In the user-centric cell-free massive MIMO (UC-mMIMO) network scheme, user mobility necessitates updating the set of serving access points to maintain the user-centric clustering. Such updates are typically performed through handoff (HO) operations; however, frequent HOs lead to overheads associated with the allocation and release of resources. This paper presents a deep reinforcement learning (DRL)-based solution to predict and manage these connections for mobile users. Our solution employs the Soft Actor-Critic algorithm, with continuous action space representation, to train a deep neural network to serve as the HO policy. We present a novel proposition for a reward function that integrates a HO penalty in order to balance the attainable rate and the associated overhead related to HOs. We develop two variants of our system; the first one uses mobility direction-assisted (DA) observations that are based on the user movement pattern, while the second one uses history-assisted (HA) observations that are based on the history of the large-scale fading (LSF). Simulation results show that our DRL-based continuous action space approach is more scalable than discrete space counterpart, and that our derived HO policy automatically learns to gather HOs in specific time slots to minimize the overhead of initiating HOs. Our solution can also operate in real time with a response time less than 0.4 ms.

We study the problem of managing handoffs (HOs) in user-centric cell-free massive MIMO (UC-mMIMO) networks. Motivated by the importance of controlling the number of HOs and by the correlation between efficient HO decisions and the temporal evolution of the channel conditions, we formulate a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading and the action space representing the association decisions of the user with the access points (APs). We develop a novel algorithm that employs this model to derive a HO policy for a mobile user based on current and future rewards. To alleviate the high complexity of our POMDP, we follow a divide-and-conquer approach by breaking down the POMDP formulation into sub-problems, each solved separately. Then, the policy and the candidate pool of APs for the sub-problem that produced the best total expected reward are used to perform HOs within a specific time horizon. We then introduce modifications to our algorithm to decrease the number of HOs. The results show that half of the number of HOs in the UC-mMIMO networks can be eliminated. Namely, our novel solution can control the number of HOs while maintaining a rate guarantee, where a 47%-70% reduction of the cumulative number of HOs is observed in networks with a density of 125 APs per km2. Most importantly, our results show that a POMDP-based HO scheme is promising to control HOs.

We consider a bidirectional communication between two single-antenna transceivers using multiple multi-antenna access points (APs) in a cell-free network architecture. In such a network, because of different propagation delays associated with different APs, the end-to-end link is a multi-path channel that results in inter-symbol-interference (ISI) in the signals received at the transceivers. To tackle ISI, we resort to cyclic prefix (CP) assisted block transmission of the information symbols and employ joint pre- and post-channel equalizers at both the transceivers to mitigate the impact of intra-block interference. Considering the amplify-and-forward technique at the APs, we cast the joint design of equalizers, beamforming matrices, and transceivers’ transmit powers as a power minimization problem while guaranteeing predefined data rates at the transceivers. Assuming symmetric beamforming matrices at the APs, we devise a semi-closed-form solution for this problem. We prove rigorously that at the optimum only a synchronous subset of the APs should participate in the information exchange between the two transceivers. This is achieved by proving that at the optimum, the pre-equalizer matrices should be unitary and the post-equalizer matrices should be invertible.

We propose an interleaved training design for multi-user massive MIMO downlink and study the performance thereof with the maximum-ratio transmission (MRT) precoding. In our proposed design, the channels are trained one BS antenna at a time and each training step is interleaved with the channel state information (CSI) feedback. The decision to continue training depends on whether or not the signal-to-interference-plus-noise (SINR) requirements of all users are satisfied with currently available instantaneous CSI. For the MRT precoding, we analyze the system performance in terms of the training length and the transmission success rate. Our simulation results show that the proposed training scheme has a significant performance advantage over existing full training scheme and fixed-length partial training scheme.

We devise a partially observable Markov decision process (POMDP) based joint antenna selection and user scheduling (JASUS) policy for a massive MIMO base station, equipped with only a small number of RF chains, that serves a large number of users. The users are served at different time slots within a frame. Relying on partial CSI obtained from training between the selected antennas and the users, at the beginning of each frame, the BS assigns each user to a time slot in the frame and selects a subset of antennas to serve the users scheduled in each time slot. Assuming that the channels evolve according to a Markov process and relying on zero-forcing beamforming, we formulate our JASUS problem using a POMDP framework to devise a real-time decision-making policy that maximizes the expected long-term sum-rate. We rigorously prove that for positively correlated two-state channel models, the myopic policy provides the optimal solution to our POMDP-based JASUS problem for any number of RF chains and for any number of users. Based on this, we model the Rayleigh fading channels as first-order Gauss-Markov processes and devise a low-complexity myopic policy-based JASUS algorithm for massive MU-MIMO systems that only relies on partial CSI.

We propose to control handoffs (HOs) in user- centric cell-free massive MIM 0 networks through a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading (LSF) and the action space representing the association decisions of the user with the access points. Our proposed formulation accounts for the temporal evolution and the partial observability of the channel states. This allows us to consider future rewards when performing HO decisions, and hence obtain a robust HO policy. To alleviate the high complexity of solving our POMDP, we follow a divide-and-conquer approach by breaking down the POMDP formulation into sub-problems, each solved individually. Then, the policy and the candidate cluster of access points for the best solved sub-problem is used to perform HOs within a specific time horizon. We control the number of HOs by determining when to use the HO policy. Our simulation results show that our proposed solution reduces HOs by 47% compared to time- triggered LSF-based HOs and by 70% compared to data rate threshold-triggered LSF-based HOs. This amount can be further reduced through increasing the time horizon of the POMDP.

An interleaved training scheme is proposed for multi-user massive multi-input-multi-output (MIMO) downlink with maximum-ratio-transmission (MRT). The base station (BS) sends pilots to train the channels antenna-by-antenna and the training steps are interleaved with the feedback of the channel state information (CSI) from the users. For each training step of the interleaved scheme, the BS decides whether to continue or to stop the training process based on the quality-of-service (QoS) provided by the available CSI. The training time and the transmission success rate of the proposed scheme are analyzed with closed-form approximations derived. Simulations show that the proposed scheme can largely save the average training time without sacrificing the QoS of users. The analytical results are also verified via simulation.

We consider the analysis and design of distributed wireless networks wherein remote radio heads (RRHs) coordinate transmissions to serve multiple users on the same resource block (RB). Specifically, we analyze two possible multiple-input multiple-output wireless fronthaul solutions: multicast and zero forcing (ZF) beamforming. We develop a statistical model for the fronthaul rate and, coupled with an analysis of the user access rate, we optimize the placement of the RRHs. This model allows us to formulate the location optimization problem with a statistical constraint on fronthaul outage. Our results are cautionary, showing that the fronthaul requires considerable bandwidth to enable joint service to users. This requirement can be relaxed by serving a low number of users on the same RB. Additionally, we show that, with a fixed number of antennas, for the multicast fronthaul, it is prudent to concentrate these antennas on a few RRHs. However, for the ZF beamforming fronthaul, it is better to distribute the antennas on more RRHs. For the parameters chosen, using a ZF beamforming fronthaul improves the typical access rate by approximately 8% compared to multicast. Crucially, our work quantifies the effect of these fronthaul solutions and provides an effective tool for the design of distributed networks.

