The evolution from fifth-generation (5G) to sixth-generation (6G) networks is driving an unprecedented demand for advanced machine learning (ML) solutions. Deep learning has already demonstrated significant impact across mobile networking and communication systems, enabling intelligent services such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. At the same time, the rapid proliferation of resource-constrained Internet-of-Things (IoT) devices has accelerated the adoption of tiny machine learning (TinyML) for efficient on-device intelligence, while large machine learning (LargeML) models continue to require substantial computational resources to support large-scale IoT services and ML-generated content. These trends highlight the need for a unified framework that integrates TinyML and LargeML to achieve seamless connectivity, scalable intelligence, and efficient resource management in future 6G systems. This survey provides a comprehensive review of recent advances enabling the integration of TinyML and LargeML in next-generation wireless networks. In particular, we(i)provide an overview of TinyML and LargeML,(ii)analyze the motivations and requirements for unifying these paradigms within the 6G context,(iii)examine efficient bidirectional integration approaches,(iv)review state-of-the-art solutions and their applicability to emerging 6G services, and(v)identify key challenges related to performance optimization, deployment feasibility, resource orchestration, and security. Finally, we outline promising research directions to guide the holistic integration of TinyML and LargeML for intelligent, scalable, and energy-efficient 6G networks and beyond.

This work investigates cellular millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems within the open radio access network (O-RAN) architecture, integrating the compatible spectrum, air interface, and networking entities of beyond fifth-generation wireless networks. To overcome O-RAN fronthaul (O-FH) load limitations and the short wavelength inherent in mmWave bands, we design a hybrid beamforming architecture with digital and analog beamformers generated at the O-RAN distributed unit and O-RAN radio unit, respectively. Using the information theory, we develop non-grid-of-beams analog beamformers to maximize the sum-spectral efficiency (SE) under constant-modulus constraints. For digital precoding, we apply a successive convex approximation method with second-order cone program procedures to maximize sum-SE, while addressing transmit power and limited O-FH load constraints, and ensuring user quality of service requirements. Sub-optimal digital combiners are also designed based on the inherent characteristics of the user side. However, the current optimization approach suffers from long execution times, posing challenges for near-real-time beamforming configurations. To address this issue, we propose an efficient deep learning (DL)-based digital precoding scheme with short execution time, low computational complexity, and high performance. Numerical results demonstrate that the proposed DL-based precoding scheme provides superior performance compared to benchmark schemes, generalizes well to environments with imperfect CSI and user mobility, and scales effectively to massive MIMO configurations.

Simultaneously meeting high spectral efficiency (SE)-oriented enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (uRLLC) remains a significant challenge. Given uRLLC requirements, this work investigates the problem of sum-SE maximization in multiple reconfigurable intelligent surfaces-assisted cell-free (CF) millimeter wave massive multiple-input multiple-output systems. The joint optimization problem of passive reflective beamforming (RBF) and hybrid active transceiver beamforming is tackled using a multi-block coordinate descent (MBCD) method, under constraints of transmit power, users’ quality-of-service, and constant-modulus characteristics. The subproblems involving analog transceiver processing and passive RBF, associated with constant-modulus constraints, are solved via MBCD-based Riemannian manifold optimization, while the subproblems involving digital transceiver beamforming are addressed using a successive convex approximation framework combined with semidefinite relaxation and Gaussian randomization. To further balance computational complexity, fronthaul overhead, and system performance, a user-centric access point selection scheme is adopted using statistical channel state information. Numerical results demonstrate that the proposed framework outperforms various practical benchmark schemes in terms of user SE. Furthermore, the user-centric CF design achieves a 99.29% reduction in computational complexity with only a 5.49% performance loss compared with full-association CF.

