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In this paper, we study amplify-and-forward (AF) schemes in two-hop one-way relay networks. In particular, we consider multigroup multicast transmission between long-distance users. Assuming that perfect channel state information is perceived, our goal is to design the AF process so that the max-min-fair signal-to-interference-plus-noise ratio (SINR) is optimized while generalized power constraints are satisfied. We propose a beamformed Alamouti (BFA) AF scheme and formulate the corresponding AF design problem as a two-block fractional quadratically constrained quadratic program (QCQP). We then tackle the two-block fractional QCQP using the semidefinite relaxation (SDR) technique and analyze the approximation accuracy of the proposed SDR. From a theoretical perspective, our results are fundamentally new and reveal that the proposed BFA AF scheme can outperform the traditional BF AF scheme, especially when there are many users in the system or many generalized power constraints in the problem formulation. From a practical perspective, our proposed BFA AF scheme improves the receivers’ SINR by offering two degrees of freedom (DoFs) in beamformer design, as opposed to only one DoF offered by the BF AF scheme. In the latter part of this paper, we demonstrate how this extra DoF leads to provable performance gain by considering two special relay scenarios, in which the AF process is shown to possess a special structure. Numerical simulations further confirm that the proposed BFA AF scheme outperforms the BF AF scheme and works well for large-scale relay systems. [ABSTRACT FROM PUBLISHER]

This paper addresses the optimal design of limited-feedback downlink multiuser spatial multiplexing systems. A multiple-antenna base-station is assumed to serve multiple single-antenna users, who quantize and feed back their channel state information (CSI) through a shared rate-limited feedback channel. The optimization problem is cast in the form of minimizing the average transmission power at the base-station subject to users' target signal-to-interference-plus-noise ratios (SINR) and outage probability constraints. The goal is to derive the feedback bit allocations among the users and the corresponding channel magnitude and direction quantization codebooks in a high-resolution quantization regime. Toward this end, this paper develops an optimization framework using approximate analytical closed-form solutions, the accuracy of which is then verified by numerical results. The results show that, for channels in the real space, the number of channel direction quantization bits should be (M-1) times the number of channel magnitude quantization bits, where M is the number of base-station antennas. Moreover, users with higher requested quality-of-service (QoS), i.e., lower target outage probabilities, and higher requested downlink rates, i.e., higher target SINR's, should use larger shares of the feedback rate. It is also shown that, for the target QoS parameters to be feasible, the total feedback bandwidth should scale logarithmically with the geometric mean of the target SINR values and the geometric mean of the inverse target outage probabilities. In particular, the minimum required feedback rate is shown to increase if the users' target parameters deviate from the corresponding geometric means. Finally, the paper shows that, as the total number of feedback bits B increases, the performance of the limited-feedback system approaches the perfect-CSI system as 2-B/M2. [ABSTRACT FROM PUBLISHER]

In a companion paper, the generalized geometric mean decomposition (GGMD) was proposed and used to design GGMD decision feedback equalizer (DFE) transceivers for arbitrary multiple-input multiple-output (MIMO) channels without zero-forcing constraint. For the application to cyclic prefix (CP) systems, the GGMD DFE transceiver has the most advantage over the GMD DFE MMSE transceiver in terms of design and implementation complexity. This paper presents the performance analysis for the GGMD DFE transceiver implementation proposed in Part I. The arithmetic mean-square error (MSE), symbol error rate (SER) and Gaussian mutual information of the proposed system are investigated. The performance advantages of GGMD DFE transceiver over popular orthogonal-frequency-division multiplexing (OFDM) and single-carrier CP MMSE systems are shown analytically and verified by numerical simulations. [ABSTRACT FROM AUTHOR]

