Reconfigurable Intelligent surface (RIS) has emerged as a candidate technology for enhancing coverage in millimeter wave wireless networks at a lower energy and cost footprint. RIS has several antenna elements that can each be configured to reflect impinging electromagnetic waves after imparting a chosen phase shift with possibly some amplitude attenuation (a.k.a. chosen reflection coefficient). Over the canonical RIS-enabled communications scenario, an optimal choice of reflection coefficients (or optimal RIS pattern) can be efficiently determined for an ideal unit-amplitude unconstrained phase alphabet. However, for most practical RIS that entail finite alphabets with amplitude imbalance and per-group-of-elements control, efficiently determining optimal patterns remain open problems. In this paper we resolve two such open problems by designing optimal and efficient pattern determination algorithms for binary and quaternary alphabets. We show that our algorithms can noticeably improve over the state-of-art conventional heuristic, especially in the presence of high amplitude imbalance and more restrictive per-group control. The designed algorithms also yield companion sets, which we show offer very significant advantages in RIS pattern selection under interference limit (leakage suppression) constraints.

Traditionally intelligent reflecting surface (IRS) has been viewed as an ideal passive phase shifter with no mutually coupled elements. For this assumption to hold, IRS inter element spacing needs to be at least Nyquist spacing, thereby increasing overall aperture size with number of IRS elements. In this paper, we consider reverse problem wherein the aperture size is fixed. Here, packing more elements in a given space to enhance system performance results in mutual coupling and correlated fading. We consider an IRS aided communication network for a point-to-point (P2P) MIMO system with closely spaced transmit, receive and IRS antenna arrays. We derive channel models for various propagation scenarios and validate the theoretical expressions with actual antenna simulations. We allow for an arbitrary array configuration made from a material with finite conductivity. We show that such a communication network can be expressed as an equivalent MIMO system whose overall channel matrix is a non-linear function of IRS loading. The capacity expression is also a non-convex function of loads attached to IRS. We propose a proximal distance based algorithm to optimize capacity and analyze the effect of coupling aware optimization. Our results indicate capacity benefits on orders of 2-2.5 times for tightly coupled arrays when well designed optimization is performed using more accurate communication model that accounts for mutual coupling as opposed to naive methods that ignore coupling.

Intelligent reflecting surface (IRS) has been promoted as a leading candidate technology for enhancing coverage as well as spectral and energy efficiencies in future wireless communication networks. An IRS comprises of a multitude of low-cost antenna elements that can be programmed to influence impinging electromagnetic waves in a desirable manner. However, performance enhancements are conditional upon availability of accurate channel estimates, which are especially hard to obtain for a passive IRS that lacks baseband processing capability. In this work, we propose novel channel reconstruction formulations for IRS-assisted communications where the IRS panel has only passive elements and the intended receiver provides just signal strength feedback reports. Our formulations simultaneously exploit low rank property and sparse beam-space representation of the unknown effective channel, and can accommodate subspace side-information whenever available. We design efficient proximal distance based algorithms to reconstruct the effective channel and demonstrate their superior performance via results generated using the open-source SimRIS platform.

We consider the uplink of a cellular network wherein each base-station (BS) simultaneously communicates with multiple users. Each BS is equipped with a large number of antenna elements and a limited number of RF chains. Each RF chain (on each BS) houses an analog-to-digital converter (ADC) whose bit resolution can be configured. We seek to jointly optimize user transmit powers and ADC bit resolutions in order to maximize the network spectral efficiency, subject to power budget constraints at each user and BS. This joint optimization becomes intractable if we insist on exactly modeling the nonlinear quantization operation performed at each ADC. On the other hand, simplistic approximations made for tractability need not be meaningful. In this work, we propose a methodology based on a constrained worst-case quantization noise formulation, along with another one that assumes quantization noise covariance to be diagonal. In each case, using a series of effective mathematical re-formulations we are able to express our problem in a form that is well-suited for alternating optimization, in which each sub-problem can be efficiently and optimally solved. Through a detailed performance analysis, we demonstrate that the optimized transmit powers and bit resolutions can yield very significant improvements in achievable spectral efficiency, at a reduced sum power consumption and an affordable complexity.

For low-resolution analog-to-digital converter (ADC) systems, only high-complexity receive antenna selection has been developed and transmit antenna selection has been limited to a single antenna selection in prior work. In this paper, we propose low-complexity receive antenna selection algorithms and analyze transmit antenna selection by considering antenna selection at a base station with large antenna arrays and low-resolution ADCs. For downlink antenna selection, we show a selection criterion with zero-forcing precoding equivalent to a perfect quantization system; sum rate increases with number of selected antennas; derivation of the sum rate loss function from using a antenna subset; and sum rate loss reaches a maximum at a point of total transmit power and decreases beyond that point to converge to zero. For wideband orthogonal-frequency-division-multiplexing (OFDM) systems, our results hold when entire subcarriers share a common subset of antennas. For uplink antenna selection, we generalize a greedy antenna selection criterion; propose a quantization-aware fast antenna selection algorithm using the criterion; and derive a lower bound on sum rate achieved by the proposed algorithm. For wideband OFDM systems, we extend our algorithm and derive a lower bound on its sum rate. Simulation results validate theoretical analyses and show increases in sum rate over conventional algorithms.

We consider the problem of channel reconstruction in FDD networks wherein each base-station (BS) employs a transmit array comprising of a multitude of antenna elements and simultaneously serves multiple different users (a.k.a. Massive MIMO networks). In channel reconstruction the BS seeks to reconstruct the instantaneous true channel seen by each user as accurately as possible, which is both critical and challenging. It is challenging since the true high-dimensional instantaneous channel must be recovered by the BS from quantized low-dimensional observations while fully exploiting other available side information. It is a critical problem in that the viability of FDD Massive MIMO directly depends on whether an effective implementable reconstruction scheme can be found. We propose a unified framework for channel reconstruction that combines instantaneous quantized feedback, long-term statistical subspace information as well other auxiliary estimates. Interestingly, the resulting problem is an NP-hard non-convex quadratically constrained quadratic programming (QCQP) problem that has received wide attention in diverse areas but hitherto lacks an efficient algorithm that meets our required stringent complexity limits. We propose a novel approach based on the K-best methodology that is well suited for implementation and demonstrate that it offers a superior performance and complexity tradeoff.

We consider a long-standing open problem pertaining to scheduling over a wideband multi-user downlink. In this problem a base-station (BS) must assign multiple subbands to its served users such that a weighted sum rate metric is maximized subject to sum power and cardinality constraints. On each subband multiple users can simultaneously be scheduled. Such scheduling is complicated by the fact that the rate achieved by a user on any subband assigned to it depends not only on its own channel condition, but on the set of other users co-scheduled on that subband as well. The latter dependence is via the transmission scheme adopted by the BS in order to simultaneously serve multiple users on the same subband. This problem has received wide attention for over a decade and while numerous heuristics have been designed, there is no known algorithm that offers provable constant-factor worst-case guarantee. In this paper we obtain an important result which demonstrates that when the transmitter employs capacity-optimal dirty paper coding, constant-factor approximation guarantee can be achieved via simple algorithms. Indeed, we show that for a wideband scheduling problem in which a permissible set of user groups is specified as input, a simple deterministic algorithm yields a constant-factor approximation guarantee. Further, for the generalized case where any user group subject to a cardinality constraint is permissible, a greedy algorithm yields a constant-factor guarantee over certain practically relevant regimes.

