This paper gives a replica analysis for the minimum mean square error (MSE) of a massive multiple-input multipleoutput (MIMO) system by using Bayesian inference. The Bayesoptimal estimator is adopted to estimate the data symbols and the channels from a block of received signals in the spatial-temporal domain. We show that using the Bayes-optimal estimator, the interfering signals from adjacent cells can be separated from the received signals without pilot information of the interfering signals. In addition, the MSEs with respect to the data symbols and the channels of the desired users decrease with the number of receive antennas and the number of data symbols, respectively. There are no residual interference terms that remain bounded away from zero as the numbers of receive antennas and data symbols approach infinity.

Pilot contamination posts a fundamental limit on the performance of massive multiple-input-multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To address this problem, we propose estimation of only the channel parameters of the desired links in a target cell, but those of the interference links from adjacent cells. The required estimation is, nonetheless, an underdetermined system. In this paper, we show that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. Our strategy is inspired by the observation that for a cellular network, the channel from user equipment to a base station is composed of only a few clustered paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. As a result, if the signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators, the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination.

This paper studies the fairness optimization of dynamic multiuser multicarrier allocation in the cellular downlink of MIMO orthogonal frequency division multiple access (OFDMA) systems. The varying capacity demands of different users motivate the fairness problem. In the resource allocation approaches that maximizing the sum rate or minimizing the total power often leads to poor fairness among users. The allocation is prone to starvation situation for the users with deep fading subchannels. Hence, this work considers the fairness issue and proposes to maximize the minimum rate surplus, where the rate surplus is defined as the difference between the demand data rate and the resulting allocated data rate. The fairness is inverse proportional to the gap of the maximum rate surplus to the minimum rate surplus among all users. In this work, the design of the precoding and decoding matrices for the MIMO structure is also developed. To solve the optimization problem, an iterative algorithm is proposed to optimize the subcarrier assignment with low complexity. Simulation results on multiuser MIMO environment show that the proposed algorithm strikes the balance between sum rate and fairness. Comparing with the state-of-the-art works, the proposed algorithm shows an advantage in keeping the sum rate while the fairness is significantly improved.

Base station (BS) cooperation can turn unwanted interference to useful signal energy for enhancing system performance. In the cooperative downlink, zero-forcing beamforming (ZFBF) with a simple scheduler is well known to obtain nearly the performance of the capacity-achieving dirty-paper coding. However, the centralized ZFBF approach is prohibitively complex as the network size grows. In this paper, we devise message passing algorithms for realizing the regularized ZFBF (RZFBF) in a distributed manner using belief propagation. In the proposed methods, the overall computational cost is decomposed into many smaller computation tasks carried out by groups of neighboring BSs and communication is only required between neighboring BSs. More importantly, some exchanged messages can be computed based on channel statistics rather than instantaneous channel state information, leading to significant reduction in computational complexity. Simulation results demonstrate that the proposed algorithms converge quickly to the exact RZFBF and much faster compared to conventional methods.

This paper presents a 4×4/8×8/12×12 and QPSK/16-QAM/64-QAM reconfigurable MIMO detector on a multi-core DSP platform for different MIMO detection algorithms, including multiple-candidate-selection QRSIC (MCS-QRSIC), distributed K-best, and sorting-reduced K-best detectors. This study uses an Eigen-value decomposition method to investigate the intrinsic degree of parallelism of MIMO detectors and allocate the processing elements to DSP cores. MCS-QRSIC outperforms other detectors in 4 × 4 detection, and distributed K-best has the best performance in 8×8 and 12×12 MIMO systems. The normalized throughput achieves 188/153/142 Mbps for 64-QAM 4×4/8×8/12×12 MIMO detections at a 1GHz clock rate with 448 cores.

The conventional linear minimum mean square error (LMMSE) estimator is commonly implemented through the sample covariance matrix. This estimator can only be implemented if the sample size N is higher than the observation dimension M. Moreover, this estimator performs poorly when the sample size is not sufficiently large. To address this problem, we propose a new shrinkage LMMSE estimator. The proposed estimator performs efficiently over a wide range of observation dimensions and sample sizes. In contrast to existing methods, the proposed estimator can be applied if M ≥ N. Even if M <; N, the proposed estimator performs more efficiently than existing estimators.

