system limit in which the number of users per cell $(K)$ and the number of antennas per BS (N)$ tend to infinity with their ratio \beta=({K}/{N}) held fixed. We show that for both types of cooperation, for some values of interfering channel gain, usually at low values, no cooperation between the BSs is preferred. This is because, for these values of cross channel gain, the channel estimates for the cross link are not accurate enough for their knowledge to contribute to improving the SINR and there is no benefit in doing BS cooperation under that condition. We also show that for the MCP scheme, unlike in the perfect CSI case, the SINR improves only when the interfering channel gain is above a certain threshold. [ABSTRACT FROM PUBLISHER]

In this paper, system design for a multiple-input–multiple-output (MIMO) relay broadcast channel with individual signal-to-interference-plus-noise ratio (SINR) constraints at mobile stations (MSs) is considered. By exploring the structure of downlink–uplink duality at either the base station (BS) or the relay station (RS), we propose two schemes of joint power allocation and beamforming design at the BS and the RS. The problem of the existence of feasible solutions under practical power constraints at the BS and the RS with given SINR targets is considered first. Then, the problem of sum power minimization is considered. Each design problem can be efficiently solved using optimal joint power allocation and beamforming under the framework of convex optimization. We also show that with subchannel pairing at the RS, the transmission power can be further reduced. Finally, a generalization to the multihop scenario is provided to further improve power efficiency. [ABSTRACT FROM PUBLISHER]

This paper considers maximizing the network-wide minimum supported rate in the downlink of a two-cell system, where each base station (BS) is endowed with multiple antennas. This is done for different levels of cell cooperation. At one extreme, we consider single cell processing where the BS is oblivious to the interference it is creating at the other cell. At the other extreme, we consider full cooperative macroscopic beamforming. In between, we consider coordinated beamforming, which takes account of inter-cell interference, but does not require full cooperation between the BSs. We combine elements of Lagrangian duality and large system analysis to obtain limiting SINRs and bit-rates, allowing comparison between the considered schemes. The main contributions of the paper are theorems which provide concise formulas for optimal transmit power, beamforming vectors, and achieved signal to interference and noise ratio (SINR) for the considered schemes. The formulas obtained are valid for the limit in which the number of users per cell, K, and the number of antennas per base station, N, tend to infinity, with fixed ratio \beta=K/N. These theorems also provide expressions for the effective bandwidths occupied by users, and the effective interference caused in the adjacent cell, which allow direct comparisons between the considered schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider a downlink communication system in which a base station (BS) equipped with M antennas and power constraint P communicates with N users each equipped with K receive antennas. It is assumed that the users have perfect channel state information (CSI) of their own channels, while the BS only knows the partial CSI provided by the receivers via a feedback channel. We study the fundamental limits on the amount of feedback required at the BS to achieve the sum-rate capacity of the system (when BS has perfect CSI for all users) in the asymptotic case of N \to \infty , considering various signal to noise ratio (SNR) regimes. The main results of this paper can be expressed as follows. 1) In the fixed-SNR regime (where the SNR does not scale with N) and low-SNR regime (where the SNR is much smaller than 1\over \ln (N)), to achieve the (1 - \varepsilon)-portion of the sum-rate capacity, the total amount of feedback should scale at least with \ln (\varepsilon ^-1). In the fixed-SNR regime, to reduce the gap between the achievable sum rate and the sum-rate capacity of the system (which is defined as the sum-rate gap) to zero, the amount of feedback should scale at least logarithmically with the sum-rate capacity, which is achievable by using the random beam-forming (RBF) scheme proposed by Sharif and Hassibi. In the low-SNR regime, we propose an opportunistic beam-forming (OBF) scheme, which is shown to be asymptotically feedback optimal. 2) In the high-SNR regime (where the SNR grows to infinity as N \to \infty ), the total amount of feedback depends on the number of receive antennas. In particular, to reduce the sum-rate gap to zero in the case of K < M, the amount of feedback in the SNR regime of \ln (P)\over \ln (N) > 1\over M-1, should scale at least logarithmically with the SNR. In the case of K \geq M, the amount of feedback does not need to scale with the SNR. Moreover, we show that RBF is asymptotically feedback optimal in the high-SNR regime. [ABSTRACT FROM AUTHOR]

