We study the Bandit Clustering (BC) problem under the fixed confidence setting, where the objective is to group a collection of data sequences (arms) into clusters through sequential sampling from adaptively selected arms at each time step while ensuring a fixed error probability at the stopping time. We consider a setting where arms in a cluster may have different distributions. Unlike existing results in this setting, which assume Gaussian-distributed arms, we study a broader class of vector-parametric distributions that satisfy mild regularity conditions. Existing asymptotically optimal BC algorithms require solving an optimization problem as part of their sampling rule at each step, which is computationally costly. We propose an Efficient Bandit Clustering algorithm (EBC), which, instead of solving the full optimization problem, takes a single step toward the optimal value at each time step, making it computationally efficient while remaining asymptotically optimal. We also propose a heuristic variant of EBC, called EBC-H, which further simplifies the sampling rule, with arm selection based on quantities computed as part of the stopping rule. We highlight the computational efficiency of EBC and EBC-H by comparing their per-sample run time with that of existing algorithms. The asymptotic optimality of EBC is supported through simulations on the synthetic datasets. Through simulations on both synthetic and real-world datasets, we show the performance gain of EBC and EBC-H over existing approaches.

We consider the problem of person-in-bed detection using accelerometer measurements in the segmented as well as streaming setting. For the segmented problem, we identify frequency domain features (4 features for each acceleration coordinate) that can be used to model the in-bed and not-in-bed hypotheses. We estimate the model parameters from the training data and apply the Generalized Likelihood Ratio (GLR) test. Using the same form as the GLR test statistic, we also propose an improvement using quadratic logistic regression. For the streaming problem, we model it as a sequential change detection problem using the models that we obtained for the in-bed and not-in-bed hypotheses and propose a GLRT-based Cumulative Sum (CuSum) algorithm.

In this paper, we consider an Orthogonal Frequency Division Multiplexing (OFDM) system with a periodic pilot insertion pattern on the Time-Frequency (TF) grid. We compute Ambiguity functions at the receiver, which are two-dimensional (2D) delay-Doppler (DD) domain correlations of transmitted and received signals. Using these ambiguity functions, we propose a channel estimation technique for sparse DD channels. The TF channel estimate can be obtained by directly computing the 2D Fourier Transform of the ambiguity functions. While this approach gives better performance than conventional frequency-domain channel estimation and interpolation for OFDM, it does not exploit the sparsity in the DD domain. Our proposed approach uses the inherent sparsity of the ambiguity functions and estimates the sparse DD channel using Orthogonal Matching Pursuit. The obtained sparse DD channel is then used to calculate the TF channel. Through simulations, we study both the direct approach and our proposed sparsity-based approach and show improved channel estimation and error performance using the DD domain approaches.

Hybrid linear precoding for mmWave multi-user multiple-input multiple-output (MU-MIMO) has been extensively studied. In this paper, we propose a robust nonlinear Tomlinson-Harashima Precoding (THP) design for MU-MIMO when only partial interfering beam feedback is available at the transmitter. In a partial interfering beam feedback system, each user sends only the information about the top-p interfering beams to the transmitter. First, we propose a THP design based on an approximate effective channel. Then, we propose an iterative method for robust THP design that minimizes the total transmit power under mean square error constraint at each user. This robust method takes into account error in the approximated channel and performs better than THP design directly for the approximated channel. Numerical results show that the proposed non-linear precoding performs better than the linear-precoding in terms of both mean bit error rate (BER) and sum-rate achieved. In addition, the robust method which considers the approximated channel and feedback errors achieves better BER compared to the non-robust hybrid THP method.

We consider a multi-hypothesis testing problem involving a$K$-armed bandit. Each arm’s signal follows a distribution from a vector exponential family. The actual parameters of the arms are unknown to the decision maker. The decision maker incurs a delay cost for delay until a decision and a switching cost whenever he switches from one arm to another. His goal is to minimise the overall cost until a decision is reached on the true hypothesis. Of interest are policies that satisfy a given constraint on the probability of false detection. This is a sequential decision making problem where the decision maker gets only a limited view of the true state of nature at each stage, but can control his view by choosing the arm to observe at each stage. An information-theoretic lower bound on the total cost (expected time for a reliable decision plus total switching cost) is first identified, and a variation on a sequential policy based on the generalised likelihood ratio statistic is then studied. Due to the vector exponential family assumption, the signal processing at each stage is simple; the associated conjugate prior distribution on the unknown model parameters enables easy updates of the posterior distribution. The proposed policy, with a suitable threshold for stopping, is shown to satisfy the given constraint on the probability of false detection. Under a continuous selection assumption, the policy is also shown to be asymptotically optimal in terms of the total cost among all policies that satisfy the constraint on the probability of false detection.

We study a nonparametric search problem to detect$L$anomalous streams from a finite set of$ S$data streams. The$L$anomalous streams are real-valued independent and identically distributed (i.i.d.) sequences drawn from the distribution$ q$, while the remaining$S-L$data streams are i.i.d. sequences drawn from the distribution$ p$. The distributions$ p$and$ q$are assumed to bearbitraryandunknown, but distinct. We consider two cases: one where$L = 1$, and the other where$0 \leq L \leq A$. In both cases, we propose universal distribution-free sequential tests that are consistent. For the first case, we also: (1) show that the test is universally exponentially consistent and stops in finite time almost surely, and (2) bound the limiting growth rate of the expected stopping time as the probability of error decreases to zero. Simulations show that the performance of the proposed test is better than that of the fixed sample size test.