In this paper, we investigate the asymptotic mutual information expression and the capacity-achieving input covariance matrices for the coordinated multi-point multiple-input multiple-output (MIMO) antenna channels. In particular, it is considered that the numbers of antennas at the transmitter and receiver approach to infinity with a fixed ratio. Our derivations are based on novel techniques from large dimensional random matrix theory. In contrast to previous studies, we consider a general model in which the correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian distributed. We show that the asymptotic capacity is invariant to all types of fading distribution. As such, the asymptotic mutual information expression is robust and has wide applicability.

This paper presents an extrinsic data compression method for double-binary turbo codes. Frame and extrinsic data memory occupy more area in turbo decoder implementations with the frame size increasing. Besides, non-binary turbo codes have much more extrinsic memory usage than single-binary turbo codes. This proposed compression method utilizes an operation in radix-4 single-binary turbo decoder and also can simplify the integration of single and double-binary turbo decoders in hardware implementations. It can reduce one-third of extrinsic memory size, about 15% of total memory usage. Simulation results show that this method only causes 0.2dB performance loss when bit error rate is equal to 10−5with slight hardware increment.

Incorporating wireless transceivers with numerous antennas (such as Massive-MIMO) is a prospective way to increase the link capacity or enhance the energy efficiency of future communication systems. However, the benefits of such approach can be realized only when proper channel information is available at the transmitter. Since the amount of the channel information required by the transmitter is large with so many antennas, the feedback is arduous in practice, especially for frequency division duplexing (FDD) systems. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing, which permits the transmitter to obtain channel information with acceptable accuracy under substantially reduced feedback load. Furthermore, by leveraging properties of compressive sensing, we present two adaptive feedback protocols, in which the feedback content can be dynamically configured based on channel conditions to improve the efficiency.

In this paper, we address statistically robust transmit design problems that maximize the average mutual information of amplify-and-forward (AF) multiple-input multiple-output (MIMO) two-hop relay channels having perfect channel state information at the receiver (CSIR) and statistical channel state information at the transmitter (CSIT). In the selected scenario, the source, relay, and destination terminals are equipped with correlated antennas where a direct link between the source and the destination terminals can be found. Moreover, the statistical CSIT consists of the channel means and covariance matrices of various links. The design problem is taken into account for regimes in large systems because there are no closed-form expressions of the average mutual information in the MIMO relay channel. The contribution of this study includes the derivation of asymptotic mutual information expressions for the MIMO relay channel in the large system limit. Additionally, an efficient optimization algorithm based on the asymptotic mutual information is proposed in order to find the asymptotically optimal source covariance matrix and the relay amplifying matrix. Numerical simulation results show that the new approach achieves indistinguishable performance compared to those of the maximization approaches in finite-dimensional systems, even for a small number of antennas at each link. The impact of the CSI of various links on the throughput of the MIMO relay channel is studied using the new approach.

In this paper, we study the capacity-achieving input covariance matrices for the jointly-correlated (or the Weichsel-berger) Rician fading multiple-input multiple-output (MIMO) antenna channel when perfect channel state information (CSI) is known at the receiver while only statistical CSI at the transmitter is available. Unlike the Kronecker model, such jointly-correlated MIMO channel accounts for the correlation coupled between the two ends and has been shown to be most accurate for representing real channels. Our contribution includes the expression for the asymptotic mutual information for the jointly-correlated Rician fading MIMO channel in the large-system regime in which the numbers of antennas at the transmitter and receiver go to infinity with a fixed ratio. Based on this expression, an efficient algorithm is also proposed to obtain the capacity-achieving input covariance matrix. Simulation results demonstrate that even for a moderate number of antennas at each end, the proposed scheme provides undistinguishable results as those obtained by the highly-complex stochastic programming (or Monte-Carlo based) approach.

