Cascaded reconfigurable intelligent surfaces (RISs) greatly improve network coverage and reliability. This paper examines the outage probability (OP) of multi-cascaded RISs (MCRISs)-aided systems over doubly-correlated Rayleigh multiple-input multiple-output (MIMO) channels. The moment-generating function is invoked to convert the OP into a numerical inversion of Laplace integral. To capture profound insights, we conduct an in-depth asymptotic investigation into the outage behavior of MCRIS-aided MIMO communications by leveraging random matrix theory in the high-SNR regime. The asymptotic results reveal that the firsthRISs with the smallest number of reflective elements primarily dictate the bottleneck of reliability performance, wherehis influenced by the variation in the number of reflective elements across the RISs. Specifically, smaller variations result in a largerh, while greater disparities reduce it. Additionally, we identify an “unsaturation effect” that occurs when the performance margin, or residual spatial degree of freedom (DoF), after propagation through the firsthRISs cannot be evenly distributed between the transmitter and the RISs. This effect slows down the decline of the OP with increasing SNR. More cascaded RISs impair the spatial DoF of wireless communications, resulting in the loss of half of the independent fading channels compared to the system without the support of RIS as the cascaded number of RISs increases. Majorization theory is subsequently applied to unveil the negative effect of doubly-spatial correlation on system reliability. Finally, Monte Carlo simulations are carried out for validations.

Many emerging artificial intelligence-driven applications rely on the availability of large-scale, stable, and fresh sensing data, underscoring the Ultra-Reliable Low-Latency Communications (URLLC). This paper proposes to amalgamate Non-Orthogonal Multiple Access (NOMA) and Hybrid Automatic Repeat reQuest with Incremental Redundancy (HARQ-IR) to accommodate reliable and real-time wireless services. The outage probability and the Average Age of Information (AAoI) are used to evaluate the transmission reliability and data freshness for HARQ-IR-NOMA schemes. To reveal physical insights as well as ease system designs, the asymptotic outage probability in the high Signal-to-Noise Ratio (SNR) regime is derived by developing a recursive dominant term approximation method. Moreover, the AAoI is deduced in terms of the outage probability by considering an M/G/1/1 queuing model. The asymptotic AAoI at high SNR is shown to be an increasing function of the diversity order-deficiency, which theoretically justifies the tradeoff between the transmission reliability and the data freshness. Furthermore, the AAoI is minimized by optimizing the transmission powers between users as well as HARQ rounds while maintaining the outage and total power constraints. The minimal AAoI is obtained by developing Geometric Programming (GP)-based and Deep Reinforcement Learning (DRL)-based methods. Finally, the numerical results are presented for verification.

The integration of pinching antenna systems with non-orthogonal multiple access (NOMA) has emerged as a promising technique for future 6G applications. This paper is the first to investigate power minimization for NOMA-assisted pinching antenna systems utilizing multiple dielectric waveguides. We formulate a total power minimization problem constrained by each user’s minimum data requirements, addressing a classical challenge. To efficiently solve the non-convex optimization problem, we propose an iterative algorithm. Furthermore, we demonstrate that the interference function of this algorithm is standard, ensuring convergence to a unique fixed point. Numerical simulations validate that our developed algorithm converges within a few steps and significantly outperforms benchmark strategies across various data rate requirements. The results also indicate that the minimum transmit power, as a function of the interval between the waveguides, exhibits an approximately oscillatory decay with a negative trend.

In this study, we investigate the resource management challenges in next-generation mobile crowdsensing networks with the goal of minimizing task completion latency while ensuring coverage performance, i.e., an essential metric to ensure comprehensive data collection across the monitored area, yet it has been commonly overlooked in existing studies. To this end, we formulate a weighted latency and coverage gap minimization problem via jointly optimizing user selection, subchannel allocation, and sensing task allocation. The formulated minimization problem is a non-convex mixed-integer programming issue. To facilitate the analysis, we decompose the original optimization problem into two subproblems. One focuses on optimizing sensing task and subband allocation under fixed sensing user selection, which is optimally solved by the Hungarian algorithm via problem reformulation. Building upon these findings, we introduce a time-efficient two-sided swapping method to refine the scheduled user set and enhance system performance. Extensive numerical results demonstrate the effectiveness of our proposed approach compared to various benchmark strategies.

