An Internet of Things (IoT) network is a network of devices. In a massive IoT network, devices are often battery-powered, and the battery life determines the network’s lifespan. The goal of this article is thus to examine the inherent relationship between device intrinsic characteristics and network performance (lifespan and capacity). The findings lead to a simple but fundamental view of IoT network longevity and, more significantly, an IoT network longevity principle. This new perception of network longevity alludes to distinct characteristics of IoT devices from those used in existing technologies and systems. These distinctions manifested by the network performance are examined through in-depth theoretical analysis. An exemplary augmentation of characteristics on a practical device demonstrates and reinforces the importance of device characteristics on network performance and the power of the longevit principle.

Sensor sharing is an important ability for vehicle platoon to reduce vehicle costs and improve driving safety. However, sensing data with a large amount of information is difficult to be shared by using traditional communication methods. Applying millimeter-wave communication can improve sensor sharing capabilities of vehicles, but millimeter wave has large propagation loss and is easy to be blocked by obstacles. Therefore, how to apply millimeter-wave communication to improve sensor sharing capabilities in vehicle platoon is still an open issue. To this end, we present a stochastic game approach and propose two distributed algorithms to achieve high-reliability and low-energy sensor sharing. First, we model the multihop millimeter-wave sensor sharing of the vehicle platoon as an optimization problem of reducing energy consumption while ensuring communication reliability, and then based on the characteristics of vehicle communication, we transform the optimization problem into a multiagent stochastic game. Second, by utilizing the characteristic that the immediate cost has nothing to do with the transfer function of the game, we prove that this stochastic game has equilibrium by proving that each subgame has a Nash equilibrium. Third, we propose an equilibrium-guided multiagent distributed (EGMD) algorithm to obtain the optimal action of each vehicle in the platoon, and then based on the analysis of the limitation of the approach, we present a multiagent distributed cooperation (MDC) algorithm to further reduce the system energy consumption. Finally, through simulation experiments, we evaluate a variety of approaches and demonstrate that our proposed methods can significantly reduce energy consumption while ensuring communication reliability.

The application of Non-Orthogonal Multiple Access (NOMA) technology to New Radio (NR) V2X can further reduce communication delay and improve the system capacity of the vehicular network. However, in NR V2X Mode 1, the base station first needs to allocate resources in each transmission period, and then the vehicles transmit information by the allocated resource. According to this, we propose a two-stage scheme of centralized resource allocation and distributed power control to meet the requirement of the NR V2X Mode 1 while NOMA technology is employed in vehicle groups. Firstly, at the beginning of a transmission period, the base station allocates resources to vehicle groups. For this centralized manner, we propose a graph-based matching approach to allocate resources to improve system capacity. Then, each vehicle group controls and adjusts transmission power for NOMA communication in the period. For this distributed manner, we put forward a cooperative game approach to control the power of the vehicle group while increase system capacity. Simulation results show that the proposed scheme can increase the system capacity.

Placement of edge computing servers at the edge of the network can reduce task transmission delay. Connecting them into a system can provide services for a wider range. However, due to the mobility of the crowd and mobile devices, the number of tasks offloaded to each edge server may be quite different, which will seriously affect the QoS of the system. To this end, we investigate the QoS improvement of the distributed edge computing system from the game-theoretic perspective and propose a multi-agent state-based learning algorithm. Firstly, by modeling the cost of an edge computing server as the deviation between its execution time and the system average execution time, we formulate the QoS improvement of the system as a state-based game where each agent competes to maximize its own utility. Then, we propose a multi-agent state-based learning algorithm to obtain the pure Nash equilibrium strategy of each agent. Finally, compared with the existing approaches, the experiments show that the proposed algorithm can improve the QoS of the distributed edge computing system.

