High-mobility and LEO satellite channels cause severe Doppler dispersion that degrades OFDM performance. Affine frequency division multiplexing (AFDM) mitigates this effect with an efficient chirp-based design. To further lower receiver complexity, this paper introduces a variational Bayesian (VB) detector using mean-field factorization and coordinate-ascent updates, eliminating matrix inversion. The proposed method achieves fast, low-complexity soft detection with superior BER for next-generation LEO systems.

Accurate localization is critical for vehicle-toinfrastructure (V2I) communication systems, especially in urban areas where GPS signals are often obstructed by tall buildings, leading to significant positioning errors, necessitating alternative or complementary techniques for reliable and precise positioning in applications like autonomous driving and smart city infrastructure. This paper proposes a multimodal contrastive learningbased regression framework for V2I localization. By integrating channel state information (CSI) with visual data, the framework achieves enhanced accuracy and reliability. The approach leverages the complementary strengths of wireless and visual data to overcome the limitations of traditional localization methods, offering a robust solution for V2I applications. Simulation results demonstrate that the proposed CSI-vision fusion model significantly surpasses both traditional and unimodal benchmarks, delivering superior localization precision in challenging urban environments.

Incorporating non-RF data in beam selection can speed up decision-making in situations that require a thorough search of all candidate options. Therefore, using camera sensory data at base stations to enhance millimeter-wave (mmWave) beam selection is becoming increasingly popular. This paper introduces a machine learning framework using a robust model along with XGBoost in beam prediction scenarios. Our approach enhances beam selection in both single and multi-candidate real-world vehicle-to-infrastructure (V2I) settings.

In this paper, we propose BeamLLM, a vision-empowered millimeter-wave (mmWave) beam prediction framework leveraging large language models (LLMs) to enhance the accuracy and robustness of beam prediction. By integrating computer vision (CV) with LLMs’ cross-modal reasoning capabilities, the framework extracts user equipment (UE) positional features from RGB images and aligns visual-temporal features with LLMs’ semantic space through reprogramming techniques. Evaluated on a realistic vehicle-to-infrastructure (V2I) scenario, BeamLLM achieves 61.01% top-1 accuracy and 97.39% top-3 accuracy in standard prediction tasks, outperforming traditional deep learning models. In few-shot prediction scenarios, performance degradation is limited to 12.56% (top-1) and 5.55% (top3) from time sample 1 to 10, demonstrating superior prediction capability.

In high-mobility communication scenarios, traditional orthogonal frequency-division multiplexing (OFDM) systems suffer from performance degradation due to the Doppler effect. In contrast, orthogonal time frequency space (OTFS) modulation represents signals in the delay-Doppler (DD) domain, effectively leveraging the sparsity and stability of the channel, making it a promising alternative. However, channel estimation in OTFS systems remains a challenge. This paper proposes an OTFS channel estimation method based on the alternating direction method of multipliers (ADMM). By designing an appropriate pilot structure, the channel estimation problem is formulated as a convex optimization problem with ℓ1-norm regularization, enabling efficient solution without requiring prior knowledge of sparsity. The paper provides a detailed description of the system model, ADMM algorithm derivation, parameter selection, and complexity analysis. Simulation results demonstrate that the proposed method outperforms existing CS-based approaches in terms of normalized mean square error (NMSE), bit error rate (BER), confirming its superiority in high-mobility scenarios. This research provides significant theoretical and practical support for the implementation of OTFS systems.

This work proposes a deep learning-based data-aided active user detection network (D-AUDN) for grant-free sparse code multiple access (SCMA) systems that leverages both SCMA codebook and Zadoff-Chu preamble for activity detection. Due to disparate data and preamble distribution as well as codebook collision, existing D-AUDNs experience performance degradation when multiple preambles are associated with each codebook. To address this, a user activity extraction network (UAEN) is integrated within the D-AUDN to extract a-priori activity information from the codebook, improving activity detection of the associated preambles. Additionally, efficient SCMA codebook design and preamble sequence association are considered to further enhance performance.

Deep learning (DL) has emerged as a transformative tool in wireless communications, offering powerful methods for optimizing complex systems. One critical challenge in deploying DL-based solutions is ensuring that models can generalize effectively across various channel conditions. This paper investigates the generalization capabilities of a DL-based Collision-Aware Multi-User Detection (CA-MUD) system within Grant-Free Sparse Code Multiple Access (GF-SCMA) frameworks, which are vital for efficient massive machine-type communication (mMTC). By exploring different training environments, such as independent and identically distributed (IID) Rayleigh and tapped delay line (TDL) channels, the study aims to identify optimal strategies to maintain robust communication performance under diverse conditions.

This paper presents a novel approach for dynamic resource allocation in cell-free massive MIMO enabled mobile edge computing (MEC) systems, employing an attention mechanism within a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to adaptively weigh the contributions of other agents. This approach allows for a more nuanced understanding of the interactions between agents, leading to a more efficient resource distribution. We demonstrate the effectiveness of the proposed algorithm in meeting delay requirements while reducing energy consumption of the users. Notably, our scheme exhibits robust generalization capabilities, effectively accommodating varying number of users beyond the training scenario.

This paper presents an attention mechanism-empowered multi-agent deep deterministic policy gradient (MADDPG) algorithm for distributed resource allocation in cell-free mobile edge computing (MEC) system. The proposed algorithm leverages the attention mechanism to adaptively weigh the contributions of other agents, outperforming the performance of conventional MADDPG algorithm.

In grant-free sparse code multiple access (SCMA) systems, SCMA codebooks (CBs) are used for efficient grant-free random access. However, CB collisions can occur when multiple active users select the same CB, degrading the performance of multi-user detection (MUD) at the base station (BS). The existing methods modify the factor graph on the message-passing algorithm (MPA) for each CB collision scenario, resulting in high computational complexity. In this paper, we aim to confirm that even in the presence of CB collisions, MUD performance can be ensured through a deep learning (DL)-based receiver and explore its limitations. We propose a single DL architecture for collision-aware MUD (CA-MUD) that can tolerate CB collisions, without resorting to the distinct MUD processes associated with individual collision scenarios. To facilitate the generation of training data for CA-MUD that comprehensively represents the grant-free SCMA scenario, we introduce a transceiver model that regulates the number of active CBs and sets the maximum tolerable CB collisions. Simulation results demonstrate that our proposed approach allows a single CA-MUD network to handle various CB collision scenarios, including 2-fold CB collision subject to a limited number of active users.

