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Dirty paper coding (DPC) scheme is the capacity achieving transmission technique in multiuser MIMO downlink channels. As a suboptimal solution to DPC, successive zero-forcing DPC (SZF-DPC) has been proposed recently. The zero-interference constraint in designing the precoding matrices limits the number of supportable users. In this correspondence, we propose three low-complexity suboptimal user scheduling algorithms to exploit the multiuser diversity gain in SZF-DPC as the number of users grows. The first algorithm greedily maximizes the true sum rate. The second algorithm is based on eigenvalues. The third algorithm relies on the diagonal elements of the effective channel matrix since eigenvalues and diagonal entries of a Hermitian matrix have a strong relationship. Simulation results show that the proposed scheduling algorithms can obtain a significant fraction of sum rate of the optimal solution. Furthermore, the performance analysis is provided to prove that the proposed user selection algorithms can achieve the same asymptotic sum rate as that of DPC. [ABSTRACT FROM AUTHOR]

Methods for information-theoretic control for networks of sensors are of interest for enabling the development of autonomous sensor systems. In this paper we revisit the fundamentals of information theoretic-based control for multi-target systems and present a systematic approach for determining information-theoretic situational awareness based on mutual information for point processes. The extension to multi-sensor systems is developed using the concept of a broadcast channel from information theory. Analytic results are presented for linear-Gaussian systems which enable low complexity solutions for determining information from multiple sensors and we consider a large number of potential sensor configurations. We consider extensions of single-target methods to multi-target scenarios and present results in simulations. [ABSTRACT FROM AUTHOR]

Nonlinear transceiver based on Tomlinson–Harashima (TH) precoding outperforms the linear minimum mean-square-error (MSE) architecture in terms of minimum achievable MSE. In this paper, we investigate nonlinear TH precoding design problem for simultaneous wireless information and power transfer in multiple-input-multiple-output broadcast channels, where there are one information-decoding (ID) receiver and multiple energy harvesting (EH) receivers. We consider the scenario that the transmitter knows imperfect channel state information of all links and the channel uncertainties are modeled by Gaussian random variables. Our objective is to jointly optimize TH precoding matrices and linear equalizer to minimize average MSE subject to transmit power constraint and EH constraints on average harvested power. We transform the robust transceiver design problem into a difference of convex programming and propose a constrained concave convex procedure (CCCP) based locally optimal solution. To reduce computational complexity, we propose a noniterative upper bound based suboptimal solution that minimizes the upper bound on average MSE. We also extend the CCCP-based locally optimal solution to the single group multicast scenario where there are multiple ID receivers. Simulation results demonstrate that our proposed TH precoding transceiver design achieves lower average MSE than linear precoding transceiver design. [ABSTRACT FROM PUBLISHER]

Recently, the duality between multiple-input multiple-output (MIMO) multiple-access channels (MAC) and MIMO broadcast channels (BCs) has been established under a total power constraint. The same set of rates for MAC can be achieved in BC exploiting the MAC-BC duality formulas while preserving the total power constraint. In this paper, we describe the BC optimal power allocation applying this duality in a downstream x-digital subscriber lines (xDSLs) context under a total power constraint for all modems over all tones. Then, a new algorithm called BC-optimal spectrum balancing (BC-OSB) is devised for a more realistic power allocation under per-modem total power constraints. The capacity region of the primal BC problem under per-modem total power constraints is found by the dual optimization problem for the BC under per-modem total power constraints which can be rewritten as a dual optimization problem in the MAC by means of a precoder matrix based on the Lagrange multipliers. We show that the duality gap between the two problems is zero. The multiuser power allocation problem has been solved for interference channels and MAC using the OSB algorithm. In this paper we solve the problem of multiuser power allocation for the BC case using the OSB algorithm as well and we derive a computational efficient algorithm that will be referred to as BC-OSB. Simulation results are provided for two VDSL2 scenarios: the first one with differential-mode (DM) transmission only and the second one with both DM and phantom-mode (PM) transmissions. [ABSTRACT FROM AUTHOR]