In this paper we formulate a general link packing problem for mmWave networks. Each link is a 4-tuple determined by the choice of receiving user, transmitting access point, transmit beamforming vector and receive beamforming vector. The problem seeks to optimize the weighted sum over active links, where each link is allowed to have any arbitrarily chosen weight or priority and an active link must satisfy a minimum link quality threshold. Our formulation models a practical scenario in which blockages due to arbitrarily placed obstacles in the propagation environment are allowed to occur, where we note that mmWave transmissions are extremely susceptible to blockages. This is a key departure from the classical link packing problem where only the signal attenuation based on propagation distance is modeled. We exploit the sparsity induced by the directional nature of propagation due to beamforming, limited diffraction and the significant signal attenuation due to high path, penetration losses. We propose a novel technique that exploits this sparsity and considers an alternate formulation which is a column-sparse binary packing problem. This alternate formulation is in general conservative and we derive sufficient conditions under which it is equivalent to the original problem. We construct an efficient iterative algorithm and show that it outperforms other heuristics and guarantees a constant factor approximation for input instances that are likely to occur in mmWave networks.

We consider network utility maximization problems over heterogeneous cellular networks (HetNets) that permit dual connectivity. Dual connectivity (DC) is a feature that targets emerging practical HetNet deployments that will comprise of non-ideal (higher latency) connections between transmission nodes, and has been recently introduced to the LTE-Advanced standard. DC allows for a user to be simultaneously served by a macro node as well as one other (typically micro or pico) node and requires relatively coarser level coordination among serving nodes. For such a DC enabled HetNet we comprehensively analyze the problem of determining an optimal user association that maximizes the weighted sum rate system utility subject to per-user rate constraints, over all feasible associations. Here, in any feasible association each user can be associated with (i.e., configured to receive data from) any one macro node (in a given set of macro nodes) and any one pico node that lies in the chosen macro node's coverage area. We show that, remarkably, this problem can be cast as a non-monotone submodular set function maximization problem, which allows us to construct a constant-factor approximation algorithm. We then consider the proportional fairness (PF) system utility and characterize the PF optimal resource allocation. This enables us to construct an efficient algorithm to determine an association that is optimal up-to an additive constant. We then validate the performance of our algorithms via numerical results.

We consider the problem of downlink scheduling in an LTE network powered by energy harvesting devices. We formulate optimization problems that seek to optimize two popular energy efficiency metrics subject to mandatory LTE network constraints along with energy harvesting causality constraints. We identify a key sub-problem pertaining to maximizing the weighted sum rate that is common for both optimization problems, and is also of independent interest. We show that the latter sub-problem can be reformulated as a constrained submodular set function maximization problem. This enables us to design constant-factor approximation algorithms for maximizing the weighted sum rate as well as the two energy efficiency metrics over an energy harvesting LTE downlink. Our proposed algorithms are simple to implement and offer superior performance.

We analyze Coordinated Multi-Point (CoMP) transmission and reception over heterogeneous wireless networks (HetNets). Our focus is on the design of a practical algorithm for joint resource allocation (joint scheduling) in each HetNet cluster. We present one such algorithm together with its detailed analysis and evaluation under two different approaches for obtaining and utilizing channel feedback from the users. The considered approaches as well as the evaluation methodology are fully compliant with the 3GPP LTE standard. Our evaluations reveal several important insights that are essential to achieve significant CoMP gains over HetNets.

We consider the problem of maximizing the alphafairness utility over the downlink of a heterogeneous wireless network (HetNet) by jointly optimizing the association of users to transmission points (TPs) and the activation fractions of all TPs. Activation fraction of each TP is the fraction of the frame duration for which it is active, and together these fractions influence the interference seen in the network. To address this joint optimization problem we adopt an approach wherein the activation fractions and the user associations are optimized in an alternating manner. The sub-problem of determining the optimal activation fractions is solved using an auxiliary function method that we show is provably convergent and is amenable to distributed implementation. On the other hand, the sub-problem of determining the user association is solved via a simple combinatorial algorithm. Meaningful performance guarantees are derived and a distributed variant offering identical guarantees is also proposed. The significant benefits of using the proposed algorithms are then demonstrated via realistic simulations.

We consider the problem of maximizing the proportional fairness (PF) utility over heterogeneous wireless networks (HetNets) by jointly exploiting cell dormancy (cell ON-OFF) - wherein some transmission nodes from a set of interest are made inactive - and load balancing (user association) - wherein users are associated to the active transmission nodes in that set with each user being associated with only one node. We establish that this joint optimization problem which is a discrete optimization problem, is strongly NP-hard. Nevertheless, we prove that the load balancing sub-problem for any given set of active transmission nodes is not NP-hard but instead can be re-formulated as an asymmetric assignment problem and hence can be optimally solved in an efficient manner. In addition, we propose another lower complexity greedy algorithm for the load balancing sub-problem which offers a near-optimal average-case performance and a worst-case performance guarantee. We then propose a low-complexity algorithm for the joint optimization problem. Simulations over an example LTE HetNet topology reveal the superior performance of the proposed algorithms and underscore the significant benefits of jointly exploiting cell dormancy and load balancing.

In this paper, we consider the design of user grouping and scheduling for large-scale multiple-input multiple-output (MIMO) frequency-division-duplexing (FDD) systems. Based on a recently proposed two-stage precoding framework, we first propose an improved K-means user grouping scheme which allocates the users to different pre-beamforming groups using the second-order channel statistics, and then a user grouping scheme that considers both load balancing and precoding design. After user groups are so determined, we present a dynamic user scheduling scheme where second-stage precoding is designed based on instantaneous channel conditions. We demonstrate the efficacy of the proposed schemes through simulations.

We consider the problem of maximizing the proportional fairness (PF) utility over heterogeneous cellular networks (HetNets) by jointly exploiting partial muting-wherein the high power macro transmission node in a set of transmission nodes of interest can be made inactive for a specified fraction of the available time-frequency resource- and load balancing (user association)-wherein each user can be associated to (i.e., configured to receive data from) any one transmission node in that set. This joint problem is a mixed optimization problem in which there are finitely many choices for the muting fraction and the user association, but there are a continuum of ways in which each transmission node can partition the available resource among the users associated to it. While this mixed optimization problem seems intractable, we systematically construct efficient approximation algorithms, one of which is approximately optimal in that it yields a system utility that is no less than the optimal utility minus a constant. In addition, we propose another lower complexity greedy algorithm and proceed to demonstrate the superior performance of the proposed algorithms via simulations over an example LTE HetNet topology.

We consider the problem of maximizing the proportional fairness (PF) system utility over heterogeneous wireless networks (HetNets) by jointly exploiting cell dormancy (cell ON-OFF)-wherein some transmission points from a set of interest can be made inactive-and load balancing (user association)-wherein users are associated to the active transmission points in that set, with each user being associated with only one point. We establish that this joint optimization problem, which is a discrete optimization problem, is NP-hard. Nevertheless, we prove that the load balancing subproblem for any given set of active transmission points is not NP-hard but instead can be reformulated as an asymmetric assignment problem and hence can be optimally solved in an efficient manner. In addition, we show that some generalized load balancing problems that incorporate multiuser diversity gains can also be optimally and efficiently solved. We propose another lower complexity greedy algorithm for the load balancing subproblem that offers a worst-case performance guarantee and describe a simple way to approximately realize a given input user association via biasing factors. We then derive low-complexity algorithms for the joint optimization problem, including one based on a successive approximation method that has hitherto been used for continuous nonconvex optimization problems. Simulations over an example Long-Term Evolution HetNet topology reveal the superior performance of the proposed algorithms and underscore the significant benefits of jointly exploiting cell dormancy and load balancing.