Ensuring timely information delivery is of paramount importance for mission-critical applications in the Internet of Things (IoT). The age of information (AoI), which measures the time elapsed from data generation to reception, is critical for assessing information freshness in time-sensitive scenarios. This work focuses on the AoI analysis of cooperative IoT systems with decode-and-forward relaying mode, where automatic repeat request (ARQ) and truncated ARQ (TARQ) protocols are employed to enhance the reliability of short packet communications. The source and relay nodes have limited battery capacity, thereby harvesting energy from the charging towers. We derive analytical expressions for the average AoI and the average peak AoI for the proposed ARQ-assisted and TARQ-assisted protocols. Simulation results validate our theoretical analysis and explore the impact of system parameters on AoI performance. Our findings highlight the existence of the optimal packet length and retransmission time that minimize AoI, providing valuable insights for designing efficient IoT systems.

This letter proposes a novel hybrid automatic repeat request with chase combining assisted sparse code multiple access (HARQ-CC-SCMA) scheme. Depending on whether the same superimposed packet is retransmitted, synchronous and asynchronous modes are considered for retransmissions. Moreover, a factor graph aggregation (FGA) method is used for multi-user detection. Specifically, a large-scale factor graph is constructed by combining all the received superimposed signals and message passing algorithm (MPA) is applied to calculate log-likelihood ratio (LLR). Monte Carlo simulations are preformed to show that FGA surpasses bit-level combining (BLC) and HARQ with incremental redundancy (HARQ-IR) in synchronous mode. Moreover, FGA performs better than BLC at high signal-to-noise ratio (SNR) region in asynchronous mode. However, FGA in asynchronous mode is worse than BLC at low SNR, because significant error propagation is induced by the presence of failed messages after the maximum allowable HARQ rounds.

The mixed Analog-to-Digital Converter (ADC) architecture is considered a promising solution in balancing the trade-off between high-resolution and low-resolution quantization over the hardware costs, power consumption, and transmission demands in massive multiple-input multiple-output (MIMO) systems. Meanwhile, the Direction of Arrival (DOA) estimation is a prerequisite for accurate beam processing in MIMO systems. Therefore, evaluating the DOA estimation performance in linear array architectures of mixed-ADC based MIMO systems is crucial. However, local bounds, such as the widely used Cramer-Rao Bound (CRB), only offer rigorous performance analysis of the estimator in the high signal-to-noise ratio (SNR) regime. In this paper, we derive a globally effective and closed-form Ziv-Zakai Bound (ZZB) to assess the DOA estimation performance of mixed-resolution quantization structures. We have also provided the CRB for DOA estimation with mixed-resolution quantization and included the classical MUSIC algorithm as a comparison. Additionally, we have analyzed the impact of different prior information, the numbers of snapshots and sensors, quantization bits and parameter settings on the ZZB. Simulation results show that the ZZB provides globally effective bounds under Gaussian and uniform distributions. In particular, in the low SNRs region, the ZZB offers a tighter and more effective bound than CRB and BCRB.

Precise positioning is becoming increasingly crucial across various applications, including rescue operations, intelligent transportation, logistics, and environmental monitoring. However, previous research often assumes that wireless sensor networks are perfectly synchronized or have unlimited communication resources. These assumptions frequently do not align with real-world scenarios, particularly in resource-constrained networks with stringent power and communication limits. In this paper, we propose a target localization and time synchronization algorithm based on the quantization of time-of-arrival (TOA) measurements. This approach accounts for both the quantization process and clock offsets caused by asynchronous clocks between sensor nodes and the target source. To tackle the problem, a variational Bayesian learning method is proposed to estimate the target source location and clock offsets jointly. This algorithm aims to find a feasible variational distribution that approximates the true posterior distribution, thereby conducting the Bayesian estimation over the approximate distributions and obtaining the accurate locations and clock offsets. Additionally, we derive the quantized Bayesian Cramr-Rao Bound (QBCRB) for quantized TOA measurements to evaluate localization and time synchronization performance. Simulation and experimental results demonstrate that the proposed method provides effective estimations and outperforms other comparison algorithms across various scenarios.

