This paper investigates the joint optimization of user association, base station (BS) beamforming, and reconfigurable intelligent surface (RIS) phase adjustment in an RIS-aided multi-BS multi-user-equipment (UE) millimeter wave (mmWave) network with load-balancing considerations. To address this complex problem, we propose a graph neural network (GNN)-based approach with enhanced generalizability to both varying numbers of BSs and UEs. An adaptive attention-based aggregation (AAA) mechanism is incorporated to mitigate oversmoothing in deep GNNs. By using uplink pilots as input, the proposed method avoids the need for explicit channel state information (CSI). Simulation results demonstrate the proposed scheme’s superior sum-rate performance compared to both optimization-based and learning-based benchmarks while satisfying load-balancing requirements, quantify the effectiveness of AAA in addressing oversmoothing, demonstrate the proposed scheme’s robustness to pilot correlation, and highlight the load-balancing benefits of deploying RISs.

Low Earth orbit (LEO) satellites is regarded as a promising solution for supporting Non-Terrestrial Network (NTN); for instance, third Generation Partnership Project (3GPP) includes LEO satelites in 5G New Radio (NR). In the LEO-enabling NTN scenario, a distinct issue is the receivers’ impairments caused by different sources of phase variations, mainly phase noise (PN) and Doppler effects. Current compensation methods are challenging to implement in Orthogonal Frequency-Division Multiple-Access (OFDMA) systems due to the high overhead of pilots required. This paper proposes a technique that can work with very low overhead of reference signals and jointly compensate the effects of PN and residual Carrier Frequency Offset. Key factors to achieve effective compensation are also identified, and significant improvement in system performance is demonstrated in simulations.

In this paper, we study the problem of aggregating heterogeneous models with low-complexity consideration in federated learning (FL). A green heterogeneous federated learning (GHFL) system, the marriage of the green learning (GL) methodology and FL, is proposed to simultaneously account for model heterogeneity and computation complexity. The unique properties of scalability and modularized design in GL are leveraged to facilitate low-complexity and personalized learning models in local clients which still can be aggregated at the server. Particularly, inspired by the subspace approximation technique developed in GL, we devise an aggregation rule by leveraging the distributed multi-stage principal component analysis (PCA) in this work. Prominent anchor vectors of local data spaces from heterogeneous local clients can be properly and reliably aggregated at the server. Numerical experiments validate the effectiveness of the proposed GHFL.

This paper considers the joint optimization of user association (UA), base station (BS) transmit beamforming, and reconfigurable intelligent surface (RIS) phase adjustment in a RIS-aided multi-BS multi-user-equipment (UE) system with load balancing. The problem is challenging due to variable coupling and nonconvexity. We propose a novel graph neural network (GNN) approach, leveraging permutation equivariance and invariance properties of GNNs for enhanced generalization compared to conventional deep neural network (DNN) methods. By using uplink pilots as input, our proposed GNN method eliminates the need for explicit channel state information (CSI), which can be challenging to acquire in RIS-aided systems. Simulation results demonstrate the superior performance of our GNN-based method over traditional UA strategies in terms of sum rate and load-balancing violation penalty. Moreover, the benefits of deploying RIS are illustrated from a new perspective of facilitating load balancing without substantial compromises in sum-rate performance.

This paper studies the previously unexamined problem of joint cooperation clustering and content caching in a cache-enabled cell-free massive multiple-input multiple-output (CF-mMIMO) network that comprises a large number of access points (APs) collaboratively serving users without cell structure limitations. A joint cooperation clustering and content caching design is motivated by the observation that forming cooperation clusters (i.e., determining the sets of serving access points (APs) for users) based on channel quality alone or caching status alone is suboptimal. We develop a deep reinforcement learning (DRL)-based joint design scheme for dynamic CF-mMIMO networks. The proposed scheme demonstrates favorable network energy efficiency (EE) performance and does not require prior information such as user content preferences.