This paper considers the integration of non-orthogonal multiple access (NOMA) into massive multi-input multi-output (MIMO) systems for downlink transmission. We consider the joint design of user clustering, transmit beamforming, and power allocation to minimize the total transmit power while meeting the signal-to-interference-and-noise ratio targets. We decompose this challenging mixed-integer programming problem into three separate subproblems to solve. We propose a low-complexity learning-based user clustering algorithm, which is a modified version of mean shift clustering with a new channel correlation based clustering metric. The proposed clustering algorithm determines the clusters to trade-off between spatial dimension and power dimension offered by respective MIMO and NOMA for user multiplexing. We then design zero-forcing transmit beamformers to eliminate inter-cluster interference and optimize power allocation to minimize the total transmit power. We provide two case studies for both co-located and distributed massive MIMO systems in spatially highly correlated prorogation environments. Simulation results show that our proposed algorithm forms NOMA clusters based on the available degrees of freedom in the system to effectively use both spatial and power dimensions, which results in a substantial performance improvement over MIMO-only methods or other existing clustering methods in such environments.

We use a partially observable Markov decision process (POMDP) framework to design an optimal antenna selection policy for downlink transmit beamforming at a multi-antenna base station (BS) equipped with only a limited number of RF chains. Assuming that the channel state evolves according to a finite-state Markov process and that only the channel coefficients which correspond to previously selected antennas, are available at the BS, we use the POMDP framework for antenna selection with the aim to maximize thelong-term expected downlink data rate. To avoid the high computational complexity of the value iteration algorithm, we focus on the myopic policy and prove thatin the case of positively correlated two-state Markov model for the channel over each antenna, the myopic policy is optimal for antenna selection for any number of RF chains. Based on this finding, for general fading channels, we propose to quantize each channel into two levels and apply the myopic policy for antenna selection. Our simulation results show that using this two-state coarse channel quantization for antenna selection results in only a small loss in performance, as compared to the antenna selection technique which uses full channel state information without quantization.

In this paper, we optimize user scheduling, power allocation and beamforming in distributed multiple-input multiple-output (MIMO) networks implementing user-centric clustering. We study both the coherent and non-coherent transmission modes, formulating a weighted sum rate maximization problem for each; finding the optimal solution to these problems is known to be NP-hard. We use tools from fractional programming, block coordinate descent, and compressive sensing to construct an algorithm that optimizes the beamforming weights and user scheduling and converges in a smooth non-decreasing pattern. Channel state information (CSI) being crucial for optimization, we highlight the importance of employing a low-overhead pilot assignment policy for scheduling problems. In this regard, we use a variant of hierarchical agglomerative clustering, which provides a suboptimal, but feasible, pilot assignment scheme; for our cell-free case, we formulate anarea-basedpilot reuse factor. Our results show that our scheme provides large gains in the long-term network sum spectral efficiency compared to benchmark schemes such as zero-forcing and conjugate beamforming (with round-robin scheduling) respectively. Furthermore, the results show the superiority of coherent transmission compared to the non-coherent mode under ideal and imperfect CSI for the area-based pilot-reuse factors we consider.

We develop two distributed downlink resource allocation algorithms for user-centric, cell-free, spatially-distributed, multiple-input multiple-output (MIMO) networks. In such networks, each user is served by a subset of nearby transmitters that we call distributed units or DUs. The operation of the DUs in a region is controlled by a central unit (CU). Our first scheme is implemented at the DUs, while the second is implemented at the CUs controlling these DUs. We define a hybrid quality of service metric that enables distributed optimization of system resources in a proportional fair manner. Specifically, each of our algorithms performs user scheduling, beamforming, and power control while accounting for channel estimation errors. Importantly, our algorithm does not require information exchange amongst DUs (CUs) for the DU-distributed (CU-distributed) system, while also smoothly converging. Our results show that our CU-distributed system provides 1.3- to 1.8-fold network throughput compared to the DU-distributed system, with minor increases in complexity and front-haul load - and substantial gains over benchmark schemes like local zero-forcing. We also analyze the trade-offs provided by the CU-distributed system, hence highlighting the significance of deploying multiple CUs in user-centric cell-free networks.

We consider robust multi-group multicast beamforming design in massive multiple-input multiple-output (MIMO) large-scale systems. The goal is to minimize the transmit power subject to the minimum signal-to-interference-plus-noise-ratio (SINR) targets under channel uncertainty. Using the exact worst-case SINR constraints, we transform the problem into a non-convex optimization problem. We develop an alternating direction method of multipliers (ADMM)based fast algorithm to solve this problem directly with convergence guarantee. Our two-layer ADMM-based algorithm decomposes the non-convex problem into a sequence of convex subproblems, for which we obtain the semi-closed-form or closed-form solutions. Simulation studies show that our algorithm provides a considerable computational advantage over the conventional interior-point method non-convex solver with nearly identical performance.

We use a partially observable Markov decision process (POMDP) framework to formulate the problem of antenna selection for a base-station, equipped with a large-scale antenna array and a smaller number of RF chains. Assuming that the fading channel evolves according to a finite-state Markov chain and that only partial channel state information (CSI) from the limited selected antennas is available at each time slot, we rely on a POMDP framework for antenna selection to maximize the long-term expected downlink data rate. To avoid the computational complexity associated with the value iteration algorithm, we herein propose to use the simple myopic antenna selection policy based on the fact that for any arbitrary number of antennas and RF chains, under the assumption of positively correlated two-state Markov channel model, the myopic policy is optimal. To apply the optimal myopic policy-based antenna selection for general fading channels, we propose to quantize the channels into two values only for the purpose of antenna selection. Interestingly, our results show that the performance of the myopic antenna selection policy is close to that of the policy which relies on un-quantized full CSI.

In this paper, we study the deployment decisions and location optimization for the remote radio heads (RRHs) in coordinated distributed networks in the presence of a wireless backhaul. We implement a scheme where the RRHs use zero-forcing beamforming (ZF-BF) for the access channel to jointly serve multiple users, while on the backhaul the RRHs are connected to their central units (CUs) through point-to-point wireless links. We investigate the effect of this scheme on the deployment of the RRHs and on the resulting achievable spectral efficiency over the access channel (under a backhaul outage constraint). Our results show that even for noise-limited backhaul links, a large bandwidth must be allocated to the backhaul to allow freely distributing the RRHs in the network. Additionally, our results show that distributing the available antennas on more RRHs is favored as compared to a more co-located antenna system. This motivates further works to study the efficiency of wireless backhaul schemes and their effect on the performance of coordinated distributed networks with joint transmission.

We study the problem of user-scheduling and resource allocation in distributed multi-user, multiple-input multiple-output (MIMO) networks implementing user-centric clustering and non-coherent transmission. We formulate a weighted sum-rate maximization problem which can provide user proportional fairness. As in this setup, users can be served by many transmitters, user scheduling is particularly difficult. To solve this issue, we use block coordinate descent, fractional programming, and compressive sensing to construct an algorithm that performs user-scheduling and beamforming. Our results show that the proposed framework provides an 8- to 10-fold gain in the long-term user spectral efficiency compared to benchmark schemes such as round-robin scheduling. Furthermore, we quantify the performance loss due to imperfect channel state information and pilot training overhead using a defined area-based pilot-reuse factor.