To address the significant capital expenditures (CAPEX) and operating expenses (OPEX) associated with each antenna having its dedicated radio frequency (RF) chain in multiple-input multiple-output configuration, various techniques have been introduced, such as analog architectures, hybrid architectures, and index modulation (IM). Moreover, novel antenna architectures, such as graphene-based and dynamic metasurface antennas (DMAs), which leverage the unique properties of graphene and metamaterials, respectively, have been proposed to further reduce CAPEX and OPEX. In this study, we propose novel DMA-based IM schemes that aim to significantly reduce the power-hungry yet expensive RF chains, thereby improving the OPEX and CAPEX. Specifically, we propose a spatial modulation (SM) technique for the DMA architecture, which is achieved by activating either a single microstrip (microstrip-wise SM) with the help of a switching mechanism or a single DMA element (element-wise SM) by manipulating both the DMA reconfigurable weight and a switching mechanism. Our analysis shows that microstrip-wise SM achieves higher signal-to-noise ratios (SNRs) and, consequently, higher spectral efficiency compared with element-wise SM. However, in the high-SNR regime, the element-wise SM performs very close to the microstrip-wise SM while requiring lower complexity. Furthermore, we propose a generalized spatial modulation (GSM) technique for the DMA architecture, achieved by simultaneously activating multiple microstrips, to further enhance the spectral efficiency. We propose the design of DMA weights to maximize the SNR for the two considered SM schemes, obtaining closed-form solutions. For the GSM scheme, we develop an alternating algorithm to optimize the DMA weight and baseband precoder to maximize the spectral efficiency. Finally, we provide simulation results to validate our analysis and demonstrate the effectiveness of the proposed schemes.

This research investigates a multi-user wireless communication system that is assisted by a dual-polarized intelligent reflecting surface (DP-IRS). The DP-IRS system includes DP antennas equipped with separate phase shifters for each polarization at both the transmitter and receivers. Our objective is to minimize the total transmit power at the access point (AP) by optimizing the precoder at the AP, operations of the reflecting elements at the DP-IRS, and vertical/horizontal passive phase shifters at the transmit/receive DP antennas while taking into consideration the individual spectral efficiency (SE) constraints of the users. In tackling this problem, we introduce a four-step alternating optimization (AO) algorithm that utilizes the semi-definite relaxation approach. Furthermore, we propose a computationally low-complexity algorithm based on closed-form solutions for a single-user case. The numerical results demonstrate that the proposed algorithms achieve a reduction in transmit power by 74.6% for multi-user scenarios and 99.0% for single-user scenarios when compared to random operations. We also assess the minimized transmit power of the DP-IRS in comparison to that of the Simple IRS (S-IRS). For instance, when targeting a SE of 5.6 bps/Hz for each user, DP-IRS requires 70% lower transmit power than S-IRS for 50 reflecting elements and eight users.

This paper addresses a multi-user multiple-input multiple-output (MIMO) wireless network enhanced by a dual-polarized intelligent reflecting surface (DP-IRS). The system encompasses multiple DP antennas at both transmitters and receivers, each equipped with a single RF chain featuring independent phase shifters. Our objective is to minimize the total transmit power at the transmitter while satisfying per data stream target spectral efficiency constraints for all users. To achieve this, we introduce a multi-step alternating optimization algorithm. Additionally, we propose a novel scheme to reduce the computational complexity of beamforming design in IRS-assisted multi-user wireless networks. Specifically, by leveraging users’ channel information, we classify them into two distinct groups, simplifying problem formulations and significantly decreasing computational costs with minimal impact on performance. Numerical results illustrate the superiority of our proposed algorithms over various benchmark schemes. In a comparison between DP-IRS and Simple IRS, DP-IRS requires 41.7% less transmit power for 50 reflecting elements and eight users, while targeting a spectral efficiency of 4.6 bps/Hz per data stream for each user. Additionally, Algorithm 2, based on the proposed user grouping scheme, yields a 75.5% reduction in computational cost compared to Algorithm 1, with negligible performance degradation.

This work proposes a novel semi-static hybrid beamforming strategy for open radio access network (O-RAN) millimeter wave (mmWave) massive multiple-input multiple-output systems, addressing the limited O-RAN wireless fronthaul (O-WFH) capacity and short wavelength of mmWave bands. Statistical analog precoders are designed at O-RAN radio units to maximize the average signal-to-leakage-plus-noise ratio using statistical channel state information under non-grid-of-beams (non-GoB) constant-modulus constraints. A joint optimization problem of dynamic digital precoders and hybrid combiners is then performed at O-RAN distributed units via a multi-block coordinate descent (MBCD) method. The joint optimization maximizes sum-spectral efficiency (SE) under constraints on transmit power, users’ quality-of-service, O-WFH capacity, and non-GoB constant-modulus constraints. The digital processing subproblems are solved using a successive convex approximation with second-order cone programming, whereas the analog combining subproblem employs an MBCD-based Riemannian manifold optimization. Notably, the resulting hybrid combiners are forwarded to users for further processing and service applications. Numerical results reveal that the proposed framework offers superior user SE performance compared to practical analog and digital benchmark schemes, while significantly reducing O-WFH information exchange compared to a theoretical benchmark scheme, with only a small user SE performance loss.