The growing reliance on mobile crowdsensing net-works for real-time data collection in various applications, from urban infrastructure monitoring to environmental sensing, ne-cessitates the reduction of latency for enhanced efficiency. In this work, we address this critical challenge by focusing on the joint subband and sensing task allocation for next-generation mobile crowdsensing networks, emphasizing the minimization of latency. To achieve this goal, an optimization problem is formulated to minimize the system's total latency, taking various practical constraints into account. Therein, the latency is comprised of sensing delay and transmission delay. The considered problem is a mixed-integer programming problem, which is also non-convex. To facilitate the analysis, we utilize the underlying structural properties of the problem and derive the optimal sensing task allocation strategy under a given sub band allocation scheme. With the discussions, we show that the original optimization problem can be transformed into a maximum weighted matching problem in a bipartite graph. This problem can be optimally solved by the Hungarian algorithm in a cubic time complexity. Building upon these analyses, we further approximate the optimal network delay in closed form under certain circumstances. Extensive simulation results validate that our proposed joint optimization method outperforms various benchmark strategies in terms of latency saving under comprehensive system settings.

This paper analyzes the block error rate (BLER) of hybrid automatic repeat request with incremental redundancy (HARQ-IR) assisted short packet communications. The small packet size leads to the occurrence of the time correlation among HARQ rounds. To accurately capture the effect of time correlation, a general correlated Nakagami-m fading channel model is first developed. However, the channel correlation, multiple transmissions, and finite blocklength information theory extremely challenge the analysis of BLER. To confront this, the average BLER is derived in a compact form by capitalizing on linearization approximation and conditional Laplace transform. To reveal more insights, the asymptotic BLER in high signal-to-noise ratio is obtained in a simple form. It is disclosed that full time diversity can be achieved by the proposed HARQ-IR-aided scheme. This justifies the feasibility of using retransmissions to offer reliable short packet communications. The numerical results finally corroborate the validity of our analysis.

This article examines the application of hybrid automatic repeat request with incremental redundancy (HARQ-IR) to reliable mission-critical Internet of Things (IoT) communications, which frequently use short packets to meet low latency of mission. We first analyze the average block error rate (BLER) of HARQ-IR-aided short packet communications. The finite-blocklength information theory and the correlated decoding events preclude the analysis of BLER. To overcome the issue, the recursive formulation of the average BLER motivates us to calculate its value through trapezoidal approximation and Gauss-Laguerre quadrature. Besides, dynamic programming is applied to implement Gauss-Laguerre quadrature to avoid redundant calculations. Moreover, the asymptotic analysis is performed to derive a simple expression for the asymptotic average BLER at high-signal-to-noise ratio (SNR). Then, we study the maximization of long-term average throughput (LTAT) via power allocation meanwhile ensuring power and BLER constraints. To tackle the fractional and nonconvex problem, the asymptotic BLER is employed to convert the original problem into a convex one through geometric programming (GP). Unfortunately, since there is a large approximation error at low SNR, the GP-based solution underestimates the LTAT performance in the circumstance. Alternatively, we develop a deep reinforcement learning (DRL)-based framework to learn the optimal power allocation policy. In particular, the optimization problem is transformed into a constrained Markov decision process problem, which is solved by integrating deep deterministic policy gradient(DDPG) and subgradient method. The numerical results demonstrate that the DRL-based method outperforms the GP-based one at low SNR, albeit at the cost of increasing computational burden.

A variable-length cross-packet hybrid automatic repeat request (VL-XP-HARQ) is proposed to boost the spectral efficiency (SE) and the energy efficiency (EE) of communications. The SE is firstly derived in terms of the outage probabilities, with which the SE is proved to be upper bounded by the ergodic capacity (EC). Moreover, to facilitate the maximization of the SE, the asymptotic outage probability is obtained at high signal-to-noise ratio (SNR), with which the SE is maximized by properly choosing the number of new information bits while guaranteeing outage requirement. By applying Dinkelbach’s transform, the fractional objective function is transformed into a subtraction form, which can be decomposed into multiple sub-problems through alternating optimization. By noticing that the asymptotic outage probability is a convex function, each sub-problem can be easily relaxed to a convex problem by adopting successive convex approximation (SCA). Besides, the EE of VL-XP-HARQ is also investigated. An upper bound of the EE is found and proved to be attainable. Furthermore, by aiming at maximizing the EE via power allocation while confining outage within a certain constraint, the methods to the maximization of SE are invoked to solve the similar fractional problem. Finally, numerical results are presented for verification.