This paper studies the problem of developing an efficient signal sampling framework for distributed blind spectrum sensing, together with the corresponding local cognitive radio (CR) decision and fusion rules. A novel collaborative sampling and binary output (CSBO) generating scheme is proposed to overcome the disadvantage of traditional energy detectors for blind spectrum sensing, in which the signal-to-noise ratio (SNR) and level of noise power at the CRs are unknown and thus the decision threshold of local energy detectors cannot be specified. In designing the fusion rule in conjunction with the proposed CSBO scheme, both fixed-sample-size tests and sequential tests are considered. It is theoretically shown that the proposed CSBO scheme outperforms traditional blind cooperative energy detectors with uniform sampling schemes in terms of its ability to increase the amount of information acquired under a given number of samples. Simulation results confirm the theoretical analyses, and demonstrate that, compared with the fixed-sample-size test, the sequential test requires fewer observations on average to achieve comparable detection performance. The reduction in the expected sample size needed in the sequential CSBO test when compared to the fixed-sample-size CSBO can reach 50% when primary signal is present.

We first investigate the accumulated loopback self-interference (ALSI) under amplify-and-forward (AF) protocol in two-way full-duplex (TWFD) relaying systems with simultaneous wireless information and power transfer (SWIPT). We analyze how SWIPT affects ALSI and establish the condition for ALSI to converge when there is no energy buffer. Unlike ALSI in TWFD relaying without SWIPT (Chang et al., 2019), ALSI in TWFD relaying with SWIPT always converges and does not grow unbounded. In addition, we study the delay performance in such a system under AF and decode-and-forward (DF) protocols. In AF protocol, the condition for successful signal transmissions is provided. When that condition is met, the received signals at the relay can always get through the channels but experience delays. Otherwise, the outage event occurs. In DF protocol, the best-effort scenario (having no energy buffer) and the QoS scenario (having energy buffer) are discussed. The probability of successful transmissions and the distribution of the delay in both scenarios are derived. Simulation results of DF protocol show that the best-effort scenario is suitable for the delay-tolerant service and the QoS scenario is suitable for the delay-sensitive service. Furthermore, AF protocol is more suitable for the service with less QoS demanding.

The joint design of content caching and user clustering (JCC) in cache-enabled heterogeneous networks is challenging, due to various unknown, possibly time-varying, system parameters which potentially give rise to various design tradeoffs in practice. This paper presents the first study that investigates the problem of JCC using the deep deterministic policy gradient (DDPG)-based reinforcement learning, with the purpose of balancing both the energy efficiency (EE) and content hit probability (CHP), while satisfying the cluster size constraint (CSC). We propose a new learning structure, termed multiDDPG (MDDPG), that demonstrates better EE performance while providing a comparable CHP to the caching scheme with known content popularity.

In this paper, we study the problem of blind spectrum sensing by exploring signal sampling at each cognitive radio (CR) in a distributed cognitive radio network. Specifically, a new cooperative sampling scheme is proposed to deal with the challenge of unknown signal-to-noise ratio and level of noise power at each CR, which is a more realistic assumption in distributed spectrum sensing. We prove that the observations acquired by the proposed collaborative sampling are informative from an information theoretic perspective. Simulation results also demonstrate better receiver operating characteristics (ROC) of the proposed sampling scheme than the traditional uniform sampling scheme.

Device-free indoor localization is a key enabling technology for many Internet of Things (IoT) applications. Deep neural network (DNN)-based location estimators achieve high-precision localization performance by automatically learning discriminative features from noisy wireless signals without much human intervention. However, the inner workings of DNN are not transparent and not adequately understood especially in wireless localization applications. In this paper, we conduct visual analyses of DNN-based location estimators trained with Wi-Fi channel state information (CSI) fingerprints in a real-world experiment. We address such questions as 1) how well has the DNN learned and been trained, and 2) what critical features has the DNN learned to distinguish different classes, via visualization techniques. The results provide plausible explanations and allow for a better understanding of the mechanism of DNN-based wireless indoor localization.

Two-way full-duplex (TWFD) transmissions in cooperative amplify-and-forward (AF) relaying systems provide a promising way of communications with the improved spectral efficiency by concurrently transmitting/receiving data in the same frequency band and by the two-way transmission protocol. However, the loopback self-interference, caused by the imperfect channel estimation in the loopback link, at the AF relay accumulates and is forwarded to the end users recurrently, which substantially degrades the end users' error performance if this accumulated loopback self-interference (ALSI) does not converge. It is therefore of critical importance to analyze the convergence behavior of the ALSI in the TWFD systems with AF relaying. In this paper, we establish sufficient conditions on the relay power to ensure the convergence of the ALSI, with which the average limiting signal-to-interference-plus-noise ratio at the infinite time horizon is derived. In addition, we specify the relay power at which the signal-to-noise ratio reaches the maximal value under different scenarios and obtain an upper bound to the symbol error rate. Finally, extensive computer simulations results are provided to corroborate the analytic derivations.