Dynamic metasurface antennas (DMAs) is a new antenna technology comprising multiple microstrips, each encompassing several reconfigurable metamaterial elements acting as radiating and receiving antennas. The whole unit is usually connected via the input/output port of each microstrip to the radio frequency (RF) chains of the baseband unit. Point-to-point (P2P) multiple-input multiple-output (MIMO) system is one of the most prominent wireless communication systems, with its use cases recently increasing with the booming of machine-to-machine communication. However, it is yet to be explored with DMAs. This work thus studies the achievable rate maximization of a DMA-based P2P MIMO system whose transmitter or receiver is equipped with DMA instead of traditional antennas. Noteworthy, this is the first work to analyze the deployment where both the BS and the user are equipped with DMA. Additionally, we numerically demonstrate how uplink and downlink communication can be achieved in each DMA deployment strategy. Finally, through the simulation results, the performance of our proposed designs is compared with various benchmark systems, such as conventional P2P and IRS-aided MIMO systems, where in both cases, it is shown that the proposed designs provide the best tradeoff in achieving high performance with significant RF chains reduction.

This study considers a dual-polarized intelligent reflecting surface (DP-IRS)-assisted multiple-input multiple-output (MIMO) single-user wireless communication system. The transmitter and receiver are equipped with DP antennas, and each antenna features a separate phase shifter for each polarization. We attempt to maximize the system’s spectral efficiency (SE) by optimizing the operations of the reflecting elements at the DP-IRS, precoder/combiner at the transmitter/receiver, and vertical/horizontal phase shifters at the DP antennas. To address this problem, we propose a three-step alternating optimization (AO) algorithm based on the semi-definite relaxation method. Next, we consider asymptotically low/high signal-to-noise ratio (SNR) regimes and propose low-complexity algorithms. In particular, for the low-SNR regime, we derive computationally low-cost closed-form solutions. According to the obtained numerical results, the proposed algorithm outperforms the various benchmark schemes. Specifically, our main algorithm exhibits a 65.6% increase in the SE performance compared to random operations. In addition, we compare the SE performance of DP-IRS with that of simple IRS (S-IRS). For$N = 50$, DP-IRS achieves 24.8%, 28.2%, and 30.3% improvements in SE for${4} \times {4}$,${8} \times {8}$, and${16} \times {16}$MIMO, respectively, compared to S-IRS.

Dynamic metasurface antennas (DMAs) have great potential to be used in the radiator/receptor elements of future wireless transmitters and receivers, replacing conventional metallic antennas. This can be attributed to their unique properties, such as the ability to be reconfigured in real-time and to reduce the radio frequency chains, resulting in low implementation cost. However, the Lorentzian constraint associated with the DMA elements poses a challenge to real-time configuration and limits the application of the DMA. In this study, we propose a DMA-based wireless network, wherein a DMA-equipped base station (BS) communicates with single and multiple users. For the single-user scenario, we develop an optimal algorithm to maximize the signal-to-noise ratio of the user, which provides the weight of each DMA element in closed form. Furthermore, for multiple users, we formulate the weighted sum rate (WSR) problem and employ techniques from the single-user case to develop an efficient alternating optimization algorithm, which optimizes both the transmit precoders and DMA weights, to enhance the WSR of the system under the transmit power constraint of the BS. The numerical results demonstrate the effectiveness of the proposed algorithms in achieving better performance than that of the benchmark schemes.

This work explores the potential of a deep learning-aided detector for narrowband signal enumeration in a coherent environment. Specifically, we introduce the logarithmic eigenvalue-based classification network (LogECNet) to detect the signal number. In the proposed scheme, the full-row Toeplitz matrices reconstruction (FTMR) algorithm is employed to avoid the rank loss of the signal covariance matrix (SCM) in highly correlated signal environments. The simulation results show that the FTMR method not only achieves the complexity reduction with respect to the prior forward/backward spatial smoothing (FBSS) algorithm, but also improves the signal number detection performance when combined with LogECNet.