The emerging immersive and autonomous services have posed stringent requirements on both communications and localization. By considering the great potential of reconfigurable intelligent surface (RIS), this paper focuses on the joint channel estimation and localization for RIS-aided wireless systems. As opposed to existing works that treat channel estimation and localization independently, this paper exploits the intrinsic coupling and nonlinear relationships between the channel parameters and user location for enhancement of both localization and channel reconstruction. By noticing the non-convex, nonlinear objective function and the sparse angle pattern, a variational Bayesian learning-based framework is developed to jointly estimate the channel parameters and user location through leveraging an effective approximation of the posterior distribution. The proposed framework is capable of unifying near-field and far-field scenarios owing to exploitation of sparsity of the angular domain. Since the joint channel and location estimation problem has a closed-form solution in each iteration, our proposed iterative algorithm performs better than the conventional particle swarm optimization (PSO) and maximum likelihood (ML) based ones in terms of computational complexity. Simulations demonstrate that the proposed algorithm almost reaches the Bayesian Cramer-Rao bound (BCRB) and achieves a superior estimation accuracy by comparing to the PSO and the ML algorithms.

This paper proposes a new scheme to enhance the secrecy performance of non-orthogonal multiple access (NOMA)-based coordinated direct relay transmission (CDRT) systems with an untrusted relay. The physical-layer network coding (PNC) and the NOMA schemes are combined to improve spectrum efficiency. Furthermore, inter-user interference and friendly jamming signals are utilized to suppress the eavesdropping ability of the untrusted relay without compromising the acceptance quality of legitimate users. Specifically, the far user in the first slot and the near user in the second slot act as jammers that generate jamming signals to ensure secure transmissions of confidential messages. We investigate the secrecy performance of the NOMA-based CDRT systems with the PNC scheme and derive the closed-form expression for the ergodic secrecy sum rate. The asymptotic analysis at a high signal-to-noise ratio is performed to obtain more insights. Finally, simulation results are presented to demonstrate the proposed scheme’s effectiveness and the theoretical analysis’s correctness.

In this paper, reconfigurable intelligent surface (RIS) and hybrid automatic repeat request with incremental redundancy (HARQ-IR) are amalgamated to lower power expenditure, shorten latency and strengthen reliability of the internet of things (IoT) communications. By considering Rician fading channels and multiple RISs, the outage probability of single-input single-output (SISO) HARQ-IR-RIS aided IoT networks is derived in closed-form, with which the asymptotic outage analysis is carried out. The asymptotic results are further extended to single-input multiple-output (SIMO)/multiple-input multiple-output (MIMO) HARQ-IR-RIS aided IoT networks by using random matrix theory. Thereafter, the asymptotic expressions are invoked to reduce the design complexity of the phase shifts, transmit powers, and rate, which aims at minimizing the age of information (AoI) while ensuring the power and outage constraints. Particularly, the optimal phase shifts are firstly determined. The alternating optimization technique is then applied to solve the transmit rate and powers, which are iteratively updated based on majorization-minimization (MM) principle and geometric programming (GP) approximation, respectively. The numerical results consequently corroborate our theoretical analysis. More interestingly, the HARQ-IR-aided scheme with fixed power is found to provide a comparable performance as the proposed scheme with variable power especially for numerous reflecting elements at RIS.

This paper investigates a reconfigurable intelligent surface (RIS)-aided multi-user multiple-input multiple-output (MIMO) system by considering only the statistical channel state information (CSI) at the base station (BS). We aim to maximize its sum-rate via the joint optimization of precoding matrix at the BS and phase shifts vector at the RIS. However, the multi-user MIMO transmissions and the spatial correlations make the optimization cumbersome. For tractability, an asymptotic sum-rate is derived under a large number of the reflecting elements. By adopting the asymptotic sum-rate as the objective function, optimal designs of the transmit precoding matrix and the phase shifts vector can be decoupled and solved individually. More specifically, a high-quality suboptimal solution of the transmit precoding matrix and phase shifts vectors can be obtained by utilizing the water-filling algorithm and the projected gradient ascent (PGA) algorithm, respectively. Comparing to the case of the instantaneous CSI assumed at the BS, the proposed algorithm based on the statistical CSI can achieve comparable performance but with much lower channel estimation overhead, information feedback overhead, and computational complexity, which is more affordable and appealing for practical applications. Moreover, the impact of spatial correlation on the asymptotic sum-rate is examined by using majorization theory.