In this paper, we consider resource allocation in the 3GPP Long Term Evolution (LTE) cellular uplink (UL), which will be the most widely deployed next generation cellular uplink. The key features of the 3GPP LTE uplink are that it is based on a modified form of the orthogonal frequency division multiplexing-based multiple access (OFDMA), which enables channel dependent frequency selective scheduling, and that it allows for multiuser (MU) scheduling wherein multiple users can be assigned the same time-frequency resource. In addition to the considerable spectral efficiency improvements that are possible by exploiting these two features, the LTE UL allows for transmit antenna selection together with the possibility to employ advanced receivers at the base-station, which promise further gains. However, several practical constraints that seek to maintain a low signaling overhead are also imposed. In this paper, we show that the resulting resource allocation problem is APX-hard and then propose a local ratio test (LRT)-based constant-factor polynomial-time approximation algorithm. We then propose two enhancements to this algorithm as well as a sequential LRT-based MU scheduling algorithm that offers a constant-factor approximation and is another useful choice in the complexity versus performance tradeoff. Further, user preselection, wherein a smaller pool of good users is preselected and a sophisticated scheduling algorithm is then employed on the selected pool, is also examined. We suggest several such user preselection algorithms, some of which are shown to offer constant-factor approximations to the preselection problem. Detailed evaluations reveal that the proposed algorithms and their enhancements offer significant gains.

Heterogeneous wireless networks are formed when a set of disparate transmission points serve a multitude of users over an available spectrum. In order to enable efficient resource allocation over such networks, the set of transmission points is partitioned into multiple clusters and each cluster is assigned a set of users that it must serve. Our contribution in this paper is the design of an approximation algorithm for the resource allocation in each cluster. We show that this resource allocation problem is strongly NP-hard and that our algorithm yields a constant factor approximation, for a fixed cluster size. Our evaluations over a realistic heterogeneous network model show that significant gains can be realized by our algorithm, provided the feedback available from the users is fully exploited.

We consider Multi-User MIMO (MU-MIMO) scheduling in the 3GPP LTE-Advanced (3GPP LTE-A) cellular uplink. The 3GPP LTE-A uplink allows for precoded multi-stream (precoded MIMO) transmission from each scheduled user and also allows flexible multi-user (MU) scheduling wherein multiple users can be assigned the same time-frequency resource. However, exploiting these features is made challenging by certain practical constraints that have been imposed in order to maintain a low signaling overhead. We show that while the scheduling problem in the 3GPP LTE-A cellular uplink is NP-hard, it can be formulated as the maximization of a submodular set function subject to one matroid and multiple knapsack constraints. We then propose constant-factor polynomial-time approximation algorithms and demonstrate their superior performance via simulations.

This paper considers the design of linear transmit precoding schemes to minimize the weighted sum delay metric over a K-user multi-antenna multicast channel. Limited by the rank and power constraints, the precoding matrices are designed under two interesting scenarios. The first scenario assumes the availability of pilots that can be precoded, using which the transmitter can convey any choice of transmit precoders to the users. Consequently, the sought transmit precoders can be any complex-valued matrices subject to given rank (dimensionality) and power (norm) constraints. A provably convergent cyclic alternating ascent based algorithm is proposed for a relaxed version of the problem, and is shown to attain at least a stationary point. Assuming that no such pilots are available, the second scenario constrains the transmit precoders to lie in a finite codebook. A concatenation based approach is adopted for constructing higher rank precoding matrices, which can facilitate the precoder search and allow for efficient signaling. A simple deterministic algorithm is proposed which involves maximizing a submodular rate function per step, and yields a worst-case performance guarantee.

In this paper, we consider the downlink (DL) multiuser (MU) multi-input-multi-output (MIMO) scheduling for the fourth generation LTE Advanced (LTE-A) cellular networks. The DL MU-MIMO scheduling can be posed as an optimization problem to maximize the weighted sum rate subject to several practical constraints, such as a per-user rank constraint. Due to these constraints finding the optimal solution becomes an NP-hard problem. Consequently, we propose an efficient albeit sub-optimal rank balancing approach that enforces the peruser rank constraint and present several rank balancing based scheduling algorithms. We show that the proposed algorithms are guaranteed to yield constant-factor approximations. Simulation results demonstrate that the proposed rank balancing based scheduling algorithms outperform other competing schemes over various relevant system scenarios.

In LTE and LTE-Advanced uplink, the interference power in a cell depends on the user scheduling and power assignment in neighboring cells. As a result, it is hard to accurately estimate the signal-to-interference-plus-noise ratio (SINR) values and make the right MCS (Modulation and Coding Scheme) selection. To address this challenge, we propose open-loop and closed-loop power control schemes to reduce both the average and the variance of the interference power in order to improve the system performance. It is shown that compared to the existing fractional power control (FPC) in LTE, the proposed schemes improve the cell-average throughput by 7-8% while maintaining the same cell-edge throughput in networks with low penetration loss, and improve the cell-average throughput by 4-6% and the cell-edge throughput by 15-23% in networks with high penetration loss.

This paper studies the design of linear precoders via instantaneous rate maximization over a K-user multicast channel, wherein multiple antennas are present at the transmitter as well as at all the receivers. We first consider the scenario wherein the linear precoder can be any complex valued matrix subject to rank and power constraints. Recognizing the resulting optimization problem to be NP-hard, we propose a cyclic alternating ascent based algorithm and establish its convergence to a stationary point. Simulation results reveal that our proposed algorithm considerably outperforms known competing solutions. We then consider a scenario in which the linear precoder can be formed by selecting and concatenating codewords from a finite codebook of precoding matrices, subject to rank and power constraints. We show that under this scenario, the instantaneous rate maximization problem is equivalent to a robust submodular maximization problem which is strongly NP-hard. We then propose a deterministic approximation algorithm and show that it yields a bicriteria approximation.

Scheduling plays a vital role in LTE downlink systems with Multiple Input and Multiple Output (MIMO) antennas. We consider the MIMO downlink scheduling problem at the base station (BS) in LTE networks under several practical constraints mandated by the 3GPP standards. We Define a new construct called transmission mode, which denotes a particular choice of MIMO operational mode, precoding matrix, transmission rank, as well as the modulation and coding schemes (MCSs) of up to two codewords and show that both LTE systems require that each scheduled user be served using only one transmission mode in every subframe. We prove that the resulting scheduling problems are NP-hard under both backlogged and finite queue traffic models, and then develop a unified low-complexity greedy algorithm that yields solutions guaranteed to be within 1/2 of the respective optima. Extensive performance evaluation in realistic settings reveals near-optimal performance of our proposed algorithm and that it significantly outperforms the state of the art, especially under the more practical, finite queue model.

Discrete Fourier transform (DFT) based channel estimation (CE) has been widely studied as a practical CE scheme over the OFDM based wireless systems. The conventional DFT-based channel estimation utilizes a transform domain cut-off filter to suppress the noise in the time domain. However, this method can suffer significant performance loss due to the channel impulse response (CIR) energy leakage, especially when the available pilot sub-carriers are confined to a small portion of the system bandwidth. In this paper, we propose an enhanced DFT-based channel estimation technique for the long term evolution (LTE) based cellular uplink. A sinc-null based noise power estimation method in conjunction with a dynamic noise removal technique is proposed to suppress the noise in the time domain and achieve better performance while keeping the complexity in check. Simulation results show that the proposed scheme not only achieves better mean square error (MSE) and block error rate (BLER) performance but also exhibits robustness to timing offsets compared to existing DFT based CE schemes.