This paper considers intelligent reflecting surface (IRS)-aided single-user (SU) massive multiple-input multiple-output (mMIMO) millimeter wave (mmWave) downlink communication system. We aim to maximize the achievable spectral efficiency by separately designing the passive beamforming and active precoding (combining) through a decoupling strategy to reduce computational complexity. We propose two algorithms for passive beamforming design, which are followed by singular value decomposition (SVD) of the effective channel matrix to generate the active precoding and combining matrices at the bases station (BS) and user equipment (UE), respectively. The first algorithm employs the SVD of the BS-IRS and the IRS-UE channel matrices to generate the unitary matrices. These matrices are used to develop the optimization problem, which is solved via a Riemannian conjugate gradient (RCG)-based algorithm, yielding a passive beamforming vector. In the second algorithm, we propose a greedy-search (GS)-based method to select the array response vectors and their corresponding path gains of the mmWave channels between the BS (IRS) and IRS (UE) required to formulate the optimization problem, which is also solved via the RCG-based algorithm, resulting in a passive beamforming vector. The simulation results show that the proposed schemes achieve an improved trade-off between the spectral efficiency and computational complexity.

This work investigates the multiple-input multiple-output system in the context of the selective decode-and-forward multihop relay network under short-packet communications to facilitate not only ultra-reliability, but also low-latency communications. For the transmit and receive diversity techniques, we analyze the transmit antenna selection (TAS) and maximum-ratio transmission (MRT) schemes at the transmit side, whereas the selection-combining (SC) and maximum-ratio combining (MRC) schemes are leveraged at the receive side. For quasi-static Rayleigh fading channels and the finite-blocklength regime, we derive the approximate closed-form expressions of the end-to-end (e2e) block error rate (BLER) for the TAS/MRC, TAS/SC, and MRT/MRC schemes. The asymptotic performance in the high signal-to-noise ratio regime is derived, from which the comparison among diversity schemes in terms of the diversity order, e2e BLER loss, and SNR gap is provided. Furthermore, based on the asymptotic results, we develop power-allocation, relay-location, and joint simplified optimizations to minimize the asymptotic e2e BLER under the system constraints. The e2e latency and throughputs are also analyzed for the considered schemes. The correctness of our analysis is confirmed via Monte Carlo simulations.

Although the sphere decoder (SD) is a powerful detector for multiple-input multiple-output (MIMO) systems, it has become computationally prohibitive in massive MIMO systems, where a large number of antennas are employed. To overcome this challenge, we propose fast deep learning (DL)-aided SD (FDL-SD) and fast DL-aided$K$-best SD (KSD, FDL-KSD) algorithms. Therein, the major application of DL is to generate a highly reliable initial candidate to accelerate the search in SD and KSD in conjunction with candidate/layer ordering and early rejection. Compared to existing DL-aided SD schemes, our proposed schemes are more advantageous in both offline training and online application phases. Specifically, unlike existing DL-aided SD schemes, they do not require performing the conventional SD in the training phase. For a$24 \times 24$MIMO system with QPSK, the proposed FDL-SD achieves a complexity reduction of more than 90% without any performance loss compared to conventional SD schemes. For a$32 \times 32$MIMO system with QPSK, the proposed FDL-KSD only requires$K = 32$to attain the performance of the conventional KSD with$K=256$, where$K$is the number of survival paths in KSD. This implies a dramatic improvement in the performance–complexity tradeoff of the proposed FDL-KSD scheme.

