MIMO System

Accurate channel state information (CSI) is critical for current and next-generation multi-antenna systems. Yet conventional pilot-based estimators incur prohibitive overhead as antenna counts grow. In this letter, we address this challenge by developing a novel framework based on Gaussian process regression (GPR) that predicts full CSI from only a few observed entries, thereby reducing pilot overhead. The correlation between data points in GPR is defined by the covariance function, known as kernel. In the proposed GPR-based CSI estimation framework, we incorporate three kernels, i.e., radial basis function, Matérn, and rational quadratic, to model smooth and multi-scale spatial correlations derived from the antenna array geometry. The proposed approach is evaluated across two channel models with three distinct pilot probing schemes. Results show that the proposed GPR with 50% pilot saving achieves the lowest prediction error, the highest empirical 95% credible-interval coverage, and the best preservation of spectral efficiency relative to the benchmarks.

We propose an adaptive multiuser QoS-based scheduler based on the users' channel measurements. In order to mitigate the complexity that results from computing non-codebook based beamformers for possibly infeasible users, we propose to preselect the optimal users using a virtual signal-to-noise ratio (vSNR). This is computed assuming non-interfering users and a total power constraint. Solely depending on vSNR, we obtain thresholds that fulfill the equivalent SINR constraints for a different number of users. In this paper, vSNR thresholds are utilized to develop a fast adaptive multiuser scheduling routine. Finally, a performance comparison to the unscheduled case will be demonstrated in the presence of channel state information uncertainty as well as with perfect channel information.

Some communication applications, like multimedia, deliver data of different importance classes allowing unequal error protection (UEP) levels. In this paper, a multiple-input multiple-output (MIMO) system using orthogonal frequency division multiplexing (OFDM) is considered with a new UEP bit-loading algorithm based on the non-UEP algorithm by Chow, Cioffi, and Bingham. In the proposed bit-loading algorithm, bits are distributed across the unitarily transformed eigenbeams in case of partial channel information (CSI) knowledge. However, ideal performance can not withstand the rapid wireless channel variation unless a restricted beamforming is used. For the case of partial CSI, we proposed a switched beamforming technique that can maintain the required performance protection levels.

Learning algorithms for an automaton operating in a multiteacher environment are considered. These algorithms are classified based on the number of actions given as inputs to the environments and the number of responses (outputs) obtained from the environments. In this paper, we present a general class of learning algorithm for multi-input multi-output (MIMO) models. We show that the proposed learning algorithm is absolutely expedient and -optimal in the sense of average penalty. The proposed learning algorithm is a generalization of Baba's GAE algorithm [16] and has applications in solving, in a parallel manner, multi-objective optimization problems in which each objective function is disturbed by noise [20].

Weight and size are the bottlenecks of portable wireless systems which are in turn dependent on area and power consumption of electronic systems. The situation becomes even worse if wireless systems are equipped with Multiple Input and Multiple Output (MIMO) technology which involves highly complex signal processing. This paper proposes an area and power efficient VLSI architecture for computing the pseudo inverse of augmented channel matrix used in MIMO systems. Results indicate 25% area and 24% power reduction compared to previous architecture in the literature without degrading the BER performance.

Multiple Input Multiple Output (MIMO) wireless technology involves highly complex signal processing which is directly related to increased power and area consumption in VLSI architecture. This paper proposes an enhanced dual strategy based VLSI architecture developed for computing the pseudo inverse of augmented channel matrix used in MIMO systems. The architecture concurrently addresses algorithmic optimization of number of multipliers while at the same time allowing for intelligent selective clock gating to disable the clock to those portions of the architecture that remain inactive during period of computation. Results indicate overall 36% power and 31% area reduction compared to previous architecture without degrading the BER performance.

MIMO systems promise high performance gains over conventional single antenna systems. To accomplish high data rates over wireless links the use of multiple transmit and receive antennas is an alternative that does not require any extra bandwidth. It has the potential to address the unprecedented demand for wireless services, particularly for the Multimedia Broadcast Multicast Service (MBMS). This service supports downlink streaming and download-and-play type services to large groups of users. From the radio perspective, MBMS includes point-to-point (PtP) and point-tomultipoint (PtM) modes. New proposed MBMS related enhancements in the physical layer specifications and their effects on resource requirements are presented in this paper, such as the use of High Speed Downlink Packet Access (HSDPA) to broadcast/multicast video streaming using the Deficit Round Robin (DRR) scheduler algorithm as a way to maintain an acceptable QoS for all the users. Other improvements, based on the use of MIMO including macro-diversity, are independent of the mode used and are dependent on deployment scenarios, but can yield substantial reduction in resource demand.