We consider a downlink multi-user multi-input-multi-output (MU-MIMO) fading channel wherein the base station can schedule several user terminals on the same time-frequency resource. A severe practical problem in MU-MIMO is that when computing its feedback report, a user does not have an accurate estimate of the interference it might see (if scheduled) from the signals intended for the other co-scheduled users. This results in a mismatch between the user reported signal-to-interference-plus-noise-ratio (SINR) and the one it actually observes in the aftermath of scheduling. To alleviate this problem we propose to inform each user (in a slow or semi-static manner) about the rank of the precoding matrix that it should report, along with an estimate of the total number of streams that the base station expects to co-schedule on a time-frequency resource. The suggested rank and the expected total number of streams can be user-specific and together convey the expected total number of co-scheduled interfering streams to the intended user. Each user then computes one or more SINRs for all the precoding matrices having the suggested rank and reports its preferred precoding matrix along with the corresponding SINRs. The SINRs are computed after assuming that the co-scheduled interfering streams will be transmitted along vectors isotropically distributed in the orthogonal complement of the range of the precoding matrix being examined. Alternatively, the SINRs can be computed after assuming that the co-scheduled interfering streams will be transmitted along the worst-case choice of mutually orthogonal vectors that lie in the orthogonal complement. We show that the proposed solutions, while requiring negligible additional signalling overhead, mitigate the mismatch problem to a large extent and result in significant improvements in system throughput.

In this paper, we consider resource allocation in the the fourth generation multi antenna (4G MIMO) cellular uplink. In particular, we consider the two 4G standards, IEEE 802.16m and 3GPP LTE-A, that have recently been approved. We show that the uplink resource allocation problems in both 802.16m and LTE-A networks are NP-hard. We then propose constant-factor polynomial-time approximation algorithms for both these problems. We also provide linear programming (LP) based upper bounds to benchmark the performance of our proposed approximation algorithms. Simulations reveal that the proposed algorithms have excellent performance, much superior to their worst-case guarantees.

In this paper, an efficient closed-loop link adaption scheme consisting of adaptive modulation and coding as well as adaptive precoding is proposed for the 3GPP LTE-A uplink. The uplink envisaged in the LTE-A cellular network will support precoded multi-rank transmission from the users and have base stations with advanced non-linear receivers. Our proposed scheme considers one such advanced receiver, namely, the turbo successive interference cancelation (Turbo-SIC) receiver. The main difficulty in designing link adaptation schemes for these advanced non-linear receivers is that the soft-outputs of such receivers cannot be simply modeled using a scalar Gaussian channel characterized by a signal-to-interference-plus-noise ratio (SINR). To circumvent this difficulty, we employ SINRs corresponding to an ordered hard-decision SIC receiver, that can be explicitly expressed in closed form. The proposed scheme accurately predicts the turbo SIC performance and results in a perfect match between the predicted and actually simulated performance in terms of block error rate (BLER) and the spectrum efficiency. Realistic and extensive simulations reveal that it provides the highest actual spectrum efficiency compared to other competing schemes.

This paper studies the robust weighted-sum rate optimization problem in the presence of channel uncertainty over a K-user Gaussian Interference Channel (GIFC), where multiple antennas are present at all transmitters and receivers. Motivated by recent results on interference alignment that show the optimality of linear precoders and simple receivers in achieving the maximum degrees-of-freedom available in the GIFC, we consider linear transmit precoding and two simple decoding schemes: single-stream decoding and single-user decoding. The resulting precoder design problems are then posed as specific optimization problems. Unfortunately, due to the hardness of these problems, optimal solutions cannot be efficiently obtained. Instead of resorting to ad-hoc algorithms, we show that it is possible to design algorithms using a systematic approach. Towards this end, this paper develops new provably convergent iterative algorithms for precoder design through ingenious sub-problem formulations such that each of these sub-problems can be solved optimally. The sub-problems are solved in closed-form for certain cases and formulated as standard convex problems for the rest. To complement these contributions on achievable schemes, we generalize the genie-MAC outer bounding technique to incorporate channel uncertainty using notions of compound-MAC capacity and then obtain computable outer bounds using an alternating optimization approach. Thus, we introduce one of the first approaches to obtain tighter outer bounds on the capacity region of the GIFC in the presence of channel uncertainty.

In this paper, we consider a downlink (DL) multiuser (MU) multi-input-multi-output (MIMO) channel with linear precoding where the base station simultaneously schedules several user terminals on the same frequency sub-band. We assume imperfect (or quantized) per-user channel state information at the base station and present two types of channel state information (CSI) reports from user terminals, namely, the CSI report that assumes the single-user (SU) MIMO transmissions and the enhanced CSI feedback report that assumes the MU-MIMO transmissions, and in the latter case we consider both uniform and nonuniform power allocations. To improve the MU-MIMO system performance, we propose signal-to-interference-plus-noise ratio (SINR) approximation techniques that utilize the quantized CSI available at the base station and improve the rate matching. We also introduce user pooling techniques which enable a reduction in feedback signaling overhead via per-user feedback mode selection. The proposed techniques also allow unconstrained user pairing at the base station scheduler and hence enable dynamic switching between SU and MU MIMO transmissions. The simulation results demonstrate the efficiency of the proposed MU-MIMO enhancement techniques.

The authors consider chase-combining hybrid automatic repeat request (HARQ) schemes over multiple antenna multi-user systems. The focus is on a multiple-access channel, where the users as well as the base-station are equipped with multiple antennas. In such chase-combining HARQ systems, the transmitters (users) re-transmit their codewords when requested by the receiver (base-station). The receiver may choose to enforce blanking, that is, it may choose to request only a subset of the transmitters to re-transmit and the remaining ones to be silent. Moreover, subject to the complexity and latency constraints, the receiver may be able to perform codeword cancellation wherein it can re-encode, re-modulate a subset of decoded codewords and subtract them from the received observations. A candidate combining technique is the conventional chase-combining, which in contrast to the optimal combining requires significantly less memory and processing capability at the receiver but can result in substantial performance degradation. The authors propose efficient combining techniques that cater to all the various scenarios that arise in such multi-codeword systems. The proposed techniques impose similar memory and complexity demands as the conventional combining but yield a significant performance improvement. The issue of limited feedback in multi-codeword multi-antenna chase-combining HARQ systems is also addressed and it can be used to further improve the system throughput.

Inter-cell interference mitigation is a key challenge in the next generation wireless networks which are expected to use an aggressive frequency reuse factor and a high-density base station deployment to improve coverage and spectral efficiency. In this work, we consider the problem of maximizing the weighted sum-rate of a wireless cellular network via coordinated scheduling and discrete power control. We present two distributed iterative algorithms which require limited information exchange and data processing at each base station. Both algorithms provably converge to a solution where no base station can unilaterally modify its status (i.e., transmit power and user selection) to improve the weighted sum-rate of the network. Numerical studies are carried out to assess the performance of the proposed schemes in a realistic system based on the IEEE 802.16m specifications. Simulation results show that the proposed algorithms achieve a significant rate gain over uncoordinated transmission strategies for both cell-edge and inner users.

In this paper, we consider the problem of resource allocation in the DFT-Spread-OFDMA (DFT-S-OFDMA) uplink. We show that the resource allocation problem can be formulated as a set packing problem, which in general is NP-hard. We propose polynomial-time message-passing based algorithms, one of which is guaranteed to yield a solution that is within a constant fraction of the optimal solution and is also asymptotically optimal in the limit as the number of subcarriers in the system goes to infinity. The message-passing based algorithm is also extended to solve the resource allocation problem over a multi-cell uplink in a distributed fashion. Our algorithms account for finite input alphabets and non-ideal practical outer codes. Extensive simulations are performed to assess the performance of the proposed algorithms and it is shown that they yield near-optimal solutions at a low complexity and with a low memory requirement.