In this paper, the problem of mode switching in multiple-input–multiple-output (MIMO) broadcast channels utilizing the Per-User Unitary Rate Control (PU2RC) transmission scheme is considered. Since there is no mathematical expression for the achievable rate of the PU2RC systems, it is proposed to approximate them using proper bounds. It is shown that certain simple feedback distributions will lead to tight bounds on the achievable rates of the PU2RC systems. The proposed bounds are used for the calculation of mode-switching points and maximization of the system rate under different multiplexing order conditions. The asymptotic behavior of the preferred modes is also studied under high-signal-to-noise-ratio (SNR) and low-SNR conditions, and it is shown that the single-user transmission mode is the preferred mode for both regimes. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of coordinated beamforming across a group of base stations (BSs) and frequency slots in the downlink of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA) cellular network. Three figures of merit are considered for system design under a per-BS power constraint: 1) the weighted sum of the rates (WSR) on the frequency slots of the coordinated BSs; 2) the global energy efficiency (GEE), defined as the ratio between the network sum rate and the corresponding consumed power; and 3) the weighted sum of the energy efficiencies (WSEE) on the frequency slots of the coordinated BSs. The Karush–Kuhn–Tucker (KKT) conditions of the considered optimization problems are first derived to gain insight into the structure of the optimal beamformers. Then, we propose a suboptimal design method that can be applied to all considered figures of merit. Numerical results are provided to assess the performance of the proposed beamforming strategies. [ABSTRACT FROM PUBLISHER]

In multi-antenna broadcast networks, the base stations (BSs) rely on the channel state information (CSI) of the users to perform user scheduling and downlink transmission. However, in networks with large number of users, obtaining CSI from all users is arduous, if not impossible, in practice. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing (CS), which permits the BS to obtain CSI with acceptable recovery guarantees under substantially reduced feedback overhead. Additionally, assuming noisy CS measurements at the BS, inexpensive ways for improving post-CS detection are explored. The proposed techniques are shown to reduce the feedback overhead, improve CS detection at the BS, and achieve a sum-rate close to that obtained by noiseless dedicated feedback channels. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel matrix decomposition method, called generalized multi-unitary decomposition (GMUD), and use it to achieve robust multi-antenna multi-user multiple-input–multiple-output (MIMO) precoding with limited and partial channel information feedback. GMUD transforms complex matrix {H} into \bf H = \bf P\theta,\, r{\bf R}r{\bf Q}\theta,\, r^{H}, where \bf Rr is a lower triangular matrix, and \bf P\theta,\, r and \bf Q\theta,\, r are unitary matrices. A unique feature of GMUD is that it gives multiple solutions of the unitary matrices \bf P\theta,\, r and \bf Q\theta,\, r based on two parameters, namely the direction parameter \theta and magnitude parameter r. This property enables the GMUD precoder to steer the precoding vectors of different users to jointly optimize their performance. Compared with regularized-inverse precoding (an existing multi-user MIMO precoding technique that we have extended to handle multiple receive antennas), the advantages of GMUD precoding are that it requires only partial channel state information (CSI) from the users, and its performance is robust to CSI quantization. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a linear precoder for the downlink of a multi-user MIMO system with multiple users that potentially act as eavesdroppers. The proposed precoder is based on regularized channel inversion (RCI) with a regularization parameter α and power allocation vector chosen in such a way that the achievable secrecy sum-rate is maximized. We consider the worst-case scenario for the multi-user MIMO system, where the transmitter assumes users cooperate to eavesdrop on other users. We derive the achievable secrecy sum-rate and obtain the closed-form expression for the optimal regularization parameter αLS of the precoder using large-system analysis. We show that the RCI precoder with αLS outperforms several other linear precoding schemes, and it achieves a secrecy sum-rate that has same scaling factor as the sum-rate achieved by the optimum RCI precoder without secrecy requirements. We propose a power allocation algorithm to maximize the secrecy sum-rate for fixed α. We then extend our algorithm to maximize the secrecy sum-rate by jointly optimizing α and the power allocation vector. The jointly optimized precoder outperforms RCI with αLS and equal power allocation by up to 20 percent at practical values of the signal-to-noise ratio and for 4 users and 4 transmit antennas. [ABSTRACT FROM AUTHOR]