To accommodate the stringent requirements of massive connectivity and ultra-high throughput, both reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) have been perceived as the key techniques for future communication networks. In this paper, a versatile framework is conceived to boost transmit power efficiency for RIS-enabled multi-group NOMA networks in the presence of coordinated multi-point (CoMP) reception and imperfect successive interference cancellation (SIC). Particularly, a group-level SIC (GSIC) method is proposed to eliminate the decoded group’s interference together with the well-designed transceivers to mitigate the aggregated interference, including the intra-group interference, the residual interference caused by imperfect SIC, and the interference of NOMA users decoded later. According to the novel framework, a power minimization problem is formulated by collaboratively optimizing the transmit power and the phase shifts. To render the problem tractable, an alternating scheme is developed to optimize the transmit power and the phase shifts iteratively. Specifically, the transmit powers for the users in the same group are devised by a parallel iteration algorithm, whilst the phase shifts are optimized by a sequential rotation method. In simulations, it is shown that the proposed scheme requires less transmit power than various benchmark methods under the constraint of each user’s quality of service.

Vehicle-to-vehicle (V2V) communications under dense urban environments usually experience severe keyhole fading effect especially for multi-input multi-output (MIMO) channels, which degrades the capacity and outage performance due to the rank deficiency. To avoid these, the integration of MIMO and hybrid automatic repeat request (HARQ) is proposed to assist V2V communications in this paper. By using the methods of integral transforms, the outage probabilities are derived in closed-form for different HARQ-assisted schemes, including Type I-HARQ, HARQ with chase combining (HARQ-CC), and HARQ with incremental redundancy (HARQ-IR). With the results, meaningful insights are gained by conducting the asymptotic outage analysis. Specifically, it is revealed that full time diversity order can be achieved, while full spatial diversity order is unreachable as compared to MIMO-HARQ systems without keyhole effect. Moreover, we prove that the asymptotic outage probability is a monotonically increasing and convex function of the transmission rate. More importantly, although HARQ-IR performs better than HARQ-CC owing to its higher coding complexity, this advantage becomes negligible in the large-scale array regime. Finally, the numerical results are verified by Monte-Carlo simulations along with some in-depth discussions.

We thoroughly investigate the outage performance of reconfigurable intelligent surface (RIS) aided multi-input multi-output (MIMO) communications by exploiting statistical channel state information (CSI). Kornecker channel model is adopted to characterize the impact of spatial correlations among MIMO antennas and reconfigurable reflectors. Mellin transform and random matrix theory are then utilized to derive the outage probability, with which we further conduct the asymptotic outage analysis to obtain insightful findings. In particular, the asymptotic analysis reveals that the number of reflecting elements at the RIS should not be smaller than the total number of MIMO transmit and receive antennas to get rid of the rank deficiency of the cascaded MIMO channels. Moreover, the asymptotic outage probability is a monotonically increasing and convex function with respect to the transmission rate. The numerical outcomes not only corroborate our analytical results, but also demonstrate the negative impact of the spatial correlation and the benefit of increasing the number of reconfigurable reflectors. Finally, we apply the asymptotic results to optimally devise the phase shifts with a low computational complexity.

Reconfigurable intelligent surface (RIS) has recently attracted a spurt of interest due to its innate advantages over massive MIMO on power consumption. In this paper, we study the outage performance of multi-RIS system with the help of hybrid automatic repeat request (HARQ) to improve the RIS system reliability, where the fading channels are modeled by Rician fading and the phase shift setting only depends on the line-of-sight (LoS) component. Both the exact and asymptotic outage probabilities under Type-I HARQ and HARQ with chase combining (HARQ-CC) schemes are derived. Particularly, the tractable asymptotic results empower us to derive meaningful insights for HARQ-aided multi-RIS systems. On the one hand, we find that both the Type-I and the HARQ-CC schemes can achieve full diversity that is equal to the maximal number of HARQ rounds. On the other hand, the closed-form expression of the optimal phase shift setting with respect to outage probability minimization is obtained. The optimal solution indicates that the reflecting link direction should be consistent with the direct link LoS component. Finally, the analytical results are validated by Monte-Carlo simulations.