Deploying femtocells faces a major challenge of causing downlink interference to the existing macrocell. This paper proposes an intelligent spectrum sharing mechanism for a two-layer cellular network, which comprises macrocells and femtocells. Because users are scheduled to access the spectrum in time-domain according to their channel conditions, the usage of spectrum is predictable by tracing the channel conditions. Therefore, the femtocell can be managed, in time-domain, to reconfigure the antenna radiation pattern in order to suppress the interference to closely-located macrocell users who are expecting to access the spectrum. Simulation results show that user throughput at the macrocell edge can be boosted by 83%, compared to deploying femtocells with fixed omni-directional antennas.

When the total transmit power for a distributed antenna system (DAS) is constrained, downlink power allocation among remote radio heads (RHHs) should be judiciously configured for multicast/broadcast services (MBS), in order to optimize the performance. The paper derives power allocation criteria for max-min fairness in single-antenna and multi-antennas cases, which can be served as power allocation metrics when the BS has perfect large-scale fading knowledge of all users. We also propose an alternative method that is appropriate for scenarios where only limited feedback is allowed. Both methods provide better outage performance as compared to uniform power allocation.

Frequency-selective fading and inter-carrier-interference (ICI) are two fundamental issues that should be dealt with before adopting orthogonal frequency division multiplexing (OFDM). Instead of using complex designs for exploiting frequency diversity and cancelling ICI, this paper explores how spreading signal, on either frequency-domain or time-domain, can improve the performance of OFDM systems. Based on the carrier interferometry technique, information symbols are spread over all the OFDM subcarriers to pursue frequency diversity. Then a packet retransmission scheme, which can be seem as spreading signal in time-domain, is proposed to suppress ICI. Numerical simulations show that frequency diversity can be pursued by spreading information symbols in OFDM subcarrier domain and that ICI can be suppressed by spreading packets in time domain. As a result, an enhanced OFDM system with low complexity signal spreading can be delivered.

This paper explores multiuser downlink beamforming in a distributed manner, where each user locally and sequentially optimizes its own beamforming phase and amplitude according to some performance measures. For phase optimization, the complex non-convex optimization problem is reformulated into a convex one. Given the optimal phase, the amplitude is pursued through the method of Lagrange multiplier. Our numerical simulations demonstrate that the proposed algorithm matches the conventional centralized two-user zero-forcing beamforming performance in terms of bit error rate. Meanwhile, our method utilizes 50% less feedback as compared to the conventional one. As a result, a low complexity distributed multiuser beamforming is delivered.

We consider a multicell random beamforming scheme where a cooperative base station (BS) can simultaneously beamform to its served (local) user via utilizing perfect channel state information and randomly beamform to its neighboring users. The rationale behind this scheme is to curtail channel information feedback in a multicell MIMO system. By sharing antenna degree of freedom and transmit power at the collaborative BSs, user terminals (UT) can enjoy macro diversity gain and system spectrum efficiency (SE) is therefore improved. In this paper, the transmit power for a BS used for collaborative random beamforming (Co-RBF) is determined by the outage probability requirement of its served user. Besides, we propose a user selection scheme based on maximizing the incremental capacity comes from Co-RBF. The upper bound of the capacity increments in high signal-to-interference and noise ratio (SINR) regime is also derived. The proposed Co-RBF scheme shows its feasibility for multicell wireless applications due to its flexibility and lower feedback overhead.

Zadoff-Chu (ZC) sequence has been proposed as the contention based ranging preamble code (RPC) in 3GPP LTE and IEEE 802.16m specifications. The performance of the initial ranging process is affected by the number of available RPCs. In scenarios with high mobility and large round-trip delay (RTD), however, the number of available RPCs is limited; this would result in a higher collision probability. In this paper, we propose two efficient schemes to reduce the collision probability, namely ranging preamble codes division (RPCD) and ranging preamble codes partition (RPCP). Both schemes can effectively increase the number of available RPCs in the initial ranging process. The superiorities of the proposed schemes are demonstrated via computer simulations.