In this paper we consider the design of multi-stream demodulators for multiple-input multiple-output (MIMO) systems. Our proposed MIMO demodulator is based on the stack tree-search strategy and provides soft-outputs in the form of exact max-log log-likelihood ratios (max-log LLRs). We prove that our proposed demodulator is optimally efficient in that it visits the least number of nodes among all optimal tree-search based demodulators. We conduct a comprehensive complexity analysis of the optimally efficient demodulators. We also identify key parameters associated with our proposed demodulator that can be tuned to realize near max-log performance with substantially reduced complexity, as demonstrated via simulations.

We consider the problem of designing robust linear transceivers for a memoryless narrowband Gaussian interference channel (GIC) where M multi-antenna sources communicate with their respective single-antenna receivers. The design of such linear transceivers heavily depends on the accuracy of the channel state information (CSI) available at the transmitters. In practice, the transmitters can acquire only imperfect or noisy CSI. We adopt a popular noisy CSI model which assumes that the noise terms (i.e., errors in the CSI) lie within known hyper-ellipsoids and design transceivers that optimize a worst-case quality of service measure. In particular, we focus on maximizing the worst-case weighted sum-rate as well as the worst-case minimum rate. For obtaining such transceiver designs, we exploit semidefinite programming methods and offer efficient centralized and distributed algorithms that entail different levels of information exchange among the transmitters.

For coordinated transmissions in multi-cell downlink channels, the base stations are required to acquire and share their channel state information (CSI). Acquiring CSI is often prone to errors and a globally-optimal coordination is not possible when the acquired CSI is imperfect. However, when the errors in the acquired CSI are guaranteed to lie within bounded regions, any quality-of-service (QoS) measure of interest will also lie within a bounded region. Motivated by this premise, by employing the notion of robustness in the worst-case sense, some worst-case guarantees on QoS can be offered. We assume that CSI perturbations belong to known hyper-spheres and aim to design linear transceivers that optimize the minimum worst-case rate of the network. We offer centralized (fully cooperative) and distributed (limited cooperation) procedures imposing different levels of complexity and information exchange among the base stations.

Achievable rate regions of multiuser fading interference channels depend on their fading realizations. Motivated by this premise we consider the problem of adapting the users' rates to fading variations. Channel-dependent rate adjustments are accomplished after each transition of the fading channel from one state to another. Such rate adjustments (increments or decrements) are constrained to meet some notion of fairness among the users and are designed to ensure that all users remain decodable. Here, we employ the notions of symmetric fair and max-min fair rate adaptations and offer algorithms for computing such fair rate adaptations. Besides fairness, the two other major features of these algorithms are that they are amenable to distributed implementation with limited information exchange among the users, and their complexities scale polynomially in the number of users.

We consider the use of linear constellation precoding and linear detection in a multicarrier OFDM system with multiple receive antennas to obtain improved performance over multipath fading channels at a low complexity. We split the full set of subcarriers into smaller groups and spread the data symbols assigned to each group via precoding matrices. We adopt the diversity-multiplexing tradeoff (DMT) framework and derive the DMT-optimal split of the subcarriers (DMT-optimal grouping) and the DMT-optimal number of symbols assigned to each group (DMT-optimal symbol loading). We determine necessary and sufficient precoder design conditions to achieve DMT optimality and give specific constructions of such precoders. Next, we consider a multiuser OFDMA system and derive an algorithm which divides the available subcarriers among the active users in order to maximize the diversity order of the system (or joint) error probability. We also extend our analysis to OFDM systems equipped with multiple transmit antennas. Finally, we obtain important insights on the role of outer codes in an OFDM system employing linear precoding and linear equalization.

Two novel communication protocols for the quasistatic coherent fading relay channel are proposed and analyzed under the diversity-multiplexing tradeoff framework. Both these protocols satisfy the half-duplex constraint and fall under the class of decode and forward (DF) protocols, wherein the relay node attempts to decode the source signal and if successful transmits the re-encoded signal. The first protocol is a static DF protocol where the relay waits for a fixed, channel independent duration before attempting to decode. It is shown that it achieves a tradeoff curve that uniformly improves upon those of the previously best known static half-duplex protocols, which are the NAF protocol and the STC3 protocol. Our second protocol is a dynamic DF protocol where the relay waits for a dynamic, channel dependent duration before attempting to decode and it is shown to achieve a tradeoff curve that uniformly improves upon the previously proposed half-duplex dynamic DF protocol.

We consider a multi-cell wireless network with universal frequency reuse and treat the problem of co-channel interference mitigation in the downlink channel. Assuming that each base station serves multiple single-antenna mobiles via space-division multiple-access, we jointly optimize the linear beam-vectors across a set of coordinated cells and resource slots: the objective function to be maximized is the instantaneous weighted sum-rate subject to per-base-station power constraints. After deriving the general structure of the optimal beam-vectors, a novel iterative algorithm is presented which attempts to solve the Karush-Kuhn-Tucker conditions of the non-convex problem at hand. The proposed algorithm admits a distributed implementation which we illustrate. Also, various approaches to choose the initial beam-vectors are considered, one of which maximizes the signal-to-leakage-plus-noise ratio. Finally, simulation results are provided to assess the performance of the proposed algorithm.

In this work we design efficient soft output demodulators (also referred to as fast soft demodulators) for the 2 × 2 Golden code, which is a promising candidate space time block code in the evolving IEEE and 3GPP cellular standards. For this code, the naive approach for maximum likelihood (ML) hard decision as well as soft-output demodulation entails a complexity of O(M4), where M denotes the cardinality of the QAM constellation from which the underlying modulated symbols are drawn. In contrast, our efficient demodulator exploits the structure of the code and yields the ML decision and soft outputs in the form of exact max-log log-likelihood ratios (LLRs) with an O(M2.5) complexity. Moreover, we also design a sub-optimal soft output demodulator that has an O(M0.5) complexity comparable to that of the linear minimum mean square error (LMMSE) based demodulator but results in substantial performance gains.

We consider decentralized multi-antenna cognitive radio networks where secondary (cognitive) users are granted simultaneous spectrum access along with license-holding (primary) users. We investigate the problem of designing beam- formers for the secondary users by maximizing the minimum rate, subject to a limited sum-power budget and constraints on the interference level imposed on each primary receiver. We consider two scenarios: the first one allows only single-user decoding at each secondary receiver whereas in the second case each secondary receiver is allowed to employ advanced multiuser decoding and is free to decode any subset of secondary users. We provide an optimal distributed algorithm for the first scenario and an explicit formulation of the optimization problem corresponding to the second scenario. This problem however is non-convex and hence cannot be efficiently solved even in a centralized setup. As a remedy, we suggest a two-step approach. In particular, the beamformers are first designed assuming single user decoding at each secondary receiver. An optimal distributed low-complexity algorithm is then proposed to allocate excess rates to the secondary users, which are made possible due to the use of advanced decoders at the secondary receivers. Simulation results demonstrate the gains yielded by the optimal beamformers as well as the rate allocation algorithms.

We consider a memoryless Gaussian interference channel (GIC) whereKsingle-antenna users communicate with their respective receivers using Gaussian codebooks. Each receiver employs a successive group decoder with a specified complexity constraint, to decode its designated user. It is aware of the coding schemes employed by all other users and may choose to decode some or all of them only if it deems that doing so will aid the decoding of its desired user. For a GIC with predetermined rates for all transmitters, we obtain the minimum outage probability decoding strategy at each receiver which satisfies the imposed complexity constraint and reveals the optimal subset of interferers that must be decoded along with the desired user. We then consider the rate allocation problem over the GIC under successive group decoding and design a sequential rate allocation algorithm which yields a Pareto-optimal rate allocation, and two parallel rate allocation algorithms which yield the symmetric fair rate allocation and the max-min fair rate allocation, respectively. Remarkably, even though the proposed decoding and rate allocation algorithms use ldquogreedyrdquo or myopic subroutines, they achieve globally optimal solutions. Finally, we also propose rate allocation algorithms for a cognitive radio system.