A multicarrier bidirectional filter-and-forward (FF) relay-assisted network in the context of device-to-device communication is investigated. The network consists of two multicarrier-based user devices that can communicate through multiple relay nodes equipped with finite impulse response (FIR) filters to equalize the frequency-selective channels. We jointly design the distributed equalization weight vector and the power allocations at the users to minimize the total transmit power under two quality of service constraints measured by the received sum-rates at the users. We propose a novel semi-closed form sub-optimal solution to this non-convex joint optimization problem. Simulation results show that the proposed design, in conjunction with the FF-based relay nodes, attains substantially better performance than the existing designs associated with amplify-and-forward and multicarrier relay nodes.

In this paper, we study the channel estimation problem in microwave correlated massive multiple-input-multiple-output systems with reduced number of radio-frequency chains. We exploit the knowledge of the transmit and receive correlation between the antennas. Leveraging the fact that the channel entries are uncorrelated in its eigen-domain, we seek to estimate the channel in this domain. Due to reduced number of radio-frequency chains, channel estimation is performed in multiple time slots. Under a total energy budget, we aim to optimally design the hybrid precoder and combiner in each training time slot, in order to estimate the channel using the minimum mean squared error criterion. We show that the optimal precoder and combiner in each time slot are aligned to transmitter and receiver eigen-directions, respectively. The energy allocation of each eigen-direction determines the significance of each eigen-direction; more energy is allocated to the stronger eigen-directions. At low training energy budget, only significant part of the channel needs to be estimated. At high training energy budget, the energy is equally distributed among all eigen-directions. Simulation results show that the proposed channel estimation scheme can efficiently estimate correlated massive multiple-input-multiple-output channels within a few training time slots.

We propose a one-bit symbol-level precoding method for massive multiuser multiple-input multiple-output (MU-MIMO) downlink systems using the idea of constructive interference (CI). In particular, we adopt a max-min fair design criterion which aims to maximize the minimum instantaneous received signal-to-noise ratio (SNR) among the user equipments (UEs), while ensuring a CI constraint for each UE and under the restriction that the output of the precoder is a vector of binary elements. This design problem is an NP-hard binary quadratic programming due to the one-bit constraints on the elements of the precoder’s output vector, and hence, is difficult to solve. In this paper, we tackle this difficulty by reformulating the problem, in several steps, into an equivalent continuous-domain biconvex form. Our final biconvex reformulation is obtained via an exact penalty approach and can efficiently be solved using a standard block coordinate ascent algorithm. We show through simulation results that the proposed design outperforms the existing schemes in terms of (uncoded) bit error rate. It is further shown via numerical analysis that our solution algorithm is computationally-efficient as it needs only a few tens of iterations to converge in most practical scenarios.

Cell-free massive multiple-input multiple-output (CF-MIMO) is a promising technological enabler for fifth generation (5G) networks in which a large number of access points (APs) jointly serve the users. Each AP applies conjugate beamforming to precode data, which is based only on the AP’s local channel state information. However, by having the nature of a (very) large number of APs, the operation of CF-MIMO can be energy inefficient. In this paper, we investigate the energy efficiency performance of CF-MIMO by considering a practical energy consumption model which includes both the signal transmit energy as well as the static energy consumed by hardware components. In particular, a joint power allocation and AP selection design is proposed to minimize the total energy consumption subject to given quality of service (QoS) constraints. In order to deal with the combinatorial complexity of the formulated problem, we employ norm $l_{2,1}-$based block-sparsity and successive convex optimization to leverage the AP selection process. Numerical results show significant energy savings obtained by the proposed design, compared to all-active APs scheme and the large-scale based AP selection.

The availability of accurate channel state information (CSI) is essential in multiuser massive multiple-input multiple-output (MIMO) systems. However, most published works focus on frequency-flat channel estimation which requires that the estimation must be repeated for every frequency slot, e.g., subcarrier, in a broadband system. Since the channels in the frequency-domain are the Fourier transform of a small set of time-domain channel coefficients, the time-domain channel estimation requires that fewer parameters be estimated. In this paper, we propose a semi-blind, time-domain, channel estimation technique for frequency-selective massive MIMO systems. Our solution depends on the subspace spanned by the signal eigenvectors of the received signal covariance matrix. Importantly, since the receiver samples at the symbol rate, time-domain-based estimation inherently has available enough samples for an accurate matrix estimate. To avoid asymptotic assumptions, we express each channel vector as a linear combination of the signal eigenvectors. We estimate the linear combination using a set of training symbols. Given the many samples of the received signal in the time-domain, we obtain a better channel estimate compared to conventional subcarrier-wise frequency-domain channel estimation. Unlike the previous published results, in this paper, we do not assume orthogonality of users' channels or knowledge of large-scale fading coefficients. In addition, our estimation procedure does not require orthogonality between the training symbols of the users in all cells.

Channel state information (CSI) is essential in massive multiple-input mulitple-output (MIMO) systems. However, most of the literature focuses on frequency-flat channel estimation; in a realistic broadband setting this implies that the estimation must be repeated for each subcarrier. In this paper, we propose a new channel estimation technique for frequency-selective channels in the time-domain. Time-domain CSI acquisition requires the estimation of fewer parameters. Importantly, since the receiver samples at the symbol rate, time-domain based estimation yields many samples of the received signal. Our approach depends on the estimation of the subspace spanned by users' channels, requiring a large number of antennas to ensure that the required rank constraints are met. Unlike previous published results, in this paper, we do not make any assumption on orthogonality of users' channel vectors or knowledge of large-scale fading coefficients. Additionally, our channel estimation, does not require orthogonality between the training symbols of the users in all cells.

We study a two-way relay network where multiple multi-antenna relays facilitate two-way communications between multiple pairs of transceivers. Each relay is equipped with a massive number of antennas. As a result, we can assume that the transceiver-relay channel vectors are approximately orthogonal, and thus, intra-and inter-pair interference will be negligible. Aiming to maintain the signal-to-noise ratio (SNR) at receiver front-end of each transceiver above a certain threshold, we obtain the relay beamforming matrices and the transceiver powers such that the total transmit power consumed in the entire network is minimized. To do so, we assume that the channel vectors between each relay and different transceivers are asymptotically orthogonal. For such power minimization problem, we derive computationally efficient solutions.