Spatial division multiple access can be used to boost spectrum efficiency if perfect channel information is available at the transmitter. By using equal-gain beamforming, this paper reformulates the difficult non-convex optimization problem of finding the optimal multiuser beamforming vectors into a convex one. Consequently, the Nash equilibrium solution can be reached based on a non- cooperative game, where each receiver feedbacks its selected beamforming vector without sharing information with the others. This paper verifies the performance over spatial correlated, multipath, time-varying Rayleigh fading channels for downlink orthogonal frequency division multiplexing systems. Based on long-term beamforming, the proposed algorithm outperforms the conventional approach in terms of received SINR, besides the reduced complexity.

In multi-cell joint processing, a concatenating precoder can be constructed and applied among multiple base stations (BS) to enhance the system performance. In practice, the precoder used by each BS is chosen from a pre-determined codebook. Exhaustive search (ES) could be employed for concatenating precoder construction, but the complexity of which might be too high for implementations, especially when the number of collaborative BS and the codebook size are large. In this paper, we present and study three alternative precoder search methods that can achieve similar performance to ES but with much lower complexities. The strengthes of these proposed schemes are illustrated clearly through computer simulations.

Geometric mean decomposition (GMD) has been proposed as an elegant tool for MIMO transceiver architecture design. Under the ideal assumptions, MIMO-GMD schemes can realize multiple spatial sub-channels with identical gains. The main goal of this paper is to provide analytical distribution functions for sub-channels gains and capacities of MIMO-GMD schemes. In particular, we have derived closed-form results for MIMO-GMD transceivers with two spatial streams. Furthermore, we show that the sub-channel gains for cases with arbitrary number of spatial streams can be approximated by Gamma random variables with excellent accuracy. Additionally, some practical applications of these results are also presented in this paper.

In recent years, multi-cell downlink (DL) collaborative multi-input multi-output (MIMO) transmission has drawn a lot of attentions due to its potential benefits in system throughput. However, the requirements on training and feedback may hamper its practical applications. In this paper, we consider a single-user collaborative random beamforming (Co-RBF) scheme to ease the training and feedback burden in a multi-cell environment. The proposed single-user collaborative random beamforming system, either with and without additional transmitter phase adjustments, are described and analyzed from outage probability perspective. From analysis and numerical results, collaborative random beamforming schemes can provide macro diversity gain with little feedback. This implies its feasibility for limited feedback collaborative wireless systems.

Worldwide interoperability for microwave access (WiMAX) suffers from inter-carrier interference (ICI) since it adopts orthogonal frequency division multiplexing in physical layer. This study applies a repetition coding scheme, which encodes the same data on a subcarrier pair, to the hierarchical modulation to suppress the ICI and thus the inter-layer interference. Consequently, the proposed method mitigates the effects of fading and interference and provides a finer granularity of modulation order for link adaptation. Besides, the repetition coding scheme is also applied to a hybrid automatic repeat request (HARQ) protocol to recover error packets because of poor channel conditions. The proposed method incorporates the ICI cancellation into the conventional incremental redundancy HARQ and automatically activates the cancellation of ICI without Doppler shift/mobility estimation. Simulations show that the proposed algorithms outperform the conventional schemes in time-varying Rayleigh fading channels, especially in high-mobility environments.

The performance of a space-time block coded orthogonal frequency-division multiplexing (STBC-OFDM) system often relies on the assumption of quasi-static channels. For the time-varying multipath channel, co-channel interference (CCI) and inter-carrier interference (ICI) occur and performance degrades seriously. In this paper, we propose an iterative interference cancellation scheme to reduce both CCI and ICI jointly. In particular, a list successive interference cancellation (List-SIC) algorithm is presented to obtain a candidate list to obtain soft information used in the CCI cancellation. Furthermore, an ICI cancellation algorithm that uses prior List-SIC information from the preceding iteration is proposed. The simulation results show that the proposed scheme achieves near maximum likelihood (ML) error performance within only two or three iterations in fast fading channels.

This paper proposes a modified sorted-QR decomposition algorithm for the high-dimensional multiple-input multiple-output (MIMO) detection. Due to the growing demands of high-dimension MIMO channels and large number of OFDM subcarriers, the sorted-QR decomposition becomes one of the computational bottlenecks in the QR-based MIMO detection. The proposed Givens-rotation-based algorithm aims to improve the throughput and the hardware utilization efficiency by relaxing the sorting condition and allowing the cross-column parallel rotation operations. The simulation results show that our proposed parallel algorithm can significantly reduce the minimal computation latency from 28% to 21% of the original algorithm, and the hardware utilization rate can be enhanced from 71% to 90% for 16times16 MIMO using four Given-rotation processing elements. The detection performance degradation is negligible and better hardware efficiency can be obtained for the larger number of MIMO antennas.