Consider a multiple-input multiple-output (MIMO) downlink multi-user channel. A well-studied problem in such a system is the design of linear beamformers for power minimization with the quality of service (QoS) constraints. The most representative algorithms for solving this class of problems are the so-called minimum mean square error (MMSE)–second-order cone programming (SOCP) algorithm [Visotksy and Madhow, “Optimum Beamforming Using Transmit Antenna Arrays,” Proc. IEEE Veh. Technol. Conf., May 1999, vol. 1, pp. 851–856] , [Wong, Zheng, and Ng, “Convergence Analysis of Downlink MIMO Antenna System Using Second-Order Cone Programming,” Proc. 62nd IEEE Veh. Technol. Conf., Sep. 2005, pp. 492–496] and the uplink-downlink duality (UDD) algorithm [Codreanu, Tolli, Juntti, and Latva-Aho, “Joint Design of Tx-Rx Beamformers in MIMO Downlink Channel,” IEEE Trans. Signal Process., vol. 55, no. 9, pp. 4639–4655, Sep. 2007]. The former is based on alternating optimization of the transmit and receive beamformers; while the latter is based on the well-known uplink-dowlink duality theory. Despite their wide applicability, the convergence to Karush–Kuhn–Tucker (KKT) solutions of both algorithms is still open in the literature. In this paper, we rigorously establish the convergence of these algorithms for QoS-constrained power minimization (QCPM) problem with both single stream and multiple streams per user cases. Key to our analysis is the development and analysis of a new MMSE-DUAL algorithm, which connects the MMSE-SOCP and the UDD algorithm. Our numerical experiments show that 1) all these algorithms can almost always reach points with the same objective value irrespective of initialization and 2) the MMSE-SOCP/MMSE-DUAL algorithm works well while the UDD algorithm may fail with an infeasible initialization. [ABSTRACT FROM AUTHOR]

This paper considers the joint design of a multiple-input multiple-output (MIMO) radar with co-located antennas and a MIMO communication system. The degrees of freedom under the designer's control are the waveforms transmitted by the radar transmit array, the filter at the radar array and the code-book employed by the communication system to form its space-time code matrix. Two formulations of the spectrum sharing problem are proposed. First, the design problem is stated as the constrained maximization of the signal-to-interference-plus-noise ratio at the radar receiver, where interference is due to both clutter and the coexistence structure, and the constraints concern both the similarity with a standard radar waveform and the rate achievable by the communication system, on top of those on the transmit energy. The resulting problem is nonconvex, but a reduced-complexity iterative algorithm, based on iterative alternating maximization of three suitably designed subproblems, is proposed and analyzed. In addition, the constrained maximization of the communication rate is also investigated. The convergence of all the devised algorithms is guaranteed. Finally, a thorough performance assessment is presented, aimed at showing the merits of the proposed approach. [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]

This paper is concerned with the game theoretical approach in designing the multiuser downlink beamformers in multicell systems. Sharing the same physical resource, the base-station of each cell wishes to minimize its transmit power subject to a set of target signal-to-interference-plus-noise ratios (SINRs) at the multiple users in the cell. In this context, at first, the paper considers a strategic noncooperative game (SNG) where each base-station greedily determines its optimal downlink beamformer strategy in a distributed manner, without any coordination between the cells. Via the game theory framework, it is shown that this game belongs to the framework of standard functions. The conditions guaranteeing the existence and uniqueness of a Nash Equilibrium (NE) in this competitive design are subsequently examined. The paper then makes a revisit to the fully coordinated design in multicell downlink beamforming, where the optimal beamformers are jointly designed between the base-stations. A comparison between the competitive and coordinated designs shows the benefits of applying the former over the latter in terms of each design's distributed implementation. Finally, in order to improve the efficiency of the NE in the competitive design, the paper considers a more cooperative game through a pricing mechanism. The pricing consideration enables a base-station to steer its beamformers in a more cooperative manner, which ultimately limits the interference induced to other cells. The study on the existence and uniqueness of the new game's NE is then given. The paper also presents a condition on the pricing factors that allow the new NE point to approach the performance established by the coordinated design, while retaining the distributed nature of the multicell game. [ABSTRACT FROM PUBLISHER]