In this paper, we study spectrum trading in cognitive radio (CR) networks with multiple primary services (PSs) and multiple secondary services (SSs) from a game-theoretic perspective. We propose a multistage Bayesian game-based trading model which accounts for unknown private information of players (for example, the number of user connections in PSs may be unknown to the SSs) as in practical network scenarios. The perfect Bayesian equilibrium (PBE) is derived by solving an involved sequential optimization problem. We formulate the joint Karush-Kuhn-Tucker (KKT) conditions and use the KKT translation technique to obtain the PBE at each stage. Simulation demonstrates the convergence of the sequence of strategies in the multistage Bayesian game.

In this paper, we study the channel selection problem for selfish and altruistic precoding in multilink multiple-input single-output (MISO) networks from a distributed game-theoretic perspective. Our goal is to find for each link a proper channel selection strategy that is robust against time-varying channel states. This motivates the development of stochastic learning that finds Nash equilibrium (NE) of an expected game. The convergence properties of the proposed learning algorithm are theoretically and numerically verified. The proposed algorithm demonstrates good sum-rate performance in the system-level simulation of a multilink MISO network based on the 3GPP-LTE model.

This paper studies spectrum trading in cognitive radio networks in which multiple service providers (SPs) sell unused spectrum to multiple unlicensed secondary users (SUs). Motivated by the nature of the problem with new considerations, spectrum trading is modeled as a multi-leader multi-follower expected Stackelberg game with two levels of competition. The SPs as leaders compete in offering subscription prices (upper-level subgame) and the SUs as followers compete in selecting service from the SPs (lower-level subgame). The lower-level subgame incorporates the time-varying spectrum availability as the external state so that the proposed scheme does not require knowledge of dynamic spectrum availability. To achieve self-organized network operation, we propose decentralized, stochastic learning-based algorithms for the game. The convergence properties of the proposed algorithms toward the Nash equilibrium (NE) are theoretically and numerically studied. The proposed scheme demonstrates good utility performance for the SUs as compared to other service selection schemes.

This paper considers a noncoherent distributed space-frequency coded (SFC) wireless relay system with multiple relays. Each relay adopts a censoring scheme to determine whether the relay will decode and forward the source's information toward the destination. We analytically obtain the achievable diversity for both cases of perfect and imperfect relay censoring. With perfect censoring, we show that the same diversity of a conventional noncoherent SFC MIMO-OFDM system is achievable in the considered noncoherent distributed SFC system with maximum-likelihood (ML) decoding, regardless of whether partial information of channel statistics and relay decoding status is available at the destination. With imperfect censoring, we analytically investigate how censoring errors affect the achievability of the system's diversity. We show that the two types of censoring errors, which correspond to useless and harmful relays, respectively, can decrease the achievable diversity significantly. Our analytical insights and numerical simulations demonstrate that the noncoherent distributed system can offer a comparable diversity as the conventional MIMO-OFDM system if relay censoring is carefully implemented.

In this paper, we study the problem of spectrum trading in cognitive radio (CR) networks from a game theoretical perspective. Particularly, we consider the CR network with multiple primary services (PS's) and a single secondary service (SS), where all PS's are sellers competing in the prices and the SS is the buyer deciding how much spectrum is demanded from each PS in the trading game. Aiming at dealing with the trading behaviors, we propose using a multistage Bayesian game based trading model to account for possible unknown private information in each player, and obtain the perfect Bayesian equilibrium (PBE) sequentially under a bandwidth constraint. By the backward induction principle, we translate the Karush-Kuhn-Tucker (KKT) condition of the SS to form a joint KKT conditions with PS in order to solve the optimization problem. Finally, in the simulations, we compare the proposed approach with that, and numerically study the convergence behaviors of the proposed multistage game.