Through offloading task to mobile ad hoc cloud (MAHC), mobile devices can execute computation-intensive tasks faster and consume less energy without infrastructures. However, when multiple resource demanders (RDs) offload tasks to resource providers (RPs) in the MAHC, the disorderly competition among RDs can cause inhomogeneous task distributions on the RPs. This can reduce the service efficiency of the MAHC and lead to a low utility of each RD. Accordingly, we propose a stochastic game approach to solve this problem. Firstly, the utility of each RD is examined as the combination of task execution time and monetary cost. Then, we model the competition for each RD pursuing its maximum utility as a static noncooperative game. After that, we transform the single-shot game in one time slot into a stochastic game in an infinite time horizon to obtain the optimal strategy of each RD. Finally, we propose the backward iteration algorithm to reduce the computational complexity for reaching the ε-Nash equilibrium of the game. Numerical results show various performances of this stochastic game. Compared with the strategy obtained from the static game, the equilibrium strategy derived from the stochastic game can effectively improve the utility of each RD.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Mobile-edge computing can reduce task execution delay and improve the Quality of Experience (QoE) for the network edge users. However, when there are multiple independent cloudlets in the network with the mobile users offloading tasks randomly, how to maintain the load balancing of the independent cloudlets, how to improve the quality of service and users' QoE are still issues need to be solved. To this end, we study these issues from the perspective of game theory and propose decentralized learning algorithms. First, we turn the cloudlets load-balancing issue into a competition that each user minimizes its task execution time, and then a stochastic congestion game with incomplete information is proposed. Second, based on the existence proof of the Nash equilibria by using potential game theory, we propose a multiuser decentralized learning algorithm to obtain the pure Nash equilibrium strategy of each user. Then, an ordinary differential equation is derived to prove the convergence of the algorithm. Finally, we propose two application scenarios, one is for static users and the other is for dynamic users, and then the performances of the algorithm in a static scenario is tested. In order to adapt to dynamic scenarios, and further improve the performance and reduce communication costs, we propose a decentralized learning algorithm with termination condition. The experiments show that this algorithm can improve the load balancing of the multicloudlet system, and enhance the quality of service.

The application of nonorthogonal multiple access (NOMA) technology to New Radio (NR) Vehicles-to-Everything (V2X) can further reduce communication delay and improve the system capacity of the vehicular network. However, due to the high mobility of the vehicles, it is difficult for the base station to obtain the channel information between vehicles in real time for resource allocation and vehicle transmission power control. To this end, we propose a two-stage scheme of centralized resource allocation and distributed power control to meet the NR V2X Mode 1 requirement while NOMA technology is employed in each vehicle group. First, at the beginning of a transmission period, the base station allocates resources to vehicle groups. For this centralized manner, we propose a graph-based matching approach to allocate resources to improve system capacity. Then, each vehicle group controls and adjusts transmission power for NOMA communication in the period. For this distributed manner, we propose a noncooperative game to control the power of the vehicle groups and then analyze the performance of the noncooperative game. Afterward, we further put forward a cooperative game approach to control the power of the vehicle group to increase system capacity. Compared with the noncooperative game, the proposed scheme can increase communication capacity by up to 5% and reduce transmission power consumption by 36%.

After the sensing-based semipersistent scheduling (SPS) is introduced in the media access control layer of the long-term evolution vehicle-to-everything (LTE-V2X) Mode 4, many tests have been conducted to measure its performance. However, until now, there is still no clear mathematical expression for the reliability of this scheduling. To this end, in this article, we attempt to provide the lower and upper bounds of the reliability and propose a distributed algorithm for improving reliability. First, we give a mathematical description of sensing-based SPS and apply packet pass rate (PPR) to represent its reliability. Then, we analyze the SPS without sensing and present a theoretical expression of the reliability, which is described as a function of channel busy rate (CBR). Second, an iterative approach is applied to analyze the sensing-based SPS, and the mathematical expressions of lower and upper-reliability bounds are derived. Third, based on the existing condition of the upper bound, we propose a reliability improvement solution, which utilizes a distributed algorithm to process added information, such as the counter and the offset for the remaining storage space. Finally, this solution is applied to LTE-V2X, and simulation shows that the proposed scheme can significantly improve the scheduling reliability.

This paper proposes a practical synchronization waveform that is resilient to frequency error for machine-type communications with applications in massive Internet of Things (IoT). Mathematical properties of the waveform are derived, which are keys to addressing the practical issues. In particular, it is shown that this type of waveform is asymptotically optimal in the presence of a frequency error, in the sense that its asymptotic performance is the same as the optimal matched-filter detector that is free of frequency error. This asymptotic property enables optimization of the waveform under the constraints imposed by an application. It is also shown that such optimized waveform comes in pairs, which facilitates the formation of a new waveform capable of frequency error estimation and timing refinement at the receiver. Detailed comparisons with the LTE narrowband IoT primary synchronization signal are provided.

Machine-type communication (MTC) is the key technology to support data transfer among devices (sensors and actuators) in Internet of Things (IoT). However, MTC, especially when applied to massive low-power IoT (mIoT), poses some unique and serious challenges due to the low-cost and low-power nature of an mIoT device. One of the most challenging issues is providing a robust way for an mIoT device to acquire the network under a large frequency offset/error (due to the use of a low-cost crystal oscillator) and a low operating SNR (due to the extended coverage). We address the issues in the existing mIoT system acquisition, particularly the initial synchronization waveform detection, and derive a new synchronization waveform that is more robust in an mIoT environment. The mathematical approach provides a useful analytical insight into the design of the synchronization signal waveform for the 5G mIoT system.