Optical access networks are envisioned to become increasingly complex as they support more and more diverse and immersive services, each with a different capacity, latency, and reliability need. While machine learning has been touted as a silver bullet that will intelligently manage network operations and resources to meet these demands, as it had been anticipated for core and metro networks, there exist various challenges that need to be addressed to progress machine learning models from research to production. We first aim motivate the continued push to advance optical access networks and rationalize the use of machine learning in these networks. We then highlight the challenges that are especially amplified due to the traffic dynamicity and heterogeneity, data scarcity, and computation-resource constraints of optical access networks. We discuss emerging machine learning approaches that are being explored to address these challenges. Finally, we consider an example of an online reinforcement learning based bandwidth allocation scheme to address concept drift in machine learning enhanced optical access network.

In this paper, we consider the minimum mean square error-based constructive interference optimization (MMSE-CIO) problems to ensure the minimum required constructive interference gain under a fixed total-power constraint, which involves critical implementation issues regarding the computational complexity. The optimum solution is derived in a closed form for a digital precoder subject to a fixed analog beam, serving as a key implementation block to reduce the computational complexity in the proposed hybrid beamforming under a more complexity situation in which the different types of modulation schemes are mixed. We also show that MMSE-CIO can achieve a significant performance gain with the advantages of link adaptation over legacy CIO scheme in nonhomogeneous scenario.

To tackle the power consumption challenges in terahertz band wireless communication, this study proposes a deep learning-driven approach for transceiver design that utilizes one-bit quantization and oversampling at the receiver. The solution also involves implementing Faster-than-Nyquist (FTN) transmission on a fading channel. Our approach employs a convolutional autoencoder (AE) to enable the transmission of higher-order modulation over a one-bit fading channel while utilizing pilots. By exploiting the AE transceiver, it is evident that performance in quantized communication has significantly improved for QPSK, 16-QAM, and 64-QAM modulation levels, approaching the theoretical lower bound for the corresponding modulation over additive white Gaussian noise (AWGN) channel. Furthermore, the study has explored how to use the robust error-correcting capabilities of the AE transceiver to boost spectral efficiency by increasing FTN rates without dire Bit-error-rate sacrifice.

With the aim of addressing power consumption issues for terahertz band wireless communication, this work presents a deep learning-based solution for transceiver design with 1-bit quantization and oversampling at the receiver, and Faster-than-Nyquist transmission over fading channel. Specifically, by implementing the transceiver using a convolutional autoencoder, our work allows higher-order modulation transmission over one-bit fading channel without pilots. Transfer learning from previously trained blocks over simple noisy channel is used to minimize the probability of bit error and outperforms the convolutional autoencoder at low oversampling rates. The biterror-rate gain offered at 20dB SNR by the transfer learning is seen to be as high as half of one order at low oversampling rates and to saturate as oversampling rate increases. Furthermore, by allowing explicit phase synchronization, the autoencoder-based transceiver with partial channel matching is able to approach unquantized performance with 4dB gap in Rayleigh fading environment.

The recent works on a deep learning (DL)-based joint design of preamble set for the transmitters and data-aided active user detection (AUD) in the receiver has demonstrated a significant performance improvement for grant-free sparse code multiple access (GF-SCMA) system. The autoencoder for the joint design can be trained only in a given environment, but in an actual situation where the operating environment is constantly changing, it is difficult to optimize the preamble set for every possible environment. Therefore, a conventional, yet general approach may implement the data-aided AUD while relying on the preamble set that is designed independently rather than the joint design. In this paper, the activity detection error rate (ADER) performance of the data-aided AUD subject to the two preamble designs, i.e., independently designed preamble and jointly designed preamble, were directly compared. Fortunately, it was found that the performance loss in the data-aided AUD induced by the independent preamble design is limited to only 1dB. Furthermore, such performance characteristics of jointly designed preamble set is interpreted through average cross-correlation among the preambles associated with the same codebook (CB) (average intra-CB cross-correlation) and average cross-correlation among preambles associated with the different CBs (average inter-CB cross-correlation).

This paper investigates a joint resource allocation problem in cell-free mobile edge computing system which intends to minimize the number of users subjected to outage, due to failure to meet user-specific delay constraints. Accordingly, the number of APs serving each user, i.e., dynamic cluster size, uplink transmit power and computing resources at the edge server are jointly optimized based on deep reinforcement learning (DRL) algorithm.

In this study, with the aim of reducing power consumption for terahertz band wireless communication, we present a deep learning-based solution for transceiver design with 1-bit quantization and oversampling at the receiver, and Faster- than-Nyquist transmission. Our simulation results illustrate that the studied system with 1-bit quantization achieves bit-error- rate performance comparable to that of an end-to-end channel autoencoder without the constraint of 1-bit quantization subject to the same spectral efficiency. It is also demonstrated that reliable communication can be achieved at rates exceeding 5.3 bits/sec/Hz which corresponds to 80% of the achievable capacity at adequate SNR.

In this paper, with the aim of addressing the stringent computing and quality-of-service (QoS) requirements of recently introduced advanced multimedia services, we consider a cell-free massive MIMO-enabled mobile edge network. In particular, benefited from the reliable cell-free links to offload intensive computation to the edge server, resource-constrained end-users can augment on-board (local) processing with edge computing. To this end, we formulate a joint communication and computing resource allocation (JCCRA) problem to minimize the total energy consumption of the users, while meeting the respective user-specific deadlines. To tackle the problem, we propose a distributed solution approach based on cooperative multi-agent reinforcement learning framework, wherein each user is implemented as a learning agent to make joint resource allocation relying on local information only. The simulation results demonstrate that the performance of the proposed distributed approach outperforms the heuristic baselines, converging to a centralized target benchmark, without resorting to large over-head. Moreover, we showed that the proposed algorithm has performed significantly better in cell-free system as compared with the cellular MEC systems, e.g., a small cell-based MEC system.