Hardware calibration is essential to restore the uplink/downlink channel reciprocity for multi-user massive multiple-input multiple-output (MIMO) systems operating in a time division duplexing mode. Unfortunately, due to the associated overhead, calibration cannot be performed frequently; furthermore, any calibration procedure leaves behind a residual mismatch between the uplink and downlink channels. In this paper, we study the effects of these calibration errors on the achievable rates in the downlink of a multi-cell, multi-user, and distributed massive MIMO system. Specifically, we develop accurate, yet simple, lower-bounds on the per-user achievable rate, assuming either zero-forcing (ZF) or matched filtering (MF) are used. We also introduce a performance loss coefficient as a measure of sensitivity of the performance to the calibration errors. Using this measure, we identify the conditions under which ZF precoding is more sensitive to calibration errors than MF. Finally, we consider the robust weighted sum-rate maximization problem to mitigate the degrading effects of non-ideal calibration. Our numerical experiments show that the rate lower-bounds developed in this paper accurately quantify the impacts of non-ideal calibration on performance. Also, the proposed robust beamforming scheme improves the average sum-rate by up to 42% compared with the other available schemes.

We consider a single-carrier asynchronous relay network where two transceivers exchange information with the help of multiple multi-antenna relays. The network is assumed to be asynchronous, meaning that the signal transmitted by any of the two transceivers arrives at different relays with different delays and also signals transmitted by different relays arrive at any of the two transceivers with different delays. We further assume that each relay obtains the vector of the relay transmit signals via multiplying the vector of the relay received signals by a beamforming matrix. For such an asynchronous two-way network with multi-antenna relays, our goal is to obtain symmetric relay beamforming matrices and the transceivers' transmit powers such that the total power consumed in the entire network is minimized while guaranteeing given data rates at the two transceivers. To this end, we develop a model for the end-to-end channel and use this model to solve the total power minimization problem. Assuming symmetric relay beamforming matrices, we present a computationally efficient solution to this problem. Our simulation results suggest that for a given total number of antennas, there appears to be an optimal number of antennas per relays which results in the lowest power consumption in the network.

In this paper a Markov decision process (MDP) model for virtualized content delivery networks is proposed. We use stochastic optimization to assign cloud site resources to each user group. We propose how quality of experience (QoE) can be included in the modeling and optimization. We then present an optimal solution for a constraint-free version of the problem, and show the improvement in accumulated revenue when our optimization model is used. A sub-optimal algorithm is proposed that would reduce the complexity of the problem. Simulation results are presented to support merits of the proposed algorithm.

Hardware calibration is a necessary procedure to establish the reciprocity of the uplink (UL) and downlink channels (DL) in a time division duplex (TDD) mode of operation. In practice, any calibration scheme is prone to errors. Such errors result in a residual mismatch between UL and DL channels, which, in turn, can significantly affect the quality of DL transmission. In this paper, we study the effects of the hardware calibration errors on the per-user achievable ergodic rate in the DL of a single cell multi-user massive MIMO system. We consider zero-forcing (ZF) precoding under two different scenarios: first we assume that the UL channel state information (CSI) is known perfectly at the BS; we then consider the case where CSI is acquired with minimum mean squared error (MMSE) estimation. We verify the accuracy of our analytical results through numerical simulations.

We consider amplify-and-forward two-way relaying in a multichannel system with two end nodes and a single relay, with a two-slot multiaccess broadcast (MABC) relaying strategy. We investigate the problem of joint subchannel pairing and power allocation to maximize the achievable sum-rate in the network under the individual power constraints. We propose an iterative approach to solve this challenging mixed-integer programming problem by decomposing it into subchannel pairing optimization and joint power allocation optimization, and solving them iteratively. For subchannel pairing at the relay, we show that, unlike in the one-way relaying case, there exists no explicit SNR-based low-complexity subchannel pairing strategy that is optimal for two-way relaying, and the optimal pairing needs to be performed numerically. Nonetheless, we propose an effective low-complexity suboptimal pairing scheme based on an effective SNR metric. For joint power allocation at all nodes, the optimization problem is nonconvex. We propose an iterative procedure to optimize the power at the two end nodes and at the relay iteratively. Using a problem transformation, we show that each power optimization subproblem turns out to be convex and can be solved efficiently. Our proposed iterative procedure is guaranteed to converge to a locally optimal solution. We then generalize our approach to the weighted sum-rate maximization problem. Simulation results demonstrate the effectiveness of the proposed pairing scheme, as well as the gain of joint optimization approach over other pairing-only or power-allocation-only optimization approaches.

Considering a bidirectional amplify-and-forward based multi-carrier multi-relay network, we formulate two different joint power allocation and network beamforming problems. In the first formulation, we aim to minimize the total transmit power of the network, subject to two constraints on the transceiver rates. In the second problem, our goal is to maximize the sum-rate of the two transceivers subject to a constraint on the total network transmit power. In both problems, the design parameters include the relay beamforming weights and the transceiver power allocations over all subcarriers. We propose a two-step iterative method to tackle each problem and show that each method leads to (at least) a locally optimum solution. Each iterative method alternates between solving the underlying problem for one set of variables while the other set is fixed and vice versa. Each subproblem is shown to be amenable to a computationally efficient solution. Our simulation results show the efficiency of the proposed techniques.

We study the problem of obtaining achievable signal-to-noise ratio (SNR) region and the corresponding rate region for an asynchronous bidirectional multi-carrier relay network which consists of two transceivers and multiple relays. We assume that each relaying path, corresponding to each relay, causes a delay in the signal transmitted by one of the transceivers when this signal goes through that relay and arrives at the other transceiver. This delay depends on the distance traveled by the signal. Hence, different relaying paths incur different delays in the signal time of arrival at each of the two transceivers. In our data model, we take into account that these delays are different for different relaying paths. Assuming distributed beamforming at the relays and power control at the transceivers, we characterize the achievable SNR region and the corresponding rate region for this network. Such a characterization is performed when each subcarrier is used to enable bidirectional communication between several outer transceivers. To do so, we present our optimization framework and examine its structure, thereby showing how it can be solved. We prove that for the case where the rates over different subcarriers at each transceiver are constrained to be equal, our approach leads to semi-closed-form solutions for the relay beamforming weights and transceivers' subcarrier powers and for the boundaries of the SNR region.

In this paper, Genetic Programming (GP) is employed to obtain an optimum transformation function for bi-level image thresholding. The GP utilizes a user-prepared gold sample to learn from. A magnificent feature of this method is that it does not require neither a prior knowledge about the modality of the image nor a large training set to learn from. The performance of the proposed approach has been examined on 147 X-ray lung images. The transformed images are thresholded using Otsu's method and the results are highly promising. It performs successfully on 99% of the tested images. The proposed method can be utilized for other image processing tasks, such as, image enhancement or segmentation.

We herein consider the problem of optimal power allocation in an OFDM two-way relay network with multiple relays. Assuming two-way relaying is performed using analog network coding, we obtain the optimal power allocation across subcarriers and among a relay and two communicating nodes by minimizing the total power consumption in the network subject to two separate rate constraints for each transceiver. We then present an algorithm to solve the proposed optimization problem. Our simulation result shows that the proposed algorithm significantly outperform an equal power allocation scheme, where all subcarriers at all nodes receive the same levels of power and the total power is equal to that consumed in our proposed solution.