In this paper, we consider the design of a reduced- rank minimum mean-squared error (MMSE) combiner in an asynchronous multicarrier-direct sequence-code division multiple access (MC-DS-CDMA) system. It is shown that the combiner weights are related to quantities whose expressions resembles the eigenvalue moments of the interference autocorrelation matrix. To lift the burden of recomputing the weights from time to time, the weights are evaluated in a large system regime and averaged over random parameters including asynchronous delays, correlated fading coefficients and spreading sequences. Explicit expressions for the asymptotic weights of the MMSE combiner are derived. The large system setting is with the spreading gain N and user number K approaching infinity, but K/N = beta and the number of sub-carriers both finite.

A distributed medium access control (MAC) algorithm for uplink OFDMA networks under the IEEE 802.16 framework is proposed and analyzed in this work. We present a simple yet efficient algorithm to enhance the system throughput by integrating opportunistic medium access and collision resolution through random subchannel backoff. Consequently, the resulting algorithm is called the opportunistic access with random subchannel backoff (OARSB) scheme. OARSB not only achieves distributed coordination among users but also reduces the amount of information exchange between the base station and users. The throughput and delay performance analysis of OARSB is conducted using a Markov chain model. The superior performance of OARSB over an existing scheme is demonstrated by analysis as well as computer simulation.

Performance analysis of multiuser orthogonal frequency division multiplexing (OFDM-TDMA) and orthogonal frequency division multiple access (OFDMA) networks in support of multimedia transmission is conducted in this work. We take a cross-layer approach and analyze several quality-of-service (QoS) measures that incilude the bit rate and the bit error rate (BER) in the physical layer, and packet average throughput/delay and packet maximum delay in the link layer. The authors adopt a cross-layer QoS framework similar to that in IEEE 802.16, where service classification, flow control and opportunistic scheduling with different subcarrier/bit allocation schemes are implemented. In the analysis, the Rayleigh fading channel in the link layer is modeled by a finite-state Markov chain, and the channel state information (CSI) is assumed to be available at the base station. With the M/G/1 queueing model and flow control results, the analysis provides important insights into the performance difference of these two multiaccess systems. The derived analytical results are verified by extensive computer simulation. It is demonstrated by analysis and simulation that OFDMA outperforms OFDM-TDMA in QoS metrics of interest. Thus, OFDMA has higher potential than OFDM-TDMA in supporting multimedia services.

A recursive algorithm for estimating and updating the effective carrier frequency offset (CFO) vector in a multicarrier code-division multiple-access (MC-CDMA) system is proposed in this work. The recursive relation is derived based on the expectation maximization (EM) algorithm with a quadratic constraint. This new approach enables the use of linear estimation theory to tackle the CFO estimation problem with or without training data, which leads to an analytic CFO estimate in closed form. Furthermore, the multiple access interference (MAI) is mitigated using the second order statistics of the interference-plus-noise vector, which is updated in a recursive manner under the EM formulation, too. When reaching a converged estimate, a fixed-norm quadratic constraint is imposed so that the final CFO estimate is robust to an imprecise covariance matrix estimate caused by insufficient data samples. It is demonstrated by computer simulation that the performance of an MC-CDMA system without the CFO information can be restored by the proposed scheme in the sense that its bit error probability (BEP) performance is close to that with perfect CFO knowledge

The delay analysis and comparison of OFDM- TDMA and OFDMA using a flow control scheme under the QoS framework of IEEE 802.16 are conducted in this work. We investigate the maximum packet delay performance by deriving delay bounds for several multiaccess modes with different subcarrier/time-slot assignment and bit allocation schemes. The analytical delay bounds provide insights into the performance difference among different multiaccess schemes, and demonstrate each scheme's capability of supporting real-time, delay-sensitive multimedia applications. The derived analytical results are verified by computer simulation.

Performance comparison of multiuser OFDM-TDMA and OFDMA systems by considering physical and link cross-layer behavior is conducted in this work. We provide a new framework for performance evaluation that centers on "scheduling" with several different subcarrier/bit allocation schemes. The performance metrics include: the bit rate, the bit error rate (BER), packet throughput and delay. The instantaneous channel conditions of users are assumed to be available at the base station, which can be achieved by channel estimation at the mobile terminal (MT), and then fed back to the base station through a control channel. To perform the link layer comparison, we model the Rayleigh fading channel with a finite-state Markov chain, and adopt the M/G/1 queueing model as the analytical tool. Computer simulation is performed to verify conducted analytical results.