We consider a time division duplex multiple-input multiple-output (n t ×nr MIMO). Using channel state information (CSI) at the transmitter, singular value decomposition (SVD) of the channel matrix is performed. This transforms the MIMO channel into parallel subchannels, but has a low overall diversity order. Hence, we propose X-Codes which achieve a higher diversity order by pairing the subchannels, prior to SVD precoding. In particular, each pair of information symbols is encoded by a fixed 2 × 2 real rotation matrix. X-Codes can be decoded using n r very low complexity two-dimensional real sphere decoders. Error probability analysis for X-Codes enables us to choose the optimal pairing and the optimal rotation angle for each pair. Finally, we show that our new scheme outperforms other low complexity precoding schemes.

In this paper, we propose a training-based channel estimation scheme for large non-orthogonal space-time block coded (STBC) MIMO systems. The proposed scheme employs a block transmission strategy where an N t × Nt pilot matrix is sent (for training purposes) followed by several N t × Nt square data STBC matrices, where N t is the number of transmit antennas. At the receiver, we iterate between channel estimation (using an MMSE estimator) and detection (using a low-complexity likelihood ascent search (LAS) detector) till convergence or for a fixed number of iterations. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed scheme at low complexities. The fact that we could show such good results for large STBCs (e.g., 16×16 STBC from cyclic division algebras) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot-based channel estimation and turbo coding) establishes the effectiveness of the proposed scheme.

Recently, we reported a low-complexity likelihood ascent search (LAS) detection algorithm for large MIMO systems with several tens of antennas that can achieve high spectral efficiencies of the order of tens to hundreds of bpsIHz. Through simulations, we showed that this algorithm achieves increasingly near SISO AWGN performance for increasing number of antennas in i.i.d. Rayleigh fading. However, no bit error performance analysis of the algorithm was reported. In this paper, we extend our work on this low-complexity large MIMO detector in two directions: i) We report an asymptotic bit error probability analysis of the LAS algorithm in the large system limit, where N" N r ~00 keeping Nt = Nr, where Nt, and NT are the number of transmit and receive antennas, respectively. Specifically, we prove that the error performance of the LAS detector for V-BLAST with 4-QAM in i.i.d. Rayleigh fading converges to that of the maximum-likelihood (ML) detector as Nt ~N r -e, x keeping Nt = NT. ii) We present simulated BER and nearness to capacity results for V-BLAST as well as high-rate non-orthogonal STBC from Division Algebras (DA), in a more realistic spatially correlated MIMO channel model. Our simulation results show that a) at an uncoded BER of 10-3, the performance of the LAS detector in decoding 16 x 16 STBC from DA with N, =Nr =16 and 16-QAM degrades in spatially correlated fading by about 7 dB compared to that in i.i.d. fading, and b) with a rate-3/4 outer turbo code and 48 bps/Hz spectral efficiency, the performance degrades by about 6 dB at a coded BER of 10-4. Our results further show that providing asymmetry in number of antennas such that N r > Nt keeping the total receiver array length same as that for N r = Nt, the detector is able to pick up the extra receive diversity thereby significantly improving the BER performance.

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-multiple-input multiple-output (MIMO) systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search ( -LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16 16 and 32 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.

In our recent work, we reported an exhaustive study on the simulated bit error rate (BER) performance of a low-complexity likelihood ascent search (LAS) algorithm for detection in large multiple-input multiple-output (MIMO) systems with large number of antennas that achieve high spectral efficiencies. Though the algorithm was shown to achieve increasingly closer to near maximum-likelihood (ML) performance through simulations, no BER analysis was reported. Here, we extend our work on LAS and report an asymptotic BER analysis of the LAS algorithm in the large system limit, where N t , N r → ∞ with N t = N r , where N t and N r are the number of transmit and receive antennas. We prove that the error performance of the LAS detector in V-BLAST with 4-QAM in i.i.d. Rayleigh fading converges to that of the ML detector as N t , N r → ∞.

