Semantic-aware Vehicular Edge Computing (VEC) has emerged as a novel paradigm to significantly reduce transmission costs and edge resource consumption by offloading extracted task-driven semantic information. However, excessive semantic extraction may impose additional computational workload. In the face of unknown environmental dynamics, the semantic extraction ratio must be jointly designed with task offloading for resource-constrained VEC. To this end, we conceive a multiple-objective (MO) semantic-aware task offloading framework for VEC by jointly optimizing semantic extraction ratio, transmit power and task scheduling strategies aimed at minimizing both long-term age-of-information (AoI) and energy consumption while guaranteeing queue stability. Subsequently, we propose a Lyapunov-guided multi-objective reinforcement learning (MORL)-based semantic-aware joint task scheduling and resource allocation (SJTSRA) solution. Specifically, Lyapunov optimization method is first leveraged to transform the original problem into a multi-objective Markov decision process (MOMDP). Then, the concave-augmented Pareto Q-learning (CAPQL) algorithm is employed to find Pareto optimal solutions through introducing uniform weight sampling and entropy regularization, where the convergence can be guaranteed theoretically. Simulation results show that the proposed solution achieves the closest approximation to the Pareto front with the highest hypervolume, and superior energy-AoI trade-offs across varying environment parameters compared to all benchmarks.

Frame slotted ALOHA (FSA) has been thede factomultiple-access protocol for many energy-efficient Internet of Things applications. To improve the age of information (AoI) that measures the freshness of the status update, we devote this paper to designing an age-threshold FSA protocol that adaptively limits the contention in each frame to users with age gains as high as possible, by focusing on a multiple-packet reception (MPR) physical layer model of practical importance for the first time. For an ideal scenario where the coordinator always knows the exact age gain of each user, we propose a low-complexity algorithm to approximate the optimal age gain threshold and frame length for maximizing the expected slot-average AoI reduction within the upcoming frame, which provides a design clue for other scenarios. With this clue, for a practical scenario where the coordinator has to estimate the age gains based on its feasible observations, we design a Bayesian method to update individual distributions of the local ages of all the users based on both the channel statuses and the AoI of each user, and propose an algorithm to approximate optimal access parameters based on these distributions. We also evaluate the computational complexity of the proposed practical scheme and discuss how to generalize it to consider random channel errors. Numerical experiments show that our proposed practical scheme outperforms state-of-the-art schemes for a wide range of MPR configurations.

We focus on the Age of Information (AoI) performance in a system where each user generates packets periodically to send to a common access point (AP) for status updating. To avoid heavy overhead, we assume that channel sensing, feedback information from the AP, and time synchronization are not available in the system. We adopt a multi-access scheme called the sequence scheme, where each user is assigned a periodic binary sequence to schedule their transmissions. In our previous work, we have thoroughly studied the AoI performance under the sequence scheme when the sequence period,L, is equal to the status generating period,T. However, the case ofT̸=Lis not covered by the previous work. Therefore, in this paper, we aim at analyzing the AoI performance for general values ofTandL, which is more challenging and requires different approaches. We conduct in-depth analysis and develop a mathematical tool based on integer partitions to facilitate the analysis. We derive low-complexity closed-form expressions for two special cases. Based on the obtained analytical results, we propose an optimization method for parameter selection in sequence construction to minimize the average AoI. Finally, we compare our proposed sequence scheme with two commonly used baselines, and show that our proposed scheme outperforms the baselines in terms of AoI performance while consuming less energy.

The Unmanned Aerial Vehicles (UAVs) communication faces challenges arising from scarce spectrum resources and malicious jamming. This paper proposes a spectrum waterfall (SW)-assisted multi-agent anti-jamming framework for UAV swarms by designing joint resource allocation and trajectory optimization (JRATO) strategies. By formulating the problem as a decentralized partially observable parameterized-action Markov Decision Process (Dec-POPAMDP), we first employ a self-attention-based convolutional neural network (CNN) to extract spatiotemporal SW knowledge, and then propose a multiagent hybrid Proximal Policy Optimization (MA-HPPO) based anti-jamming scheme to maximize the long-term utility-cost trade-off. Simulation results show that the proposed scheme outperforms the benchmarks in terms of both the convergence and the long-term utility-cost trade-off, while achieving higher success rate with lower energy consumption with varying numbers of channels.