The hybrid precoding and combining algorithms for mmWave massive multiple-input multiple-output (MIMO) systems must consider the trade-off between the complexity and performance of the system. Unfortunately, because of the unit-norm constraint imposed by the use of phase shifters, the optimization of the radio frequency (RF) precoder and combiner becomes a non-convex problem. As a consequence, the algorithm for hybrid precoding and combining design often incurs high complexity. This paper proposes a low-complexity algorithm for hybrid precoding and combining design based on array response vectors. The proposed algorithm considers a decoupled optimization scheme between the RF and baseband domains for the spectral efficiency-maximization problem. In the RF domain, we propose an incremental successive selection method to find a subset of array response vectors from a dictionary, which forms the RF precoding/combining matrices. For the digital domain, we employ singular-value decomposition (SVD) of the low-dimensional effective channel matrix to generate the digital baseband precoder and combiner. Through numerical simulation, we show that the proposed algorithm achieves nearoptimal performance with 89.9 % - 99.4% complexity reduction compared to the conventional state-of-the-art hybrid precoding and combining algorithm.

In this paper, we propose an adaptive perturbation-aided opportunistic hybrid beamforming (AP-OHBF) scheme for temporally correlated millimeter wave (mmWave) channels. The basic idea is that the proposed scheme perturbs the channel parameters to track the channels of the scheduled mobile stations (MSs), whose channels are in favorable conditions, based on only the signal-to-interference-plus-noise ratio (SINR) information of MSs. The BS utilizes these parameters to generate the precoders and adopts the successful perturbation for data transmission, which achieves performance improvements. By contrast, if a perturbation results in performance degradation, it is discarded, whereas the precoders corresponding to the best-known parameters in the memory are instead used.

A variety of hybrid analog-digital beamforming architectures have recently been proposed for massive multiple-input multiple-output (MIMO) systems to reduce energy consumption and the cost of implementation. In the analog processing network of these architectures, the practical sub-connected structure requires lower power consumption and hardware complexity than the fully connected structure but cannot fully exploit the beamforming gains, which leads to a loss in overall performance. In this work, we propose a novel unequal sub-connected architecture for hybrid combining at the receiver of a massive MIMO system that employs unequal numbers of antennas in sub-antenna arrays. The optimal design of the proposed architecture is analytically derived, and includes antenna allocation and channel ordering schemes. Simulation results show that an enhancement of up to 10% can be attained in the total achievable rate by unequally assigning antennas to sub-arrays in the sub-connected system at the cost of a marginal increase in power consumption. Furthermore, in order to reduce the computational complexity involved in finding the optimal number of antennas connected to each radio frequency (RF) chain, we propose three low-complexity antenna allocation algorithms. The simulation results show that they can yield a significant reduction in complexity while achieving near-optimal performance.

In this study, we consider the application of deep learning (DL) to tabu search (TS) detection in large multiple-input multiple-output (MIMO) systems. First, we propose a deep neural network (DNN) architecture for symbol detection, termed the fast-convergence sparsely connected detection network (FS-Net), which is obtained by optimizing the prior detection networks called DetNet and ScNet. Then, we propose the DL-aided TS algorithm, in which the initial solution is approximated by the proposed FS-Net. Furthermore, in this algorithm, an adaptive early termination (ET) algorithm and a modified searching process are performed based on the predicted approximation error, which is determined from the FS-Net-based initial solution, so that the optimal solution can be reached earlier. The simulation results show that the proposed algorithm achieves approximately 90% complexity reduction for a 32 × 32 MIMO system with QPSK with respect to the existing TS algorithms, while maintaining almost the same performance.

Linear interference alignment (IA) is considered a promising interference management technique for future cellular networks. However, its application to practical cellular networks still faces many challenges, two of which are the overhead of sharing global channel state information (CSI) and the cost of signalling dimensionality. These two challenges are resolved in this study via a new linear IA scheme that not only prevents global CSI sharing but also keeps the number of signalling dimensions manageable. While making linear IA more applicable, the proposed scheme provides comparable degrees of freedom to the existing IA schemes in symmetric multicell multiuser multiple‐input multiple‐output (MIMO) uplink channels.

This study presents novel strategies to improve the decoding performance through base‐station cooperation. In the proposed schemes, the soft output of the demapper at a serving base station (SBS) is replaced by a combination of log‐likelihood ratio information, which is generated through demodulation by neighbouring base stations (NBSs) as well as the SBS. For the information used in the cooperation, soft information is first considered. Combining the soft information transmitted from all NBSs provides close‐to‐optimal performance, but it still requires a high amount of overhead to be passed over the backhaul links. In the proposed schemes, the SBS only cooperates with the reliable NBSs, which are identified on the basis of the magnitude of the metrics transmitted from the NBSs, to achieve a trade‐off between performance and overhead. In addition, the use of hard information instead of soft information is also proposed to further reduce the backhaul overhead. In this scheme, the metric values as well as the hard information are exploited to generate the updated soft information for the channel decoder. In the simulation results, it is shown that the proposed schemes provide better error performance compared with the conventional schemes under limited backhaul capacities.