We consider large MIMO systems, where by 'large' we mean number of transmit and receive antennas of the order of tens to hundreds. Such large MIMO systems will be of immense interest because of the very high spectral efficiencies possible in such systems. We present a low-complexity detector which achieves uncoded near-exponential diversity performance for hundreds of antennas in V-BLAST (i.e., achieves near SISO AWGN performance in a large MIMO fading environment) with an average per-bit complexity of just O(NtNr), where Nt and Nr denote the number of transmit and receive antennas, respectively. With an outer turbo code, the proposed detector achieves good coded bit error performance as well. For example, in a 600 transmit and 600 receive antennas V-BLAST system with a high spectral efficiency of 200 bps/Hz (using BPSK and rate-1/3 turbo code), our simulation results show that the proposed detector performs close to within about 4.6 dB of the theoretical capacity. We also adopt the proposed detector for the low-complexity decoding of high-rate non-orthogonal spacetime block codes (STBC) from division algebras (DA). We have decoded the 16 × 16 full-rate STBC from DA using the proposed detector and show that it performs close to within about 5.5 dB of the capacity using 4-QAM and rate-3/4 turbo code at a spectral efficiency of 24 bps/Hz. The practical feasibility of the proposed high-performance low-complexity detector could trigger wide interest in the implementation of large MIMO systems.

We propose a method for vector precoding suited to singular multiple-input multiple-output (MIMO) channels were the number of data streams is larger than the number of transmit antennas. We calculate the probability that our method, due to the channel singularity, fails to provide the data without crosstalk to the receiver, analytically, and show that this probability goes to zero exponentially with the size of the MIMO system if the number of QPSK data streams in smaller than twice the number of transmit antennas.

In this paper, the results of an on-body, line-ofsight (LOS), multiple-input, multiple-output (MIMO) channel sounding investigation are reported. Transmission takes place between on-body antennas and a uniform linear array, positioned at close range and within line-of-sight of each other. Due to the on-body location of the antennas, the influence of actual radio channel can no longer be separated from the effects of user behaviour and body proximity. This paper therefore argues that the composite channel must be considered. Post-processing of the recorded channels shows that their capacities outperform those from equivalent Rayleigh channels. Taking into account the clear LOS in the experiment, this appears counter-intuitive to the well-known fact that uncorrelated scattering results in high capacity. An analysis of the results highlights some less familiar facts and leads to an overview of capacity generating mechanisms in MIMO systems. The paper adds an analysis of the sensitivity of the capacity to the properties of the channel matrix, which is used for a discussion of the practical use of MIMO in personal and body area networks.

We report on a channel measurement campaign using body and personal area network devices, placed both in an office and on-body. Inspired by the theoretical work on cooperation between devices, which predicts MIMO-like capacity gains for co-operative systems, the capacity of MIMO and cooperating devices are examined. Significant capacity gain is observed, even under line-of-sight conditions in the body area network. Power imbalance between individual channels due to shadowing or orientation, however, degrades the capacity increase when significant compared to the signal-to-noise ratio.

A closed-form expression for information capacity of the random signature RS multipleinput multiple-output MIMO channel is given. A direct calculation of the capacity i s provided additional to the proof involving the capacity of MMSE systems provided in 8 .

This paper deals with MIMO methods for which the Singular Value Decomposition of the channel matrix is calculated and exploited as part of the transmission and reception process. For these methods it is shown that a severe inter-sub-streams cross-talk effect arises due to imperfect channel matrix estimates. It is shown that the mean cross-talk SNR has a simple functional dependency on the singular values of the channel matrix, and that there exist singular values for which the mean cross-talk SNR is significantly higher than for others. A pre-equalization method is proposed to transform a random original channel into a modified 'virtual' channel with superior mean cross-talk SNR. The penalty associated with this pre-equalizer operation is described along with a possible optimization strategy to select an optimal modified channel. Comparative performance simulation results are presented. Significant performance gain of the proposed method, relative to methods where no such pre-equalization is applied, is demonstrated. Symbol processing complexity is shown to consist of merely a single matrix-vector multiplication.