Grant-free (GF) access has been one of the enablers for the various use cases in 5th generation (5G) mobile system, especially for time-critical massive machine-type communication (mMTC). However, these use cases have diverse quality of service (QoS) requirements, which can be measured in terms of an access success rate from a GF random access perspective. Consequently, a GF scheme that enables supporting of diverse QoS is highly sought. This article proposes a GF access scheme in which high-QoS users superpose multiple preambles to improve their access success rate as a result of the diversity in access collision and multiple access interference seen by multiple preambles. We further show that in the presence of multiple antennas in the base station (BS), a low-complexity receiver can correctly detect active preambles with a significantly high probability, even under severe multiple access interference caused by non-orthogonal preamble transmission. A theoritical performance analysis is conducted by modeling the preamble reception as a multiple measurement vector-based compressive sensing problem. The preamble misdetection probability is shown to decrease exponentially as the number antennas at BS increases. Numerical results demonstrate multiple-order improvement in terms of the access success rate for critical-QoS users, even under severe noise and multiple access contamination.

The performance of grant-free random access (GF-RA) is limited by the number of accessible random access resources (RRs) due to the absence of collision resolution. Compressive sensing (CS)-based RA schemes scale up the RRs at the expense of increased non-orthogonality among transmitted signals. This paper presents the design of multi-sequence spreading random access (MSRA) which employs multiple spreading sequences to spread the different symbols of a user as opposed to the conventional schemes in which a user employs the same spreading sequence for each symbol. We show that MSRA provides code diversity, enabling the multi-user detection (MUD) to be modeled into a well-conditioned multiple measurement vectors (MMVs) CS problem. The code diversity is quantified by the decrease in the average Babel mutual coherence among the spreading sequences. Moreover, we present a two-stage active user detection (AUD) scheme for both wideband and narrowband implementations. Our theoretical analysis shows that with MSRA activity misdetection falls exponentially while the size of GF-RA frame is increased. Finally, the simulation results show that about 82% increase in utilization of RRs, i.e., more active users, is supported by MSRA than the conventional schemes while achieving the RA failure rate lower bound set by random access collision.

Enhanced licensed-assisted access (eLAA) is an operational mode that allows the use of unlicensed band to support long-term evolution (LTE) service via carrier aggregation technology. The extension of additional bandwidth is beneficial to meet the demands of the growing mobile traffic. In the uplink eLAA, which is prone to unexpected interference from WiFi access points, resource scheduling by the base station, and then performing a listen before talk (LBT) mechanism by the users can seriously affect the resource utilization. In this paper, we present a decentralized deep reinforcement learning (DRL)-based approach in which each user independently learns dynamic band selection strategy that maximizes its own rate. Through extensive simulations, we show that the proposed DRL-based band selection scheme improves resource utilization while supporting certain minimum quality of service (QoS).

The data-aided symbol-level precoding scheme has been known as a useful means of improving the system efficiency in multi-user MISO system. Even if it was mostly evaluated under the condition of an ideal channel state information (CSI), it is conjectured that its performance is critically governed by imperfect received CSI (CSIR). Furthermore, as the downlink channel can be estimated only in a block level, i.e., without tracking an effective channel on each symbol, its performance would be more seriously degraded by symbol-by-symbol channel variation. Meanwhile, we also investigate a channel coding gain for symbol-level preceding, which is not clear when the minimum distance between received signals can be increased by a nature of cumulative constructive interference gain (CCIG). The objective of this paper is to provide a complete performance comparison between the conventional block-level precoding and symbol-level precoding schemes under more realistic system assumptions.

In 5G mobile communication system design and standard specification, a system-level simulator is an indispensable element of research & development (R&D). Due to various usage scenarios, 5G systems require the development of different simulators that deal with individual evaluation scenarios and environments. To minimize the time and effort required for the implementation, we have designed and implemented 5G K-SimSys, which is a system-level simulator with modular & flexible structure, facilitating its reuse thorough open-source code sharing. This paper describes the 5G ultra-reliable low latency communication (URLLC) specification in 3GPP and its system-level evaluation to illustrate that 5G K-SimSys can provide reconfiguration between various usage scenarios.

Massive MIMO is a key feature for improving spectral efficiency in 5G system. Since massive MIMO has been standardized in 3GPP Re1-13 (LTE-Advanced Pro), a next generation of massive MIMO standard is now available in Rel-15, a.k.a New Radio (NR), for 5G, which is aiming at further improving the average spectral efficiency with more antenna elements over the higher frequency band. In particular, beam-based air interface for above 6GHz band involves with various antenna configurations and feedback schemes, requiring a more complex testbed for system-level performance evaluation. In this paper, we examine the multi-antenna technologies in 3GPP NR specification, so as to develop its system-level model in 5G K-SimSys, which has been designed and implemented to provide an open platform and testbed for evaluating the system-level performance for 5G system. Its actual implementation is detailed along with the overall platform architecture for modular and flexible design. Then, its actual performance is demonstrated for one of the precoding matrices in the NR specification as an example.

Unlike the general concept of eliminating or avoiding inter-user interference in the downlink multi-user multiple-input single-output (MISO) system, the data-aided precoding scheme attempts to exploit the constructive interference at symbol level. Positive interference can be constructed to enhance the received signal gain by predicting the phase and magnitude of the inter-user interference. In this paper, we formulate a constructive interference optimization problem that minimizes a sum of minimum mean square error (MMSE) for all users, while ensuring the minimum required constructive interference gain with a fixed total power constraint. As opposed to the existing scheme, such as constructive zero-forcing precoding or minimum power precoding subject to strict phase conservation for phase-shift keying (PSK), our proposed scheme exploits a full range of relaxation for the constructive interference region, while ensuring the link performance by minimizing the sum of mean-square error (MSE) for all users. In fact, the relaxed requirements lead to more degrees of freedom for improving the cumulative constructive interference gain (CCIG) under the varying channel conditions. Furthermore, our MMSE-based optimization approach allows for a semi-closed-form optimal solution to the data-aided precoding scheme, providing a more CCIG without incurring unacceptable complexity than other state-of-the-art schemes.