We propose a resource sharing scheme between a primary pair (which owns the spectrum resources) and a secondary pair (which owns a relay infrastructure) in a collaborative manner. The secondary network allows the primary pair to use the relays in order to establish a bidirectional communication between its transceivers. In exchange for this cooperation, the primary pair assigns a portion of its spectral resources to the secondary pair, thereby enabling a two-way communication between the secondary transceivers. Assuming an amplify-and-forward relaying scheme, the relays collectively build two network beamformers, each of which enables communication between the two transceivers in one pair. We propose a max-min optimization problem to calculate the two network parameters in semi-closed forms.

This paper studies the effect of mobility on the sensing performance of a cognitive radio network with mobile nodes. The secondary nodes sense the spectrum using a distributed compressive sensing approach to detect the available channels. Distributed compressive sensing is suggested to reduce the number of samples by exploiting correlation between the samples. Channel occupancy at the two nodes will be jointly estimated and a channel available at the location of both nodes is chosen for communication. We show that mobility can be exploited to further decrease the number of samples by increasing the average level of correlation among the sensed samples over time.

In this paper, we examine the problem of throughput maximization in an energy-harvesting two-hop amplify-and-forward relay network. This problem is investigated over a finite time horizon and in an online setting, where the causal knowledge of the harvested energy and that of fading are available. We use Markov decision process (MDP) formulation to present a mathematically tractable solution to the throughput maximization problem. In this solution, optimal power-use policy is obtained using backward induction algorithm of the corresponding discrete dynamic programming problem. We also present properties of the optimal policy for an important special case, where the power control at transmitters is limited to on-off switching. These properties facilitate the implementation of the MDP based solution. Our numerical simulations show that the proposed method outperforms existing solutions to this problem.

In this paper, we adopt a novel efficiency measure, namely the received signal-to-noise-ratio (SNR) per unit power, in amplify-and-forward (AF) relay networks. The measure is addressed as the power-normalized SNR (PN-SNR). For several relay network scenarios, we solve the PN-SNR maximization problems and analyze the network performance. First, for single-relay networks, we find the optimal relay power control scheme that maximizes the PN-SNR for a given transmitter power. Then, for multi-relay networks with a sum relay power constraint, we prove that the PN-SNR optimization problem has a unique maximum, thus the globally optimal solution can be found using a gradient-ascent algorithm. Finally, for multi-relay networks with an individual power constraint on each relay, we propose an algorithm to obtain the globally optimal solution and also a low complexity algorithm for a suboptimal solution. Our results show that with the same average relay transmit power, the PN-SNR maximizing scheme is superior to the fixed relay power scheme not only in PN-SNR but also in the outage probability for both single and multi-relay networks. Compared with SNR-maximizing scheme, it is significantly superior in PN-SNR with moderate degradation in outage probability. Our results show the potential of using PN-SNR as efficiency measure in network design.

This paper addresses the problem of throughput maximization in an energy-harvesting two-hop amplify-and-forward relay network. We obtain optimal policies for transmission power for two cases. First, we assume non-causal knowledge of the harvested energy and that of the fading channel states. Then, we assume that this information is known only causally. We propose an effective algorithm to solve the power-use problem in the non-causal (offline) case. For the causal (online) case, we cast the problem as a Markov decision process (MDP) and solve the resulting optimization problem using only causal knowledge of the fading and the harvested energy. This MDP approach yields good performance, but at the cost of computational complexity. To address this issue, we consider the case where the power control at the transmitting nodes is limited to on-off switching. We derive interesting properties for the optimal solutions to the MDP formulation for this special case. Furthermore, using these properties, we propose a computationally simple power allocation scheme. The performances of the proposed schemes are evaluated using computer simulations and are compared to existing methods which address the same problem.

In this paper, we adopt a novel efficiency measure, namely, the received signal to noise ratio (SNR) per unit power, in relay network design. First, limitations of conventional efficiency measures, spectral efficiency and energy efficiency, are discussed to motivate the SNR-per-unit-power (SNR-PUP) measure. Then for a single-relay network which uses amplify-and-forward (AF) protocol, we find the optimal relay power that maximizes the SNR-PUP for a given transmitter power. The average relay power, the SNR-PUP, and the outage probability of the proposed design are investigated analytically and numerically, and are compared with the conventional design where the relay power is fixed. We also consider a general multi-relay network and use gradient-ascent method for the SNR-PUP maximization. Our results show that with the same average relay transmit power, the proposed design is superior not only in the SNR-PUP but also in the outage probability for both single and multi-relay networks.

We consider an asynchronous two-way relay network, where multiple asynchronous relays cooperate to establish a connection between two transceivers. In such an asynchronous relay network, a certain signal path (originating from one transceiver and going through a certain relay) introduces a propagation and/or relaying delay to the corresponding relayed signal. We assume that such delays are different for different signal paths which correspond to different relays. Based on this model, the end-to-end communication link can be viewed as a multi-path channel, and thus, it can cause inter-symbol-interference (ISI) at the two transceivers when the data rate is sufficiently high. To tackle such an ISI, the two transceivers are herein assumed to employ orthogonal frequency division multiplexing (OFDM) technology. The relays however use amplify-and-forward relaying to materialize a distributed beamforming scheme. For such a communication scheme, we use a max-min fair design approach to optimally obtain the relay beamforming weights and the transceivers' subcarrier powers such that the smallest subcarrier signal-to-noise ratio (SNR) ismaximized under a total power budget. Furthermore, we prove that this approach (which has been shown to equivalent to a SNR balancing scheme) leads to certain relay selection solution. We then present a semi-closed-form solution to obtain the relay beamforming weights and the associated maximum balanced SNR. Simulation results show that the performance of this solution is superior to an equal power allocation approach, where all relays and two transceivers consume the same level of power.

In this letter, we study the problem of joint power allocation and channel design for an active link which conveys information from a source to a destination through multiple orthogonal subchannels. In such a link, the power can be injected into the channel not only at the source but also at each subchannel. For such a parallel channel, we study the problem of sum-rate maximization under the assumption that the source power as well as the total power of the active channel are limited. Although this problem is not convex, we present an efficient solution to this sum-rate maximization. An interesting aspect of this solution is that it requires only a subset of the subchannels to be active and the remaining subchannels should be turned off. This is in contrast with passive parallel channels with equal subchannel signal-to-noise-ratios (SNRs), where water-filling solution to the sum-rate maximization under a source total power constraint leads to an equal power allocation among all subchannels. Furthermore, we prove that the number of active subchannels depends on the product of the source and channel powers. We also prove that if the total power available to the source and to the channel is limited, then in order to maximize the sum-rate via optimal power allocation to the source and to the active channel, half of the total available power should be allocated to the source and the remaining half should be allocated to the active channel.

In this paper, considering a mobile wireless sensor network, we study the problem of exploiting sensor mobility information in the process of sensor localization under two range measurement models, namely the time-of-arrival (TOA) model and the received signal strength (RSS) model. To do so, for each model, we first derive the maximum likelihood (ML) location estimator for the case of error-free velocity measurements. As the corresponding optimization problems are non-convex, we resort to semi-definite relaxation (SDR) techniques to find approximate solutions to each problem using semi-definite programming (SDP). We then extend our results to the cases where the velocity measurements are subject to measurement errors. Our simulation results show that exploiting the mobility information in the localization process can significantly improve the performance of the sensor localization. Moreover, mobility-aided localization has the potential to address some of typical positioning problems, such as sensitivity to the ranging measurement errors and the requirement on the number of the anchors needed to uniquely localize the sensor nodes.