We consider beamformer design for multiuser multiple-input multiple-output (MIMO) interference channels where each transmitter communicates to its intended receivers while sharing the same spectro-temporal resources with some other unintended receivers. This scenario is often referred to as MIMO interference broadcast channel (IBC) where by using interference alignment (IA), it is possible to cancel out inter and intracell interferences so that the achievable degrees of freedom could be linearly scaled up with the number of users. In this paper, we propose two distinct and novel IA algorithms, one of which is IA via signal matching (SIGMA) that revolves around interference leakage minimization and further takes the desired signal subspaces into account to output higher sum rates. The second algorithm is called bipartite rank fitting, which is founded on the prior knowledge of the rank of receive beamformers and interference matrices. Therefore, this algorithm is quite a unique approach towards interference alignment in MIMO IBCs since it performs the optimization over only receive beamformers. Furthermore, we propose a robust design of SIGMA algorithm to achieve better performance in the presence of imperfect channel state information (CSI). The key tenet of our design is that we formulate the optimization problems involved in these two algorithms as plain semidefinite programs. It is shown that the proposed algorithms are able to outperform state-of-the-art IA techniques under perfect and imperfect CSI, and the suitability of each algorithm for different network configurations is further discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a statistical-eigenmode space-division multiple-access (SE-SDMA) transmission approach for a two-user downlink system where two transmit antennas are deployed at the base station and each mobile user has only one receive antenna, assuming that only statistical channel state information (SCSI) is available at the transmitter. By maximizing a lower bound of the ergodic signal-to-leakage-and-noise ratio (SLNR), the proposed SE-SDMA approach selects two users with orthogonal principal statistical eigen-directions and then transmits to each user along the corresponding eigenmode. For this approach, we derive an exact expression of the achievable ergodic rate, and compare it with the zero-forcing beamforming (ZFBF) system exploiting instantaneous CSI. We show that SE-SDMA can achieve the maximum ergodic sum-rate of the two selected users, and provide significant user selection gains. For comparison with ZFBF, we show that the rate gap between SE-SDMA and ZFBF with perfect CSI at the transmitter (CSIT) tends to zero in highly correlated channels. Further, when ZFBF is implemented with inaccurate CSIT, the operating regions of SE-SDMA and ZFBF are investigated for different ranges of signal-to-noise ratio (SNR), instantaneous CSIT accuracy levels, and spatial correlation levels. Numerical results confirm the analytically derived conclusions, and physical insights are provided. [ABSTRACT FROM PUBLISHER]

The communication between a multiple-antenna transmitter and multiple receivers (users) with either a single or multiple-antenna each can be significantly enhanced by providing the channel state information at the transmitter (CSIT) of the users, as this allows for scheduling, beamforming and multiuser multiplexing gains. The traditional view on how to enable CSIT has been as follows: In time-division duplexed (TDD) systems, uplink (UL) and downlink (DL) channel reciprocity allows the use of a training sequence in the UL direction, which is exploited to obtain an UL channel estimate. This estimate is in turn recycled in the next downlink transmission. In frequency-division duplexed (FDD) systems, which lack the UL and DL reciprocity, the CSIT is provided via the use of a dedicated feedback link of limited capacity between the receivers and the transmitter. In this paper, we focus on TDD systems and put their classical approach in question. We show that the traditional TDD setup above fails to fully exploit the channel reciprocity in its true sense. In fact, we show that the system can benefit from a combined CSIT acquisition strategy mixing the use of limited feedback and that of a training sequence. This combining gives rise to a very interesting joint estimation and detection problem for which we propose two iterative algorithms. An outage rate based framework is also developed which gives the resource split between training and feedback. We demonstrate the potential of this hybrid combining in terms of the improved CSIT quality under a global training and feedback resource constraint. [ABSTRACT FROM PUBLISHER]

Abstract-Linear transmit beamforming and receive combining is a low complexity approach for communications in the multiuser multiple-input multiple-output (MIMO) channel. This paper proposes a full search and two low complexity coordinated algorithms where each receiver uses a quantized combining vector. Since the optimization is performed at the transmitter with channel state information from all the receivers, the receive combining vectors are (either jointly or independently) quantized at the transmitter and sent to the receivers via a low-rate feedforward control channel. Rate bounds are provided to estimate the impact of quantization loss on the achievable rate in Rayleigh channels. Simulations show that the sum rate of the proposed methods using Grassmannian codebooks is close to the sum capacity of the MIMO broadcast channel, especially in the high signal-to-noise ratio (SNR) regime. [ABSTRACT FROM AUTHOR]