For the 3GPP LTE uplink transmissions, the DFTspread OFDM technique has been adopted as the air interface in order to reduce the peak-to-average-power ratio (PAPR). In this scheme, each data symbol is spread over many tones by a discrete Fourier transform (DFT) operation at the transmitter before being sent to the orthogonal frequency division multiplexing (OFDM) modulator. Moreover, more than one user can be scheduled over the same frequency and time resource block (RB) via space-division multiple-access (SDMA). The conventional receiver technique for such DFT-spread OFDM systems involves tone-by-tone single-tap equalization followed by an inverse DFT operation. In this paper, we propose a more powerful receiver technique for DFT-spread OFDM systems that consists of an efficient linear pre-filter and a two-symbol soft output demodulator. The proposed method can be applied to both single-user per RB (DFT-S-OFDMA) and multiple users per RB (DFT-S-OFDMSDMA) systems and it offers significant performance gains over the conventional method, especially in the high-rate regime, with little attendant increase in computational complexity.

We consider a memoryless narrowband Gaussian interference channel (GIC) where K single-antenna users communicate with their respective receivers using Gaussian code- books. Each receiver employs a successive group decoder with a specified complexity constraint, to decode its designated user. It is aware of the coding schemes employed by all other users and may choose to decode some or all of them only if it deems that doing so will aid the decoding of its desired user. For a GIC with predetermined rates for all users, we obtain the minimum outage probability decoding strategy at each receiver, which satisfies the imposed complexity constraint and reveals the optimal channel-dependent subset of interferers that must be decoded along with the desired user. We then consider the rate allocation problem over the GIC and design two distributed rate allocation algorithms which yield the symmetric fair rate allocation and the max-min fair rate allocation, respectively.

We propose resource allocation algorithms based on the auction method for uplink OFDMA cellular networks. We consider cellular systems that employ the traditional static frequency reuse as well as the next-generation systems that aim to achieve a universal frequency reuse via base-station coordination. Our algorithms are designed for finite input alphabets and also account for non-ideal practical outer codes, and they can be implemented in a distributed manner, when applied for multi-cell resource allocation. The proposed algorithms have a complexity of O(N) per user per iteration, where N denotes the number of subcarriers in the system, and are also well suited for parallel implementations. We also address power and bandwidth constraints that are motivated by practical concerns. The proposed algorithms exhibit very low complexity and simulation results demonstrate that they offer near-optimal performance.

We consider a block fading multiple access relay channel (MARC) where K users communicate with a single destination in the presence of Q ≥ 1 half-duplex relays. Each relay employs a successive group decoder with a specified complexity constraint and a decode and forward (DF) protocol. We design an outage minimizing relaying strategy for the scenario where no channel dependent feedback is possible between the destination and any user but a limited amount of such feedback is possible between the destination and each relay. We also design a rate allocation algorithm which yields the symmetric fair rate allocation. Remarkably, even though the outage minimization and rate allocation algorithms use low-complexity ‘greedy’ or myopic sub-routines at the relays, they achieve globally optimal solutions.

We consider the sum-rate maximization via linear preceding in downlink MIMO-OFDM systems with quantized feedback. We address the preceding codebook design based on the capacity measure by introducing a new distance metric. We propose a codebook structure and its associated design algorithm that allows for significant reduction in the memory requirement and computational complexity in real-time system implementation. We then provide a system design approach comprising of four main ingredients: (i) a multi-rank beamforming (MRBF) scheme, (ii) an efficient CQI-based precoder selection algorithm, (iii) reduced feedback strategies, and (iv) novel channel quality indicator (CQI) combining. Our simulation results show that the proposed MRBF scheme can approach the precoding upper bounds with relatively few feedback bits. Moreover, with the same number of bits, the proposed scheme simultaneously achieves higher throughput and lower computational complexity in comparison to the other existing precoding schemes.

We consider the multi-input multi-output (MIMO) downlink channel in the next-generation cellular networks and propose two improved interference suppression receivers for combating out-of-cell interference. The proposed receivers exploit the fact that the co-channel interference seen on the downlink channel (especially the downlink control channel) has a particular structure, in order to obtain significantly improved performance while ensuring low decoding complexity. The first receiver does not require the user to decode the interference or be aware of the particular inner codes employed by the interfering transmitters. The second receiver decodes and subtracts a subset of interferers in a channel-dependent order before processing the desired signal. Each interferer is decoded at most once and the choice of the ordered subset mitigates error propagation. Simulation results are presented to demonstrate the significant gains obtained by the proposed low-complexity receivers over their conventional counterparts.

In this paper, we propose a systematic procedure for designing spherical lattice (space–time) codes. By employing stochastic optimization techniques we design lattice codes which are well matched to the fading statistics as well as to the decoder used at the receiver. The decoders we consider here include the optimal albeit of highest decoding complexity maximum-likelihood (ML) decoder, the suboptimal lattice decoders, as well as the suboptimal lattice-reduction-aided (LRA) decoders having the lowest decoding complexity. For each decoder, our design methodology can be tailored to obtain low error-rate lattice codes for arbitrary fading statistics and signal-to-noise ratios (SNRs) of interest. Further, we obtain fundamental lower bounds on the error probabilities yielded by lattice and LRA decoders and characterize their asymptotic behavior.

Embodiments of the diagonal Bell Laboratories layered space-time (D-BLAST) architecture for multiple-input-multiple-output (MIMO) communication are developed wherein information symbol vectors are encoded using codewords from a lattice code [called a diagonally layered lattice (DLL) code], which are formatted onto the diagonals of a space-time frame. Decoding is done using a sphere decoder for each diagonal based on soft statistics obtained after zero forcing (ZF) or minimum-mean-square-error (MMSE) filtering and decision feedback. These operations give rise to an effective parallel channel model with channel gains with nonidentical statistics and additive noise which is Gaussian in the ZF-filtering case and non-Gaussian in the MMSE-filtering case. The so-called full modulation diversity (FMD) property is nevertheless shown to yield the maximum achievable diversity orders over the MIMO channel for both the ZF- and the MMSE-filtering-based decoders respectively, for any arbitrary fading distribution. In the case of the independent, identically distributed (i.i.d.) Rayleigh fading MIMO channel withK-transmit andN-receive antennas (withNgesK), these diversity orders areNK-K(K-1)/2 andNKfor ZF- and MMSE-filtering-based decoding, respectively. The error probability analysis also yields a design criterion for optimizing transmit power allocations. Several lattice design methods are proposed for the effective parallel channel models. Two methods are proposed to achieve high coding gain in the Rayleigh fading MIMO channel; a third method is proposed that minimizes the exact symbol error probability (SEP) and can be tailored for any given fading distribution. A novel soft decision feedback decoder is also proposed based on the list sphere decoder to mitigate error propagation due to hard decision feedback. The salient feature of the proposed DLL schemes is that they have nearly full rate and full (or high) diversity order and yet a much lower decoding complexity than other existing full rate, full diversity space-time block codes (STBCs). The frame error probability (FEP) performance of the optimized DLL schemes for moderate-to-high spectral efficiencies and a wide range of signal-to-noise ratios (SNRs) can be quite close to the performance of the best performing, but more complex to decode, STBCs. Moreover, the proposed DLL schemes significantly outperform other existing MIMO systems of comparable decoding complexity.