This short correspondence serves as en errata to our paper titled, "Achievable rate region under joint distributed beamforming and power allocation for two-way relay networks," published in IEEE Transactions on Wireless Communications, vol. 11, no. 11, pp. 4026-4037, Nov. 2012. We do not present any novelty in this errata.

We consider an asynchronous bi-directional relay network, consisting of two single-antenna transceivers and multiple single-antenna relays, where the transceiver-relay paths are subject to different relaying and/or propagation delays. Such a network can be viewed as a multipath channel which can cause inter-symbol-interference (ISI) in the signals received by the two transceivers. Hence, we model such a communication scheme as a frequency selective multipath channel which produces ISI at the two transceivers, when the data rates are high. To tackle ISI, the transceivers can employ an orthogonal frequency division multiplexing (OFDM) scheme to diagonalize the end-to-end channel. The relays use simple amplify-and-forward relaying, thereby materializing a distributed beamformer. For such a scheme, we propose two different algorithms, based on the max-min fair design approach, to calculate the subcarrier power loading at the transceivers as well as the relay beamforming weights. We develop computationally efficient solutions to these two approaches. Simulation results are presented to show that our proposed schemes outperform equal or maximum power allocation schemes.

In this paper, an asynchronous bidirectional relay network is considered. We assume that the signal path going through each relay has a propagation delay which is different from those of the other relaying paths. Such an assumption leads to inter-symbol-interference (ISI) at the two transceivers. As such, orthogonal frequency division multiplexing (OFDM) is deployed at the two transceivers to tackle ISI, while the relays, for the sake of simplicity, use amplify-and-forward relaying technique. Using a max-min fair design approach, an SNR balancing technique is presented to jointly obtain the beamformer weights and transceivers' subcarrier power loading under a total transmit power budget. Simulation results are presented to show the performance of this approach.

We consider an asynchronous two-way relay network where the signal paths, going through different relays, are subject to different propagation and/or processing delays. Such a relay channel can be viewed as an artificial multi-path channel which causes inter-symbol-interference (ISI). To combat such an ISI, orthogonal frequency division multiplexing (OFDM) is deployed at the two transceivers while the relays are to use simple amplify-and-forward (AF) relaying protocol, thereby realizing a beamformer in a distributed manner. For such a communication scheme, we present two max-min design approaches to obtain jointly optimal subcarrier power loading at the transceivers and distributed beamforming weights at the relays. Numerical examples are presented to compare the performance of the two algorithms.

We obtain the achievable beamforming rate region for a two-way cooperative network consisting of two transceivers and multiple relays, all single-antenna nodes. Assuming that the relay beamforming weights as well as the transceiver transmit powers are the design parameters, this region is characterized under a constraint on the total (network) transmit power consumption. Using the shape of the rate region, we then use a sum-rate maximization approach to obtain the jointly optimal relay beamforming weights and transceiver transmit powers. Interestingly, we show that the sum-rate maximization approach yields the same solution as the max-min fair design approach does. Using this relationship, we further present a semi-closed-form solution to the underlying distributed beamforming problem. We then prove that the transmit power of any of the two transceivers can be obtained as the solution to a one-dimensional optimization problem using a simple bisection method which enjoys a low computational complexity. Furthermore, we extend these results to obtain the relay beamforming weights and transceiver transmit powers corresponding to any point on the boundary of the rate region, through a weighted sum-rate maximization approach.

In cooperative networks, users share their resources to establish reliable connections between each other. If two users want to communicate through a cooperative network, different transmission schemes are possible in which other users serve as relays. In this work, we compare different relaying schemes on the basis of their maximal capacity. We assume that the channel state information is available and the relays use the amplify-and-forward protocol. An optimal technique for one-directional transmissions forms the basis for capacity maximization of the bi-directional four-phase scheme. The analysis for the asymptotic behavior of the one-directional scheme also provides simple relations for the maximal sum-capacity of the bidirectional two-phase scheme. For the third bi-directional scheme with three-phases, upper bounds on the maximal capacity are obtained.

In this paper, we introduce the novel concept of mobility diversity as the diversity gained by transmitting the information of the nodes of a mobile network over different network topologies. Due to the mobility of the nodes, different network topologies emerge which can benefit the information transmission throughout the network. In traditional diversity schemes, such as frequency, time, or spatial diversity, a signal is transmitted over different diversity dimensions (e.g., different frequency bands, different time intervals, or different spatial paths) to combat the destructive effects of fading in each individual channel. In a mobile wireless network, the nodes can exploit the topology diversity to communicated with their corresponding destinations more reliably as compared to the case when the topology of the network is fixed. In fact, in a fixed topology, the probability of a source having a poor connectivity to its destination is higher than the case when there are multiple topologies over which the communication can occur.

In this letter, we present an optimal joint relay selection (RS) and power allocation scheme for two-way relay networks which aim to establish a communication link between two transceivers with the help of one relay. Our approach is based on the maximization of the smaller of the received signal-to-noise-ratios (SNRs) of the two transceivers under a total transmit power budget. We show that this problem has a closed-form solution and requires only a single integer parameter (i.e, the index of the optimally selected relay) to be broadcasted to all relays. We also show that for large values of the total transmit power, the selection criterion can be approximated as the harmonic mean of the amplitudes of the relays' local channel coefficients. We evaluate the performance of our scheme numerically.

A new approach to distributed cooperative beamforming in two-way half-duplex relay networks with frequency selective channels is proposed. In our scheme, two transceivers simultaneously transmit the signals they wish to exchange to the relays. The relay received signals are the sum of the channel-convoluted versions of the transmitted signals. Each relay retransmits a filtered version of its received signal to both transceivers. The proposed distributed beamforming approach is based on minimizing the total relay transmitted power subject to the quality-of-service requirements for both transceivers. We show that this problem is convex, and thus, it can be efficiently solved as a second-order cone program. Simulation results demonstrate that the transmitted power can be significantly reduced and the problem feasibility can be substantially improved by using such a filter-and-forward relaying strategy instead of the traditional amplify-and-forward relaying approach.

In this paper, we derive a theoretical performance evaluation scheme of Kalman filter based channel tracking and data decoding for multiple-input multiple-output orthogonal frequency division multiplexed (MIMO-OFDM) communication systems that are based on orthogonal space-time block codes. The derivation is approximate, however, it is novel and demonstrated accurate for practical scenarios. Assuming a prior distribution for the initial channel we have derived the instantaneous signal to interference and noise ratio (SINR) for consecutive transmission blocks in the absence of training by exploiting Kalman filtering to track the channel. A theoretical estimation of BER is then derived based on such instantaneous SINR values. The resulting analysis is able to study the effect of different parameters of the system such as the number of antennas, number of sub-carriers, mobile velocity and the assumed channel length on the BER performance of the system. Numerical examples confirm the validity of the theoretical analysis.