Intelligent reflective surface (IRS) is an emerging technology that can significantly improve the performance of visible light communication (VLC) systems by adding digitally controllable reflection links. In this paper, we focus on the IRS-aided multiple-input multiple-output (MIMO) VLC systems integrating with tunable meta-surface material, the reflection coefficient of which is tunable. To minimize the mean square error (MSE) between the transmit signal and the received signal, a joint optimization problem of the IRS association, the IRS reflection coefficients and the power allocation is formulated based on the singular value decomposition (SVD) precoding scheme. To tackle this non-convex optimization problem, a deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) algorithm is applied based on the corresponding Markov decision process (MDP) model. The numerical results show the convergence and effectiveness of the proposed algorithm. Moreover, the joint optimization outperforms the benchmarks optimizing fewer variables and the AO algorithm benchmark.

This paper considers utilizing the knowledge of age gains to reduce the average age of information (AoI) in random access with event-driven periodic updating for the first time. Built on the form of slotted ALOHA, we require each device to determine its age gain threshold and transmission probability in an easily implementable decentralized manner, so that the contention can be limited to devices with age gains as high as possible. For the basic case that each device utilizes its knowledge of age gain of only itself, we provide an analytical modeling by a multi-layer discrete-time Markov chains (DTMCs), where an external DTMC manages the jumps between the beginnings of frames and an internal DTMC manages the evolution during an arbitrary frame, for obtaining optimal fixed access parameters offline. For the enhanced case that each device utilizes its knowledge of age gains of all the devices, we require each device to adjust its access parameters for maximizing the estimated network expected AoI reduction per slot, through maintaining the a posteriori joint probability distribution of local age and age gain of an arbitrary device in a Bayesian manner. Numerical results validate our study and demonstrate the advantage of the proposed schemes over other schemes.

A conflict-avoiding code (CAC) is a deterministic transmission scheme for asynchronous multiple access without feedback. When the number of simultaneously active users is less than or equal tow, a CAC of lengthLwith weightwcan provide a hard guarantee that each active user has at least one successful transmission within every consecutiveLslots. In this paper, we generalize some previously known constructions of constant-weight CACs, and then derive several classes of optimal CACs by the help of Kneser’s Theorem and some techniques in Additive Combinatorics. Another spotlight of this paper is to relax the identical-weight constraint in prior studies to study mixed-weight CACs for the first time, for the purpose of increasing the throughput and reducing the access delay of some potential users with higher priority. As applications of those obtained optimal CACs, we derive some classes of optimal mixed-weight CACs.

Vehicular edge computing (VEC) has driven the proliferation of computation-intensive and delay-sensitive vehicular services by deploying computing and energy resources at the edge. However, the exploitation of edge resources faces challenges due to unpredictable environmental dynamics and partial observability. To this end, this paper investigates the computing energy efficiency (CEE) problem in twin-timescale VEC scenarios by dynamically adjusting the offloading policy. Based upon modeling the problem as a decentralized partially observable Markov decision process (Dec-POMDP), a pair of non-cooperative and cooperative offloading solutions are proposed relying on multi-agent reinforcement learning (MARL), respectively. Specifically, the non-cooperative solution employs multi-agent independent proximal policy optimization (IPPO) to enable vehicular user equipments (VUEs) to learn their policies in a fully distributed manner without any information sharing. By contrast, the cooperative solution combines the multi-agent shared PPO with graph attention networks (MAPPO-GAT), where the relationship among agents is learned cooperatively and the historical learning experience is shared. Additionally, we compare the computational complexity and analyze the convergence. Simulation results show that in terms of the trade-off between offloading delay and offloading energy consumption, the proposed cooperative solution is superior to the non-cooperative counterpart with the cost of moderate training overhead for cooperative learning.