In this paper, we aim to solve the multiuser multi-input multi-output (MIMO) downlink beamforming problem where one multi-antenna base station broadcasts data to many users. Each user is assigned multiple data streams and has multiple antennas at its receiver. Efficient solutions to the joint transmit-receive beamforming and power allocation problem based on iterative methods are proposed. We adopt the group maximum signal-to-interference-plus-noise-ratio (SINR) filter bank (GSINR-FB) as our beamformer which exploits receiver diversity through cooperation between the data streams of a user. The data streams for each user are subject to an average SINR constraint, which has many important applications in wireless communication systems and serves as a good metric to measure the quality of service (QoS). The GSINR-FB also optimizes the average SINR of its output. Based on the GSINR-FB beamformer, we find an SINR balancing structure for optimal power allocation which simplifies the complicated power allocation problem to a linear one. Simulation results verify the superiority of the proposed algorithms over previous works with approximately the same complexity. [ABSTRACT FROM PUBLISHER]

In this paper, we examine single user and multiuser multiple-input multiple-output (MIMO) beamforming networks with channel distribution information (CDI). Since CDI changes infrequently compared to channel state information (CSI), algorithms based on CDI can achieve significant savings in feedback compared to algorithms based on CSI. With CDI, we can only guarantee quality of service for a specified outage probability in the network. Assuming correlated Rayleigh fading on all the links, we derive a closed-form expression for the outage probability. Then, using this expression, we derive algorithms for joint transmit/receive beamforming and power control to minimize the weighted sum power in the network while guaranteeing these outage probabilities. For both single-user and multiuser MIMO scenarios, we present optimal algorithms under the Kronecker model assumption, and we present near-optimal algorithms assuming general correlation structures on the links. We then show that using these algorithms based on CDI, if we are willing to accept given outages on the links, we can achieve comparable power usage in the network relative to algorithms based on CSI. [ABSTRACT FROM AUTHOR]

This paper considers the jointly optimal pilot and data power allocation in single-cell uplink massive multiple-input-multiple-output systems. Using the spectral efficiency (SE) as performance metric and setting a total energy budget per coherence interval, the power control is formulated as optimization problems for two different objective functions: the weighted minimum SE among the users and the weighted sum SE. A closed form solution for the optimal length of the pilot sequence is derived. The optimal power control policy for the former problem is found by solving a simple equation with a single variable. Utilizing the special structure arising from imperfect channel estimation, a convex reformulation is found to solve the latter problem to global optimality in polynomial time. The gain of the optimal joint power control is theoretically justified, and is proved to be large in the low-SNR regime. Simulation results also show the advantage of optimizing the power control over both pilot and data power, as compared to the cases of using full power and of only optimizing the data powers as done in previous work. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate a beamforming (BF) scheme for a dual-hop multiple-input multiple-output (MIMO) amplify-and-forward (AF) relay network in the presence of multiple cochannel interferences (CCIs) at both the relay and the destination nodes. In order to maximize the signal-to-interference-plus-noise ratio at the destination node, the optimal BF weights for the relay network in the presence of CCIs are derived. A tight lower bound on the outage probability (OP) is derived in a closed form by assuming that multiple CCIs have equal power at the relay and destination nodes. Through computer simulations, it is shown that the proposed BF scheme can outperform the existing schemes. Furthermore, the impacts of antenna configurations, CCIs, and power allocation on the outage performance of the MIMO AF relay systems have been demonstrated with simulations. [ABSTRACT FROM PUBLISHER]

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]

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]

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]

In this paper, we consider linear MIMO transceiver design for a cellular two-way amplify-and-forward relaying system consisting of a single multiantenna base station, a single multiantenna relay station, and multiple multiantenna mobile stations (MSs). Due to the two-way transmission, the MSs could suffer from tremendous multiuser interference. We apply an interference management model exploiting signal space alignment and propose a transceiver design algorithm, which allows for alleviating the loss in spectral efficiency due to half-duplex operation and providing flexible performance optimization accounting for each user's quality of service priorities. Numerical comparisons to conventional two-way relaying schemes based on bidirectional channel inversion and spatial division multiple access-only processing show that the proposed scheme achieves superior error rate and average data rate performance. [ABSTRACT FROM PUBLISHER]