We propose a learning-based joint sparse recovery method for the multiple measurement vector (MMV) problem using deep unfolding. We unfold an iterative alternating direction method of multipliers (ADM) algorithm for MMV joint sparse recovery algorithm into a trainable deep network. This ADM algorithm is first obtained by modifying the squared error penalty function of an existing ADM algorithm to a back-projected squared error penalty function. Numerical results for a massive random access system show that our proposed modification to the MMV-ADM method and deep unfolding provide significant improvement in convergence and estimation performance.

In this paper, we consider a multi-cell multi-band downlink where the base station (BS) in each cell has multiple transmit antennas. Each cell has one active mobile station (MS) with a single receive antenna and treats interference from the other cells as noise. There is a sum transmit power constraint for each BS over all the bands. An alternating maximization (AM) algorithm is proposed to determine the optimal power allocation among the bands and the optimal beamforming vectors for each BS in each band. This algorithm can be implemented in a distributed manner with limited exchange of interference constraints between the BSs, and only local channel state information at each BS. The proposed algorithm alternates between: (1) weighted sum-rate (WSR) optimization for the beamformers in each band for a given power allocation, and (2) optimal power allocation across bands for a given set of beamformers. For the 2-cell and 3-cell settings the WSR optimization in each band is significantly simplified using analytical solutions for the sub-problems. The power allocation across bands for a given set of beamformers is obtained analytically in all cases. Numerical results show good convergence properties and significant performance gain using the proposed AM algorithm compared to: (i) equal power allocation across bands and weighted sum-rate optimization in each band, (ii) zero-forcing (ZF) beamforming, and (iii) maximal ratio transmission (MRT) beamforming.

The sum capacity of the general K-user Gaussian Interference Channel (GIC) is known only when the channel coefficients are such that treating interference as noise (TIN) is optimal. The Han-Kobayashi (HK) scheme achieves the best known achievable rate region for the K-user interference channel (IC). Simple HK schemes are HK schemes with Gaussian signaling, no time sharing, and no private-common power splitting. The class of simple HK (S-HK) schemes includes the TIN scheme and schemes that involve various levels of interference decoding and cancellation at each receiver. We derive conditions under which simple HK schemes achieve sum capacity for general K-user Gaussian ICs. These results generalize existing sum capacity results for the TIN scheme to the class of simple HK schemes.

We determine the capacity-optimal transmission strategy for a multiple-input-multiple-output (MIMO) Gaussian channel under multiple power constraints, namely joint sum power constraint (SPC), per group power constraints (PGPC), and per antenna power constraints (PAPC). First, we focus on cases where we can analytically determine the optimal transmit strategy under joint SPC-PGPC-PAPC. We obtain results for the following cases: 1)$n_{t} \times 1$multiple-input-single-output (MISO); 2) MIMO channel with full column rank and full rank optimal covariance matrix; and 3)$2 \times n_{r}$MIMO channel. These results generalize some recent results for the special cases of PAPC only and joint SPC-PAPC. Then, we propose a projected factored gradient descent (PFGD) algorithm for the general MIMO Gaussian channel under joint SPC-PGPC-PAPC including the possibility of additional rank constraints. This algorithm matches the solution of standard convex optimization tools with lower complexity. The algorithm also overcomes the limitations of existing algorithms, in terms of accuracy and applicability to low rank channels.

We present results from an extensive simulation study, conducted to understand the properties of coverage and percolation in infrastructure-based wireless networks that comprise sink and relay nodes. Specifically, we compute vacancy (complement of coverage) and percolation probabilities as functions of sink and relay node densities. Further, we identify that the vacancy probability in an alternate model that is motivated from traditional coverage processes, referred to as independent-disc model, constitutes a lower bound for the vacancy in the original infrastructure-based model. For the case of percolation, we identify a threshold boundary (in the space of sink-relay densities pair) where the percolation probability transits rapidly from 0 to 1 (i.e., from no-percolation to full-percolation).

In this paper, we consider a multi-cell multi-band downlink where the base-station (BS) in each cell has multiple transmit antennas and each cell has one active mobile station (MS) with a single receive antenna and treats interference from the other cells as noise. There is a sum transmit power constraint for each BS over all the bands. An alternating maximization (AM) algorithm is proposed to determine the optimal power allocation among the bands and the optimal beamforming vectors for each BS in each band. This algorithm can be implemented in a distributed manner with limited exchange of interference constraints between the BSs. Using simulations, the algorithm is shown to converge to the weighted sum rate optimal point on the Pareto boundary of the achievable rate region. Furthermore, significant performance gain is observed compared to: (i) equal power allocation across bands and weighted sum rate optimization in each band, (ii) zero-forcing (ZF) beamforming, and (iii) maximal ratio transmission (MRT) beamforming.