We propose a simple search algorithm to reduce the computational complexity and power consumption for equal gain precoder. Simulation results show that the equal gain precoder with the proposed search algorithm can achieve nearly the same performance as that of the Grassmannian precoder. The proposed search algorithm shows several advantages in practical implementation as will be explained later in this paper.

The convergence speed of layered message-passing decoding (LMPD) is faster than that of standard two phase message passing (TPMP) decoding. We propose to use dynamic scheduling and early termination to reduce computational complexity for a modified LMPD algorithm which was proposed for quasi-cyclic low-density parity-check (QC-LDPC) codes. We dynamically skip or redo the decoding operations for some layers based on appropriate criteria. An early termination strategy which is efficient in hardware implementation is also proposed in this paper. The modified MPD using these two techniques simultaneously can reduce the computational complexity with similar error performance as compared to the case of not using these two techniques.

Hierarchical modulation provides layered transmission and is a very promising mechanism to reliably provide basic services to receivers with various reception conditions. Because the high priority layer is allocated with more power, the low priority layer is vulnerable to noise and interference. This paper applies a repetition coding to either of the layers of hierarchical modulation to cancel the inter-carrier-interference, and thus the inter-layer-interference. More importantly, the proposed scheme provides a finer granularity of modulation order, which makes it more flexible to support various radio resource allocation.

The standards for next-generation wireless communications have adopted collaborative spatial multiplexing (CSM) as an advanced feature to increase the spectral efficiency. In this paper, we aim to compare a few user pairing modes and MIMO channel-dependent selection metrics of uplink scheduling for CSM, with the inclusion of proposing a novel selection metric that is related to channel condition number. The performances of these pairing modes and selection metrics are compared through simulations, and the results show that the proposed metric can provide lower error rates and better fairness than the traditional schedulers based on maximum capacity.

Cooperative spatial multiplexing (CSM) can increase the system spectral efficiency via collaboration among multiple distributed nodes. How the relays for cooperation are chosen is an important issue as it may affect the system performance. In this paper, we propose a new relay selection method for CSM in a clustered environment where the source and relays are close in proximity. Instead of directly optimizing the channel capacity, our algorithm considers the suitability of spatial multiplexing based on the condition number of the effective channel. The simulation results have shown that our method can achieve better error performance than some other relay selection schemes.

Inspired by the success of the low-density parity-check (LDPC) codes in the field of error-control coding, in this paper we propose transforming the downlink multiuser multiple-input multiple-output scheduling problem into an LDPC-like problem using the normal graph. Based on the normal graph framework, soft information, which indicates the probability that each user will be scheduled to transmit packets at the access point through a specified angle-frequency sub-channel, is exchanged among the local processors to iteratively optimize the multiuser transmission schedule. Computer simulations show that the proposed algorithm can efficiently schedule simultaneous multiuser transmission which then increases the overall channel utilization and reduces the average packet delay.

This paper presents a low complexity algorithmic framework for finding a broadcasting schedule in a low-altitude satellite system, i.e., the satellite broadcast scheduling (SBS) problem, based on the recent modeling and computational methodology of factor graphs. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, in this paper, we transform the SBS problem into an LDPC-like problem through a factor graph instead of using the conventional neural network approaches to solve the SBS problem. Based on a factor graph framework, the soft-information, describing the probability that each satellite will broadcast information to a terminal at a specific time slot, is exchanged among the local processing in the proposed framework via the sum-product algorithm to iteratively optimize the satellite broadcasting schedule. Numerical results show that the proposed approach not only can obtain optimal solution but also enjoys the low complexity suitable for integral-circuit implementation.