In this paper, we study K-user multiple-input multiple-output (MIMO) interference channels (IC) in the presence of antenna correlations. Although interference alignment (IA) achieves the maximum multiplexing gain, i.e., degrees of freedom (DOF), the actual performance is degraded due to ill-conditioned channels caused by the antenna correlations. To enhance the overall system performance, we thus consider a transmission mode selection scheme which adaptively determines the number of data streams at each transmitter-receiver pair in a distributed fashion using local channel state information (CSI). A filter update process is exploited in order to estimate the performance regarding each transmission mode successively by considering the actual channel conditions as well as signal-to-noise ratio (SNR). Simulation results show that our mode selection scheme enhances the sum rate performance compared to the conventional full data stream transmissions in spatially correlated MIMO IC. [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]

This paper studies the max-min weighted signal-to-interference-plus-noise ratio (SINR) problem in the multiple- input-multiple-output (MIMO) downlink, where multiple users are weighted according to priority and are subject to a weighted-sum-power constraint. First, we study the multiple-input-single-output (MISO) and single-input-multiple- output (SIMO) problems using nonlinear Perron–Frobenius theory. As a by-product, we solve the open problem of convergence for a previously proposed MISO algorithm by Wiesel, Eldar, and Shamai in 2006. Furthermore, we unify our analysis with respect to the previous alternate optimization algorithm proposed by Tan, Chiang, and Srikant in 2009, by showing that our MISO result can, in fact, be derived from their algorithm. Next, we combine our MISO and SIMO results into an algorithm for the MIMO problem. We show that our proposed algorithm is optimal when the channels are rank-one, or when the network is operating in the low signal-to-noise ratio (SNR) region. Finally, we prove the parametric continuity of the MIMO problem in the power constraint, and we use this insight to propose a heuristic initialization strategy for improving the performance of our (generally) suboptimal MIMO algorithm. The proposed initialization strategy exhibits improved performance over random initialization. [ABSTRACT FROM PUBLISHER]

We perform an asymptotic study of the performance of filter bank multicarrier in the context of massive multi-input multi-output. We show that the effects of channel distortions, i.e., intersymbol interference and intercarrier interference, do not vanish as the base station (BS) array size increases. As a result, the signal-to-interference-plus-noise ratio (SINR) cannot grow unboundedly by increasing the number of BS antennas, and is upper bounded by a certain deterministic value. We show that this phenomenon is a result of the correlation between the multiantenna combining tap values and the channel impulse responses between the mobile terminals and the BS antennas. To resolve this problem, we introduce an efficient equalization method that removes this correlation, enabling us to achieve arbitrarily large SINR values by increasing the number of BS antennas. We perform a thorough analysis of the proposed system and find analytical expressions for both equalizer coefficients and the respective SINR. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a max–min weighted signal-to-interference-plus-noise-ratio (SINR) problem in a full-duplex multiuser multiple-input-multiple-output system, where a full-duplex-capable base station (BS) equipped with multiple antennas communicates with multiple half-duplex downlink and uplink users under the same system resources. Specifically, we consider a practical scenario where the downlink minimum weighted SINR is maximized under specific SINR constraints for uplink users. Moreover, the optimization is conducted by jointly considering the BS transmit power, the transmit power of uplink users, and BS transmit and receive beamforming. This optimization problem is, therefore, subject to multiple uplink SINR constraints and multiple transmit power constraints. Due to the SINR constraints, negative matrix components arise and hence the optimization problem cannot be directly solved by the standard approach, i.e., Perron–Frobenius theory. We have provided an explicit iterative scheme to solve this joint optimization problem with a strict proof. The proposed algorithm is proved to converge to Karush–Kuhn–Tucker points. Simulation results show that our proposed algorithm has a fast convergence rate and leads to a better performance compared with other optimization techniques that do not jointly consider all parameters. [ABSTRACT FROM PUBLISHER]

Second order cone (SOC) restrictions have been widely employed in the design of wireless communication systems to recast precoding optimizations in convex form. This paper highlights the flexibility of this framework to handle various transmit power and interference limitations also in the multistream multiple-input multiple-output broadcast channel with imperfect channel state information at the transmitter. In particular, we propose an iterative solution for mean square error (MSE) balancing with such constraints via the Lagrangian dual formulation. The MSE balancing criteria, contrary to signal-to-interference plus-noise ratio and rate criteria, exhibit an interesting property when considering different targets for the receivers. Users with low target values are strictly prioritized in comparison to users with large MSE targets that can even be switched off. Therefore, only a subset of the receivers is served at the solution with stringent limitations for the precoders. An example of such stringent limitations are small power constraints and probabilistic interference temperature constraints that set a measurable threshold on the generated interference in a cognitive radio system. We provide four different approaches to approximate these probabilistic constraints with deterministic SOC constraints, and compare their accuracy. [ABSTRACT FROM PUBLISHER]