This paper considers the max-min weighted signal-to-interference-plus-noise ratio (SINR) problem subject to multiple weighted-sum power constraints, where the weights can represent relative power costs of serving different users. First, we study the power control problem. We apply nonlinear Perron-Frobenius theory to derive closed-form expressions for the optimal value and solution and an iterative algorithm which converges geometrically fast to the optimal solution. Then, we use the structure of the closed-form solution to show that the problem can be decoupled into subproblems each involving only one power constraint. Next, we study the multiple-input-single-output (MISO) transmit beamforming and power control problem. We use uplink-downlink duality to show that this problem can be decoupled into subproblems each involving only one power constraint. We apply this decoupling result to derive an iterative subgradient projection algorithm for the problem. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

Owing to the special structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), the associated capacity region computation and beamforming optimization problems are typically non-convex, and thus cannot be solved directly. One feasible approach is to consider the respective dual multiple-access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality has been established via BC-MAC signal transformation, and is applicable only for the case in which the MIMO BC is subject to a single transmit sum-power constraint. An alternative approach is based on minimax duality, which can be applied to the case of the sum-power constraint or per-antenna power constraint. In this paper, the conventional BC-MAC duality is extended to the general linear transmit covariance constraint (LTCC) case, which includes sum-power and per-antenna power constraints as special cases. The obtained general BC-MAC duality is applied to solve the capacity region computation for the MIMO BC and beamforming optimization for the multiple-input single-output (MISO) BC, respectively, with multiple LTCCs. The relationship between this new general BC-MAC duality and the minimax duality is also discussed, and it is shown that the general BC-MAC duality leads to simpler problem formulations. Moreover, the general BC-MAC duality is extended to deal with the case of nonlinear transmit covariance constraints in the MIMO BC. [ABSTRACT FROM AUTHOR]

This paper studies generalized single-stream transmit beamforming employing receive array co-channel interference reduction algorithms under slow and flat fading multiuser wireless systems. The impact of imperfect prediction of channel state information for the desired user spatially uncorrelated transmit channels on the effectiveness of transmit beamforming for different interference reduction techniques is investigated. The case of over-loaded receive array with closely-spaced elements is considered, wherein it can be configured to null specified interfering sources. Both dominant interference reduction and adaptive interference reduction techniques for statistically ordered and unordered interferers powers, respectively, are thoroughly studied. The effect of outdated statistical ordering of the interferers powers on the efficiency of dominant interference reduction is studied and then compared against the adaptive interference reduction. For the system models described above, new analytical formulations for the statistics of combined signal-to-interference-plus-noise ratio are presented, from which results for conventional maximum ratio transmission and single-antenna best transmit selection can be directly deduced as limiting cases. These results are then utilized to obtain quantitative measures for various performance metrics. They are also used to compare the achieved performance of various configuration models under consideration. [ABSTRACT FROM PUBLISHER]

This paper considers joint linear processing at multi-antenna sources and one multiple-input multiple-output (MIMO) relay station for both one-way and two-way relay-assisted wireless communications. The one-way relaying is applicable in the scenario of downlink transmission by a multi-antenna base station to multiple single-antenna users with the help of one MIMO relay. In such a scenario, the objective of join linear processing is to maximize the information throughput to users. The design problem is equivalently formulated as the maximization of the worst signal-to-interference-plus-noise ratio (SINR) among all users subject to various transmission power constraints. Such a program of nonconvex objective minimization under nonconvex constraints is transformed to a canonical d.c. (difference of convex functions/sets) program of d.c. function optimization under convex constraints through nonconvex duality with zero duality gap. An efficient iterative algorithm is then applied to solve this canonical d.c program. For the scenario of using one MIMO relay to assist two sources exchanging their information in two-way relying manner, the joint linear processing aims at either minimizing the maximum mean square error (MSE) or maximizing the total information throughput of the two sources. By applying tractable optimization for the linear minimum MSE estimator and d.c. programming, an iterative algorithm is developed to solve these two optimization problems. Extensive simulation results demonstrate that the proposed methods substantially outperform previously-known joint optimization methods. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a distributed beamforming scheme to realize a full-duplex relay (FDR) in multiuser multiple-input–multiple-output (MIMO) relaying systems. In the proposed scheme, each mobile and relay station performs distributed transmit beamforming and receive combining, whereas the base station performs ordinary multiuser MIMO transmission. Using the channel reciprocity, the proposed FDR scheme can be realized in an iterative way with no cooperation among nodes. Numerical results show that the proposed scheme provides substantial performance improvement compared with the half-duplex relay. [ABSTRACT FROM PUBLISHER]