This extended single-column survey reviews tensor decompositions used in signal processing (SP) with a primary focus on two application pillars: (i) wireless communications and (ii) array processing (radar/sonar/acoustic arrays and related localization/beamforming problems). We cover modeling pathways from physical measurement equations to low-rank tensor structures (CP/PARAFAC, structured CP, Tucker, and coupled models), identifiability and ill-conditioning phenomena, and algorithmic building blocks (ALS/first-order methods, MTTKRP, regularization/constraints) that recur across both pillars. We also highlight the practical bridge from factor estimates to physical parameters (angles, delays, Dopplers) under Vandermonde/shift-invariance structure, and we discuss common failure modes such as collinearity, degeneracy, and protocol-driven rank deficiencies. Compared with earlier drafts, we add a detailed treatment of CP identifiability (Kruskal-type conditions, generic rank and typical ranks, and degeneracy/illconditioning) as well as an appendix deriving ALS updates and common regularized variants with complexity analysis.

This short survey reviews tensor decompositions used in signal processing (SP) with a primary focus on two application pillars: (i) wireless communications and (ii) array processing (radar/sonar/acoustic arrays and related localization/beamforming problems). We cover modeling pathways from physical measurement equations to low-rank tensor structures (CP/PARAFAC, structured CP, Tucker, and coupled models), identifiability and ill-conditioning phenomena, and algorithmic building blocks (ALS/first-order methods, MTTKRP, regularization/constraints) that recur across both pillars. We also highlight the practical bridge from factor estimates to physical parameters (angles, delays, Dopplers) under Vandermonde/shift-invariance structure, and we discuss common failure modes such as collinearity, degeneracy, and protocol-driven rank deficiencies. Compared with earlier drafts, we add a detailed treatment of CP identifiability (Kruskal-type conditions, generic rank and typical ranks, and degeneracy/illconditioning) as well as an appendix deriving ALS updates and common regularized variants with complexity analysis.

MIMO-OFDM (multiple inputs and multiple output orthogonal frequency division multiplexing) system is a new wireless broadband technology which has gain great popularity for its capability of high rate transmission and its strength against multipath fading. In this paper turbo coding, Zero Forcing and Minimum Mean Square Error equalization techniques are presented for reduction in bit error rate for the 16QAM modulation technique. The channel used in this paper is Rayleigh frequency selective channel.

The support of MIMO communication in OFDM-WLAN systems conforming to IEEE 802.11n standard requires the design and use of compact antennas and arrays with low correlation ports. In this paper, the design and analysis of a compact space-multimode diversity provisioning stacked circular microstrip patch uniform linear antenna array (SCP-ULA) for MIMO-OFDM WLAN systems and the associated spatial and modal correlation, ergodic spectral efficiency and compactness gains with respect to dipole (DP-ULA) and circular microstrip patch uniform linear arrays (CP-ULA) with dominant-mode operation are presented.

In mobile communications, time varying channels make the available channel information out of date. Timely updating the channel state is an obvious solution to improve the system performance in a time varying channel. However, a better knowledge of the channel comes at the cost of a decrease in the system throughput. Thus, predicting the future channel conditions can improve not only the performance but also the throughput of many types of wireless systems. This is especially true for a wireless system where multiple antennas are applied at both link ends. In this paper we propose and evaluate the performance of a prediction scheme for multiple input multiple output (MIMO) systems that apply spatial multiplexing. We aim at predicting the future precoder/decoder directly without going through the prediction of the channel matrix. The results show that in a slowly time varying channel an increase in the system performance by a factor of two is possible

This letter introduces a new frequency domain approach for either MIMO System Identification or Source Separation of convolutive mixtures in cyclostationary context. We apply the joint diagonalization algorithm to a set of cyclic spectral density matrices of the measurements to identify the mixing system at each frequency up to permutation and phase ambiguity matrices. An efficient algorithm to overcome the frequency dependent permutations and to recover the phase, even for non-minimum-phase channels, based on cyclostationarity is also presented. The new approach exploits the fact that each input has a different and specific cyclic frequency. A comparison with an existing MIMO method is proposed.