We consider a change detection problem with an unknown post-change parameter. The optimal algorithm in minimizing worst case detection delay subject to a constraint on average run length, referred as parallel CUSUM, is computationally expensive. We propose a low complexity algorithm based on parameter estimation using Kiefer-Wolfowitz (KW) method with CUSUM based change detection. We also consider a variant of KW method where the tuning sequences of KW method are reset periodically. We study the performance under the Gaussian mean change model. Our results show that reset KW-CUSUM performs close to the parallel CUSUM in terms of worst case delay versus average run length. Non-reset KW-CUSUM algorithm has smaller probability of false alarm compared to the existing algorithms, when run over a finite duration.

In this paper, we propose new decode-and-forward (DF) protocols for the M-relay diamond relay channel with multicarrier transmission. The DF protocols proposed for the case of multicarrier transmission are motivated by recent capacity gap results for the 2-relay single-carrier diamond channel. The protocol for the M-relay case is an extension of the 2-relay case using appropriate relay selection. The various DF protocols studied differ in the choice and number of network states used, the allocation of subcarriers and power in each state, and the resulting operating rate vector for each state. First, we compare the performance of the proposed DF protocol with the cutset upper bound and existing DF relaying protocols for the 2-relay case with multicarrier transmission. We focus on the case of Rayleigh fading models for each of the links and also study the performance for various relay locations. Then, we consider the 3-relay case for performance comparison. From the numerical results, we observe that: (1) the proposed DF protocols perform significantly better than the existing DF protocols, and (2) appropriately chosen DF relaying protocols can perform close to the cutset bound.

In this paper, we determine the optimal transmit strategy that achieves the capacity of a n_t x 1 Multiple-Input Single-Output (MISO) and 2 x n_r Multiple-Input-Multiple-Output (MIMO) channels under: (1) Per-group Power constraints (PGPC), i.e., sum power constraints on groups of antennas, and (2) Joint power constraints, e.g., sum power constraint and per-group power constraints. Sum power constraint over all antennas (SPC) and per-antenna power constraints (PAPC) are special cases of PGPC. The results in this paper generalize some recent results on MISO capacity under: (1) PAPC and (2) joint SPC and PAPC. PGPC and joint power constraints arise naturally in distributed antenna systems, and in systems with some practical implementation constraints.

In this paper, we determine the optimal rank-constrained transmission strategy for the Multiple-Input Multiple-Output (MIMO) channel under Per-group Power constraints (PGPC). PGPC includes sum power constraint and per-antenna power constraints as special cases. We propose a Projected Factored Gradient Descent (PFGD) algorithm to obtain optimal rank-constrained strategy. If the rank constraint is greater than or equal to the rank of the unconstrained optimal covariance matrix, then the rank-constrained capacity coincides with the MIMO capacity under PGPC. The study of rank-constrained transmission under PGPC is important in the context of mmWave systems where the number of antennas could be large, but the number of streams is limited by the rank of the channel and the complexity of implementation of spatial multiplexing with a large number of streams. The proposed algorithm has lower complexity compared to standard approaches for semi-definite programs especially for low rank transmission. Numerical results are shown to study the behavior of the resulting rank-constrained capacity in the context of some existing mmWave channel models and to illustrate the convergence of the algorithm. For a special case where the channel has full column rank and the optimal covariance matrix is also full rank, we determine the MIMO capacity under PGPC analytically.

We consider a large class of bidirectional relaying scenarios with physical layer network coding, and analytically characterize the relay's error performance in decoding the network-coded combination at high signal-to-noise ratio (SNR). Our analysis applies to scenarios with 1) binary or higher order real/complex modulation, 2) real or complex channel coefficients, and 3) linear or non-linear network maps for network coding at the relay. We consider block fading and allow the relay to choose from a set of network maps based on the channel coefficients of the source to relay links in every block. We derive expressions for pairwise error probability and approximate expected overall error probability. We also derive lower bounds for these error probabilities. We validate these expressions using simulations and show that our approximations are tight in the high SNR regime.

In this paper, the Gaussian many-to-one X channel (XC), which is a special case of general multiuser XC, is studied. In the Gaussian many-to-one XC, communication links exist between all transmitters and one of the receivers, along with a communication link between each transmitter and its corresponding receiver. As per the XC assumption, transmission of messages is allowed on all the links of the channel. This communication model is different from the corresponding manyto-one interference channel (IC). Transmission strategies, which involve using Gaussian codebooks and treating interference from a subset of transmitters as noise, are formulated for the above channel. Sum-rate is used as the criterion of optimality for evaluating the strategies. Initially, a 3 × 3 many-to-one XC is considered and three transmission strategies are analyzed. The first two strategies are shown to achieve sum-rate capacity under certain channel conditions. For the third strategy, a sum-rate outer bound is derived and the gap between the outer bound and the achieved rate is characterized. These results are later extended to the K × K case. Next, a region in which the many-to-one XC can be operated as a many-to-one IC without the loss of sum-rate is identified. Furthermore, in the above region, it is shown that using Gaussian codebooks and treating interference as noise achieve a rate point that is within K/2 - 1 bits from the sum-rate capacity. Subsequently, some implications of the above results to the Gaussian many-to-one IC are discussed. Transmission strategies for the many-to-one IC are formulated, and channel conditions under which the strategies achieve sum-rate capacity are obtained. A region where the sum-rate capacity can be characterized to within K/2 - 1 bits is also identified. Finally, the regions where the derived channel conditions are satisfied for each strategy are illustrated for a 3 × 3 many-to-one XC and the corresponding many-to-one IC.