A conflict-avoiding code (CAC) is a deterministic transmission scheme for asynchronous multiple access without feedback. When the number of simultaneously active users is less than or equal to$w$, a CAC of length$L$with weight$w$can provide a hard guarantee that each active user has at least one successful transmission within every consecutive$L$slots. To deal with different individual performance requirements in heterogeneous systems, in this paper, we relax the identical-weight constraint in prior studies to study mixed-weight CACs for the first time. We first derive a new class of optimal CACs with constant weights, and then propose a general construction of mixed-weight CACs consisting of three different weights. Finally, we obtain a class of optimal mixed-weight CACs containing two different weights by the help of Kneser's Theorem and some techniques in Additive Combinatorics.

This paper focuses on protocol sequences for age of information (AoI) in a multiple-packet reception (MPR) channel without feedback and synchronization. Unlike traditional probabilistic schemes, protocol-sequences-based schemes allow each user to deterministically decide when to transmit only according to its assigned sequence. When the MPR capability$\gamma=2$, we use a previously known construction to generate user-irrepressible (UI) sequences that are favorable for the AoI improvement. Under this construction, by studying the reverse Hamming cross-correlations of the corresponding sequences, which is more complicated than that in prior studies, we provide an analytical approach for evaluating the AoI for$\gamma=2$. When$\gamma > 2$, we also propose a new construction to produce UI sequences for the AoI improvement. Simulation results show that the proposed schemes outperform slotted ALOHA in terms of average AoI and worst-case AoI for various settings.

We consider the tracking problem of an object that can randomly appear on a line of a fixed length. We want to determine the position of the object and rely on sensors that can detect objects with a predefined range and report that to a remote observer. Sensors are equipped with a transmitter that can transmit at a limited data rate. Once a sensor is triggered, it transmits its own ID to inform. A straightforward protocol is to label each sensor with different ID. However, this would require a large number of unique IDs and many bits to represent. As a result, higher data rate is required. We propose a newly defined protocol using multiset color coding with optimal design or efficiency in reusing a much smaller number of IDs for the whole system. We only require the minimal number of bits for labeling each sensor. We derive the factor of reduction and show its significance. We present some optimal constructions for the required multiset color coding sequence. Besides, we derive the general upper and lower bounds for the maximum length (size) of the system. Numerical examples have also demonstrated the effectiveness and improvement by the proposed new method.

In the face of increasingly computing-intensive and delay-sensitive vehicular applications, vehicular edge computing (VEC) has become a promising computing paradigm by deploying computing resources at the edge. This paper investigates an age of information (AoI)-aware vehicular edge offloading problem by dynamically adjusting the edge offloading ratio and selecting the VEC server, taking into account the computing energy efficiency (CEE). To adapt to the time-varying network topology of VEC, we propose a multi-agent cooperative edge offloading solution relying on actor-attention-critic framework, where each vehicular user equipment (VUE) employs an attention mechanism to regulate its attention to other VUEs, facilitating selective focus on important information to enhance policy learning. The simulation results show that the proposed solution can achieve a more compelling trade-off between AoI and CEE compared with the baseline solutions.

In this correspondence, a corrected version of the upper bound on the number of codewords for a multichannel CAC of weight three is presented.

This paper considers designing an optimal policy for deadline-constrained access in cognitive radio networks, where a secondary user needs to complete a packet transmission over the vacant spectrum within a delivery deadline. To minimize the total access cost, it is desirable to design an optimal opportunistic access policy by utilizing channel dynamics and sensing outcomes. We take non-negligible switching overheads, a state-dependent overtime penalty, and practical switching operations into consideration in the Markov decision process formulation of such an access problem under wide-band sensing. Moreover, we establish the existence of monotone optimal decision rules to reduce the complexity of computing an optimal policy. Simulation results verify our theoretical studies and the cost advantage over other policies.