Zadoff-Chu (ZC) sequences have been used as synchronization sequences in modern wireless communication systems, replacing the conventional pseudorandom noise sequences due to their perfect autocorrelation properties. We first study the problem of ambiguity between a timing offset and a frequency offset, which arises when a ZC sequence is used as a synchronization signal. We then show how a frequency offset can impair the timing property of a ZC sequence, causing irreducible timing errors. An analytical framework, particularly the timing spectrum, is developed, which fully characterizes a ZC sequence's timing properties and its fundamental limitations as a time synchronization sequence in the presence of a frequency offset between the transmitter and the receiver. This analytical framework provides a powerful analytical tool for timing signal design and performance analysis of ZC sequences.

The FCC new regulation for cognitive use of the TV white space spectrum provides a new means for improving traditional cellular network performance. But it also introduces a number of technical challenges. This paper studies one of the challenges: given the significant differences in the propagation property and the transmit power limitations between the cellular band and the TV white space, how both bands can be jointly utilized such that the benefit from the TV white space is maximized for overall cellular network performance improvement. Both analytical and simulation results are provided.

Orthogonal frequency division multiple access (OFDMA) and Single Carrier Frequency Division Multiple Access (SC-FDMA) are the two major multiple access schemes for 4G wireless communications. In long term evolution (LTE) the downlink multiple access scheme is based on OFDMA and the uplink multiple access scheme is based on SC-FDMA. In this letter, we derive the fundamental performance difference between OFDMA and SC-FDMA, and present the general performance comparison between them. Analytical results show that OFDMA performance upper bounds SC-FDMA in the sense of capacity. The conclusion is further confirmed by numerical results.

In this paper, we review the signal-to-leakage-plus-noise ratio (SLNR) transmit precoding criterion and the active antenna selection (AAS) strategy in a multi-user MIMO system. We then address the limitations of the original SLNR and AAS schemes and provide a solution that generalizes the SLNR precoding model by incorporating the antenna-receiver structure into the optimization process to further improve the multi-user MIMO performance under various receiver structures.

Zadoff-Chu (ZC) sequences have been used as random access sequences in modern wireless communication systems, replacing the conventional pseudo-random-noise (PN) sequences due to their superior autocorrelation properties. An analytical framework quantifying the ZC sequence's performance and its fundamental limitation as a random access sequencein the presence of frequency offset between the transmitter and the receiver is introduced. We show that a ZC sequence's perfect autocorrelation properties can be severely impaired by the frequency offset thereby limiting the overall performance of the random access signals formed from these sequences. First, we derive the autocorrelation function of these random access sequences as a function of the frequency offset. Next, we introduce the concept of critical frequency offsets and the spectrum associated with a ZC sequence set to characterize the frequency offset properties of the random access signals. Finally, we demonstrate that the frequency offset immunity of a ZC sequence set can be controlled by shaping the spectrum of the ZC sequence set.

This paper proposes a unique discovery signal as an enabler of peer-to-peer (P2P) communication which overlays a cellular network and shares its resources. Applying P2P communication to cellular network has two key issues: 1. Conventional ad hoc P2P connections may be unstable since stringent resource and interference coordination is usually difficult to achieve for ad hoc P2P communications; 2. The large overhead required by P2P communication may offset its gain. We solve these two issues by using a special discovery signal to aid cellular network-supervised resource sharing and interference management between cellular and P2P connections. The discovery signal, which facilitates efficient neighbor discovery in a cellular system, consists of un-modulated tones transmitted on a sequence of OFDM symbols. This discovery signal not only possesses the properties of high power efficiency, high interference tolerance, and freedom from near-far effects, but also has minimal overhead. A practical discovery-signal-based P2P in an OFDMA cellular system is also proposed. Numerical results are presented which show the potential of improving local service and edge device performance in a cellular network.

Accurate channel information at the transmitter is crucial to multi-user MIMO performance. Unfortunately, the bandwidth of the control channel by which the feedback is conveyed is often limited. An important issue is how to improve multi-user MIMO performance with minimal feedback. Conventional feedback techniques focus on improving the quantized codebook performance using various quantization criteria. In this paper, instead of trying to optimize a single codebook, we apply multiple alternating codebooks to effectively reduce the multi-user MIMO quantization error in addition to the reduction provided by the single codebook optimization techniques. That is, we use an existing quantization codebook design methodology to create not one but multiple such similar codebooks. The codebooks are alternated at each feedback instance creating a larger virtual codebook with the same number of feedback bits as the single smaller codebook. Simulation results show that significant performance gain in multi-user MIMO systems is obtained via the alternating codebook scheme.