In this paper, the concept of faster-than Nyquist (FTN) transmission is used as a non-orthogonal multiple access scheme to interlace sporadic traffic into a broadband service traffic so that radio resources can be efficiently shared by both traffics, beyond the Nyquist rate. We have extended the concept of a 2-dimensional faster- than Nyquist (2D-FTN) transmission where some time-frequency resources are offloaded (turned off) from a broadband transmission to a time- frequency- space 3DFTN system. A broadband user transmitting in multiple- input multiple-output (MIMO) mode turns-off some set of transmit antennas for few subcarriers, which will be allowed for sporadic traffic. Furthermore, we show that turning-off subcarriers with the ill- conditioned channel matrices reduces the performance penalty of FTN transmission. For example, we have have shown that turning-off 12.5% of subcarriers from broadband transmission of 256 OFDM-symbol block costs only 0.3dB transmit power per bit while allowing up to 64 single- carrier grant-free transmission over the offloaded subcarriers.

In the Third Generation Partnership Project (3GPP) standardization for LTE services in the unlicensed band, i.e., aggregation of LTE on licensed and unlicensed spectrum, known as “Licensed-Assisted Access using LTE” (LAA), carrier sensing via Listen-Before-Talk (LBT) procedure is a vital feature for fair sharing and friendly operation in the unlicensed spectrum. Meanwhile, along with the recent contribution to performance enhancement with the frequency reuse-1 for Licensed-Assisted Access using LTE (LAA) system, we propose a new scheme, referred to as alignment reference interval adaptation-based LAA (ARIA-LAA), by combining the advantages of each conventional scheme into a unified access framework. Our simulation results demonstrate that ARIA-LAA is a novel scheme of spectrum sharing with spatial reuse for LAA that can increase the overall system capacity while satisfying the fair-coexistence requirement. Its implementation is simple and robust as only a single design parameter is dynamically controlled and optimized for adaptation to the varying traffic load.

In this paper, we introduce a comprehensive grant-free random access scheme for machine-type communication which is characterized by massive connectivity and low latency. The scheme presented in here is comprehensive in a sense that, synchronization, channel estimation, and users identification & data detection (multi-user detection) are performed all in a single shot. The scheme employs compressive sensing by exploiting two sparse phenomena: sparsity in users activity and sparsity in multi-path channel. Furthermore, the scheme is designed in such a way that channel estimation and multi-user detection have a bi-directional mutual relationship, enabling one to reinforce the other for accurate detection and estimation. Moreover, the iterative order recursive least square (IORLS) estimation algorithm is modified & employed in such a way that it exploits the joint structure in multi-path channel and multi-user signal sparsity.

The compressive sensing-based random access may get impacted largely by other cell interference (OCI), which may shatter the sparsity of the received signal model unless the signature space assignment among interfering cells is carefully handled. In this paper, we investigate the performance of multi-sequence spreading-based random access (MSRA) scheme under multi-cell environment. In particular, a grant-free random access scenario is considered to indicate its particular attributes that fundamentally affect the cellular performance. We also give the performance of MSRA in comparison with sparse code multiple access (SCMA). We show that if a proper signature assignment is undertaken, the OCI in MSRA and other CS-based schemes can be modeled as a dispersed noise which does not critically undermine the underlying sparsity.

For the frequency division duplexing-based large-scale multiple-input multiple-output (MIMO) systems, we consider a multi-user MIMO (MU-MIMO) non-orthogonal multiple access (NOMA) scheme, referred to as beam division-NOMA (BD-NOMA), which is featured by a long-term feedback-based user grouping and clustering, along with a short-term feedback-based BD-NOMA scheduling. The performance of BD-NOMA is not warranted in practice because near users are severely interfered by strong signals for the far users of other beams, and furthermore, the long-term feedback-based user grouping and clustering might result in short-term inter-beam interference. We consider the short-term feedback-based joint optimization problem of user selection and power allocation for NOMA in each beam and system-wide power allocation for MU-MIMO, dealing with both intra-beam and inter-beam interference at the same time. We formulate a joint optimization problem for a large-scale MIMO system with the multiple channel quality indicators-based short-term feedback. In our analysis, we reformulate the original problem into an equivalent one that can be solved iteratively with the weighted minimum mean square error-based algorithm. Then, the performance analysis is presented to conclude that the proposed optimization is essential for enabling BD-NOMA to outperform beam division multiple access, especially when inter-beam interference becomes critical.

A fundamental performance trade-off of multicell multiuser multiple-input multiple-output (MU-MIMO) systems is explored for achieving intercell and intracell interference-free conditions. In particular, we analyze the three-dimensional diversity-multiplexing-nulling trade-off (DMNT) among the diversity order (i.e., the slope of the error performance curve), multiplexing order (i.e., the number of users that are simultaneously served by MU-MIMO), and nulling order (i.e., the number of users with zero interference in a victim cell). This trade-off quantifies the performance of MU-MIMO in terms of its diversity and multiplexing order, while nulling the intercell interference toward the victim cell in the neighbor. First, we design a precoding matrix to mitigate both intercell and intracell interference for a linear precoding-based MU-MIMO system. Then, the trade-off relationship is obtained by analyzing the distribution of the signal-to-noise ratio (SNR) at the user terminals. Furthermore, we demonstrate how DMNT can be applied to estimate the long-term throughput for each mobile station, which allows for determining the optimal number of multiplexing order and throughput loss due to the interference nulling.