As the ultra-reliability and low-latency are essential requirements for grant-free access, in this paper we consider Chinese reminder theorem (CRT) based sequences, which are binary and periodic sequences used for deterministic multiple- access without feedback. Some CRT-based sequences are proved to have user-irrepressible (UI) property, which means they are able to provide a hard guarantee that each user has a successful transmission within a fixed period of time. In this paper, we provide a general sufficient condition of constant weight CRT-based sequence sets being UI, show the obtained sufficient condition is necessary in some cases, and characterize the condition when the best access delay performance occurs under CRT structure by numerical studies. We also provide an example to claim that our approach is a potential way to find UI sequences with a shorter common period. Finally, the reliability issue is concerned in the case when the UI property is not guaranteed.

This paper considers a system in which multiple users send periodically generated status information to a common access point (AP) over a collision channel. To avoid high overhead, there is no time synchronization and no feedback information from the AP to indicate whether a transmission is successful or not. The performance metric that we focus on is the age-of-information (AoI), which represents the freshness of the status information received at the AP. For this model, we propose a sequence based MAC scheme in which each user is pre-assigned a periodic sequence to schedule transmissions. This scheme guarantees each user at least one successful packet transmission within a sequence period, in the absence of time synchronization and feedback information from the AP. To the best of our knowledge, this is the first study investigating AoI performance under a sequence based MAC scheme. We derive the closed-form expressions for average AoI, average peak AoI and average age penalty under the sequence based scheduling. Besides, we derive several critical properties of the sequences to optimize the AoI performance. Comparison results show that our proposed sequence scheme outperforms slotted ALOHA and framed ALOHA in various settings.

Designing efficient random access is a vital problem for urgency-constrained packet delivery in uplink Internet of Things (IoT), which has not been investigated in depth so far. In this paper, we focus on unpredictable frame-synchronized traffic, which captures a number of scenarios in IoT communications, and generalize prior studies on this issue by considering a general ALOHA-like protocol, a general single-packet reception (SPR) channel, urgency-dependent throughput (UDT) based on a general urgency function, and the dynamic programming optimality. With a complete knowledge of the number of active users, we use the theory of Markov Decision Process (MDP) to explicitly obtain optimal policies for maximizing the UDT, and prove that a myopic policy is in general optimal. With an incomplete knowledge of the number of active users, we use the theory of Partially Observable MDP (POMDP) to seek optimal policies, and show that a myopic policy is in general not optimal by presenting a counterexample. Because of the prohibitive complexity to obtain optimal or near-optimal policies for this case, we propose two practical policies that utilize the inherent property of our MDP framework and channel model. Simulation results show that both outperform other alternatives. The robustness under relaxed system settings is also examined.

In this paper, we investigate the energy-efficiency (EE) problem in edge association for heterogeneous Internet of Vehicles (IoV), when the dynamic environmental information can not be known in advance. Aiming to maximize the long-term tradeoff between EE and handover (HO) overhead, we propose a cooperative multi-agent edge association solution, where vehicular user equipments (VUEs) make decisions cooperatively relying on their local observations under centralized training. Specifically, we first construct a multi-agent partially observable Markov decision process (MA-POMDP) problem and decompose the system value function into the local value functions for implicit individual learning. Next, through sharing learning experience and approximating the global state, each VUE is able to obtain its own optimal/suboptimal policy given its local observations and historical information. Simulation results show that the proposed solution outperforms the non-cooperative counterpart and other baselines in terms of improving EE with the most appropriate number of HOs.

Vehicular edge computing (VEC) has become a promising enabler for ultrareliable and low-latency communications (URLLC) vehicular networks by providing computational resources for task offloading. In this article, we investigate an online task offloading problem for heterogeneous VEC (HVEC) network in the face of unknown environment dynamics. To overcome the unavailability of state information, we aim for minimizing the expectation of total offloading energy consumption while satisfying stringent delay requirements by learning the relationship between historical observations and rewards. Hence, this problem constitutes a contextual multiarmed bandit (MAB) problem. By grouping users according to their task preferences, we propose a contextual clustering of bandits-based online vehicular task offloading (CBTO) solution, which is aware of the task popularity. Simulation results reveal that the proposed solution outperforms other contextual and context-free benchmarkers in terms of both offloading energy consumption and delay performance.