Based on the principle of spread spectrum systems, this paper presents a hybrid automatic-repeat-request (HARQ) scheme with the mitigation of intercarrier interference (ICI) by orthogonally encoding transmitted signals on subcarriers for orthogonal frequency division multiplexing (OFDM) systems. The proposed scheme not only increases the signal-to-noise ratio (SNR) but also reduces the ICI. Simulations demonstrates that it outperforms the conventional Chase Combining HARQ in terms of uncoded bit error rate (BER) with nearly no complexity increase.

In this paper, a 2times2 multiple-input multiple-output- orthogonal frequency division multiple access (MIMO-OFDMA) uplink transceiver based on IEEE 802.16e-2005 standard is proposed. Carrier frequency offset (CFO) and multipath channel impairments are considered. To mitigate the CFO problems in multiuser environment, an inter-carrier-interference- cancellation-based (ICI-cancellation-based) CFO estimator is proposed. The proposed CFO estimator measures energy dispersion of data compensated by ICI cancellation and channel equalization. With measured energy dispersion, correct CFOs can be obtained by running an iterative search. Compared with other designs, the proposed CFO estimator is more robust in transmission conditions and types of subcarrier allocations. Simulation results demonstrate that the proposed transceiver outperforms others in mean-square-error (MSE) and bit-error- rate (BER) performance.

In this paper an efficient channel estimation algorithm under different mobile velocities in orthogonal frequency division multiplexing (OFDM) systems is considered. Conventional channel estimation algorithms track adaptively time-varying channels using high performance approaches. However, the power consumption for these algorithms are usually tremendous. On the other hand, users usually move with low speeds in most cases and they do not require such a complicated channel tracker. In view of this we classify the moving speeds into three modes. We then propose a multi-mode channel tracking scheme with different complexities and tracking capabilities. A novel inverse mean-square error (IMSE) velocity metric estimator is also provided along with a mode selection procedure to detect the proper tracking mode. Simulation results show that under the OFDMA mode of the IEEE 802.16e-2005 standard, the proposed algorithm can track the velocity variation very well and perform channel estimation using the most efficient tracking mode. The power consumption is thus decreased significantly without degrading the system performance.

For antenna-array-based multiple-input multiple- output orthogonal-frequency-division-multiplexing (MIMO-OFDM) wireless systems, gain in channel throughput reduced through sufficient feedback of the channel state information (CSI) is significant, particularly when the number of transmit antennas is larger than the number of receive antennas. In this letter, we demonstrate that, in such scenarios, (1) the CSI of each OFDM sub-carrier can be parameterized into a short bit stream by a proposed low-complexity QR decomposition on the corresponding MIMO channel matrix, (2) the overall CSI can be reliably represented by a proposed parameter interpolation on the above bit streams of only a fraction of sub-carriers, and (3) a MIMO-OFDM system with a low-rate CSI feedback parameterized above can provide a channel throughput comparable to the channel capacity.

With extrinsic information transfer (EXIT) chart code design tool, people can more easily design a capacity-approaching low-density parity-check (LDPC) codes for antenna-array-based multiple-input multiple-output (MIMO) systems. However, extremely time-consuming Monte-Carlo simulations are required to evaluate the statistics used in the EXIT charts, which makes capacity-approaching LDPC code design almost impossible for large-scale MIMO systems. By making use of large-system performance analysis technique, we propose a time-efficient code design approach which eliminates the necessity of time-consuming Monte Carlo simulations and then makes capacity-approaching LDPC code design possible for large-scale MIMO systems. Numerical experiments show that the so-constructed LDPC codes attain comparable bit error rate performance with existing well-designed LDPC codes. In addition, we demonstrate that redesigning LDPC code is very helpful only when the ratio of number of transmit and that of receive antennas of a MIMO system varies.

In this paper, we are interested in performance analyses of a multiple-input multiple-output (MIMO) multiple-access (MA) wireless network where Wyner's linear cellular array model is adopted. Notwithstanding some recent results have shown that, in the Wyner's model, the joint processing of the entire received signal with access point (AP) cooperation can dramatically enhance the system throughput because of the macrodiversity. However, in practice, the channel estimation quality from the providers of the macrodiversity is usually poor. Furthermore, existing literatures do not take channel estimation error into account. The channel estimation error provides us the motive to derive analytical solutions to the large-system throughput (or channel sum-rate) of the MIMO cellular network with imperfect channel estimations. Unlike the most large-system results, our derived results can not only be applied in the scenarios with any given input distributions (not necessarily Gaussian) from the mobile transmitters, but also clarify the performance of the network which was originally involved. Several issues we address including (1) how much of coherence interval should be spent for training and (2) the impact of AP cooperation is highlighted through the analytical results.