In this paper, superimposed pilots are introduced as an alternative to time-multiplexed pilot and data symbols for mitigating pilot contamination in massive multiple-input multiple-output (MIMO) systems. We propose a non-iterative scheme for uplink channel estimation based on superimposed pilots and derive an expression for the uplink signal-to-interference-plus-noise ratio (SINR) at the output of a matched filter employing this channel estimate. Based on this expression, we observe that power control is essential when superimposed pilots are employed. Moreover, the quality of the channel estimate can be improved by reducing the interference that results from transmitting data alongside the pilots, and an intuitive iterative data-aided scheme that reduces this component of interference is also proposed. Approximate expressions for the uplink SINR are provided for the iterative data-aided method as well. In addition, we show that a hybrid system with users utilizing both time-multiplexed and superimposed pilots is superior to an optimally designed system that employs only time-multiplexed pilots, even when the non-iterative channel estimate is used to build the detector and precoder. We also describe a simple approach to implement this hybrid system by minimizing the overall inter- and intracell interference. Numerical simulations demonstrating the performance of the proposed channel estimation schemes and the superiority of the hybrid system are also provided. [ABSTRACT FROM PUBLISHER]

The characterization of the global maximum of energy efficiency (EE) problems in wireless networks is a challenging problem due to their nonconvex nature in interference channels. The aim of this paper is to develop a new and general framework to achieve globally optimal solutions. First, the hidden monotonic structure of the most common EE maximization problems is exploited jointly with fractional programming theory to obtain globally optimal solutions with exponential complexity in the number of network links. To overcome the high complexity, we also propose a framework to compute suboptimal power control strategies with affordable complexity. This is achieved by merging fractional programming and sequential optimization. The proposed monotonic framework is used to shed light on the ultimate performance of wireless networks in terms of EE and also to benchmark the performance of the lower-complexity framework based on sequential programming. Numerical evidence is provided to show that the sequential fractional programming framework achieves global optimality in several practical communication scenarios. [ABSTRACT FROM PUBLISHER]

This work focuses on the downlink and uplink of large-scale single-cell multiuser multiple-input multiple-output systems in which the base station (BS) endowed with $M$ antennas communicates with $K$ single-antenna user equipments (UEs). Particularly, we aim at reducing the complexity of the linear precoder and receiver that maximize the minimum signal-to-interference-plus-noise ratio (SINR) subject to a given power constraint. To this end, we consider the asymptotic regime in which $M$ and $K$ grow large with a given ratio. Tools from random matrix theory (RMT) are then used to compute, in closed form, accurate approximations for the parameters of the optimal precoder and receiver, when imperfect channel state information (modeled by the generic Gauss–Markov formulation form) is available at the BS. The asymptotic analysis allows us to derive the asymptotically optimal linear precoder and receiver that are characterized by a lower complexity (due to the dependence on the large scale components of the channel) and, possibly, by a better resilience to imperfect channel state information. However, the implementation of both is still challenging as it requires fast inversions of large matrices in every coherence period. To overcome this issue, we apply the truncated polynomial expansion (TPE) technique to the precoding and receiving vector of each UE and make use of RMT to determine the optimal weighting coefficients on a per-UE basis that asymptotically solve the max–min SINR problem. Numerical results are used to validate the asymptotic analysis in the finite system regime and to show that the proposed TPE transceivers efficiently mimic the optimal ones, while requiring much lower computational complexity. [ABSTRACT FROM PUBLISHER]