The problem of interference alignment (IA) for uplink channels in multicell multiuser cellular networks with arbitrary numbers of transmit and receive antennas is considered. The proposed IA scheme aligns the interference from K users into a subspace established by the interference from p users among those K users. The value of p can be adaptively chosen based on the network configuration. The IA solution is realized by designing precoder matrices from a system of linear equations built upon the IA constraints. The closed-form result for the degrees of freedom (DoF) achievable by the proposed scheme is derived. Numerical analysis shows that, in most cases, the proposed scheme provides higher DoF than other related schemes.

In this study, two novel decoding algorithms for cooperative beamforming (CBF) systems are proposed. In decode‐and‐forward‐based CBF, multiple relay nodes decode the packets transmitted from the source node and forward them to the destination node. However, decoding errors at the relay nodes cause erroneous retransmission of packets, which degrades the signal decoding performance at the destination node. To solve this problem, the optimal decoding strategy is derived; this strategy takes the potential errors in forwarded signals into account in the computation of the likelihood ratio of coded bits. Furthermore, to reduce the required computational complexity, a suboptimal decoding algorithm, which assumes that only one of the multiple relayed signals potentially contains errors in each time slot, is also proposed. In simulation results, it is observed that the proposed decoding algorithms for CBF provide performance improvements in terms of the achievable packet error rates with respect to the conventional schemes.

In this paper, the optimal decoding strategy for cooperative spatial multiplexing (CSM) aided systems is derived. In CSM systems, the multiple relay stations (RSs), which compose a virtual antenna array (VAA), independently decode the packets received from the mobile stations (MS) and forward them to the base station (BS). When the BS decodes the signal forwarded from the RSs, the potential decoding errors encountered at the RSs will result in erroneous forwarding, but their effects are mitigated by the proposed solution. Our simulation results show that when the direct link has a significantly higher signal-to-noise ratio than the relay link, the proposed decoding algorithm achieves an approximately 3 dB better performance than conventional CSM, which does not consider the deleterious effects of erroneous forwarding from the RSs.

This paper proposes two types of new decoding algorithms for a network coding aided relaying (NCR) system, which adopts multiple antennas at both the transmitter and receiver. In the NCR system, the relay station (RS) decodes the data received from both the base station (BS) as well as from the mobile station (MS) and combines the decoded signals into a single data stream before forwarding it to both. In this paper, we consider the realistic scenario of encountering decoding errors at the RS, which results in erroneous forwarded data. Under this assumption, we derive decoding algorithms for both the BS and the MS in order to reduce the deleterious effects of imperfect decoding at the RS. We first propose a decoding algorithm for a hard decision based forwarding (HDF) system. Then, for the sake of achieving further performance improvements, we also employ soft decision forwarding (SDF) and propose a novel error model, which divides the error pattern into two components: hard and soft errors. Given this error model, we then modify the HDF decoder for employment in SDF systems. We also derive estimation algorithms for their parameters that are required for the efficient operation of the proposed decoders. Our simulation results show that the proposed algorithms provide substantial performance improvements in terms of the attainable packet error rate as a benefit of our more accurate error model.

In this letter, the optimal decoding strategy for cooperative spatial multiplexing (CSM) aided systems is derived. In CSM systems, the multiple relay stations (RSs), which compose a virtual antenna array (VAA), independently decode the packets received from the mobile stations (MS) and forward them to the base station (BS). When the BS decodes the signal forwarded from the RSs, the potential decoding errors encountered at the RSs will result in erroneous forwarding, but their effects are mitigated by the proposed solution. Our simulation results show that when the relay link has a significantly higher signal-to-noise ratio than the direct link, the proposed decoding algorithm achieves an approximately 3 dB better performance than conventional CSM, which does not consider the deleterious effects of erroneous forwarding from the RSs.