In this paper, we analyze the Gaussian X channel in the mixed interference regime. In this regime, multiple access transmission to one of the receivers is shown to be close to optimal in terms of sum rate. Three upper bounds are derived for the sum capacity in the mixed interference regime, and the subregions where each of these bounds dominate the others are identified. The genie-aided sum capacity upper bounds derived also show that the gap between sum capacity and the sum rate of the multiple access transmission scheme is small for a significant part of the mixed interference region. For any δ > 0, the region where multiple access transmission to one of the receivers is within δ from sum capacity is determined.

In this paper, we study the Gaussian MIMO Z channel and the Gaussian MIMO X channel. The MIMO X channel (XC) consists of two multiple antenna transmit-receive pairs, where each transmitter communicates with both receivers. The MIMO Z channel (ZC) is obtained from the MIMO X channel by eliminating one of the links and its corresponding message. First, we derive a sum-rate upper bound for the MIMO Z channel and compare it with an existing bound in literature. Next, we consider the MIMO X channel and propose a new sum-rate upper bound by utilizing the sum-rate upper bound for the MIMO ZC. Subsequently, we derive another upper bound for the MIMO XC by assuming receiver cooperation and deriving the worst noise covariance matrix for the resulting two-user MAC. We compare the above two upper bounds for the MIMO XC with the MaddahAli-Motahari-Khandani (MMK) scheme. Then, we consider some consequences of the above results for the MIMO interference channel. Finally, we present some numerical results. The numerical results suggest that the proposed sum-rate capacity upper bounds are tighter than existing bounds.

We study the use of Low-Density Parity-Check (LDPC) codes for two-phase, network-coded bidirectional relaying with higher-order modulation. In the multiple-access phase, the sum of transmitted symbols scaled by the channel gains is the received relay constellation, which is network-mapped (clustered) to a transmit constellation for the ensuing broadcast phase. This operation at the relay is termed Clustered-Scaled-Sum (CSS) decoding. We propose a CSS coding scheme for bidirectional relaying using a single LDPC code over a ring with higher-order PAM or QAM alphabets. We design a message-passing decoder for CSS decoding with trade-offs possible between complexity and performance. We suggest a method for completing a Constrained Partially-filled Latin Square (CPLS) to a latin square, which is used in the construction of network maps at the relay for any channel fading state. The performance of the CSS coding scheme with LDPC codes over rings is shown to be very close to information-theoretic outer bounds.

We address the problem of optimal downlink resource allocation in an OFDMA system, in a scenario where very limited channel quality information (CQI) is available at the base station. This paper is particularly applicable in the context of the LTE downlink since the feedback mechanism that we consider closely resembles one of the CQI reporting modes in LTE. Specifically, the users only report the indices of their best M subbands and an effective CQI corresponding to these best M bands. Our policy simultaneously performs optimal subband assignment and rate allocation, by taking into account channel quality and the queue backlogs of each user. The technical novelty of our work lies in exploiting a limit theorem on the best SNRs reported by the users, and combining it within a Lyapunov stability framework. We show that our policy is throughput maximizing among all policies, which are constrained to the CQI mechanism considered. Numerical results indicate that, in terms of throughput and average delay, our policy compares favorably to existing resource allocation policies such as proportional fair.

We study the Gaussian many-to-one interference network which is a special case of general interference network, where only one receiver experiences interference. We allow transmission of messages on all the links of the network. This communication model is different from the corresponding many-to-one interference channel. We formulate three transmission strategies for the above network, which involve using Gaussian codebooks and treating interference from a subset of the transmitters as noise. We use sum-rate as the criterion of optimality for evaluating the strategies. For the first two strategies, we characterize the sum-rate capacity under certain channel conditions, while for the other strategy, we derive a sum-rate outer bound and characterize the gap between the outer bound and the achievable sum-rate of the strategy. Finally, we illustrate the regions where the derived channel conditions are satisfied for each strategy.

We study the Gaussian one-to-many interference network which is obtained as a special case of a general interference network, where only one transmitter generates interference in the network. We allow transmission of messages on all the links of the network. This communication model is different from the corresponding one-to-many interference channel. We formulate two transmission strategies for the above network, which involve using Gaussian codebooks and treating interference as noise at a subset of the receivers. We use sum-rate as the criterion of optimality for evaluating the strategies. For the first strategy, we characterize the sum-rate capacity under certain channel conditions, while for the second strategy, we derive a sum-rate outer bound and characterize the gap between the outer bound and the achievable sum-rate of the strategy. Next, we show that the solution approach for the second strategy has applications to the cascade Gaussian Z network, a network consisting of parallel point-to-point links, where each transmitter except the last has a communication link to the adjacent receiver. Lastly, we illustrate the regions corresponding to the derived channel conditions for each strategy.

We address the problem of optimal downlink resource allocation in an OFDMA system, in a scenario where very limited channel quality information (CQI) is available at the base-station. Our work is particularly applicable in the context of the LTE downlink, since the feedback mechanism we consider closely resembles one of the CQI reporting modes in LTE. Specifically, the users only report the indices of their best M sub-bands and an effective CQI corresponding to these best M bands. Our policy simultaneously performs optimal sub-band assignment and rate allocation, by taking into account channel quality as well as the queue backlogs of each user. The technical novelty of our work lies in exploiting a limit theorem on the best SNRs reported by the users, and combining it within a Lyapunov stability framework. We show that our policy is throughput maximizing among all policies which are constrained to the CQI mechanism considered. Numerical results indicate that in terms of throughput and average delay, our policy compares favorably to existing resource allocation policies such as proportional fair.