In this paper, we consider an interference multiple-input multiple-output (MIMO) relay system where multiple source nodes communicate with their desired destination nodes concurrently with the aid of distributed relay nodes all equipped with multiple antennas. We aim at minimizing the total source and relay transmit power such that a minimum signal-to-interference-plus-noise ratio (SINR) threshold is maintained at each receiver. An iterative joint power control and beamforming algorithm is developed to achieve this goal. The proposed algorithm exploits transmit-relay-receive beamforming technique to mitigate the interferences from the unintended sources in conjunction with transmit power control. In particular, we apply the semidefinite relaxation technique to transform the relay transmission power minimization problem into a semidefinite programming (SDP) problem which can be efficiently solved by interior point-based methods. Numerical simulations are performed to demonstrate the effectiveness of the proposed iterative algorithm. [ABSTRACT FROM PUBLISHER]

Recent works on multiuser MIMO study have shown that the linear growth of capacity in single user MIMO system can be translated to the multiuser MIMO scenario as well. In this paper, we propose a method pursuing performance gain of vector perturbation in multiuser downlink systems. Instead of employing the maximum number of mobile users for communication, we use small part of them as virtual users for improving reliability of users participating communication. By controlling parameters of the virtual users including information and perturbation vector, we obtain considerable improvement in the effective SNR. Simulation results on the realistic multiuser MISO and MIMO downlink systems show that the proposed method brings substantial performance gain over the standard vector perturbation with marginal overhead in computations. [ABSTRACT FROM AUTHOR]

In a cellular-like system, the multiple-input multiple-output (MIMO) mode used by a base station (BS) can be adaptively varied among several candidate MIMO modes, including space-time block coding (STBC), open-loop spatial multiplexing (OLSM), closed-loop spatial multiplexing (CLSM), MIMO beamforming (MBF), and maximal ratio transmission (MRT). In practice, during any one given period of time, when the desired BS performs downlink transmission with a specific MIMO mode, each co-channel interfering BS will use a MIMO mode the same as or different from that used in the desired link and any two co-channel interfering BSs may use different MIMO modes, so that the interference scenario becomes quite complicated. In this paper, considering realistic propagation conditions including both path-loss and Rayleigh fading and considering the existence of unequal-power interferers, the statistical distributions of the post-processing signal-to-interference ratios (SIRs) for either MBF or STBC downlink transmissions are analyzed when co-channel interfering BSs employ arbitrary MIMO modes. In particular, closed-form expressions for the probability density function (PDF) of the interference terms in the post-processing SIRs are derived. Further, the statistical distributions of the post-processing SIRs are analyzed. Simulation results verify the validity of the theoretical analyses. [ABSTRACT FROM PUBLISHER]

Two-way relaying has demonstrated significant gain in spectral efficiency by applying network coding when a pair of source nodes exchange information via a relay node. This paper is concerned with the scenario where multiple pairs of users exchange information through a common relay node equipped with multiple antennas. We aim to design linear precoding at the relay based on amplify-and-forward strategy. The goal is to maximize the minimum achievable rate among all the users subject to a peak relay power constraint so as to achieve the max-min fairness. We first convert this nonconvex problem into a series of semidefinite programming problems using bisection search and certain transformation techniques. A quasi-optimal solution is then obtained by using semidefinite relaxation (SDR). To reduce the design complexity, we further introduce a pair-wise zero-forcing (ZF) structure that eliminates the interference among different user pairs. By applying this structure, the precoding design problem is simplified to a power allocation problem which can be optimally solved. A simplified power allocation algorithm is also proposed. Simulation results show that the proposed SDR-based precoding not only achieves high minimum user rate but also maintains good sum-rate performance when compared with existing schemes. It is also shown that the proposed pair-wise ZF precoding with simplified power allocation strikes a good balance between performance and complexity. [ABSTRACT FROM PUBLISHER]