We investigate two different relay selection (RS) methods for precoded decode-and-forward (DF) cooperative OFDM systems: one scheme is to select the best relays for individual precoding groups (IPG-RS), and the other is to choose a single relay for the entire precoding groups (EPG-RS). We derive their diversity performances from the pairwise error probability (PEP) analysis, indicating that both schemes can achieve the full frequency and cooperative diversity with a proper choice of precoding size. Numerical results also show that IPG-RS can either reduce the decoding complexity or improve the error performance significantly as compared to existing solutions whereas EPG-RS has some performance loss as a result of less communication overhead.

Next generation wireless communications rely on large bandwidths and orthogonal frequency division multiple access (OFDMA) techniques to achieve high data rates. In an OFDMA-based cellular network, interference management is key to system performance characteristics, such as capacity, coverage and stability. Conventional cellular systems typically employ frequency reuse, such as 1/3, or 1/7 frequency reuse, to reduce inter-cell interference, resulting in low bandwidth efficiency. This study introduces a frequency resource and interference management scheme for use in an OFDMA cellular communication system, referred to as dynamic frequency resource allocation, in which the network divides frequency band into multiple partitions with various inter-sector interference blocking capabilities. In addition, an interference identification scheme that allows the network to acquire a terminal's interference properties is also proposed. Therefore the terminals under different inter-sector interference can be dynamically assigned with the most efficient frequency resources depending on the interference sources, enabling the network to optimise both bandwidth usage and cell edge performance with increased flexibility and accuracy.

Orthogonal frequency division multiplexing (OFDM) has become a promising physical layer modulation technology for beyond 3G or 4G wireless communications due to effective inter-symbol interference mitigation for high speed data transmission. However, the timing of the OFDM symbol, i.e., the placement of the DFT collection window in a multi-path time dispersive channel remains an important and challenging issue in OFDM receiver design. An erroneous timing decision creates inter-symbol interference (ISI), inter-carrier interference (ICI), channel attenuation, and channel estimation error, which leads to a penalty on the collected OFDM symbol signal to noise ratio (SNR) resulting in an irreducible error floor. In this paper we quantify such effects and derive an optimal OFDM symbol timing solution in the sense of maximizing the signal to interference ratio (SIR) of the collected OFDM symbol. A practical timing algorithm, referred to as the equilibrium algorithm, is then developed to approximate the optimal timing decision. Compared with existing schemes in the literature, the proposed approach does not rely on explicit detection of individual channel paths or the delay spread boundary and therefore greatly reduces timing complexity. The equilibrium algorithm performs nearly as well as the optimal solution over a variety of channel delay spreads, is simple to implement, and is robust to channel estimation errors.

The wide choices of deployment parameters in next generation wireless communication systems, such as flexible bandwidth allocation, synchronous/asynchronous modes, FDD/TDD, full/half duplex, and configurable cyclic prefix duration, etc., present significant challenges in preamble and system acquisition design. This paper addresses these challenges, as well as the solutions provided by the 3GPP2 Ultra Mobile Broadband (UMB) standard. The proposed preamble design facilitates the maximal flexibility of the system configuration and yet has low overhead, low acquisition latency, and low complexity. Although the design is discussed under the UMB context, it also serves as a preamble design paradigm for an OFDMA communication system in general.

In this paper, we describe a CRC-based optimal blind transport format determination algorithm with application to the UMTS uplink. The derivation, based on MAP detection, is presented and simulation results under both AWGN and fading channel conditions are given.

In digital communication systems, variable-length Walsh codes, also called orthogonal variable spreading factor codes, are used to support simultaneous variable-rate data transmission. This paper describes algorithms for dynamic variable- length Walsh code assignment. Optimal assignment criterion is derived and an efficient data structure for maintaining variable- length Walsh codes is introduced. The benefits of code reallocation are also studied. Performance is evaluated via simulation.

The channel code bit log likelihood ratio (LLR) for soft decision decoding is derived for quadrature amplitude modulated signals (QAM). The effect of imperfect channel knowledge on soft decision decoding performance is studied. Our results indicate this effect increases with channel estimation error and/or QAM modulation level. A metric based on generalized log likelihood ratio (GLLR) is derived for soft decision channel decoding with imperfect channel knowledge. Numerical results show that the GLLR-based metric outperforms the conventional minimum distance decoding metric that does not take into account channel estimation error.