Low-complexity SAGE-based MMSE channel estimation for CoMP multi-user systems

Coordinated multiple point(CoMP) transmission/reception has been investigated recently as a promising technology to increase the cell-edge user performance of long term evolution-advanced(LTE-A),and channel estimation is a crucial technology for CoMP systems.In this paper,we consider a reduced-complexity minimum mean square error(MMSE) channel estimator for CoMP systems.The estimator uses space alternating generalized expectation maximization(EM)(SAGE) algorithm to avoid the inverse operation of the joint MMSE estimator.In the proposed scheme,the base stations(BSs) in the CoMP system estimate the channels of all the coordinated users serially and iteratively.We derive the SAGE-based estimators and analyze complexity.Simulation results verify that the performance of the proposed algorithm is close to the joint MMSE estimation algorithm while reducing the complexity greatly.     

Enhanced Unitary Beamforming Scheme for Limited-Feedback Multiuser MIMO Systems

In this letter, we consider practical downlink multiuser multiple-input multiple-output systems where each user has multiple antennas and codebook-based limited channel feedback is available at base station. We propose a preprocessing scheme for downlink transmission where a unitary beamforming matrix is used. Firstly, we propose how to construct the unitary matrix that maximizes sum rate. Secondly, we propose a codebook-based channel feedback method for the proposed beamforming method. Numerical results show that the proposed scheme outperforms conventional zero-forcing beamforming scheme in terms of sum rate.     

CSI Feedback for Dynamic Switching Between Single User and Multiuser MIMO

We consider channel state information (CSI) feedback in 3GPP Long Term Evolution (LTE)-Advanced context. In LTE-Advanced, switching between single user and multiuser transmission schemes is possible without higher layer signaling, which means that the feedback should support both single user and multiuser transmissions. Typically, the CSI feedback consists of a precoding matrix index (PMI) and channel quality indication(s) (CQI). For PMI feedback, we consider different PMI selection schemes and study whether there is a tradeoff between single user and multiuser specific codeword selection metrics. For multiuser CQI, we consider different CQI estimation strategies for two paired users, which is the primary case in LTE-Advanced. The schemes include single user single stream and two stream CQIs and several multiuser specific CQI estimation options. We find that estimating the multiuser CQI as an average over unitary pairs or as the minimum of the signal-to-interference and noise ratios of the unitary pairs offers a stable, well-performing options for different signal-to-noise ratio (SNR) ranges and antenna correlation values.     

Pilot-Assisted and Blind Joint Data Detection and Channel Estimation in Partial-Time Jamming

We consider a communication scenario in which a message is received in the presence of partial-time Gaussian jamming and additive white Gaussian noise. We consider a quasi-static channel, in which the amplitude and phase are constant over each packet transmission. The receiver does not know the amplitude and phase of the incoming signal, which symbols are jammed, or even the statistics of the jammer, such as the jamming power and jamming probability. In this scenario, the receiver must accurately estimate the parameters of the channel and the jamming to achieve good performance. We apply the expectation-maximization (EM) algorithm to iteratively approximate the maximum-likelihood (ML) estimator for all of the parameters. We find that the overall performance of the EM algorithm is very sensitive to the initial estimates, so we propose a new initial estimator that offers good performance. The EM algorithm approach requires pilot symbols to resolve a phase ambiguity. Thus, we also present a blind estimation algorithm to avoid the reduction in overall code rate from the use of pilot symbols     

Joint Admission Control and Channel Selection Based on Multi Response Learning Automata (MRLA) in Cognitive Radio Networks

We use multi response learning automata (MRLA) to control how secondary users should access the licensed primary channels in cognitive radio networks. We seek two aims in this paper: (1) estimating the availability probability of each primary channel and (2) admission control of secondary users to decrease the rate of collisions between them. We consider single and multiple secondary user scenarios. In the first scenario, the secondary user deploys learning automata to estimate the primary channel availability probability for efficient exploitation. In the second scenario, each secondary user deploys an algorithm based on MRLA to estimate primary traffic as well as the behavior of other secondary users in order to control the rate of collisions. Then, to have a better control on the rate of secondary collisions, when the number of secondary users is greater than the number of primary channels, we proposed an admission control scheme. In this scheme, some of secondary users are blocked in each time slot and do not have any interaction with the environment. The convergence of the proposed algorithms with and without admission schemes is analyzed. Simulation results are provided to show the improvement in the secondary users’ total throughput and switching cost while maintaining the fairness between them.     