The signal to interference plus noise ratio (SINR) feedback has been utilized in random beamforming (RBF) to select users for multiple-input multiple-output (MIMO) systems where a large number of users are required to obtain a multi-user diversity gain. However, if the number of users is not large enough, it may be difficult to obtain the performance gain and be easy to have a performance degradation conversely. To resolve this problem, it is necessary to find orthogonal random beams close to the channel matrix of selected users. In this paper, we present RBF using a non-orthogonal beamforming matrix (RBF NOBM), demonstrating an efficient search of random beams correlated with the channel matrix of selected users over a small number of users. For single user RBF using NOBM (SU-RBF NOBM) with two transmit antennas, the beamforming matrix for user data service is obtained through closed form expressions, and SU-RBF NOBM is then expanded to more than two transmit antennas. We also propose user selection algorithms for SU-RBF NOBM and multi-user RBF using NOBM (MU-RBF NOBM). Via simulation results, we demonstrate that the performances of RBF NOBM represent significant improvements compared to conventional beamforming schemes. [ABSTRACT FROM PUBLISHER]

This paper investigates transmit antenna selection for linearly precoded multiuser multiple-input–multiple-output (MU-MIMO) systems. First, in some precoded single-user MIMO systems, using all transmit antennas does not always lead to the best performance due to ill-conditioned channel matrices. This condition motivates us to investigate whether a similar result can be obtained in MU-MIMO systems. Based on the derived analytical results, we found that, for a given number of transmit antennas, decreasing the number of active transmit antennas [number of radio frequency (RF) units] always degrades system performance in the linearly precoded MU-MIMO systems. However, in practical systems, RF units are expensive. To reduce the hardware cost, antenna selection is usually used to reduce the number of RF units. Thus, we further analyze the performance loss due to transmit antenna selection (TAS). These analytical results provide good design references for using TAS in practical systems. Moreover, based on the analytical results, we proposed several simple TAS algorithms for linearly precoded MU-MIMO systems. Complexity analysis and simulation results show that the computational complexity of the proposed algorithms can significantly be reduced, whereas the performance is still comparable with the optimal selection scheme. As a result, the analyzed results enable us to better understand how TAS affects the MU-MIMO systems. In addition, the proposed algorithms make TAS more feasible to be used in practical systems. [ABSTRACT FROM PUBLISHER]

We consider the transceiver design for multiple-input–multiple-output (MIMO) systems when the channel state information (CSI) is available at the transmitter as well as the receiver. First, we propose an open-loop low-complexity MIMO spatial multiplexing scheme based on the energy spreading transform (EST-SM). The EST-SM can spatially multiplex multiple data streams and iteratively detect the data streams with almost negligible interstream interference at sufficiently high SNR. Then, we propose a closed-loop precoding scheme suitable for the EST-SM called root mean square decomposition (RMSD) scheme. The RMSD precoding scheme combined with the EST-SM decomposes a MIMO channel into multiple subchannels with identical SNRs. This desired property minimizes bit error rate (BER) when different bit allocations on different subchannels, which cause a significant increase in system complexity, are not used. We show that when the EST-SM is used the RMSD scheme is optimal in BER performance and it achieves full diversity. Simulation results show that the RMSD scheme outperforms other existing techniques such as the geometric mean decomposition (GMD) scheme (Jiang , IEEE Trans. Signal Process., vol. 53, no. 10, pp. 3791–3803) and the uniform channel decomposition (UCD) schemeref refid="fn1"/ asterisk="no" id="fn1"paraThroughout this paper, the GMD and UCD schemes mean GMD-VBLAST in refid="ref12"/ and UCD-VBLAST in refid="ref13"/, respectively.para (Jiang , IEEE Trans. Signal Process., vol. 53, no. 11, pp. 4283–4294) in BER performance. [ABSTRACT FROM PUBLISHER]