We consider space-time transceiver architectures for space-division multiple-access (SDMA) fading channels with simultaneous transmissions from multiple users. Each user has up to four transmit antennas and employs a space-time orthogonal or a quasi-orthogonal design as an inner code. At the multiple-antenna receiver, efficient successive group interference cancellation strategies based on zero-forcing or minimum mean-square error (MMSE) filtering are employed in some fixed or channel-dependent order. These strategies are efficient in the sense that they exploit the special structure of the inner codes to yield much higher diversity orders than would be otherwise possible, while at the same time preserving what we call thedecouplingpropertyof the constituent inner codes which enables the use of low-complexity outer encoders/decoders for each user. Motivated by the special structure of the effective channel matrix induced by the inner codes, we obtain several new distribution results on the QR and eigenvalue decompositions of certain structured random matrices. These results are the key to a comprehensive performance analysis of the proposed multiuser transceiver architectures including the characterization of diversity-multiplexing tradeoff (DMT) curves and exact per-user bit-error rates (BERs) without making simplifying assumptions about error propagation.

We consider a slow-fading narrowband multiple-input multiple-output (MIMO) multiple-access channel (MAC) in which multiple users, each equipped with multiple transmit antennas, communicate to a receiver equipped with multiple receive antennas. The users are unaware of the channel state information (CSI) whereas the receiver has perfect CSI and employs a successive group decoder (SGD). We obtain achievable outage probabilities for the case where an outage must be declared simultaneously for all users (common outage) as well as the case where outages can be declared individually for each user (individual outage). We then derive the optimum successive group decoder (OSGD) that simultaneously minimizes the common outage probability and the individual outage probability of each user, over all SGDs of permissible decoding complexity. For each channel realization, the OSGD is also shown to maximize the error exponent of the decodable set of users. An adaptive SGD is derived which not only retains the outage optimality of the OSGD but also minimizes the expected decoding complexity. Asymptotically tight (in the limit of high signal-to-noise ratio (SNR)) affine approximations are then obtained for the weighted sum common and individual outage capacities and the symmetric outage capacity yielded by the OSGD. Limiting expressions for the relevant capacities as the number of users and the number of receive antennas approach infinity are also obtained and it is shown that the OSGD yields symmetric capacity gains commensurate with the decoding complexity allowed. Simulation results with practical low-density parity-check (LDPC) outer codes show that the OSGD offers significantly improved performance at low decoding complexity.

A new technique is proposed for designing decision feedback detectors (DFDs) wherein the per-symbol decision rules are obtained by exploiting modulation constraints. It is presented in the context of the multi-input, multi-output (MIMO) fading channel with K transmit and N receive antennas. Three cases are considered to illustrate the technique where all the transmitters employ real-valued pulse-amplitude modulation (PAM), or phaseshift keying (PSK), or complex quadrature amplitude modulation (QAM). In each case, the corresponding per-symbol decision rules are obtained by imposing the modulation constraints on a likelihood function maximization problem. When all transmitters employ PAM, it is shown that the resulting DFD (the PAM-DFD) is equivalent to the decorrelating decision feedback detector (DDFD) when the latter is used on a modified received statistic. Two DFDs are then derived (the PSK-DFDs and the QAMDFDs) for the cases when all transmitters employ PSK and QAM, respectively. To illustrate the performance benefit, we consider the Rayleigh fading channel and derive the exact joint error probability (JEP) of the PAM-DFD and show that the (possibly fractional) diversity order of the JEP is equal to N - K-1/ 2 . This greatly improves on the diversity order of N - K + 1 of the JEP obtained by the D-DFD without exploiting the real modulation constraint. Through simulations, it is shown that the PSK-DFDs and the QAM-DFDs result in significant performance improvements over the D-DFD as well. It is conjectured that one of the PSK-DFD achieves the improved diversity order of N - K-1/ 2 as well and the QAM-DFDs result in an improvement in effective SNR gain.

We consider the use of linear constellation preceding and linear equalization in a multicarrier system to obtain improved performance over multipath fading channels at a low complexity. We split the full set of subcarriers into smaller groups and spread the data symbols assigned to each group via precoding matrices. To obtain diversity order gains at a given data rate, we derive the optimal split of the subcarriers (optimal grouping) and the optimal number of symbols assigned to each group (optimal loading) using a diversity-multiplexing tradeoff analysis. We also derive the necessary and sufficient optimality conditions for the precoder design and provide examples of such optimal precoders.

We consider the problem of designing space-time- frequency linear dispersion (LD) codes in wideband multiple- input multiple-output (MIMO) antenna systems employing orthogonal-frequency-division-multiplexing (OFDM). Three design methods are presented and discussed, which involve: (1) minimizing the average block error rate, (2) maximizing the ergodic mutual information, and (3) a two-step procedure considering the optimization of the mutual information as well as the average block error rate, respectively. For any set of subcarriers, any number of OFDM symbol intervals, any number of transmit/receive antennas and any statistical fading channel model, the corresponding optimized LD code matrices are numerically computed via a stochastic gradient descent algorithm. Code design examples are provided and discussed for communication systems operating over a realistic 3GPP spatial channel model.

We consider space-time transceiver architectures for multiple access fading channels with K users, each equipped with multiple transmit antennas. Each user employs an orthogonal or a quasi-orthogonal design as an inner code. At the multi-antenna receiver, successive group interference suppression strategies based on the linear zero-forcing or linear MMSE filters are employed in some fixed or channel dependent order. These strategies exploit the specific structure of the inner codes to yield high diversity orders while preserving the decoupling property of the constituent inner codes thereby enabling the use of simple demodulators. Motivated by the special structure of the effective channel matrix induced by the inner codes, we obtain several new results on the QR and eigenvalue decompositions of certain structured random matrices. Using these random-matrix distribution results, we characterize the high-SNR performance limits of the transceiver architectures under consideration by obtaining their diversity-multiplexing tradeoff curves.

We propose two novel communication protocols for the quasi-static coherent fading relay channel and analyze them under the diversity-multiplexing tradeoff framework. Both these protocols satisfy the half-duplex constraint and fall under the class of decode and forward (DF) protocols, wherein the relay node attempts to decode the source signal and if successful transmits the re-encoded signal. Our first protocol is a static DF protocol where the relay waits for a fixed (channel independent) duration before attempting to decode. We show that it achieves a tradeoff curve that uniformly improves upon those of the previous best known static half-duplex protocols, which are the NAF protocol and the STC3 protocol. Our second protocol is a dynamic DF protocol where the relay waits for a dynamic (channel dependent) duration before attempting to decode and it is shown to achieve a tradeoff curve that uniformly improves upon those of all previously proposed half-duplex protocols, including the dynamic DF protocol.

In this paper we propose a systematic procedure for designing optimal lattice (space-time) codes. By employing stochastic optimization techniques we design lattice codes with minimum error rates when lattice decoders are employed at the receiver. Our design methodology can be tailored to obtain optimal lattice (space-time) codes for any fading statistics and SNR of interest. Further, we obtain fundamental lower bounds on the error probabilities yielded by lattice decoders and characterize their asymptotic behavior

Outage capacity and throughput are the two key metrics through which the fundamental limits of delay-sensitive wireless MIMO links can be studied. In this paper, we show that these metrics are intimately related, and consequently, as in the case of outage capacity, the growth rate of throughput with SNR rho is t log rho for a general class of fading channels (with channel state information at the receiver (CSIR) and with or without CSI at the transmitter (CSIT)) whose channel matrix is of rank t with probability one. However, while asymptotically tight affine lower bounds of the form t log rho + 0(1) were recently derived for outage capacity for such channels, in the sense that the limit as rho rarr infin of the difference between the outage capacity and the lower bound is zero, such affine lower bounds are not possible in general for the throughput. Using the t log rho + O(1) bounds on outage capacity however, lower bounds on throughput are specified where the high SNR limit of the ratio of the throughput and its lower bound is unity. These bounds reveal that the throughput optimal outage probability approaches zero as rho rarr infin. An important exception is the scenario where both the transmitter and receiver have CSI under the long-term power constraint (LTPC), for which we obtain a lower bound of the form t log rho + O(1) which is asymptotically tight (in the stronger sense) and interestingly, this lower bound is identical to the asymptotic delay-limited capacity. The throughputs of MISO and SIMO fading channels are extensively analyzed and it is shown that asymptotically, isotropic Gaussian input is throughput optimal, correlation is detrimental whereas increase in the Rice factor is beneficial and that throughput is schur-concave in the correlation eigenvalues