A limited sensing random-access algorithm with binarysuccess-failure feedback

The authors consider the problem of random access communication over a time-slotted channel, with binary success/failure feedback. The feedback informs the users only whether or not there was a success (single transmission) in the previous slot. They propose and analyze a limited feedback-sensing algorithm (each user is required to observe the channel feedback, from the time he generates a packet to the time that this packet is successfully transmitted). The algorithm requires central control implemented by a central receiver. The limit Poisson user model is adopted. The algorithm achieves a throughput of 0.322 and induces low delays for relatively low input rates     

Blind asynchronous multiuser CDMA receivers for ISI channels usingcode-aided CMA

A code-aided constant modulus algorithm (CMA) based approach is presented for blind detection of asynchronous short-code DS-CDMA (direct sequence code division multiple access) signals in intersymbol interference (ISI)/multipath channels. Only the spreading code of the desired user is assumed to be known; its transmission delay may be unknown. A linear equalizer is designed by minimizing the Godard/CMA cost function of the equalizer output with respect to the equalizer coefficients subject to the fact that the equalizer lies in a subspace associated with the desired user's code sequence. Constrained CMA leads to the extraction of the desired user's signal whereas unconstrained minimization leads to the extraction of any one of the active users. The results are further improved by using unconstrained CMA initialized by the results of the code-aided CMA. Identifiability properties of the approach are analyzed. Illustrative simulation examples are provided     

Optimum Power Allocation for Single-User MIMO and Multi-User MIMO-MAC with Partial CSI

We consider both the single-user and the multi-user power allocation problems in MIMO systems, where the receiver side has the perfect channel state information (CSI), and the transmitter side has partial CSI, which is in the form of covariance feedback. In a single-user MIMO system, we consider an iterative algorithm that solves for the eigenvalues of the optimum transmit covariance matrix that maximizes the rate. The algorithm is based on enforcing the Karush-Kuhn-Tucker (KKT) optimality conditions of the optimization problem at each iteration. We prove that this algorithm converges to the unique global optimum power allocation when initiated at an arbitrary point. We, then, consider the multi-user generalization of the problem, which is to find the eigenvalues of the optimum transmit covariance matrices of all users that maximize the sum rate of the MIMO multiple access channel (MIMO-MAC). For this problem, we propose an algorithm that finds the unique optimum power allocation policies of all users. At a given iteration, the multi-user algorithm updates the power allocation of one user, given the power allocations of the rest of the users, and iterates over all users in a round-robin fashion. Finally, we make several suggestions that significantly improve the convergence rate of the proposed algorithms.     

Power control and channel allocation for real‐time applications in cellular networks

The next‐generation packet‐based wireless cellular network will provide real‐time services for delay‐sensitive applications. To make the next‐generation cellular network successful, it is critical that the network utilizes the resource efficiently while satisfying quality of service (QoS) requirements of real‐time users. In this paper, we consider the problem of power control and dynamic channel allocation for the downlink of a multi‐channel, multi‐user wireless cellular network. We assume that the transmitter (the base‐station) has the perfect knowledge of the channel gain. At each transmission slot, a scheduler allots the transmission power and channel access for all the users based on the instantaneous channel gains and QoS requirements of users. We propose three schemes for power control and dynamic channel allocation, which utilize multi‐user diversity and frequency diversity. Our results show that compared to the benchmark scheme, which does not utilize multi‐user diversity and power control, our proposed schemes substantially reduce the resource usage while explicitly guaranteeing the users' QoS requirements. Copyright © 2007 John Wiley & Sons, Ltd.     