Listen-Before-Talk (LBT) protocol is an essential mechanism that allows Wi-Fi and LTE systems to share the unlicensed band while maintaining the performance of each individual system. However, a conventional LBT mechanism in this coexisting environment operates with the fixed channel occupancy time (COT) even under the varying traffic load in each system. In practice, if COT is set too short or large, one of two systems becomes inefficient when it is overloaded. Furthermore, Wi-Fi and LTE systems operate with different back-off mechanisms, which may violate the requirement of fair-coexistence. It implies that the operational parameters in LBT protocol must be dynamically adapted to the varying traffic load and back-off window size of Wi-Fi system. Toward this end, we propose an adaptive LBT protocol that can increase the overall system throughput without compromising a performance of the Wi-Fi system in the coexisting environment. Two different adaptation mechanisms, on-off adaptation for COT of LTE system and short-long adaptation for idle time (IT), are designed to implement the adaptive LBT scheme. Our simulation results demonstrate that the overall system throughput can be improved by 30–105% depending on the traffic load, while warranting the performance of the Wi-Fi system.

As compared to asynchronous contention-based random access, synchronous & distributed link scheduling for OFDM system turns to be a viable solution to improve the system throughput for device-to-device (D2D) communication. In particular, a spatial spectral efficiency can be improved by scheduling the concurrent D2D links as many as the individual signal-to-interference ratio (SIR) requirement is satisfied. In this paper, we propose an adaptive yielding mechanism that can further improve the spatial spectral efficiency by allowing for more concurrent D2D links whenever more interference can be accepted, e.g., when the instantaneous bandwidth efficiency requirement is less than the current link capacity. Even if the system throughput varies with the link density, it has been shown that the average system throughput can be significantly improved by the proposed yielding mechanism.

For interference alignment (IA) with no channel extension, determining the existence of IA solution for general system parameters is still an open problem. This same problem also exists in interference alignment and cancellation (IAC), which combines IA with interference cancellation in the receivers, involving a new receiver architecture associated with signaling over backhaul links among them. As IAC is expected to achieve more degrees of freedom (DoF) than IA, the existence of IAC solution would be another interesting, yet challenging issue to investigate. In this paper, we have proposed a specific alignment scheme, which allows for the derivation of closed-form IAC solution. Furthermore, we establish and prove the general necessary and sufficient condition to determine the existence of closed-form IAC solution for a Gaussian interference multiple access channel (MAC) with K receivers and J users, each with M antennas.

This paper proposes a distributed coordination framework with opportunistic scheduling among the multiple users as opposed to the existing works on the multi-cell cooperative beamforming problem subject to a single active user in each cell. This framework deals with the best throughput trade-off between throughput gain by interference nulling, and opportunistic scheduling gain in a cellular MISO system. In this framework, multi-user scheduling and inter-cell coordination issues are jointly considered for improving the cell-edge throughput performance without compromising the overall system performance. Our simulation results have demonstrated that its performance can reach up to 85% of its upper bound at the cell boundary.

In this paper, we present an economic cost analysis, including the capacity analysis subject to deployment cost constraint in the relay-enhanced cellular networks. Based on stochastic geometry analysis of throughput performance, we provide a framework to determine the maximum areal capacity with the given deployment cost, which involves the optimum RS and BS densities. Based upon the throughput analysis, a linear capacity-scaling property of relay system has been shown. Furthermore, we justify the advantage of low-cost relay nodes by deriving the capacity-scaling property that is governed by the economies of scale. Furthermore, our framework allows for analyzing the explicit benefit of relay system over the conventional cellular system as a unit cost of relay station varies. In particular, our asymptotic analysis has shown that the maximum possible gain is limited to 2.12 times as much as the maximum areal capacity of the conventional cellular system, no matter how many relay stations are employed.

In this paper, we consider an interference-limited dual-hop relay system, in which the overall system performance is governed not by an additive white Gaussian noise (AWGN), but by co-channel interference (CCI), due to aggressive frequency reuse for high spectrum utilization. We consider that the received signal-to-interference power ratio (SIR) at each hop is affected by large-scale fading (path-loss and shadowing) as well as small-scale Rayleigh fading. Assuming that the multiple CCIs in each hop are distributed as a Poisson point process on the infinite plane, we investigate the outage performance of the AF and DF relay systems. In order to analyze the performance of AF relaying, we exploit the upper and lower bounds, which can be directly obtained from the outage probability of the DF relay system. Even though the closed-form expression for the outage probability of the DF relaying cannot be found, the results can be obtained by simple numerical method, requiring only one integration calculus. For the various propagation parameters and interferer density, we present how the CCIs influence on the outage performance of dual-hop relay systems, and show that the performance gap between the upper and lower bounds is not significant.

The feasibility condition for interference alignment (IA) with no channel extension has not been known yet. In this paper, for the first time, we explore the feasibility of interference alignment and cancellation (IAC) which has been proposed for IA with no channel extension. We investigate IAC for the Gaussian interference multiple access channel (MAC) with K receivers and J users, each with M antennas. We establish and prove the general feasibility conditions of IAC. A graph theoretic approach is central to the results.

In this paper, we consider the BER analysis of the constellation rearrangement (CoRe), which is not subject to the Gray-encoding constraint for the first transmission. This type of CoRe can be applied in a parallel transmission system such as cooperative relaying networks. Furthermore, it allow us to construct the accumulate constellation, which obtained by separating and rearranging the received symbols over two different frequency bands. Based on this accumulated constellation, we derive the tight bound on the BER of CoRe over Nakagami-m fading channel. We show that the proposed bound is much tighter than the existing loose bound, e.g., Chernoff bound. Especially, for the Rayleigh fading channel, i.e., m=1, we show that our bound is virtually identical to the simulation result over the acceptable range of Eb/No.