In this paper, we analyze the bit-error-rate (BER) performance of the optimum multiuser detection (MUD) with channel mismatch in multicarrier code-division-multiple-access (MC-CDMA) systems. The BER performance of the optimum MUD without channel mismatch in MC-CDMA systems has been recently derived using the replica method. However, it is left unjustified, since the replica method is not a rigorous approach. In addition, it is NP-hard to implement an optimum MUD algorithm. To justify the BER performance and to make the optimum MUD feasible, based on Pearl's belief propagation (BP) scheme, we put together a low-complexity iterative MUD algorithm for MC-CDMA systems. Furthermore, channel mismatch is introduced into the BP-based MUD algorithm to make the scenario general. With channel mismatch, the analytical results of the BP-based MUD algorithm conform perfectly to, and the simulation results of the BP-based MUD algorithm conform very closely to the BER performance of the optimum MUD derived using the replica method, which is a nontrivial extension of the existing replica approach mentioned above. Without channel mismatch, the problem becomes a special case of our contribution.

With a unified approach, this paper investigates the asymptotic performance or, equivalently, the large-system properties, of various point-to-point systems with antenna-array-based multiple-input multiple-output (MIMO) channels having spatial correlations. Using the replica method originally developed for statistical physics, we provide analytical solutions to the input-output mutual information of MIMO systems: 1) at the transmit side, with arbitrary inputs, and 2) at the receive side, with either the optimum space-time joint decoding or various suboptimum spatial equalizers followed by a bank of temporal decoders. Important physical meanings revealed though the analytical solutions to those more practical combinations, such as how the input-output mutual information is affected by the channel spatial correlations, are highlighted along our derivation. Moreover, we provide a novel waterfilling algorithm to determine the data-rate-maximizing transmit signal covariance matrix when only the channel long-term spatial correlations are available at the transmitter.

By considering the large-system regimes, we analyze the asymptotic performance of a multiple-input multiple-output (MIMO) multiple-access (MA) wireless network in the presence of spatial correlation at the transmitters (i.e., mobile stations) and the receiver (i.e., base station) with particular emphasis on the receive antennas utilizing pattern diversity. Using the replica method originally developed in statistical physics, we are able to derive analytical solutions to the spectral efficiency of the MIMO-MA systems for any given arbitrary input signal distributions at the transmitters assuming the information-theoretic optimum decoding at the receivers. Our results are general in that they encompass lots of previously published results for degenerate cases. In addition, based on the asymptotic solution, we propose a computation-efficient algorithm to determine the asymptotic optimum transmit signal covariance matrices that can maximize the spectral efficiency of the MA network when only the slow-varying channel spatial covariance information is available at the transmitters.

Most researches into multiple-input multiple-output (MIMO) antenna systems have aimed to determine the capacity-achieving input covariance given certain degree of channel state information (CSI) at the transmitter side. In practice, however, it is much preferred to find the input signal covariance that requires the least average transmit power to support a given rate based on only long-term channel information. In this paper, we analyze the characteristics of the optimal (in the sense of power-saving) input covariance for spatially correlated MIMO channels where only the long-term (slow-varying) channel spatial covariance information is available at the transmitter. Sufficient and necessary conditions of the optimal input covariance are derived. By considering the large-system regimes, we devise an efficient iterative algorithm to compute the asymptotic optimal power-saving input covariance. Remarkably, simulation results will demonstrate that the asymptotic solution is very effective in that it gives promising results even for MIMO systems with only a few antennas at the transmitter and the receiver.