The problem of optimally allocating power for half-duplex two-way relaying in an OFDM system is considered. Assuming two-way relay is performed using analog network coding, and with total network power constraint, we obtain the optimal power allocation across subcarriers and among a relay and two communicating nodes to maximize the achievable sum rate in the network. We show that the resulting solution is a combination of two (usually opposite) power allocation strategies, i.e., water-filling across subcarriers and SNR-balancing between communicating end nodes. Further analysis also shows the optimal power allocation on the relay itself is a water-filling solution.

Two closed-form blind channel estimators for orthogonally coded multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems are proposed. The key idea of our approaches is to estimate the channel parameters in the time domain instead of doing this in the frequency domain. Our first approach is based on the maximum likelihood (ML) technique with the relaxed finite alphabet constraint, while the second approach uses the generalized Capon estimator to obtain the channel parameters. The proposed techniques amount to solving eigenvector problems.

Distributed beamforming is a powerful approach to reliable communications in relay networks. In this paper, a novel multiple peer-to-peer beamforming technique is proposed for relay networks with multiple source-destination pairs and frequency selective source-to-relay and relay-to-destination channels. Our technique uses a filter-and-forward relaying strategy to compensate for channel frequency selectivity and mitigate inter-symbol interference. Simulation results clearly demonstrate that the proposed technique substantially outperforms the existing amplify-and-forward multiple peer-to-peer beamforming techniques in frequency selective fading environments.

We study the sum-rate maximization approach when applied to design a decentralized beamformer for two-way relay networks. Considering a constraint on the total transmit power consumed in the whole network, we prove that sum-rate maximization is equivalent to an SNR balancing approach where the smallest of the two receive SNRs is maximized subject to the same total power constraint.

In this paper, we present a semi-closed form solution to the SNR balancing problem, first considered in, in the context of network beamforming design for two-way relay networks. This solution relies on a simple bisection method to obtain the transmit power of one of the two transceivers. Given this transmit power, the relay beamforming weight vector is shown to have a closed-form solution. Simulation results show that the proposed solution has significantly lower computational complexity. We also present a suboptimal solution which does not use the aforementioned bisection algorithm while performs very closely to the optimal beamformer.

A computationally efficient distributed beamforming technique for multi-user relay networks is developed. The channel state information is assumed to be known at the relays or destinations, and the total relay transmitted power is minimized subject to the destination quality-of-service constraints. It is shown that this problem can be approximately converted to a convex second-order cone programming form. As a result, the proposed network beamforming technique offers a substantially reduced computational complexity than earlier state-of-the-art techniques that are based on semidefinite relaxation.

A half-duplex distributed beamforming technique for relay networks with frequency selective fading channels is developed. The network relays use the filter-and-forward (FF) strategy to compensate for the transmitter-to-relay and relay-to-destination channels using finite impulse response (FIR) filters. With the channel state information (CSI) being available at the receiver, the transmit relay power is minimized subject to the destination quality-of-service (QoS) constraint. This distributed beamforming problem is shown to have a closed-form solution. Simulation results demonstrate substantial improvements in terms of the relay transmitted power and feasibility of the destination QoS constraint as compared to amplify-and-forward (AF) distributed beamforming techniques.

We consider a relay network which consists of two transceivers and r relay nodes. We study a half-duplex two-way relaying scheme. First, the two transceivers transmit their information symbols simultaneously and the relays receive a noisy mixture of the two transceiver signals. Then each relay adjusts the phase and the amplitude of its received signal by multiplying it with a complex beamforming coefficient and transmits the so-obtained signal. Aiming at optimally calculating the beamforming weight vector as well as the transceiver transmit powers, we minimize the total transmit power subject to two constraints on the receive signal-to-noise ratios (SNRs) at the two transceivers. We show that the optimal weight vector can be obtained through a simple iterative algorithm which enjoys a linear computational complexity per iteration.

We consider a wireless network consisting of a single-antenna transmitter, a single-antenna receiver, and a multi-antenna relay node. For such a network, we introduce the novel approach of general rank beamforming. In this approach, the relay multiplies the vector of its received signals by a general-rank complex matrix to obtain a new vector. Each entry of this new vector is then transmitted on one of the antennas available at the relay. We show that maximizing the receiver SNR subject to total relay power constraint yields a closed-form solution for the beamforming matrix. We also prove that if the channel coefficients from the transmitter to the relay antennas and those from the relay antennas to the receiver are statistically independent, the general rank beamforming approach results in a rank-one solution for the beamforming matrix.

We consider an ad hoc network consisting of d source-destination pairs and R relaying nodes. Each source wishes to transmit its data to its corresponding destination through the relay network. Each relay in the network transmits a properly scaled version of its received signal thereby cooperating with other relays to deliver each source's data to the corresponding destination. Assuming a minimal cooperation among the relaying nodes, we design a distributed beamformer such that the total relay transmit power dissipated by all relays is minimized while, at the same time, the quality of services at all destinations are guaranteed to be above certain pre-defined thresholds. We show that using a semi-definite relaxation approach, the power minimization problem can be turned into a semi-definite programming (SDP) optimization, and therefore, it can be solved efficiently using interior point methods. Our results show that the distributed relay multiplexing is possible and may be beneficial depending on the channel conditions.

The problem of distributed beamforming is considered for a network which consists of a transmitter, a receiver, and r relay nodes. Assuming that the second order statistics of the channel coefficients are available, we design a distributed beamforming technique via maximization of the receiver signal-to-noise ratio (SNR) subject to individual relay power constraints. We show that using semi-definite relaxation, this SNR maximization can be turned into a convex feasibility semi-definite programming problem, and therefore, it can be efficiently solved using interior point methods. We also obtain a performance bound for the semi-definite relaxation and show that the semi-definite relaxation approach provides a c-approximation to the (nonconvex) SNR maximization problem, where c = O((log r)-1) and r is the number of relays.

A new blind channel estimation technique for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems is proposed. It estimates the channel parameters in the time domain jointly for all subcarriers instead of doing this in the frequency domain independently for each subcarrier. This results in a substantially improved parsimony of the channel parameterization along with the ability to use coherent processing across the subcarriers. It is shown that using semi-definite relaxation (SDR), our channel estimation problem can be transferred to a convex form and then solved efficiently using modern convex optimization tools.

In this paper, a new information theoretic algorithm is proposed for signal enumeration in DS-CDMA networks. The approach is based on the predictive description length (PDL) of the observation vector. The PDL is the length of a predictive code of observations. For signal detection, the PDL criterion is computed for the candidate models and is minimized to determine the number of signals. The proposed technique uses the maximum likelihood (ML) estimate of the correlation matrix. The only information used in the ML estimation of the correlation matrix is the multiplicity of the smallest eigenvalue. The PDL algorithm has a signal-to-noise ratio resolution threshold that is smaller than that of the minimum description length (MDL). The proposed method can be used on-line and can be applied to time-varying and non-stationary systems.