In this paper, we study the information theoretic maximal degrees of freedom (DoF) for the symmetric multi-input–multi-output (MIMO) interfering broadcast channel (IBC) with arbitrary antenna configurations. For the G-cell K-user M\times N MIMO-IBC network, we find that the information theoretic maximal DoF per user are related to three DoF bounds: 1) the decomposition DoF bound , a lower-bound of asymptotic IA; 2) the proper DoF bound d^{\rm{Proper}}=(M+N)/(GK+1), an upper-bound of linear IA; and 3) the quantity DoF bound d^{\rm{Quan}}$ , a zigzag piecewise linear function of M$ and N$. For most configurations in Region I where , the maximal DoF are the decomposition DoF bound and achieved by the asymptotic IA. For all configurations in Region II where \mathcal{R}^{\rm{II}}=\lbrace M/N | d^{\rm{Proper}} \geq d^{\mathrm{Decom}}\rbrace , the maximal DoF are the quantity DoF bound and achieved by the linear IA. To obtain the tight upper-bound, we propose a unified way to construct genies to help each base station or user resolve the maximal number of interference. According to the feature that the designed genies with the same dimension can derive identical DoF upper-bound, we convert the information theoretic DoF upper-bound problem into a linear algebra problem and obtain the closed-form DoF upper-bound expression. Moreover, we develop a non-iterative linear IA transceiver to achieve the DoF upper-bound for antenna configurations in Region II. The basic principles to derive the DoF upper-bound and design the linear IA transceiver to achieve the DoF upper-bound can be extended into general asymmetric networks. [ABSTRACT FROM PUBLISHER]

Weighted sum rate maximization (WSRMax) with user specific quality-of-service (QoS) constraints and general convex transmit power constraints is considered in multi-cell multi-user multiple-input multiple-output system. The particular focus in the proposed joint transmitter-receiver design is on tractability in terms of implementation and moderately fast changing/time correlated channel conditions. The non-convex transmit precoder design problem is formulated as a difference of convex functions program, for which a locally optimal solution is achieved by successive convex approximation (SCA). To achieve practically realizable designs, two decentralized approaches with low signaling overhead are proposed. Primal decomposition based solution provides better compliance of the provided QoS constraints in slowly fading channel conditions. On the other hand, solution based on Lagrangian relaxation of the coupling rate constraints is proposed for relaxed feasibility conditions and improved convergence properties. As a special case, an iterative solution via the Karush–Kuhn–Tucker conditions of the precoder design problem with per base station transmit power constraints is also proposed. Finally, we propose a heuristic extension of the SCA method, which is shown to significantly improve the rate of convergence while achieving comparable sum rate with recently published methods. [ABSTRACT FROM AUTHOR]

We study the problem of minimizing the sum transmit power in mutliple-input multiple-output (MIMO) downlink channels with linear transceivers if per-user quality of service (QoS) constraints (expressed in terms of rates) have to be fulfilled. To find a suboptimal solution of the arising non-convex optimization problem, we introduce new auxiliary variables representing the division of the per-user rate constraints into per-stream rate targets, and we optimize these variables by means of gradient-projection steps. This new method is combined with alternating updates of the transmit and receive filters. Furthermore, the proposed algorithm ensures that the mean square error (MSE) matrices of all users are diagonal, and in the course of the execution of the algorithm, it is possible that inactive streams get activated if this leads to a decreased sum transmit power. In numerical simulations, the new algorithm turns out to be superior to the various existing methods since these methods either lead to a higher sum transmit power than the proposed scheme or have a higher computational complexity or make restrictive assumptions on the system parameters. [ABSTRACT FROM PUBLISHER]

On a multiantenna broadcast channel, simultaneous transmission to multiple users by joint beamforming and scheduling is capable of achieving high throughput, which grows double logarithmically with the number of users. The sum rate for channel state information (CS!) feedback, however, increases linearly with the number of users, reducing the effective uplink capacity. To address this problem, a novel space division multiple access (SDMA) design is proposed, where the sum feedback rate is upper bounded by a constant. This design consists of algorithms for CSI quantization, threshold-based CSI feedback, and joint beamforming and scheduling. The key feature of the proposed approach is the use of feedback thresholds to select feedback users with large channel gains and small CS! quantization errors such that the sum feed- back rate constraint is satisfied. Despite this constraint, the proposed SDMA design is shown to achieve a sum capacity growth rate close to the optimal one. Moreover, the feedback overflow probability for this design is found to decrease exponentially with the difference between the allowable and the average sum feedback rates. Numerical results show that the proposed SDMA design is capable of attaining higher sum capacities than existing ones, even though the sum feedback rate is bounded. [ABSTRACT FROM AUTHOR]