This paper describes a new approach to the problem of enhancing the performance of a multiuser multiple-input-multiple-output (MIMO) system for communication from one base station to many mobile stations in both frequency-flat and frequency-selective fading channels. This problem arises in space-division multiplexing systems with multiple users where many independent signal streams can be transmitted in the same frequency and time slot through the exploitation of multiple antennas at both the base and mobile stations. Our new approach is based on maximizing a lower bound for the product of signal-to-interference plus noise ratio (SINR) of a multiuser MIMO system. This provides a closed-form (noniterative) solution for the antenna weights for all the users, under the constraint of fixed transmit power. Our solution is shown by simulation to have better performance than previously proposed iterative or noniterative solutions. In addition, our solution requires significantly reduced complexity over a gradient search-based method that directly optimizes the product SINRs while still maintaining similar performance. Our solution assumes channel state information is present at the base station or transmitter.

Subcarrier based space processing was conventionally employed in Orthogonal Frequency Division Multiplexing (OFDM) systems under Multiple-Input and Multiple-Output (MIMO) channels to achieve optimal performance. At the receiver of such systems, multiple Discrete Fourier Transform (DFT) blocks, each corresponding to one receive antenna, are required to be used. This induces considerable complexity. In this paper, we propose a pre-DFT processing scheme for the receiver of MIMO-OFDM systems with space-time-frequency coding. With the proposed scheme, the number of DFT blocks at the receiver can be any number from one to the number of receive antennas, thus enabling effective complexity and performance tradeoff. Using the pre-DFT processing scheme, the number of input signals to the space-time-frequency decoder can be reduced compared with the subcarrier based space processing. Therefore, a high dimensional MIMO system can be shrunk into an equivalently low dimension one. Due to the dimension reduction, both the complexity of the decoder and the complexity of channel estimation can be reduced. In general, the weighting coefficients calculation for the pre-DFT processing scheme should be relevant to the specific space-time-frequency code employed. In this paper, we propose a simple universal weighting coefficients calculation algorithm that can be used to achieve excellent performance for most practical space-time-frequency coding schemes. This makes the design of the pre-DFT processing scheme independent of the optimization of the space-time-frequency coding, which is desirable for multiplatform systems.

We propose an Iterative Detection and Decoding (IDD) scheme with Low Density Parity Check (LDPC) codes for Multiple Input Multiple Output (MIMO) systems for blockfading F = 2 and fast fading Rayleigh channels. An IDD receiver with soft information processing that exploits the code structure and the behaviour of the log likelihood ratios (LLR)'s is developed. Minimum Mean Square Error (MMSE) with Successive Interference Cancellation (SIC) and with Parallel Interference Cancellation (PIC) schemes are considered. The soft a posteriori output of the decoder in a block-fading channel with Root-Check LDPC codes has allowed us to create a new strategy to improve the Bit Error Rate (BER) of a MIMO IDD scheme. Our proposed strategy in some scenarios has resulted in up to 3dB of gain in terms of BER for block-fading channels and up to 1dB in fast fading channels.

Sensor networks are becoming closer towards wide-spread deployment so security issues become a vital concern. Selective forwarding attack is one of the harmful attacks against sensor networks and can affect the whole sensor network communication. The variety of defense approaches against selective forwarding attack is overwhelming. In this paper we have described all the existing defensive schemes according to our best knowledge against this attack along with their drawbacks, thus providing researchers a better understanding of the attack and current solution space. This paper also classifies proposed schemes according to their nature and defense. Nature of scheme classifies into Distributed and Centralized. Defense of scheme classifies into detection and prevention.

The code that combines channel estimation and error protection has received general attention recently, and has been considered a promising methodology to compensate multi-path fading effect. It has been shown by simulations that such code design can considerably improve the system performance over the conventional design with separate channel estimation and error protection modules under the same code rate. Nevertheless, the major obstacle that prevents from the practice of the codes is that the existing codes are mostly searched by computers, and hence exhibit no good structure for efficient decoding. Hence, the time-consuming exhaustive search becomes the only decoding choice, and the decoding complexity increases dramatically with the codeword length. In this paper, by optimizing the signal-tonoise ratio, we found a systematic construction for the codes for combined channel estimation and error protection, and confirmed its equivalence in performance to the computer-searched codes by simulations. Moreover, the structural codes that we construct by rules can now be maximum-likelihoodly decodable in terms of a newly derived recursive metric for use of the priority-first search decoding algorithm. Thus, Submission to IEEE Transactions on Information Theory. Prof. P.-N.