In order to combat the challenges of scarce spectrum bandwidth and energy supply in the Internet-of-Things networks, this article investigates a wireless-powered massive multiple-input-multiple-output (MIMO) underlay cognitive radio (CR) system, where the secondary user (SU) harvests the energy from the primary network through a time-switching (TS) strategy. Moreover, the detection technique of maximum-ratio combining (MRC) is applied at base stations (BSs) due to its low complexity. Thereon, the achievable rate of the secondary network is thoroughly analyzed by accounting for spatial correlations at both users and BSs, which significantly differentiates the analysis from the prior literature. With the analytical results, the impacts of the number of antennas and the spatial correlations are quantified. In particular, the negative impact of the spatial correlation on the achievable rate is theoretically proved based on the majorization theory. Furthermore, the TS factor and/or power allocation coefficients are optimally designed based on the statistical channel state information (CSI) only to maximize the achievable rate while ensuring the maximum endurable interference constraint. It is found that the optimal power allocation matrix is a variant of water-filling solution with two water levels associated with sum power constraint and sum weighted power constraint, respectively. The weighting matrix is aligned with the transmit spatial correlation in the SU. The joint design of TS factor and power allocation coefficients is also shown to reach a superior performance over the algorithms-based solely on TS factor or power allocation coefficients optimizations. Finally, the analytical results are validated by conducting simulations.

In this paper, a full-stack perspective that involves content coding, caching and recommendation is presented for wireless edge caching networks. Therein, content coding and recommendations are utilized to ensure the reliability of the network and to boost the gain of edge caching, respectively. Specifically, with a well-designed coding pattern, the concurrently occurred data corruptions of multiple access points (APs) can be successfully recovered. Whilst, the successful content retrieval is warranted with highly selective retrieval sets. Further, by offering personalized recommendations, the cache hitting at network edges can be promoted. On these grounds, a cache hit ratio maximization problem for reliability-aware wireless edge caching system is formulated, considering the successful content retrieval and repair, total repair cost requirements, cache capacity budget of each AP, and recommendation quality and quantity per user. To solve this intractable problem, a framework with two steps is developed, which leads to construction of a suitable coding schema with the minimum repair cost per content to satisfy the retrieval and repair criteria, thus achieving a stabilized optimal solution regarding the joint cache placement and recommendation decisions from a game-theoretical viewpoint. Finally, extensive numerical simulations are performed to confirm the superior performance of our devised algorithms compared with benchmark baselines.

To support massive connectivity and boost spectral efficiency for Internet of Things (IoT), a downlink scheme combining virtual multiple-input-multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to demultiplex the input data streams. Only statistical channel state information (CSI) is available at the base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering the Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of the Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed form, with which the joint power and rate selection is realized by using alternately iterating optimization. Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with a higher transmission rate under benign channel conditions.

The combination between non-orthogonal multiple access (NOMA) and hybrid automatic repeat request (HARQ) is capable of realizing ultra-reliability, high throughput and massive concurrent connections particularly for emerging communication systems. This paper focuses on characterizing the asymptotic scaling law of the outage probability of the HARQ with chase combining (HARQ-CC)-aided downlink NOMA scheme with respect to the transmit power, i.e., diversity order. The diversity order of the two- user HARQ-CC-aided NOMA system is derived in closed-form, where an integration domain partition trick is developed to obtain the upper and lower bounds of the outage probability. The analytical results show that the diversity order is a decreasing step function of transmission rate given the ratio between the transmit powers allocated to the two users. Moreover, full time diversity can only be achieved under a sufficiently low transmission rate. Additionally, the users' diversity orders follow a descending order according to their respective average channel gains. Monte Carlo simulations finally confirm the analysis.