Concurrent presence of inter-cell and intra-cell interferences constitutes a major impediment to reliable downlink transmission in multi-cell multiuser networks. Harnessing such interferences largely hinges on two levels of information exchange in the network: one from the users to the base-stations (feedback) and the other one among the base-stations (cooperation). We demonstrate that exchanging a finite number of bits across the network, in the form of feedback and cooperation, is adequate for achieving the optimal capacity scaling. We also show that the average level of information exchange is independent of the number of users in the network. This level of information exchange is considerably less than that required by the existing coordination strategies which necessitate exchanging infinite bits across the network for achieving the optimal sum-rate capacity scaling. The results provided rely on a constructive proof. [ABSTRACT FROM AUTHOR]

We consider a downlink multi-cell multiple-input multiple-output (MIMO) interference broadcast channel (IBC) using orthogonal frequency division multiplexing (OFDM) with multiple users contending for space-frequency resources in a given scheduling instant. The problem is to design precoders efficiently to minimize the number of backlogged packets queuing in the coordinating base stations (BSs). Conventionally, the queue weighted sum rate maximization (Q-WSRM) formulation with the number of backlogged packets as the corresponding weights is used to design the precoders. In contrast, we propose joint space-frequency resource allocation (JSFRA) formulation, in which the precoders are designed jointly across the space-frequency resources for all users by minimizing the total number of backlogged packets in each transmission instant, thereby performing user scheduling implicitly. Since the problem is nonconvex, we use the combination of successive convex approximation (SCA) and alternating optimization (AO) to handle nonconvex constraints in the JSFRA formulation. In the first method, we approximate the signal-to-interference-plus-noise ratio (SINR) by convex relaxations, while in the second approach, the equivalence between the SINR and the mean squared error (MSE) is exploited. We then discuss the distributed approaches for the centralized algorithms using primal decomposition and alternating directions method of multipliers. Finally, we propose a more practical iterative precoder design by solving the Karush–Kuhn–Tucker expressions for the MSE reformulation that requires minimal information exchange for each update. Numerical results are used to compare the proposed algorithms to the existing solutions. [ABSTRACT FROM AUTHOR]

We consider the problem of waveform design for Multiple-Input Multiple-Output (MIMO) radar in the presence of signal-dependent interference embedded in white Gaussian disturbance. We present two sequential optimization procedures to maximize the Signal to Interference plus Noise Ratio (SINR), accounting for a constant modulus constraint as well as a similarity constraint involving a known radar waveform with some desired properties (e.g., in terms of pulse compression and ambiguity). The presented sequential optimization algorithms, based on a relaxation method, yield solutions with good accuracy. Their computational complexity is linear in the number of iterations and trials in the randomized procedure and polynomial in the receive filter length. Finally, we evaluate the proposed techniques, by considering their SINR performance, beam pattern as well as pulse compression property, via numerical simulations. [ABSTRACT FROM AUTHOR]

The problem of weighted sum-rate maximization (WSRMax) in multicell downlink multiple-input single-output (MISO) systems is considered. The problem is known to be NP-hard. We propose a method, based on branch and bound technique, which solves globally the nonconvex WSRMax problem with an optimality certificate. Specifically, the algorithm computes a sequence of asymptotically tight upper and lower bounds and it terminates when the difference between them falls below a pre-specified tolerance. Novel bounding techniques via conic optimization are introduced and their efficiency is demonstrated by numerical simulations. The proposed method can be used to provide performance benchmarks by back-substituting it into many existing network design problems which relies on WSRMax problem. The method proposed here can be easily extended to maximize any system performance metric that can be expressed as a Lipschitz continuous and increasing function of signal-to-interference-plus-noise ratio. [ABSTRACT FROM PUBLISHER]

Multiple–input multiple-output (MIMO) beamforming (MBF) can greatly increase the signal gain and reduce the effects of multipath fading for cell-edge users. The objective of this work is to investigate the impact of co-channel unequal-power MIMO interferers on an MBF desired receiver under realistic propagation conditions, including path-loss and Rayleigh fading. Two major contributions are made in this work: i) for the co-channel interference term in the signal-to-interference ratio (SIR) expressions, a closed-form expression of the probability density function (PDF) is derived; ii) for the desired signal term, a simple approximation is proposed. Moreover, closed-form PDF expressions for the SIRs are obtained for some special cases. Simulation results verify the validity of the theoretical analyses. [ABSTRACT FROM PUBLISHER]