The connection between the average codeword or frame error probability (FEP) of space-time codes and the outage probability over general block-fading multiple-input multiple-output (MIMO) channels is established. Three archetypal problems are considered under general fading distributions in a single framework wherein the receiver has channel state information whereas the transmitter knows a) the fading distribution but not the channel realization b) the channel realization but must follow a short term (per codeword) average power constraint, and c) the channel realization but is constrained only by a long-term average power constraint. Three telescoping sets of space-time codes are defined for a given rate and it is shown that average FEPs arbitrarily close to the respective outage probabilities for each of the three cases a)-c) can be achieved by codes in each set for sufficiently large frame lengths. For the smallest set among the three which contains codes with a spectral norm constraint that is stricter than the average or maximum energy constraints commonly assumed, firm sphere-packing lower bounds on the FEP are obtained, and, consequently, strong converse theorems are proved which assert that the respective outage probabilities also represent the best achievable FEP in the large frame-length limit. Moreover, the set of spectral norm constrained codes are also shown to be large enough to contain universal codes that can communicate reliably over any channel realization for which the mutual information exceeds the information rate of the code

In this paper we propose a systematic procedure for designing minimum-error-rate lattice (space-time) codes. By employing stochastic optimization techniques we design lattice (space-time) codes with minimum error rate when maximum likelihood (ML) detection is employed. Our design methodology can be tailored to optimize lattice (space-time) codes for any fading statistics and SNR of interest

We consider a multi-input multi-output (MIMO) fading channel with coherent reception and provide a sharp characterization of the outage capacity in the form of an asymptotically tight (in the limit of high signal-to-noise ratio) affine lower bound, under only mild assumptions on the fading distribution. The bound is simpler to compute than the original capacity and succinctly captures the various features particular to the channel. For systems where both the transmitter and the receiver have perfect channel state information, we derive asymptotically tight affine lower bounds on the outage capacities under a long-term and a short-term power constraint as well as on the delay-limited capacity. Further, when only the receiver has perfect channel state information, and when the rank of the channel matrix is equal to the number of transmit antennas with probability one, we show that an isotropic Gaussian input is asymptotically optimal. Moreover, for Ricean channels the asymptotic effect of the Rice factor on the delay-limited capacity is also characterized

We consider a wireless network with multiple terminals (m/spl ges/2) as well as multiple receive antennas at the destination (N/spl ges/1) and obtain diversity multiplexing tradeoff upper bounds for some recently proposed cooperative diversity protocols. The results obtained show that irrespective of the coding scheme employed none of these protocols can yield a diversity order greater than N+m-1. Further, the tradeoff bounds indicate that the protocol yielding the best diversity order may change with the multiplexing gain. Using these bounds we propose a switching strategy that yields the best tradeoff bound which also significantly outperforms the optimal tradeoff curve for the baseline noncooperative system at all possible multiplexing gains.

This paper considers non-ergodic multi-input multi-output (MIMO) fading channels. We compare the outage capacities yielded by the optimal unconstrained system and some recently proposed space-time architectures for i.i.d. Gaussian inputs. All the systems considered here are known to yield outage capacities having the same (maximum) rate of growth with the signal-to-noise ratio (SNR). For each system, an asymptotically tight lower bound on the outage capacity is derived. The analysis reveals that the diagonal BLAST architecture with the zero-forcing front end is asymptotically optimal with respect to the outage capacity. Another space-time architecture is shown to be optimal at all SNR with respect to the outage capacity.

For an uncoded, K-transmit, N-receive antenna coherent narrow-band communication system employing a decorrelating decision feedback detector (D-DFD), the exact average (over channel realizations) joint error probability (JEP) as well as the average per-symbol error probabilities (SEPs) are derived without making any simplifying assumptions on error propagation. It is proved that the diversity orders of the JEP and the SEP (of every symbol) is limited by error propagation to N-K+1. Based on our exact error probability analysis, however, we suggest an optimization of JEP over nonnegative quadrature amplitude modulation (QAM) constellation sizes (rates) and average powers across transmitters which yield significant improvements over the usual equal power and equal rate assignment. In fact, the JEP of such an optimized design has the much improved diversity order of N (which is also the diversity order obtained through the optimum maximum-likelihood (ML) detector). Moreover, it is seen that these simple optimized designs can achieve a significant fraction of the /spl epsi/-outage capacity even without outer codes. It is also known-but only through simulations-that when the symbols are detected in certain channel realization-dependent orders it is possible to improve substantially over fixed-order detection in the case of the equal rate and equal power assignment. We provide an analysis for a recently proposed channel-dependent ordering rule and show that it does not provide an improvement of the diversity order of the JEP beyond N-K+1. Another ordering rule that was proposed earlier to maximize the worst case post-detection signal-to-noise ratio (SNR) under the perfect feedback assumption is shown to be optimal under a more compelling criterion that does not involve that simplifying assumption. While efficiently computable, this ordering rule is seen to perform almost as well as the optimal channel-dependent ordering rule that minimizes the conditional JEP (and hence the JEP). Nevertheless, a multiple-input multiple-output (MIMO) system with an optimized rate and power allocation and a fixed order of detection is not only less complex but also has a significantly lower JEP than that of the equal-power, equal-rate system, where transmitters are detected in a channel-dependent order, optimal or otherwise.

We propose a stacked orthogonal space-time architecture for the quasi-static, MIMO Rayleigh fading channel where the K transmit antennas are divided into K/2 groups. Each group employs the Alamouti design as the inner code with the indeterminates being uncoded symbols or symbols from some outer code. Successive group interference suppression strategies based on decorrelating and MMSE filters are used to decode the component codes in some fixed or channel dependent order. These strategies exploit the specific structure of the inner codes to yield high diversity orders while preserving the decoupling property, thereby enabling the use of single-input, single-output (SISO) outer codes. The problems of finding the exact frame error probability (FEP) or outage probability of the interference suppression schemes are challenging. Nevertheless, these analysis problems are solved for the decorrelating case. Alternatively, the outage probability is minimized over channel dependent ordering rules via a greedy algorithm for any given rate and power tuples. We also demonstrate the intimate connection between outage probability and the achievable FEP for long frame-lengths. In the case of the optimized stacked space-time systems with (SISO) outer coding, we show that much better diversity orders and frame error probabilities (of about 4 dB in one example) are obtained relative to the codes of V. Tarokh et al. (1999) and these improvements are also obtained with a lower decoding complexity.

Intellectual property cores are being widely used to enable rapid integration of entire systems onto chips. While allowing for rapid system prototyping and design, this methodology complicates the problem of testing them. Various design for test techniques and guidelines are evolving across design groups for embedded core based systems. This paper discusses a framework: for evaluating these techniques and the tradeoffs therein, to drive a cost effective test methodology. Its main contributions include: (i) it inspects various techniques to improve the test coverage and test quality in embedded core based systems. (ii) It explains important test cost measures, and proposes a framework for making design time decisions to minimise the cost. (iii) It presents the results of various experiments carried out on representative DSP core based systems, built around Texas Instruments' new DSP core, TMS320C27xx. These results have highlighted various design, and test tradeoffs, and are being profitably used to drive a cost effective test methodology on newer cores and devices.