In this paper, we analyze the outage performance of relay-enhanced cellular systems in an interference-limited environment, where the co-channel interference (CCI) is a crucial factor that governs the overall system performance as compared to an AWGN (additive white Gaussian noise). Furthermore, the relay station and destination are subject to the different but arbitrary number of CCIs on each node. Assuming that the interference channels are statistically independent and non-identically distributed (i.ni.d), we derive the outage probabilities for amplify-and-forward (AF) and decode-and-forward (DF) relay systems. We show that the statistics obtained from our analyses, e.g., probability density function (PDF) and cumulative density function (CDF), including the outage probability, closely coincide with simulation results.

In this paper, we consider an optimal topology control problem that dynamically configures the path and capacity under the varying traffic load for constructing a multihop backbone network in the TDD-OFDMA system. In order to deal with the co-channel interference among the different relay stations, K orthogonal subzones of OFDMA data regions are spatially reused. We present a joint path and capacity design problem that maximizes the average throughput while properly trading off the outage performance. Due to the complexity of optimal solution approach to this problem, we resort to a genetic algorithm (GA) to solve the dynamic topology design problem and compare its solution to the results from the existing heuristic algorithms.

In this paper, we consider the relay station (RS) selection schemes for an orthogonal amplify-and-forward relaying system with half-duplexing in a multi-cell environment. The selection schemes are to to maximize the instantaneous received SINR either when the channel gains for the other cells are fully known or not. We propose a sub-optimal RS selection scheme, which is given by the harmonic mean of the ratios of the SNRs (signal-to-noise ratios) and INRs (interference-to-noise ratios) among the adjacent cells. Furthermore, we analyze its outage performance. Our analysis presents the performance comparison between the optimal and sub-optimal RS selection policies, including the existing selection schemes. The proposed sub-optimal scheme provides a simple yet useful performance metric to improve the outage performance without resorting to the full channel knowledge in the multi-cell environment.

This paper considers a design of signal constellations for trans-modulation (constellation rearrangement: CoRe) in a relay system with the multiple links in which the same signal is decoded-and-forwarded to the destination over N time slots, each assigned to the different relay node. Demonstrating that the minimal accumulated squared Euclidean distance (MASED) is not an ultimate design metric to minimize the bit error rate (BER), we choose the BER for the joint log-likelihood ratio (LLR) combining scheme as a design metric, so as to find bit-to-symbol mapping for the multiple relay links. To handle the prohibitive amount of computational complexity associated with an exhaustive search over enumeration of all possible mappings, which grows exponentially with the number of relay links, genetic algorithm is employed for searching for the sub-optimal bitto-symbol mappings in the multiple relay links. We show that a new type of CoRe design without the gray-mapping constraint is critical for warranting the LLR-combining gain over the multiple relay links, e.g., approximately 4 dB gain for 4 relay links at high SNR as compared to the CoRe design for hybrid ARQ.

The paper considers a hybrid ARQ (HARQ) scheme for a linear pre-coding-based closed-loop multiple-input multiple- output (MIMO) system, in which a pre-coding matrix is dynamically selected for every retransmission by taking into account a symbol-level combining gain obtained from the previous receptions. A new retransmission structure with a sequence of multiple codebooks is proposed that are optimized for combining gain, rather than using a single identical codebook for every retransmission. Furthermore, a criterion for selecting a pre-coding matrix for each retransmission can be provided so as to minimize the retransmission error probability. Under the proposed multiple codebook structure for MIMO-HARQ systems, a sequence of optimum codebooks is constructed for the subsequent retransmissions. Simulation results are presented for showing that the proposed scheme achieves significant error performance advantages over conventional schemes with a single codebook and furthermore, a near-optimal performance is achieved as the codebook size chosen is large enough.

In this paper, we consider a modulation scheme for a network-coded bi-directional relaying (CBR) scheme in which two different signals from mutually communicating nodes are network-coded in the relay node. It employs dual constellations as a useful means of dealing with its performance degradation in the CBR over asymmetric channel (i.e., the different signal-to-noise ratio in each link), which provide not only a diversity gain from the symbol repetition but also a coding gain from the constellation rearrangement to protect the network-coded symbols. We study the channel utilization and bit error rate performance of the different CBR schemes to show that the proposed diversity-enhanced CBR system always outperforms other existing systems as long as traffic load is properly balanced along the bi-directional links.

While the conventional codebook is defined only for the initial transmission in the closed-loop MIMO system, this paper proposes a new retransmission structure with a sequence of multiple codebooks, rather than using a single identical codebook for every retransmission. The pre-coding matrices, in each codebook, are sequentially linked so that each one can be used for a sequential hybrid ARQ transmission attempt, without invoking any index feedback from the receiver. It has been shown that the proposed codebook structure provides a performance gain of more than 3 dB over the conventional scheme in the long-term static channel.

This paper introduces a notion of divide-by-K reuse strategy, which allows for K-orthogonal chunks (sub-zones) of OFDMA data regions to be fully reused for all relay links throughout each cell in a TV-hop relay-enhanced cellular TDD-OFDMA system with KlesN. Our objective is to configure a topology of relay stations subject to the divide-by-K reuse strategy, such that the end-to-end routes for relay stations are determined while maximizing the bandwidth efficiency of resource available for relay links. We propose a sub-optimal algorithm for a topology control that reduces co-channel interference while balancing the time-varying traffic load in a dynamical manner. Our system-level simulation results show that the average system throughput can be significantly improved by the proposed algorithm for the divide-by-K reuse strategy, especially with K = 3.

Depending on the position of mobile stations in the multi-hop relay system, various types of cooperative relaying schemes show the different level of throughput and outage performance, due to their inherent trade-off between diversity gain and overhead associated with individual cooperation scheme. In order to improve both throughput and outage performance at the same time, we propose a unified selection criterion to deal with the different level of combining gain and overhead associated with each scheme, which allows for employing the different cooperative relaying scheme in the varying position of mobile station. Our system-level simulation results for IEEE 802.16j multi-hop relay confirm the varying level of trade-off among the different cooperative relaying schemes and demonstrate the possible performance that can be achieved by using the various types of cooperative relaying schemes in the multi-hop relay system.