Both small cells and non-orthogonal multiple access (NOMA) are cutting-edge technologies to boost the system capacity for the future wireless communications. In this paper, an analytical framework for small cell uplinks using NOMA transmissions is developed. To facilitate the detection of both near and far user signals, dual path loss compensation factors (PLCFs) are exploited for uplink power control. With the aid of stochastic geometry, the average successful transmission probability are derived for small cell networks with opportunistic NOMA transmissions and dual PLCFs. Based on the derived results, optimal PLCFs are obtained by two-dimension search methods. It is shown by simulation that the proposed NOMA scheme with optimal dual PLCFs outperforms the existing NOMA scheme with a unique PLCF in terms of both average successful transmission probability and spectrum efficiency.

Considering the widespread use of effective capacity in cross-layer design and the extensive existence of renewal service processes in communication networks, this paper thoroughly investigates the effective capacity for renewal processes. Exact expressions of the effective capacity at a given quality of service (QoS) exponent are derived for the renewal processes with either constant or variable reward. The simple expressions reveal meaningful insights, such as the monotonicity and bounds of the effective capacity. The analytical results are then applied to evaluate the cross-layer throughput for diverse hybrid automatic repeat request (HARQ) systems, including fixed-rate HARQ (FR-HARQ, e.g., Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR)), variable-rate HARQ (VR-HARQ), and cross-packet HARQ (XP-HARQ). Furthermore, aiming at maximizing the effective capacity via the optimal rate selection, it is disclosed that the VR-HARQ and XP-HARQ attain almost the same performance, and both of them perform better than FR-HARQ. In contrast, most of the prior outcomes approximate the effective capacity with the lack of insightful discussions that hampers the optimal design of HARQ systems. Finally, the numerical results corroborate the analytical ones.

This paper investigates the performance of Multipleinput multiple-output non-orthogonal multiple access (MIMONOMA) systems with randomly deployed users, where the randomly deployed NOMA users follow Poisson point process (PPP), the spatial correlation between MIMO channels are characterized by using Kronecker model, and the composite channel model is used to capture large-scale fading as well as small-scale fading. The spatial randomness of users' distribution, the spatial correlation among antennas and large-scale fading will severally impact the system performance, but they are seldom considered in prior literature for MIMO-NOMA systems, and the consideration of all these impact factors challenges the analysis. Based on zeroforcing (ZF) detection, the exact expressions for both the average outage probability and the average goodput are derived in closedform. Moreover, the asymptotic analyses are conducted for both high signal-to-noise ratio (SNR) (/small cell radius) and low SNR (/large cell radius) to gain more insightful results. In particular, the diversity order is given by δ = Nr- M + 1, the average outage probability of k-th nearest user to the base station follows a scaling law of O (Dα(Nr-M+1)+2k), the average goodput scales as O(D2) and O(D-2) as D → 0 and D → ∞, respectively, where Nr, M, α and D stand for the number of receive antennas, the total number of data streams, the path loss exponent and the cell radius, respectively. The analytical results are finally validated through the numerical analysis.

This paper develops an interference aware design for cooperative hybrid automatic repeat request (HARQ)-assisted non-orthogonal multiple access (NOMA) scheme for large-scale device-to-device (D2D) networks. Specifically, interference aware rate selection and power allocation are considered to maximize long term average throughput (LTAT) and area spectral efficiency. The design framework is based on stochastic geometry that jointly accounts for the spatial interference correlation at the NOMA receivers as well as the temporal interference correlation across HARQ transmissions. It is found that ignoring the effect of the aggregate interference, or overlooking the spatial and temporal correlation in interference, highly overestimates the NOMA performance and produces misleading design insights. An interference oblivious selection for the power and/or transmission rates leads to violating the network outage constraints. To this end, the results demonstrate the effectiveness of NOMA transmission and manifest the importance of the cooperative HARQ to combat the negative effect of the network aggregate interference. For instance, comparing to the non-cooperative HARQ-assisted NOMA, the proposed scheme can yield an outage probability reduction by 21%. Furthermore, an interference aware optimal design that maximizes the LTAT given outage constraints leads to 17% throughput improvement over HARQ-assisted orthogonal multiple access scheme.