We consider a multiuser system where a single transmitter equipped with multiple antennas (the base station) communicates with multiple users each with a single antenna. Regularized channel inversion is employed as the precoding strategy at the base station. Within this scenario we are interested in the problems of power allocation and user admission control so as to maximize the system throughput, i.e., which users should we communicate with and what power should we use for each of the admitted users so as to get the highest sum rate. This is in general a very difficult problem but we do two things to allow some progress to be made. Firstly we consider the large system regime where the number of antennas at the base station is large along with the number of users. Secondly we cluster the downlink path gains of users into a finite number of groups. By doing this we are able to show that the optimal power allocation under an average transmit power constraint follows the well-known water filling scheme. We also investigate the user admission problem which reduces in the large system regime to optimization of the user loading in the system. [ABSTRACT FROM PUBLISHER]

In this paper, we propose jointly-optimized beamforming algorithms for cognitive networks to maximize the achievable rates, where primary and cognitive users share the same spectrum and are equipped with multiple antennas. We consider the transmission of a single information stream in both primary and secondary links. No coordination is required between the primary and cognitive users and the interference cancellation is done at the cognitive user. Specifically, the beamforming vectors of the cognitive link are designed to maximize the achievable rate under the condition that the interference both at the primary and cognitive receivers is completely nullified. Furthermore, it is proved that the achievable rate of a general N_t^Cx 2 (N_t^C transmit antennas at the cognitive transmitter and 2 receive antennas at the cognitive receiver) cognitive multiple-input multiple-output (MIMO) link employing the optimal proposed beamformers (which completely nullifies the interference to and from the primary link while maximizing its own achievable rate) is the same as the rate of an interference-free (N_t^C-1)x 1 multiple-input single-output (MISO) link employing an optimal maximal ratio transmission beamforming vector. The sum rate performance of the proposed algorithms is evaluated by Monte Carlo simulations. The impact of the number of transmit and receive antennas on the proposed algorithm is also discussed. [ABSTRACT FROM AUTHOR]

In downlink multiuser multiple-input multiple-output (MIMO) systems, system performance highly depends on the reliability of downlink channel state information (CSI) at the base station (BS). In frequency division duplexing, the most practical solution is to have downlink CSI from the users fed back to the BS. Most work on this feedback design has assumed independent block fading channels. However, this paper proposes a new differential feedback scheme using the observation that the channel realizations are usually temporally correlated. The sum-rate loss assuming differential feedback is analyzed for a system with the number of users K equal to the number of transmit antennas M. When K > M, a user selection algorithm based on an approximated signal-to-interference plus noise ratio (SINR) estimation is proposed. In simulation results, the proposed differential feedback scheme increases the sum-rate compared to previous differential feedback schemes. Moreover, the proposed user selection algorithm outperforms semi-orthogonal user selection in the moderate signal-to-noise ratio (SNR) region, despite requiring less feedback information. In low mobility channels, utilizing the channels' time correlation during quantization is shown to play a bigger role in determining sum-rate performance than multiuser diversity for most SNR regimes when a practical number of users is considered. [ABSTRACT FROM AUTHOR]

We propose an opportunistic two-stage feedback and scheduling algorithm that is based upon zero-forcing beamforming with semi-orthogonal user selection (ZFBF-SUS) to reduce its feedback load. In an SUS algorithm, a base station schedules semi-orthogonal users using the feedback of all users' channel information. However, such feedback overhead significantly increases with the number of users. To reduce the feedback load of ZFBF-SUS systems, the proposed two-stage feedback scheme opportunistically and separately exploits the signal-to-interference-plus-noise ratio and orthogonality in each stage such that only a fraction of users feed back their channel information. Based on this opportunistic feedback scheme, we decouple the entire process of the SUS into two sub-processes. The proposed two-stage feedback and scheduling scheme effectively exclude the inappropriate users for ZFBF transmission in both feedback and scheduling periods, and thus save the scarce resources consumed for the feedback process. We derive an analytical expression for the average number of feedback bits of the proposed system and optimize it in an averaged sense. Further, the sum-rate of the proposed system is theoretically investigated. Both analytical and simulation results show that the proposed algorithm achieves the performance of conventional ZFBF-SUS systems with a significantly reduced number of feedback bits. [ABSTRACT FROM PUBLISHER]