Joint Estimation of MIMO Channel Parameters Using Space–Time Correlation Matrix for Different Angular Distributions

In this paper, we propose a new low-complexity method to jointly estimate the multiple-input multiple-output (MIMO) channel parameters namely the mean angle of arrival (AoA), the angular spread (AS) and the maximum Doppler spread (DS). We consider Gaussian and Laplacian angular distributions for the incoming AoAs in the case of a Rayleigh channel model. Our estimator is based on the magnitudes and phases of the space–time correlation functions of the received signals. To this end, closed-form expressions of the required functions were derived. Two different approaches are studied using these cross-correlation functions; first at a non zero time lag and second at two different time lags. To evaluate the robustness of the proposed estimator, the two Stage approach and the improved maximum likelihood method based on the Gauss Newton algorithm are taken as benchmarks for the mean AoA and the AS estimation. For the maximum DS, the two Rays and the auto-correlation based algorithms are chosen. Simulation results show that the proposed estimator offers more accurate estimates in almost all considered scenarios. We also compare our work to a recent joint estimator which exploits the Derivatives of the cross-correlation function (DCCF). Our method outperforms the DCCF algorithm at a lower computational cost.     

Optimal training sequences for channel estimation in bi-directional relay networks with multiple antennas

In this letter, we consider a bi-directional relay network in which two users, U _{and U, exchange their information via a relay station, RS. Multiple antennas are deployed at both users and at RS. Single carrier cyclic prefix (SCCP) is used to combat intersymbol interference (ISI) in frequency-selective fading channels. The transmission process is divided into two time slots. At the first time slot, both users send their information to RS concurrently. RS then amplifies and broadcasts its received signals at the second time slot. We propose an algorithm to estimate the channel information at end users based on the leastsquare (LS) principle. To further minimize the mean square error (MSE) of the estimate, a method to design the optimal training sequences is also proposed. Simulation results show that the performance achieved by our optimal design is close to that with perfect channel information.}     

Dynamic resource allocation in mobile heterogeneous cellular networks

User mobility is a challenging issue in macro and femto cellular networks for the fifth-generation and newer mobile communications due to the time-varying interference and topology experienced. In this paper, we consider an OFDMA-based two-tier network with one macro cell and several femto cells, wherein each macro user and/or femto user can leave or enter its serving cell frequently, referred to as user mobility. A resource allocation problem with different rate requirements of mobile users is then formulated. Assuming well knowledge of the user locations and the channel state information, we propose a dynamic algorithm with static and dynamic parts for a better trade-of between computational complexity and system throughput. The static algorithm, named interference weighted cluster algorithm in this paper, is based on the graph theory to cluster the femtocells by minimizing the interference between clusters, while the dynamic algorithm is to deal with the user mobility by sharing the resource blocks under the constraints of rate requirements. Numerical results are demonstrated to show the effectiveness of the proposed dynamic resource allocation algorithm in terms of capacity, computational time, and outage probability.

     

Turbo Estimation and Equalization for Asynchronous Uplink MC-CDMA

In this contribution, we propose a new code-aided synchronization and channel estimation algorithm for uplink MC-CDMA. The space alternating generalized expectation- maximization (SAGE) algorithm is used to estimate the channel impulse responses, propagation delays and carrier frequency offsets of the different users. The estimator, multi-user detector, equalizer, demapper and channel decoder exchange soft information in an iterative way. The performance of the proposed algorithm is evaluated through Monte Carlo simulations. Impressive performance gains are visible as compared to a conventional data-aided estimation scheme.     

User cooperation for enhanced throughput fairness in wireless powered communication networks

This paper studies a novel user cooperation method in a wireless powered cooperative communication network (WPCN) in which a pair of distributed terminal users first harvest wireless energy broadcasted by one energy node and then use the harvested energy to transmit information to a destination node (DN). In particular, the two cooperating users exchange their independent information with each other so as to form a virtual antenna array and transmit jointly to the DN. By allowing the users to share their harvested energy to transmit each other’s information, the proposed method can effectively mitigate the inherent user unfairness problem in WPCN, where one user may suffer from very low data rate due to poor energy harvesting performance and high data transmission consumptions. Depending on the availability of channel state information at the transmitters, we consider the two users cooperating using either coherent or non-coherent data transmissions. In both cases, we derive the maximum common throughput achieved by the cooperation schemes through optimizing the time allocation on wireless energy transfer, user message exchange, and joint information transmissions in a fixed-length time slot. We also perform numerical analysis to study the impact of channel conditions on the system performance. By comparing with some existing benchmark schemes, our results demonstrate the effectiveness of the proposed user cooperation in a WPCN under different application scenarios.