In this paper, outage performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) is analyzed. Unlike prior analyses, time-correlated Nakagami-m fading channel is considered. The outage analysis thus involves the probability distribution analysis of a product of multiple correlated shifted Gamma random variables and is more challenging than prior analyses. Based on the findings of the conditional independence of the received signal-to-noise ratios, the outage probability is exactly derived by using conditional Mellin transform. Specifically, the outage probability of HARQ-IR under time-correlated Nakagami-m fading channels can be written as a weighted sum of outage probabilities of HARQ-IR over independent Nakagami fading channels, where the weightings are determined by a negative multinomial distribution. This result enables not only an efficient truncation approximation of the outage probability with uniform convergence but also asymptotic outage analysis to further extract clear insights, which have never been discovered for HARQ-IR even under fast fading channels. The asymptotic outage probability is then derived in a simple form, which clearly quantifies the impacts of transmit powers, channel time correlation, and information transmission rate. It is proved that the asymptotic outage probability is an inverse power function of the product of transmission powers in all HARQ rounds, an increasing function of the channel time correlation coefficients, and a monotonically increasing and convex function of information transmission rate. The simple expression of the asymptotic result enables optimal power allocation and optimal rate selection of HARQ-IR with low complexity. Finally, numerical results are provided to verify our analytical results and justify the application of the asymptotic result for optimal system design.

This paper conducts outage analysis for cooperative hybrid automatic repeat request with incremental redundancy (C-HARQ-IR). A general time-correlated Nakagami fading channel including fast fading and Rayleigh fading as special cases is considered. An efficient truncated inverse moment matching method is developed to derive the outage probability in closed- form. The accuracy of the analytical results is verified by Monte Carlo simulations and the results reveal that C-HARQ-IR protocol can benefit from high fading order and low channel time correlation.

A full duplex amplify-and-forward relay network is thoroughly investigated in this paper. To be practical and general, loop interference and Nakagami-m fading channels are considered. Outage probability and ergodic capacity are particularly analyzed and exactly derived in close-forms. Asymptotic outage probability and bounds of the ergodic capacity are also derived in simple forms with clear insights. The analytical results unveil the impacts of loop interference and other system parameters in details. Specifically, it is found that the capacity and outage performance are enhanced via the increase of transmit power at the source, the average fading power and fading order associated with the source-to-relay link. However, under high signal-to-noise ratio (SNR), the increases of the transmit power at the relay and the average fading power corresponding to the loop interference would deteriorate the capacity and outage performance. The results also show that the outage probability obeys an inverse-m1law with respect to the ratio between the transmit powers in the source and relay, where m1is the fading order corresponding to the source-to-relay Nakagami fading channel. The accuracy of the analytical results is validated by Monte-Carlo simulations and they thus can serve as solid foundation for system design and optimization.

In this paper, performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) over Rayleigh fading channels is investigated. Different from prior analysis, time correlation in the channels is considered. Under time-correlated fading channels, the mutual information in multiple HARQ transmissions is correlated, making the analysis challenging. By using polynomial fitting technique, probability distribution function of the accumulated mutual information is derived. Three meaningful performance metrics including outage probability, average number of transmissions, and long term average throughput (LTAT) are then derived in closed-forms. Moreover, diversity order of HARQ-IR is also investigated. It is proved that full diversity can be achieved by HARQ-IR, i.e., the diversity order is equal to the number of transmissions, even under time-correlated fading channels. These analytical results are verified by simulations and enable the evaluation of the impact of various system parameters on the performance. Particularly, the results unveil the negative impact of time correlation on the outage and throughput performance. The results also show that although more transmissions would improve the outage performance, they may not be beneficial to the LTAT when time correlation is high. Optimal rate design to maximize the LTAT is finally discussed and significant LTAT improvement is demonstrated.

In this paper, an opportunistic cooperative HARQ system is analyzed. Different from prior analyses, time correlated fading channels are considered. Based on moment generation function and Laplace transform, the outage probability and throughput in terms of long-term average transmission rate (LATR) of this opportunistic cooperative HARQ system are derived in closed-forms. The accuracy of the analytical results is verified by computer simulations. From the analytical results, the impacts of the time correlation and other system parameters on the performance are investigated and the optimal packet rate selection to maximize the LATR is discussed.