We study the design of linear precoders for secure transmission in the two-user multiple-input single-output (MISO) broadcast channel with confidential messages (BC-CM). The transmitter has multiple antennas, and each user has a single receive antenna. Two independent messages are simultaneously transmitted, one intended for each user, and each message should be kept confidential from the other user. Assuming real-valued transmitted signals, we design the linear precoders subject to total and per-antenna average power constraints, and also subject to amplitude constraints. In both cases, we tackle the design problem via weighted secrecy sum rate maximization. The resulting problem, however, involves a fractional objective, making it nonconvex and difficult to solve. Nevertheless, we show that this difficult problem can be transformed into a more tractable problem, for which a solution can be obtained by an iterative search algorithm. In addition, we characterize a condition under which the obtained solution is guaranteed to be optimal. Furthermore, we show that the problem formulation and solution approach can be easily extended to handle the robust version of the design problem with uncertain channel information. We provide numerical examples to demonstrate the performance of the proposed precoder in terms of the achievable secrecy rate regions subject to the aforementioned constraints. We also demonstrate the performance of the robust precoder under different channel uncertainty levels. [ABSTRACT FROM PUBLISHER]

A wireless data network with K static nodes is considered. The nodes communicate simultaneously over the same narrowband channel and each node uses superposition coding to broadcast independent messages to individual nodes in the network. The goal herein is to find optimal data routes and power allocations to maximize a weighted sum of the data rates injected and reliably communicated over the network. Two instances of this problem are considered. In the first instance, each node uses a fixed power budget, whereas in the second instance the power used by each node is adjustable. For the latter case, two variants are considered: in the first there is a constraint on the power used by each node and in the second there is constraint on the total power used by all nodes. It will be shown that while the instance in which the power of each node is fixed can be cast in the form of an efficiently solvable geometric program (GP), the second instance in which the node powers are adjustable cannot be readily cast in this form. To circumvent this difficulty, an iterative technique is proposed for approximating the constraints of the original optimization problem by GP-compatible constraints. Numerical simulations suggest that this technique converges to a locally optimal solution within a few iterations. [ABSTRACT FROM AUTHOR]

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of the transmit and the receive beamformers according to different optimization criteria, including weighted sum rate maximization, weighted sum mean square error minimization, minimum signal-to-interference-plus-noise ratio (SINR) maximization and sum power minimization under a minimum SINR constraint. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The optimization problems encountered in the beamformer design (e.g., covariance rank constraint) are not convex in general. Therefore, the problem of finding the global optimum is intrinsically nontractable. However, by exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. Even though each subproblem is optimally solved, there is no guarantee that the global optimum has been found due to the nonconvexity of the problem. However, the simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient. [ABSTRACT FROM AUTHOR]

Knowledge of accurate and timely channel state information (CSI) at the transmitter is becoming increasingly important in wireless communication systems. While it is often assumed that the receiver (whether base station or mobile) needs to know the channel for accurate power control, scheduling, and data demodulation, it is now known that the transmitter (especially the base station) can also benefit greatly from this information. For example, recent results in multiantenna multiuser systems show that large throughput gains are possible when the base station uses multiple antennas and a known channel to transmit distinct messages simultaneously and selectively to many single-antenna users. In time-division duplex systems, where the base station and mobiles share the same frequency band for transmission, the base station can exploit reciprocity to obtain the forward channel from pilots received over the reverse channel. Frequency-division duplex systems are more difficult because the base station transmits and receives on different frequencies and therefore cannot use the received pilot to infer anything about the multiantenna transmit channel. Nevertheless, we show that the time occupied in frequency-duplex CSI transfer is generally less than one might expect and falls as the number of antennas increases. Thus, although the total amount of channel information increases with the number of antennas at the base station, the burden of learning this information at the base station paradoxically decreases. Thus, the advantages of having more antennas at the base station extend from having network gains to learning the channel information. We quantify our gains using linear analog modulation which avoids digitizing and coding the CSI and therefore can convey information very rapidly and can be readily analyzed. The old paradigm that it is not worth the effort to learn channel information at the transmitter should be revisited since the effort decreases and the gain increases with the number of antennas. [ABSTRACT FROM AUTHOR]