     

Efficient multimedia packet forwarding for multihomed users in wireless LANs

In many cases, a mobile user has the option of connecting to one of several 802.11 access points (APs), each using an independent channel. We consider the case when users multihome, i.e., split their traffic among all available APs, based on the channel burstiness, transmission rates and required application resilience. We consider two solution approaches for this problem: static and dynamic traffic splitting algorithms. Static algorithms solve a nonlinear program, which maximizes the average effective transmission rate satisfying the required resiliency against channel losses, and determines the number of packets transmitted over each channel a priori to the first packet transmission. In contrast, dynamic algorithms progressively select for each packet the channel that is most likely to be successful based on the most recent channel outcomes. The resiliency against channel losses is explicitly calculated, and accurate approximations suitable for the solution of the nonlinear program are given. We provide detailed characterization of static and dynamic policies with respect to varying channel conditions, resiliency, transmission rates, and number of available channels. We show that using a simple algorithm which keeps track of the most recent channel outcomes, it is possible to significantly improve the system performance over algorithms that only consider long-term channel statistics.     

Iterative receivers for space-time block-coded OFDM systems indispersive fading channels

We consider the design of iterative receivers for space-time block-coded orthogonal frequency-division multiplexing (STBC-OFDM) systems in unknown wireless dispersive fading channels, with or without outer channel coding. First, we propose a maximum-likelihood (ML) receiver for STBC-OFDM systems based on the expectation-maximization (EM) algorithm. By assuming that the fading processes remain constant over the duration of one STBC code word and by exploiting the orthogonality property of the STBC as well as the OFDM modulation, we show that the EM-based receiver has a very low computational complexity and that the initialization of the EM receiver is based on the linear minimum mean square error (MMSE) channel estimate for both the pilot and the data transmission. Since the actual fading processes may vary within one STBC code word, we also analyze the effect of a modeling mismatch on the receiver performance and show both analytically and through simulations that the performance degradation due to such a mismatch is negligible for practical Doppler frequencies. We further propose a turbo receiver based on the maximum a posteriori-EM algorithm for STBC-OFDM systems with outer channel coding. Compared with the previous noniterative receiver employing a decision-directed linear channel estimator, the iterative receivers proposed here significantly improve the receiver performance and can approach the ML performance in typical wireless channels with very fast fading, at a reasonable computational complexity well suited for real-time implementations     

CMA-based code acquisition scheme for DS-CDMA systems

In direct-sequence code-division multiple access, a code synchronization must take place before the multiuser detector. As the initial synchronization stage, a code acquisition scheme is used to estimate the relative timing phase for the desired transmission within one chip interval. In this paper, a blind code acquisition scheme using adaptive linear filtering based on a linearly constrained constant modulus algorithm (CMA) is proposed. The uncertainty of a desired user's delay is initially discretized and translated into a number of hypotheses. The lock convergence property of CMA is exploited, where the filter at the steady state can lock onto the desired user while nulling all other interfering users (i.e., a decorrelator). For each delay hypothesis, the filter is initialized as the corresponding shifted spreading sequence of the desired user. It is shown that lock convergence always occurs for the correct hypothesis, while all incorrect hypotheses will be hovered around some saddle regions, given sufficiently small step sizes. Then, the correct hypothesis is the one which has the converged filter to yield the maximum lock onto the desired user, or a maximum output energy     

Blind equalization of IIR channels using hidden Markov models andextended least squares

In this paper, we present a blind equalization algorithm for noisy IIR channels when the channel input is a finite state Markov chain. The algorithm yields estimates of the IIR channel coefficients, channel noise variance, transition probabilities, and state of the Markov chain. Unlike the optimal maximum likelihood estimator which is computationally infeasible since the computing cost increases exponentially with data length, our algorithm is computationally inexpensive. Our algorithm is based on combining a recursive hidden Markov model (HMM) estimator with a relaxed SPR (strictly positive real) extended least squares (ELS) scheme. In simulation studies we show that the algorithm yields satisfactory estimates even in low SNR. We also compare the performance of our scheme with a truncated FIR scheme and the constant modulus algorithm (CMA) which is currently a popular algorithm in blind equalization