We consider all-cast and multicast flow problems where either all of the nodes or only a subset of the nodes may be in session. Traffic from each node in the session has to be sent to every other node in the session. If the session does not consist of all the nodes, the remaining nodes act as relays. The nodes are connected by undirected links whose capacities are independent and identically distributed random variables. We study the asymptotics of the capacity region (with network coding) in the limit of a large number of nodes, and show that the normalized sum rate converges to a constant almost surely. We then provide a decentralized push-pull algorithm that asymptotically achieves this normalized sum rate without network coding.

In an M × N interference network, there are M transmitters and N receivers with each transmitter having independent messages for each of the 2N- 1 possible non-empty subsets of the receivers. We consider the 2 × 2 interference network with 6 possible messages, of which the 2 × 2 interference channel and X channel are special cases obtained by using only 2 and 4 messages respectively. Starting from an achievable rate region similar to the Han-Kobayashi region, we obtain an achievable sum rate. For the Gaussian interference network, we determine which of the 6 messages are sufficient for maximizing the sum rate within this rate region for the low, mixed, and strong interference conditions. It is observed that 2 messages are sufficient in several cases. Finally, we show that sum capacity is achieved using only 2 messages for a subset of the mixed interference conditions.

We propose a multihopping decode and forward relaying protocol for two-stage Gaussian relay networks with half-duplex nodes. We analytically show that the achievable rates in suitably defined strong and weak interference regimes are close to the cut-set bound.

We address the problem of allocating a single divisible good to a number of agents. The agents have concave valuation functions parameterized by a scalar type. The agents report only the type. The goal is to find allocatively efficient, strategy proof, nearly budget balanced mechanisms within the Groves class. Near budget balance is attained by returning as much of the received payments as rebates to agents. Two performance criteria are of interest: the maximum ratio of budget surplus to efficient surplus, and the expected budget surplus, within the class of linear rebate functions. The goal is to minimize them. Assuming that the valuation functions are known, we show that both problems reduce to convex optimization problems, where the convex constraint sets are characterized by a continuum of half-plane constraints parameterized by the vector of reported types. We then propose a randomized relaxation of these problems by sampling constraints. The relaxed problem is a linear programming problem (LP). We then identify the number of samples needed for “near-feasibility” of the relaxed constraint set. Under some conditions on the valuation function, we show that value of the approximate LP is close to the optimal value. Simulation results show significant improvements of our proposed method over the Vickrey-Clarke-Groves (VCG) mechanism without rebates. In the special case of indivisible goods, the mechanisms in this paper fall back to those proposed by Moulin, by Guo and Conitzer, and by Gujar and Narahari, without any need for randomization. Extension of the proposed mechanisms to situations when the valuation functions are not known to the central planner are also discussed.

We analyze the asymptotic performance of rate adaptation for Transmit Antenna Selection (TAS) and Maximum Eigenmode Beamforming (MEB) schemes in Multiple-Input Multiple-Output (MIMO) systems under imperfect channel state information (CSI) and feedback delay. The rate is adapted according to a target outage probability. We derive lower and upper bounds to this rate. We also asymptotically characterize the multi-step prediction error when MMSE prediction is used to combat feedback delay. Using the bounds and the prediction error asymptotics, we show that the rate gap from the ideal CSI scenario asymptotically grows logarithmically with SNR. The slope is at most the target outage probability. We find that when the target outage probability is decreased faster than an identified growth rate and prediction error goes to zero, then the rate gap remains bounded.

Dirty paper coding (DPC) refers to methods for pre-subtraction of known interference at the transmitter of a multiuser communication system. There are numerous applications for DPC, including coding for broadcast channels. Recently, lattice-based coding techniques have provided several designs for DPC. In lattice-based DPC, there are two codes - a convolutional code that defines a lattice used for shaping and an error correction code used for channel coding. Several specific designs have been reported in the recent literature using convolutional and graph-based codes for capacity-approaching shaping and coding gains. In most of the reported designs, either the encoder works on a joint trellis of shaping and channel codes or the decoder requires iterations between the shaping and channel decoders. This results in high complexity of implementation. In this work, we present a lattice-based DPC scheme that provides good shaping and coding gains with moderate complexity at both the encoder and the decoder. We use a convolutional code for sign-bit shaping, and a low-density parity check (LDPC) code for channel coding. The crucial idea is the introduction of a one-codeword delay and careful parsing of the bits at the transmitter, which enables an LDPC decoder to be run first at the receiver. This provides gains without the need for iterations between the shaping and channel decoders. Simulation results confirm that at high rates the proposed DPC method performs close to capacity with moderate complexity. As an application of the proposed DPC method, we show a design for superposition coding that provides rates better than time-sharing over a Gaussian broadcast channel.