In Internet of Vehicles (IoV), information freshness is a significant indicator to indemnify road traffic safety, which is measured by Age of Information (AoI). In this article, we consider the coexistence scenario of vehicular user pairs and cellular users, where the base station (BS) acts as an agent to allocate channels and transmit power for vehicular user pairs. With the goal of minimizing the sum of the average AoI of all links and the average power consumption of all vehicular user pairs, we formulate this optimization problem as a discrete-time Markov decision process (MDP) problem and adopt the trust region policy optimization (TRPO) algorithm, which has the advantage of fast convergence and high stability. Then, an AoI-aware joint spectrum and power dynamic allocation scheme based on the TRPO algorithm is proposed. Simulation results show that the TRPO-based scheme significantly outperforms both the deep$Q$network (DQN)-based scheme and the random scheme in terms of average cumulative reward, convergence speed, and stability.

In this article, we investigate the anti-jamming problem with joint channel and power allocation for unmanned aerial vehicle (UAV) networks. In particular, we focus on avoiding both mutual interference among UAVs and external malicious jamming to maximize the system Quality of Experience (QoE) relevant to the power consumption. To simultaneously capture the competition and coordination among UAVs, we first model the problem as a local interaction Markov game and then prove it as an exact potential game with at least one Nash equilibrium. Next, we propose a collaborative multiagent layered Q learning (MALQL)-based anti-jamming communication algorithm to reduce the high dimensionality of the action space and analyze the asymptotic convergence of the proposed algorithm. Simulation results show the effectiveness of the proposed algorithm, which outperforms the traditional multiagent$Q$learning algorithm when suffering from different jamming strategies.

We formulate and investigate the problem of distributed channel rendezvous in collision-prone wireless networks. Existing researches on this topic are mainly devoted to designing channel hopping sequences, each pair of which can overlap on a common channel within bounded delay. However, this overlap-based canonical rendezvous design does not take into account channel collision, which may render existing rendezvous algorithms fail to achieve bounded delay in collision-prone environment. Motivated by this observation, we formulate and investigate the collision-aware channel rendezvous problem in a generic scenario, where a collision occurs if more than$C$packets overlap in time on a same channel. Our generic formulation allows to model both the baseline single packet reception model with$C=1$and the more sophisticated multiple packet reception model with$C > 1$. We further abstract the collision-aware rendezvous problem as the problem of constructing a robust rendezvous system. We establish the theoretical limit of the problem, guided by which we design a collision-resilient distributed rendezvous algorithm with truly bounded rendezvous delay. We then demonstrate the performance of our rendezvous algorithm both analytically and numerically.

A multichannel conflict-avoiding code of length$L$and weight$w$for$M$orthogonal channels, denoted by MC-CAC(M, L, w), is used for deterministic multiple-access without feedback. When the number of simultaneously active users is less than or equal to w, an MC-CAC(M, L, w) is able to provide a hard guarantee that each active user has a successful transmission within every consecutive$L$time slots. An upper bound on the number of potential users, or codewords, that an MC-CAC(2, L, 3) can support is derived in this paper. By means of (hooked) Skolem sequences, we propose a new construction that can provide a series of optimal MC-CAC(2, L, 3)s in the sense that the code sizes achieve the obtained upper bounds.

Conflict-avoiding codes (CACs) were introduced by Levenshtein as a single-channel transmission scheme for a multiple-access collision channel without feedback. When the number of simultaneously active source nodes is less than or equal to the weight of a CAC, it is able to provide a hard guarantee that each active source node transmits at least one packet successfully within a fixed time duration, no matter what the relative time offsets between the source nodes are. In this article, we extend CACs to multichannel CACs for providing such a hard guarantee over multiple orthogonal channels. Upper bounds on the number of codewords for multichannel CACs of weights three and four are derived, and constructions that are optimal with respect to these bounds are presented.