The diversity-multiplexing tradeoff (DMT) of a multiple-input multiple-output (MIMO) multiple-access system is analyzed, where a successive cancellation (SC) receiver is employed. In order to analyze the asymptotic performance of each user in a practical MIMO multiple-access system, we derive the per-user DMT considering error propagation due to imperfect cancellation of the SC process. The diversity gain of each user is shown to be expressed as a simple function of the multiplexing gains of the previously detected users as well as its own multiplexing gain. Also, we show that the diversity gain of each user who suffers from error propagation is strictly smaller than that under the assumption of perfect cancellation in a range of multiplexing gains of the users. Numerical results show that our DMT analysis matches well with the performance of practical multiple-access systems. [ABSTRACT FROM AUTHOR]

The multiuser MIMO (MU-MIMO) systems tend to have inter-user interference in practical systems with limited feedback. Practical MU-MIMO systems with limited feedback can be divided into two classes in general. One is zero-forcing beamforming (ZFBF), and the other is per user unitary rate control (PU^2RC) which is an extended version of random beamforming (RBF). To improve performance, ZFBF needs more feedback bits, and PU^2RC needs a larger number of users. We propose a new hybrid MU-MISO system which has the advantages of the two MU-MIMO schemes simultaneously, and analyze the sum-rate performance and the quantization error of the hybrid scheme. The analysis shows how the number of feedback bits, the number of users, and the signal power affect the sum-rate. The throughput scaling laws are also derived in the high and the medium SNR regimes. Simulation results show that the proposed hybrid MU-MISO system performs better than either of the two MU-MIMO systems with the same number of feebback bits. Another advantage is that it has lower complexity for codeword search since it has a hierarchical codebook structure. [ABSTRACT FROM PUBLISHER]

In a quantization based multiuser multiple-input/multiple-output (MU-MIMO) broadcast channel, the effective channel gain (i.e., the norm of a channel vector obtained from the receive combiner) needs to be considered in addition to a reduction of quantization errors for maximizing the signal-to-interference plus noise ratio (SINR). In this work, we prove that the effective channel gains in the space limited by an N_R x M_T MIMO channel form a min(M_T,N_R)-dimensional ellipsoid in the channel space. Utilizing the geometric proof, the achievable effective channel gain is derived as a function of its direction for a given MIMO channel. Based on the derivation, we finally propose a quantization vector selection criterion and an optimal receive combiner to maximize the SINR. The proposed maximum SINR based combining (MSC) is proven to be a better solution for maximizing the SINR compared to quantization based combining (QBC) and maximum gain based combining (MGC), each of which attempts to minimize quantization errors and maximize the effective channel gain. [ABSTRACT FROM AUTHOR]

We develop an interference alignment (IA) technique for a downlink cellular system. In the uplink, IA schemes need channel-state-information exchange across base-stations of different cells, but our downlink IA technique requires feedback only within a cell. As a result, the proposed scheme can be implemented with a few changes to an existing cellular system where the feedback mechanism (within a cell) is already being considered for supporting multi-user MIMO. Not only is our proposed scheme implementable with little effort, it can in fact provide substantial gain especially when interference from a dominant interferer is significantly stronger than the remaining interference: it is shown that in the two-isolated cell layout, our scheme provides four-fold gain in throughput performance over a standard multi-user MIMO technique. We also show through simulations that our technique provides respectable gain under a more realistic scenario: it gives approximately 28% gain for a 19 hexagonal wrap-around-cell layout. Furthermore, we show that our scheme has the potential to provide substantial gain for macro-pico cellular networks where pico-users can be significantly interfered with by the nearby macro-BS. [ABSTRACT FROM AUTHOR]