In this paper, we propose two types of new decoding algorithms for a network coding aided relaying (NCR) system, which adopts multiple antennas at both the transmitter and receiver. We consider the realistic scenario of encountering decoding errors at the relay station (RS), which results in erroneous forwarded data. Under this assumption, we derive decoding algorithms for both the base station (BS) and the mobile station (MS) in order to reduce the deleterious effects of imperfect decoding at the RS. We first propose a decoding algorithm for a hard decision based forwarding (HDF) system. Then, for the sake of achieving further performance improvements, we also employ soft decision forwarding (SDF) and propose a novel decoding error model, which divides the decoding error pattern into two components: hard and soft errors. Given this error model, we then modify the HDF decoder for employment in SDF systems. Our simulation results show that the proposed algorithms provide substantial performance improvements in terms of the attainable packet error rate as a benefit of our more accurate decoding error model.

In this paper we investigate a realistic code acquisition assisted cooperative non-coherent (NC) multiple-input multiple-output (MIMO) DS-CDMA downlink scenario, when communicating over uncorrelated single-path and multi-path Rayleigh channels. The probabilities of correct detection and false alarm have been derived analytically. Furthermore, a mean acquisition time (MAT) formula is provided for the cooperative transmission scenario considered. The associated MAT performance trends are characterised as a function of both the number of relay stations (RSs), as well as that of the receive antennas and link imbalance. As opposed to the classic scenario of having co-located MIMO elements, our findings suggest that employing distributed MIMO elements acting as RSs combined with multiple receive antennas leads to an improved MAT performance.

In cellular mobile communication systems, the link performance can be remarkably improved by deploying relays between the base station and the mobile station. In this paper we propose an efficient duplexing scheme so that both spatial and temporal gain by adding relays can be increased. Using the proposed relaying scheme, the conventional system of four-phase relaying can be simplified to a two-phase relaying system and the additional resource consumption due to relays can be minimized. We show the capacity gain for cell edge users with numerical results.

We propose a new closed-loop MIMO (Multiple- Input Multiple-Output) signal processing scheme that combines transmit beamforming and spatial multiplexing in Rayleigh frequency-flat fading channel environment. Diversity gain is achieved by beamforming, and multiplexing gain by spatial multi- plexing. The proposed scheme is particularly useful for downlink communication, where the number of transmit antennas at the base station is greater than the number of receive antennas at the mobile. Beamforming weight vectors are computed at the receiver using the estimated channel matrix and are sent to the transmitter. The computation is based on the mutual information maximization criterion which reduces to a symmetric eigenvalue problem. The BER (Bit Error Rate) performance comparisons with other known schemes are also presented for the cases of the ML (Maximum Likelihood) decoding and the MMSE (Minimum Mean-Squared Error) nulling and ordered-cancellation decod- ing, through extensive Monte-Carlo simulation. The simulation results show that the proposed scheme has lower BER than the other schemes.

We introduce a modified vertical Bell laboratories layered space-time (V-BLAST) detection algorithm to reduce unexpected effects of the channel estimation errors. Assuming that the channel estimation errors are independent and identically distributed Gaussian random variables, we derive a better nulling weight, which minimizes the power of various terms generated by the channel estimation errors. The symbol ordering operation is also modified by choosing the minimum mean-square error symbol at each iteration. The proposed detection algorithm requires the knowledge of channel estimation error's variance, but its performance is robust to the inaccurate estimation of it. The simulation results show that the performance of the proposed algorithm is superior to that of the conventional V-BLAST detection algorithm when channel estimation errors exist

In this letter, we investigated the optimal minimum-mean-squared-error (MMSE) based successive interference cancellation (SIC) strategy designed for lattice-reduction aided multiple-input multiple-output (MIMO) detectors. For the sake of generating the MMSE-based MIMO symbol estimate at each SIC detection stage, we model the so-called effective symbols generated with the aid of lattice-reduction as joint Gaussian distributed random variables. However, after lattice-reduction, the effective symbols become correlated and exhibit a non-zero mean. Hence, we derive the optimal MMSE SIC detector, which updates the mean and variance of the effective symbols at each SIC detection stage. As a result, the proposed detector achieves a better performance compared to its counterpart dispensing with updating the mean and variance, and performs close to the maximum likelihood detector.