Adaptive transmit beamforming based on channel state information (CSI) is a key feature in next generation wireless cellular systems. However, CSI available for adaptation is imperfect due to feedback delay and estimation errors. In this work, we analyze the outage performance of maximum eigen-mode beamforming with imperfect CSI. First we analyze the outage probability in terms of the correlation coefficient ¿ between the CSI available at the transmitter (CSIT) and the CSI available at the receiver (CSIR). The analysis shows that feedback delay leads to significant degradation at medium and high signal-to-noise ratios (SNR). Furthermore, the effect of delay can be overcome only if ¿ tends to one with increasing SNR. Then, we study whether linear minimum mean squared error (MMSE) prediction can achieve the required behavior in ¿. The length of the prediction filter required is numerically evaluated and shown to increase with SNR. Finally, the asymptotic diversity order is analyzed as a function of the rate at which 1 - ¿ approaches 0 as the SNR ¿ ¿. Results show that for 1 - ¿ proportional to SNR-1, the asymptotic diversity order remains unaltered.

We investigate the effect of feedback delay on the outage probability of multiple-input single-output (MISO) fading channels. Channel state information at the transmitter (CSIT) is a delayed version of the channel state information available at the receiver (CSIR). We consider two cases of CSIR: (a) perfect CSIR and (b) CSI estimated at the receiver using training symbols. With perfect CSIR, under a short-term power constraint, we determine: (a) the outage probability for beamforming with imperfect CSIT (BF-IC) analytically, and (b) the optimal spatial power allocation (OSPA) scheme that minimizes outage numerically. Results show that, for delayed CSIT, BF-IC is close to optimal for low SNR and uniform spatial power allocation (USPA) is close to optimal at high SNR. Similarly, under a longterm power constraint, we show that BF-IC is better for low SNR and USPA is better at high SNR. With imperfect CSIR, we obtain an upper bound on the outage probability with USPA and BF-IC. Results show that the loss in performance due to imperfection in CSIR is not significant, if the training power is chosen appropriately.

The downlink scheduling problem in multi-queue multi-server systems under channel uncertainty is considered. Two policies that make allocations based on predicted channel states are proposed. The first is an extension of the well-known dynamic backpressure policy to the uncertain channel case. The second is a variant that improves delay performance under light loads. The stability region of the system is characterised and the first policy is argued to be throughput optimal. A recently proposed policy of Kar et al has lesser complexity, but is shown to be throughput suboptimal. Further, simulations demonstrate better delay and backlog properties for both our policies at light loads.

Antenna selection in Multiple-Input-Multiple-Output (MIMO) systems preserves diversity gain while significantly reducing hardware complexity. However, imperfect Channel State Information (CSI) affects performance. In this paper, we first analyze the performance of a MIMO system employing antenna selection at the transmitter and Maximal Ratio Combining (MRC) at the receiver in the presence of feedback delay and channel estimation errors. Then, we determine whether channel prediction can compensate for the effect of feedback delay. Outage probability is analyzed as a function ofρ, the correlation coefficient between the CSI used at the receiver for decoding (CSIR) and the CSI used at the transmitter for selection (CSIT). Analytical results show that the effect of feedback delay is more significant than the effect of estimation error. In order to overcome the effect of delay,ρshould increase with SNR. For a given SNR, the length of the Linear Minimum Mean Square Error (LMMSE) prediction filter required is calculated and shown to increase with SNR. Finally, we determine the asymptotic diversity order as a function of the feedback quality. Results show that if1 - ρ ∝ SNR-1, the diversity order with imperfect CSI is same as that with perfect CSI.

Performance of spatial multiplexing multiple-input multiple-output (MIMO) wireless systems can be improved with channel state information (CSI) at both ends of the link. This paper proposes a new linear diagonal MIMO transceiver, referred to as co-ordinate interleaved spatial multiplexing (CISM). With CSI at transmitter and receiver, CISM diagonalizes the MIMO channel and interleaves the co-ordinates of the input symbols (from rotated QAM constellations) transmitted over different eigenmodes. The analytical and simulation results show that with co-ordinate interleaving across two eigenmodes, the diversity gain of the data stream transmitted over the weaker eigenmode becomes equal to that of the data transmitted on the stronger eigenmode, resulting in a significant improvement in the overall diversity. The diversity-multiplexing tradeoff (DMT) is analyzed for CISM and is shown that it achieves higher diversity gain at all positive multiplexing gains compared to existing diagonal transceivers. Over rank n MIMO channels, with input symbols from rotated n-dimensional constellations, the DMT of CISM is a straight line connecting the endpoints (0,NtNr) and (min{Nt,Nr}, 0), where Nt, and Nrare the number of transmit and receive antennas, respectively.

In this work, we analyze the diversity-multiplexing performance of a MIMO multiple access wireless system with non-cooperating transmitters. Each of the transmitters and receiver use noisy and mismatched versions of the channel estimate to implement decentralized power control. While accounting for the resources consumed in training, we show that with relatively simple power control, regardless of the number of transmitters, we can achieve double the maximum diversity order of a system with no instantaneous channel state information at the transmitters. Intuitively, the gain can be attributed to using temporal degrees of freedom enabled by power control without coding over multiple coherence intervals.