The problem of joint channel and carrier frequency offset (CFO) estimation is addressed in the context of multiple-input multiple-output (MIMO) communications using orthogonal space-time-block codes (OSTBCs). A new semiblind method is proposed to jointly estimate the channel matrix and the CFO parameter. Our method blindly estimates the CFO parameter along with a low-dimensional subspace where the channel is located, and then uses a few training blocks to extract the channel parameters from this subspace.

In this paper, a new technique is proposed for estimating the total spatial power spectral density (PSD) caused by multiple incoherently distributed sources. Our approach is based on the fact that the array covariance matrix can be represented through the moments of the spatial PSD. Based on this representation, we develop a computationally efficient technique to estimate the moments from the array covariance matrix. The so-obtained moments are then used to estimate the total spatial PSD.

A new approach to multi-antenna downlink beam- forming is proposed that provides an improved robustness against uncertainty in the downlink channel covariance matrices caused by errors between the actual and estimated channel values. The proposed method uses the knowledge of the statistical distribution of such a covariance uncertainty to minimize the total downlink transmit power under the constraint that the outage probability does not exceed a certain threshold value. Although our approach initially leads to a non-convex optimization problem, it can be reformulated in a convex form using the semidefinite relaxation technique. The resulting convex optimization problem can be solved efficiently using the well-established interior point methods. Computer simulations verify performance improvements of the proposed technique as compared to the robust transmit beamforming method based on the worst-case performance optimization with judicious selection of the upper bounds on channel covariance errors.

We address the problem of estimating the frequencies and damping factors of a multidimensional signal which consists of several damped complex exponentials. Such a problem is of interest in several applications, such as nuclear magnetic resonance spectroscopy, where the 2-dimensional (2D) frequencies and damping factors are used to determine the structure of proteins. We herein propose a new algorithm which exploits the multiple-invariance structure that exists in the data model. Unlike search-based parameter estimation techniques, such as D-MUSIC of Y. Li et al. (1998), which have been developed for multidimensional harmonic retrieval of damped exponentials, our algorithm uses polynomial rooting to obtain the parameters of interest efficiently in a search-free fashion.

In this paper, we improve the performance of robust linear receivers for multi-user multiple-input multiple-output (MIMO) wireless systems by exploiting the inherent structure of orthogonal space-time block codes (OSTBC). This particular structure results in a worst-case optimization problem with structured uncertainty set. Exploiting this structure, an improved robust linear receiver with a combination of fixed diagonal loading and adaptive non-diagonal loading of the data covariance matrix is obtained.

We consider the problem of simultaneous estimation of the channel state information (CSI) of several transmitters that use orthogonal space-time block codes to communicate with a single receiver. Based on the generalizations of the Capon and MUSIC techniques, we propose two novel algorithms to estimate multi-user MIMO channels. These algorithms estimate the subspace spanned by the user channels blindly and use only a few training blocks to extract the users' CSI from this subspace.

The problem of joint space-time decoding and interference rejection in multiple-access MIMO wireless communication systems is considered in the case of erroneous or limited channel state information (CSI) at the receiver. Linear beamforming-type techniques that have an improved robustness in such an imperfect CSI case are proposed.

A new computationally simple approach to blind decoding of orthogonal space-time block codes (STBC) is proposed. Our approach estimates the channel matrix in a closed form and uses this estimate in the maximum likelihood (ML) receiver to decode the symbols. It exploits specific properties of the orthogonal STBC and is free of major drawbacks of other blind space-time decoding schemes.

We propose a new blind multiuser receiver which is robust against the effects of erroneously presumed desired user signature and short data length. Our approach is based on the explicit modeling of possible mismatches in the mean-square error cost function and worst-case performance optimization. We show that this approach leads to a multiuser receiver which uses the data covariance matrix with an adaptive diagonal loading. Simulation results show performance improvements achieved by our approach relative to existing techniques.

The problem of joint space-time decoding and multiple-access interference (MAI) rejection in multiple-access multiple-input multiple-output (MIMO) wireless communication systems is addressed. We assume that both the receiver and multiple transmitters are equipped with multiple antennas and that space-time block codes are used to send the data simultaneously from each transmitter to the receiver. A new linear minimum variance (MV) receiver structure is developed to decode the data sent from the transmitter-of-interest and to reject MAI, self-interference, and noise. Simulation results show that in multiple-access MIMO scenarios, the proposed receivers have a substantially lower symbol error rate as compared to the matched filter (MF) receiver which is equivalent to the maximum likelihood (ML) space-time decoder in the point-to-point MIMO communications case.

A new computationally simple approach to blind decoding of orthogonal space-time block codes is proposed. Our approach estimates the channel matrix in a closed form and uses this estimate in the maximum likelihood (ML) receiver to decode the symbols. It exploits specific properties of the orthogonal space-time block codes (STBCs) and is free of most of the shortcomings of other blind space-time decoding schemes.

The performance of blind multiuser detection methods is known to degrade severely in the presence of even small mismatches between the actual and the presumed desired user signatures. Such mismatches may occur in practical situations due to an imperfect knowledge of the channel impulse response. We propose a new robust approach to blind multiuser detection in the presence of unknown arbitrary-type mismatches of the desired user signature. Two different formulations of a robust multiuser receiver are considered. The proposed formulations are based on the explicit modeling of uncertainties in the covariance matrix of the desired user signature and/or data covariance matrix and optimization of the worst-case performance. Simple closed-form solutions to the considered robust multiuser detection problems are derived. The proposed methods have a computational complexity comparable to that of the traditional blind multiuser detection algorithms, and, at the same time, offer an improved robustness and faster convergence rates.

A new robust power adjustment method is proposed for transmit beamforming in cellular communication systems that use antenna arrays at base stations (BSs). Our method provides an improved robustness against imperfect knowledge of the wireless channel by means of maintaining the required quality of service (QoS) for the worst-case channel uncertainty.

The performance of blind multiuser detection methods is known to degrade in the presence of mismatches between the actual and the presumed desired user signatures. Such mismatches may occur in practical situations due to an imperfect knowledge of the channel impulse response. We propose a new robust approach to blind multiuser detection in the presence of unknown arbitrary-type mismatches of the desired user signature. The formulations of our robust multiuser receivers are based on the explicit modeling of uncertainties in the covariance matrix of the desired user signature/data covariance matrix and optimization of the worst-case performance. The proposed methods have a computational complexity comparable to that of the traditional blind multiuser detection algorithms, while offering an improved robustness and faster convergence rates.

The performance of adaptive beamforming methods may degrade in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. This paper addresses the problem of robust adaptive beamforming in the presence of unknown arbitrary (yet norm-bounded) mismatches of such type as well as interference-plus-noise covariance matrix mismatch. Our approach is developed for the case of an arbitrary dimension of the signal subspace and, therefore, it can be applied to both rank-one and higher-rank signal models. The proposed beamformer is based on the optimization of the worst-case signal-to-interference-plus-noise ratio (SINR). The obtained closed-form solution combines two different types of diagonal loading (DL) applied to the signal and data covariance matrices. An efficient on-line implementation of our beamformer is developed. Simulations validate substantial performance improvements relative to other popular adaptive beamforming techniques.