In this letter, we propose a generalized version of the window-based pseudonoise (PN) acquisition scheme verifying one or more best hypotheses rather than the best one per search window. The false alarm probability, detection probability, and mean acquisition time (MAT) of the proposed scheme are derived by using the well-known flow graph approach in additive white Gaussian noise (AWGN) and fading channels. Numerical results demonstrate that the acquisition performances are improved through the proposed generalization, particularly when the pilot energy to total received energy ratio (pilot Ec/Io) is low.

In this paper, we consider a novel spatial diversity scheme for a mutually cooperative relay (MCR) system in which two active users are simultaneously communicating with the same destination node. Integrating the existing rotation code into the MCR system, we now have a spatially coordinate-interleaved design (SCID), which inherits its diversity gain (with order of two) over the simple repetition code without any bandwidth expansion. Our simulation results demonstrate that the proposed SCID-based MCR scheme is useful for improving the uplink performance as long as one node can find another active node in the close neighbor that is communicating with the same destination, e.g., base station in the cellular system. More specifically, it outperforms non-cooperative and classical MCR schemes approximately by 8 dB and 2 dB at target BER of 10"3, respectively, as long as a reliable link is provided between the cooperative nodes.

Multi-hop relay (MR) and repeater are useful means of improving system throughput and coverage in the cellular mobile packet access system, as carrier-to-interference ratio can be improved with deploying them in the heavily shadowed region. In this paper, we investigate the bandwidth efficiency and the associated service outage performance for the different relay scenarios, using the system level simulation for a cellular OFDMA-TDD system. It is demonstrated that a capacity gain by an optical repeater which has a simple amplify-and-forwarding capability only could be minimal due to co-channel interference from all repeaters in the neighbor cells, even though they are usually useful for warranting the outage performance with a multiple order of combining gain, especially in the destructive area, e.g., edge of sub-cell coverage. Meanwhile, multi-hop relays are shown to increase the average system capacity (almost doubling the system throughput) by fully reusing the frequency in every relay station, while improving the per-user data rate in the cell edges or heavily shadowed areas.

In this paper, we propose a practically realizable version of a proportional fairness scheduling algorithm that is applicable to emerging broadband wireless access systems with multiple channels, whose individual channel quality vary for every user, e.g., as in the band AMC (adaptive modulation and coding) mode of a subcarrier allocation in an OFDMA (orthogonal frequency division multiplexing) system. In order to circumvent the complexity of the optimal multi-channel PF algorithm (which grows exponentially with the number of channels), we introduce the notion of the multiphase predictive approach which takes the channel conditions over the multiple slots into account, as opposed to the conventional multichannel PF scheduling algorithm which deals with the channels in each time slot. When a simple channel-by-channel scheduling algorithm is implemented with the proposed approach, it has been shown that the network utilization with respect to proportional fairness can be further improved over the conventional multichannel PF scheduling algorithm while resorting to much less complexity. We also present specific examples of implementation for the proposed approaches, along with their performance.

In this paper, we propose two different types of cooperative transmission protocols, referred to as spatial multiplexing with receive diversity (SMRD), that can support a full rate, i.e., one symbol transmission per each time slot. We show that the BER performance can be significantly improved with a proper design of SMRD under the AF (amplify-and-forward) mode of relaying, when there is no interference among all symbols transmitted in the same time slot. Furthermore, we consider a more practical implementation of SMRD with successive interference cancellation (SIC) to deal with co-channel interference. Our simulation result shows that the proposed transmission protocol achieves 6 dB gain over the case of noncooperation (direct transmission) without any bandwidth expansion.

In this paper, a packet scheduling algorithm for non-real-time service is considered, with a soft QoS requirements, which allows for degrading the QoS level, e.g., typically the packet delay, whenever necessary, in mobile broadband wireless internet access systems. This is designed to optimally trade off system throughput and delay performance, depending on soft QoS requirements, as opposed to most existing packet scheduling algorithms for non-real-time service, which are simply designed to maximize the system throughput without a delay constraint in any sense. The proposed adaptive exponential scheduling algorithm intentionally introduces additional delay to some users under bad channel conditions, allowing for serving users only under good channel conditions, as long as the resulting QoS degradation is acceptable for non-real-time service users. The results from system-level simulation demonstrate that the system capacity can be significantly increased, over existing algorithms, by as much as 65%, using the adaptive exponential scheduling algorithm, and satisfying the given QoS level requirements.

In this paper, we propose a generalized version of window-based pseudonoise (PN) code acquisition scheme selecting one or more largest hypothesis energies rather than selecting the single largest one per search window for verification. By performing the numerical analysis based on the flow graph approach, we show that our proposed scheme selecting the plural largest hypothesis energies outperforms the conventional one in both the AWGN and Rayleigh fading channels. Furthermore, it is shown that we can obtain the optimum window size robust to the variation of the pilot Ec/Io with our proposed scheme.

In this paper, a partial sphere decoding approach is proposed to reduce the computational complexity of implementing the iterative MAP receiver for spatial multiplexing system, in which the complexity prohibitively increases with the number of transmit antennas and modulation order. The proposed approach considers only a subset of symbols with high a priori probabilities, i.e., more reliable ones, which allows for reducing the dimension of detection space. We design a novel reliability estimator which is used for dividing two subvectors according to reliability of individual symbol in the initial stage. Furthermore, a cost function-based partial MAP rule is applied to those subvectors so as to correct the initial estimation error in the course of iterative detection and decoding process. We have shown that the overall complexity of sphere decoding can be reduced by almost order of 2 in terms of floating point operations while maintaining the BER performance as close as 0.7dB to the conventional approach.