The performance of iterative decoding of low density parity check (LDPC) codes over binary erasure channels can be completely characterized by the study of stopping sets. Therefore, the burst erasure correction capability of a given LDPC code can be readily quantified by searching for stopping sets within consecutive bit nodes. In this work we study the optimal permutation of the bit nodes that will result in the maximum possible burst erasure correction capability for a given LDPC code. Noting that this is essentially a combinatorial optimization problem that is highly likely to be NP-hard, we adopt a simulated annealing based approach for finding the optimal permutation. We present bounds based on stopping sets that limit the burst erasure correction capability. As part of our results, we provide interleavers that greatly improve the burst erasure correction capability of protograph quasi-cyclic LDPC codes used in the WiMax standard.

When channel state information is available at the transmitter, we can diagonalize a MIMO channel with SVD transceivers. However, outage of the weaker eigenchannels limits the performance of such a transceiver. In the current work, we propose and analytically characterize co-ordinate interleaving of multi-dimensional symbols over sets of eigenchannels. We show that in our method, the diversity multiplexing trade-off (DMT) is determined by the strongest eigenchannel in each set. We also calculate the optimal (DMT) for different possible sets and constellation dimensions with rate allocation among the sets.

We propose an improved channel estimator for reuse-1 orthogonal frequency division multiplexing (OFDM) cellular systems. The proposed channel estimation technique exploits delay subspace structure in reducing the interference on channel estimation. The proposed pilot-based channel estimation technique initially estimates the multipath delay locations of both the desired and interference channels. In estimating multipath delays, we assume that the time-of-flight difference between the desired and interfering signals ensures that the multipath delay locations of the corresponding channels are distinct. This information is used to suppress interference in the multipath-delay domain, and define a channel interpolator with a lower normalized mean squared error (NMSE) when compared to the conventional modified least-squares technique (mLS). We also derive the analytical expression for the bit-error-rate of a zero- forcing (ZF) receiver based on the proposed channel estimator. In particular, we show that for uncoded OFDM, the match between the estimated BER and analytical BER is very good and the proposed estimator can outperform mLS by more than a order of magnitude in BER if the interference on the data subcarriers is significantly lower than the interference seen on the pilot subcarriers. Simulation results are also presented with turbo-coded OFDM which further demonstrates the efficacy of the proposed algorithm.

In an orthogonal frequency division multiplexing (OFDM) downlink scenario, we propose joint subcarrier and power allocation for channel-aware queue-aware scheduling while allowing multiple users to share a single OFDM symbol. Our approach is to combine subcarrier and power allocation by optimizing a user's power allocation immediately after the user has been allocated a subcarrier. Simulation results show that joint subcarrier and power allocation yields a significant performance improvement compared to other existing schemes which perform subcarrier allocation with a fixed (uniform) power allocation assumption. Joint subcarrier and power allocation is also extended to band-wise allocation of subcarriers in order to help reduce signaling overhead in time varying channels. We examine the trade-off between increasing the sub-band size and the corresponding degradation in system performance for different values of the channel multipath delay spread.

In this work, we present an algorithm for parametric estimation of a wireless channel for OFDMA transmission, tailored to the pseudo-random "tile" allocation pattern prevalent in multi-user allocation schemes1. Such tile allocations are usually common in the uplink where a tile is usually a small number of adjacent data subcarriers with a few pilot subcarriers. The algorithm estimates the delay subspace of the parametric channel description, and shows robustness for high RMS delay-spread channels. The estimator error convergence performance improves with increase in the channel Doppler frequency. Although the proposed algorithm requires more intensive computation than straight-forward intra-tile linear interpolation, it offers a greatly enhanced Bit-Error-Rate (BER) performance. In mobile channel nel environments, the need for numerous re-transmissions is therefore decreased, making this algorithm suitable for low BER applications such as video and data.

In this letter, we investigate the effect of hopping pilots on the parametric channel estimation in orthogonal frequency-division multiplexing (OFDM) systems. Channel estimation algorithms based on parametric channel modeling require the multipath delays to be estimated at the receiver. The use of a fixed pilot pattern leads to longer training overhead in multipath delay estimation for slow fading channels. However, if hopping pilot patterns are available, then we show that the normalized mean-squared error (NMSE) convergence rate of the channel estimates can be significantly improved. We also show that hopping pilot patterns in OFDM systems effectively allow the eigenvectors of the delay subspace (of the autocorrelation matrix) to be estimated faster. Simulation results are provided to show the faster convergence rate of the NMSE for the hopping pilot pattern over the fixed pilot pattern.

Channel estimation techniques for code-division multiple access (CDMA) systems need to combat multiple access interference (MAI) effectively. Most existing estimation techniques are designed for CDMA systems with short repetitive spreading codes. However, current and next-generation wireless systems use long spreading codes whose periods are much larger than the symbol duration. We derive the maximum-likelihood channel estimate for long-code CDMA systems over multipath channels using training sequences and approximate it using an iterative algorithm to reduce the computational complexity in each symbol duration. The iterative channel estimate is also shown to be asymptotically unbiased. The effectiveness of the iterative channel estimator is demonstrated in terms of squared error in estimation as well as the bit error rate performance of a multistage detector based on the channel estimates. The effect of error in decision feedback from the multistage detector (used in the absence of training sequences) is also shown to be negligible for reasonable feedback error rates using simulations. The proposed iterative channel estimation technique is also extended to track slowly varying multipath fading channels using decision feedback. Thus, an MAI-resistant multiuser channel estimation and tracking scheme with reasonable computational complexity is derived for long-code CDMA systems over multipath fading channels.