Recently, several published papers have been drawing attention to multicarrier code division multiple access (MCCDMA) systems, especially their applications in the field of broad-band wireless communications. In such applications, existing suboptimal multiuser detectors (MUDs) usually do not achieve acceptable performance. Unfortunately, it is NP-hard to implement an optimum MUD algorithm. In this paper, to make the optimum MUD feasible, we put together a low-complexity iterative MUD algorithm for MC-CDMA systems based on Pearl's belief propagation (BP) scheme. The BP-based MUD algorithm can be implemented in a practical time scale. The analytical dynamic biterror-rate (BER) performance of the BP-based iterative detector can be traced accurately and its BER in the stationary state is shown to achieve the performance obtained by the optimum MUD algorithm.

This paper presents a novel self-organizing distributed algorithm for finding a broadcasting schedule in a packet radio network via only local collaborative interactions among neighboring network stations. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, we transform the broadcast scheduling problem (BSP) into an LDPC-like problem through a factor graph. In the proposed algorithm, the constraint rules of the BSP are divided into many simple local rules, each of which is enforced by a local processing unit in the factor graph. The soft-information, describing the probability that each station will transmit a data packet, is then efficiently exchanged among the local processing units by using the sum-product algorithm to iteratively optimize the broadcasting schedule. Simulation results indicate that the proposed algorithm performs better than the other existing central-processing algorithms in terms of the channel utilization and the average packet delay. This is true especially when the network scenario is very complex. Furthermore, the proposed algorithm is both low in complexity and completely distributed, which makes it suitable for implementation in practical network applications.

We use the replica method originally developed in statistical physics to investigate the asymptotic sum-rate of a Gaussian antenna-array-based multiple-input multiple-output (MIMO) multiple-access wireless channel having spatial correlations at both the transmitters and the receiver. The asymptotic solution is not only rigorously valid for systems with large array sizes, hut it also produces highly accurate ergodic results for systems with only a few antenna elements at each transmitter and receiver. This otters the asymptotic solution important practical values in analyzing and designing a MIMO multiple access system that makes best use of the wireless channel structure. Furthermore, with the asymptotic solution, we provide an efficient iterative water-filling algorithm to determine the optimum transmit signal covariance matrices when only the slow-varying channel spatial covariance information is available.

In a unified approach, this paper investigates the asymptotic performance, or equivalently the large-system properties, of various point-to-point systems with antenna-array-based multiple-input multiple-output (MIMO) channels having spatial correlations at both the transmitter and the receiver. Using the replica method originally developed for statistical physics, we provide closed-form solutions to the input-output mutual information of MIMO systems: 1) at the transmit side, with either Gaussian inputs or various nonGaussian arbitrary inputs, and 2) at the receive side, with either the optimum space-time joint-decoding, or various suboptimum spatial equalizers followed by a bank of temporal decoders. Among all these combinations, in Part I, we focus on the performance analysis of the MIMO systems with the optimum space-time joint-decoding, i.e., the information-theoretic optimum decoding. Based on the framework developed in this paper, some known performance results are recovered and some new performance results are discovered. Furthermore, we provide a novel water-filling algorithm to determine the data-rate-maximizing transmit signal covariance matrix when only the channel long-term spatial correlations are available at the transmitter. In Part II, we focus on the performance analysis of the MIMO systems with various suboptimum spatial equalizers, each followed by a bank of temporal decoders.

For pt.I, see ibid., p.3504-8 (2004). In Part I of this paper, we analyzed the asymptotic mutual information of the joint-decoding MIMO systems. Here in Part II, we focus on the performance analysis of the MIMO systems with various separate decoding schemes, i.e., with various suboptimum spatial equalizers followed by a bank of temporal decoders. Among the suboptimum spatial equalizers, under the proposed framework, we cover both 1) linear spatial equalizers such as linear MMSE spatial equalizer, linear zero-forcing spatial equalizer, and matched-filter equalizer, and 2) nonlinear spatial equalizers such as individually optimal spatial equalizer and marginal optimal spatial equalizer. Closed-form solutions are derived for all the possible combinations of 1) various transmit-side arbitrary input distributions and 2) the receive-side decoding schemes listed above. Important physical meanings revealed though the closed-form solutions to those more practical combinations, as how the input-output mutual information is affected by the transmit-side and the receive-side channel spatial correlations, are highlighted along our derivation.