The carrier-frequency offset effect on the performance of asynchronous multicarrier direct-sequence code-division multiple-access (MC-DS-CDMA) systems with aperiodic random spreading and correlated Rayleigh fading is studied in this paper. We obtain the optimum combining filter that maximizes the signal-to-interference-plus-noise ratio (SINR) of the combined statistics and exploits correlated information among subchannels. A closed-form expression for the unconditional covariance matrix of the interference-plus-noise vector, which forms the basis of our theoretical analysis of the maximum SINR and the average bit error probability formula, is derived by averaging several random parameters including asynchronous delays, correlated Rayleigh fading, and signature sequences. The analytic results obtained are applicable to MC-CDMA with appropriate modifications. Furthermore, we show that the MC-CDMA system with a common random signature sequence over all subcarriers for a given user outperforms that with distinct sequences over different subcarriers. Finally, the performance of MC-CDMA systems using the optimum combining technique is compared with that of different combining filters in the simulation.

A blind recursive algorithm to effectively update the carrier frequency offset (CFO) vector in a multicarrier code-division multiple-access (MC-CDMA) system is studied in this research. The recursive relation is developed based on the expectation maximization (EM) algorithm with a quadratic constraint. This new perspective enables the use of linear estimation theory to deal with the blind CFO estimation problem, leading to an analytic CFO estimate in closed-form. The multiple access interference (MAI) is mitigated using the second order statistics of the interference-plus-noise vector, which can also be updated in a recursive manner under the EM formulation. Finally, we verify the effectiveness of the proposed algorithm with computer simulation.

The effects of inter-carrier interference (ICI) and aperiodic random spreading sequences on the performance of asynchronous multicarrier code-division multiple-access (CDMA) systems with correlated fading between sub-carriers are investigated in this research. To conduct theoretical analysis for the maximal ratio combining (MRC) receiver, random parameters including asynchronous delays, correlated Rayleigh fading, and spreading sequences are averaged to find the unconditional covariance matrix of the interference-plus-noise vector. We demonstrate that the ICI in the system proposed by Kondo and Milstein (1996) can be mitigated by assigning a common random spreading sequence over all sub-carriers for each user, rather than using a set of distinct spreading sequences, where code sequences are assumed perfectly time-synchronized. Moreover, the analytic expression for the bit error probability (BEP) can be obtained with the Gaussian approximation. Simulation results are used to demonstrate the accuracy of our analysis. Various design tradeoffs including the number of sub-carriers, fading correlations, ICI and multipath effect are presented in simulation. We conclude that, when properly choosing a sufficiently large number of sub-carriers, the benefit of mitigating multipath interference and exploiting potential hidden frequency diversity shall prevail the impairment brought by increased ICI.

The effects of frequency offsets and aperiodic random spreading sequences on the performance of asynchronous multicarrier code division multiple access (CDMA) systems with correlated fading are investigated in this research. Random parameters including asynchronous delays, correlated Rayleigh fading and spreading sequences are averaged to determine the covariance matrix of the interference-plus-noise vector. An analytic expression for the average signal-to-interference-plus-noise ratio (SINR) is obtained. Furthermore, average bit error probability (BEP) based on the Gaussian approximation is also derived. We show that the system degrades significantly if the frequency offset of the desired user is present, while the system is insensitive to interferers' frequency offsets. In consequence, it is crucial to estimate and compensate the desired user's synchronization imperfection. Finally, design tradeoff among the number of subcarriers, fading correlations, and inter-carrier interference (ICI) are presented in simulation results.

The effect of inter-carrier interference (ICI) and aperiodic random spreading sequences on the performance of asynchronous multicarrier code division multiple access (CDMA) systems with correlated fading is investigated in this research. Random parameters including asynchronous delays, correlated Rayleigh fading and spreading sequences are averaged to determine the unconditional covariance matrix of the interference-plus-noise vector. An analytic expression for the bit error probability is obtained based on the Gaussian approximation. Then, we extend the result by considering situations where eigenvalues are not necessarily all identical or distinct. Finally, design tradeoffs among the number of sub-carriers, fading correlations, ICI and multipath effect are presented in simulation results.