Multiple antenna transmission and reception have been shown to significantly increase the achievable data rates of wireless systems. However, most of the existing analysis assumes perfect or no channel information at the receiver and transmitter. The performance gap between these extreme channel assumptions is large and most practical systems lie in between. Therefore, it is important to analyze multiple antenna systems in the presence of partial channel information. We upper bound the outage probability performance of multiple antenna systems with preamble-based channel estimation and quantized feedback. We design causal feedback and power control schemes to minimize this upper bound on outage probability. We consider the following practical issues in our analysis and design: (1) the channel information is imperfect both at the receiver and at the transmitter and (2) part of the total available resources for the system need to be used for estimation and feedback. Our results demonstrate that for block fading channels, sending a periodic preamble and causally receiving channel state information via a feedback channel can lead to substantial gains in the outage performance over any nonfeedback scheme. Most of the gains achieved by perfect feedback can be achieved by very few bits of feedback. Furthermore, it is demonstrated that these outage probability gains can be translated into improvements in frame error rate performance of systems using space-time codes. Thus, implementing a power control, even at the cost of reduced spectral resources for the forward channel is beneficial for block fading channels.

This paper presents algorithms and architecture designs that can meet real-time requirements of multiuser channel estimation and detection in future code-division multiple-access-based wireless base-station receivers. Sophisticated algorithms proposed to implement multiuser channel estimation and detection make their real-time implementation difficult on current digital signal processor-based receivers. A maximum-likelihood based multiuser channel estimation scheme requiring matrix inversions is redesigned from an implementation perspective for a reduced complexity, iterative scheme with a simple fixed-point very large scale integration (VLSI) architecture. A reduced-complexity, bit-streaming multiuser detection algorithm that avoids the need for multishot detection is also developed for a simple, pipelined VLSI architecture. Thus, we develop real-time solutions for multiuser channel estimation and detection for third-generation wireless systems by: (1) designing the algorithms from a fixed-point implementation perspective, without significant loss in error rate performance; (2) task partitioning; and (3) designing bit-streaming fixed-point VLSI architectures that explore pipelining, parallelism, and bit-level computations to achieve real-time with minimum area overhead.

A real-time VLSI architecture is designed for multiuser channel estimation, one of the core baseband processing operations in wireless base-station receivers. Future wireless base-station receivers will need to use sophisticated algorithms to support extremely high data rates and multimedia. Current DSP architectures are unable to fully exploit the parallelism and bit level arithmetic present in these algorithms. These features can be revealed and efficiently implemented by task partitioning the algorithms for a VLSI solution. We modify the channel estimation algorithm for a reduced complexity fixed-point hardware implementation. We show the complexity and hardware required for three different area-time tradeoffs: an area-constrained, a time-constrained and an area-time efficient architecture. The area-constrained architecture achieves low data rates with minimum hardware, which may be used in pico-cell base-stations. The time-constrained solution exploits the entire available parallelism and determines the maximum theoretical data rates. The area-time efficient architecture meets real-time requirements with minimum area overhead. The orders-of-magnitude difference between area and time constrained solutions reveals significant inherent parallelism in the algorithm. All proposed VLSI solutions exhibit better time performance than a previous DSP implementation.

Channel estimation techniques for code-division multiple access (CDMA) systems need to combat multiple access interference (MAI) effectively. Most existing estimation techniques are designed for CDMA systems with short repetitive spreading codes. However, current and next generation wireless systems use long spreading codes whose period is much larger than the symbol duration. In this paper, we derive the maximum likelihood channel estimate for long code CDMA systems over multipath channels using training sequences and approximate it using an iterative algorithm to reduce the computational complexity in each processing window. The asymptotic convergence of the mean of the iterative estimate to the actual channel is also shown. The effectiveness of the iterative channel estimator is demonstrated in terms of squared error in estimation as well as the bit error rate performance of a multistage detector based on the channel estimates. Finally, the proposed iterative channel estimation technique is extended to track slowly varying multipath fading channels using decision feedback. Thus, an MAI resistant multiuser channel estimate with reasonable computational complexity is derived for long code CDMA systems over multipath fading channels.

The mobile wireless channel affords inherent diversity to combat the effects of fading. Existing code-division multiple-access systems, by virtue of spread-spectrum signaling and RAKE reception, exploit only part of the channel diversity via multipath combination. Moreover, their performance degrades under fast fading commonly encountered in mobile scenarios. In this paper, we develop new signaling and reception techniques that maximally exploit channel diversity via joint multipath-Doppler processing. Our approach is based on a canonical representation of the wireless channel, which leads to a time-frequency generalization of the RAKE receiver for diversity processing. Our signaling scheme facilitates joint multipath-Doppler diversity by spreading the symbol waveform beyond the intersymbol duration to make the channel time-selective. A variety of detection schemes are developed to account for the intersymbol interference (ISI) due to overlapping symbols. However, our results indicate that the effects of ISI are virtually negligible due to the excellent correlation properties of the pseudorandom codes. Performance analysis also shows that relatively small Doppler spreads can yield significant diversity gains. The inherently higher level of diversity achieved by time-selective signaling brings the fading channel closer to an additive white Gaussian noise channel, thereby facilitating the use of powerful existing coding techniques for Gaussian channels.