In broadband wireless OFDMA systems where a set of subcarriers is shared among multiple users, the overall system throughput can be improved by a band AMC mode that selects subbands preferential to individual users. As long as channel qualities for the subbands of all users are known a priori, multi-user and frequency diversity gains can be simultaneously achieved with an appropriate scheduling algorithm. In this paper, an analytical means of evaluating the maximum system throughput that can be achieved with a band AMC mode is presented under the various system parameters. In particular, practical features of resource management for the OFDMA system are carefully modeled within the analytical framework. Also, we validate the proposed analytical model through a simulation. The numerical results demonstrate that the band AMC mode outperforms the diversity mode as long as sufficient feedback information for channel quality indication is provided for the subbands of all users

The delay threshold-based priority queueing (DTPQ) scheme has been shown useful for scheduling both real-time (RT) and non-real-time (NRT) service traffic in mobile broadband wireless access (MBWA) systems. The overall system capacity can be maximized subject to their QoS requirement by the DTPQ scheme, which takes the urgency of the RT service into account only when their head-of-line (HOL) packet delays exceed a given delay threshold. In practice, the optimum delay threshold must be configured under the varying service scenarios, e.g., the number of RT and NRT users in the system. In this paper, we propose an adaptive version of DTPQ scheme, which updates the delay threshold by taking the urgency and channel conditions of RT service users into account. By evaluating the proposed approach in an orthogonal frequency division multiple access/time division duplex (OFDMA/TDD)-based mobile access system, it has been found that our adaptive scheme significantly improves the system capacity as compared to the existing DTPQ scheme with a fixed delay threshold

In this paper, we propose a new type of window-based pseudonoise (PN) code acquisition scheme, which does not resort to the window threshold, as opposed to the conventional scheme. In particular, it is selecting one or more largest hypothesis energies rather than selecting single largest hypothesis energy subject to the window threshold per search window for verification. By performing the numerical analysis based on a flow graph approach in AWGN and Rician fading channels, we show that the proposed scheme outperforms the conventional one under a low pilot Ec/Io while maintaining the similar level of performances under medium and high pilot Ec/Io's.

This paper presents a mobile-assisted frame-based PRMA (MAF-PRMA) protocol, which optimizes random access control between heterogeneous traffic aiming at more efficient voice/data integrated services in dynamic reservation TDMA-based broadband wireless access networks. In order to achieve a differentiated quality-of-service (QoS) guarantee for individual service plus maximal system resource utilization, the MAF-PRMA independently controls the random access parameters, frame-by-frame basis. In addition, we have adopted a mobile-assisted random access mechanism where the voice terminal readjusts a global permission probability from the central controller, to handle the 'fair access' issue resulting from distributed queuing problems inherent in the access network. Our extensive simulation results indicate that the MAF-PRMA achieves significant improvements in terms of voice capacity, delay, and fairness over most of the existing MAC schemes for the integrated services.

This paper proposes a new packet rate scheduling scheme for a non-real time data service over the uplink of a burst switching-based direct sequence code division multiple access (DS/CDMA) system to support the integrated voice/data service. We consider the most general form of optimization problem formulation to determine the optimal number of transmission-time groups along with their data rates, which minimize the average packet transmission delay. An ordered packet length-based group-wise transmission (OLGT) scheme is proposed as a simple heuristic solution approach to this problem and present some analytical results for performance comparison with other possible schemes.

In this paper, an iterative receiver for joint data detection and channel estimation scheme is presented for wireless OFDM systems. The main feature of the proposed scheme is to design a rather simple iterative receiver by employing a two-dimensional channel estimator in the time/frequency domain with a reduced complexity. The analytical expression of error variance is derived to evaluate the performance as a function of the number of iterations. Furthermore, the analytical properties are discussed in terms of the error variances of the channel estimation and decoding process subject to the iterative principle. The simulation results show that the bit error rate (BER) of the proposed iterative receiver achieves near-ideal performance within about 0.2 dB in signal-to-noise power ratio (SNR). We also investigate the mismatch effect of the estimation filter subject to unknown channel statistics.

This paper proposes a novel MAC protocol for wireless ATM networks, which is characterized by a contention-free mechanism of the reservation request and a deterministic nature of mobile-assisted (distributed) uplink scheduling under a framework of the dynamic reservation TDMA, as discussed in the current standardization activities of ETSI Project BRAN (broadband radio access network) and the wireless ATM working group in the ATM Forum. The design objective of the proposed MAC protocol is to guarantee the real-time constraint of the real-time VBR (rt-VBR) traffic class while maximizing the multiplexing gain among all ATM traffic classes, especially with a fixed length frame. The proposed deterministic scheduling scheme for the rt-VBR traffic class lends itself to implementing the minimal configuration of control data units for reservation request as desired under the limited wireless resources. Simulation experiments using statistically multiplexed MPEG-2 video streams are performed for a 25 Mbits/s wireless ATM access link scenario. It has been shown that the proposed framework guarantees the delay constraint of rt-VBR sessions along with its cell loss rate significantly reduced, while improving the average delay performance of the nrt-VBR in the range of 10%-30% without compromising the channel utilization as compared to the DSA++ system.

The performance of two adaptive multistage interference cancellation (IC) schemes for direct sequence code division multiple access (DS/CDMA) systems, successive and parallel IC schemes, is investigated under a fading channel. The performance analysis is strictly based on the analytical approach to provide a complete framework to predict BER performance and system capacity for the IC schemes under investigation. We further investigate the performance of the successive IC scheme subject to the delay constraint, which may be imposed typically on most speech and video applications. It has been shown that the successive interference cancellation (SIC) scheme is more resistant to fading than the parallel interference cancellation (PIC) scheme. Our analysis also demonstrates that the performance may be significantly improved by the groupwise successive interference cancellation (GSIC) scheme, which can be properly optimized to meet the given delay constraint.

A theoretical analysis of the cell loss probability performance of an explicit priority assignment partial buffer sharing scheme for asynchronous transfer mode (ATM) networks is provided. A two-service-class traffic model is used in which each service class consists of bursty traffic generated by a multiple number of discrete-time Markov sources. The effect of cell loss probability on both traffic load and burstiness characteristics, as well as on system parameters such as buffer size and buffer sharing threshold, is examined. These results are useful for system design. The many examples examined yield no significant priority separation for either high- or low-priority cells. This is particularly desirable for fulfilling the objective of two quality of services (QOS) bearer services to provide only two cell loss probability levels of service.>