In this paper, we consider a multiuser uplink status update system, where a monitor aims to timely collect randomly generated status updates from multiple end nodes through a shared wireless channel. We adopt the recently proposed metric, termed age of information (AoI), to quantify the information timeliness and freshness. Due to the random generation of the status updates at the end node side, the monitor only grasps a partial knowledge of the status update arrivals. Under such a practical scenario, we aim to address a fundamental multiuser scheduling problem: how to schedule the end nodes to minimize the network-wide AoI? To solve this problem, we formulate it as a partially observable Markov decision process (POMDP), and develop a dynamic programming (DP) algorithm to obtain the optimal scheduling policy. By noting that the optimal policy is computationally prohibitive, we further design a low-complexity myopic policy that only minimizes the one-step expected reward. Simulation results show that the performance of the myopic policy approaches that of the optimal policy and is superior to that of the baseline policy.

The collision channel without feedback (CCw/oFB) introduced by Massey and Mathys, depicts a scenario where multiple users share a communication channel but have arbitrary time offsets, and can never learn these time offsets due to the lack of feedback. This paper considers an extension of the CCw/oFB, which allows the receiver to use successive interference cancellation (SIC) to cancel the interference caused by those collided packets whose contents have been known by the receiver. We derive the zero-packet-loss throughput regions of this model for both the unsynchronized and slot-synchronized cases. Given an arbitrary number of users and a packet alphabet of arbitrary size, it is shown that these two regions coincide, and the outer boundary of this common region is the set of all points with only nonnegative components that add up to one. It is further shown that all points on this outer boundary with only rational components can be achieved without packet loss in the slot-synchronized case. The constructive proofs are based on a joint design of protocol sequences, identification/location algorithm and erasure correcting codes. These findings indicate that the negative impact of the lack of time synchronization on the throughput performance can be removed by the help of SIC.

In this paper, the problem of content caching in 5G cellular networks relying on social-aware device-to-device communications (DTD) is investigated. Our focus is on how to efficiently select important users (IUs) and how to allocate content files to the storage of these selected IUs to form a distributed caching system. We aim at proposing a novel approach for minimizing the downloading latency and maximizing the social welfare simultaneously. In particular, we first model the problem of maximizing the social welfare as a many-to-one matching game based on the social property of mobile users. We study this game by exploiting users' social properties to generate the utility functions of the two-side players, i.e., content providers (CPs) and IUs. Then we model the problem of minimizing the downloading latency as a many-to-many matching problem. For solving these games, we design a many-to-one IU selection (MOIS) matching algorithm and a many-to-many file allocation (MMFA) matching algorithm, respectively. Simulation and analytical results show that the proposed mechanisms are stable, and are capable of offering a better performance than other benchmarks in terms of social welfare and network downloading latency.

This paper considers a multiple-access system with multiple-packet reception (MPR) capability γ, i.e., a packet can be successfully received as long as it overlaps with γ -1 or fewer other packets at any instant during its lifetime. To efficiently utilize the MPR capability, this paper generalizes p-persistent carrier-sense multiple access (CSMA) to consider that a user with carrier sensing capability c adopts the transmission probability p, if this user has sensed n ongoing transmissions for n = 0, 1,⋯, c - 1. This paper aims to model the characteristics of such CSMA and to design transmission probabilities for achieving maximum saturation throughput. To this end, we first formulate such CSMA as a parameterized Markov decision process (MDP) and use the long-run average performance to evaluate the saturation throughput. Second, by observing that the exact values of optimal transmission probabilities are in general infeasible to find, we modify this MDP to establish an upper bound on the maximum throughput, and modify this MDP again to propose a heuristic design with near-optimal performance. Simulations with respect to a wide range of configurations are provided to validate our study. The throughput performance under more general models and the robustness of our design are also investigated.

Protocol sequences are deterministic binary sequences of a common period, which enjoy some special Hamming cross-correlation property by design. In contrast to random or contention-based medium access control schemes, a protocol sequence-based scheme can serve to provide at least a certain number of contention-free packet transmissions within a bounded delay for each asynchronous user in a feedback-free multiple access system. However, all protocol sequence-based schemes in the literature require that all sequence entries are mapped to slots with the same time duration, which produces a relatively low channel utilization. To overcome this inefficiency that is undesirable in delay-constrained wireless sensor networks, building on the idea of combining sequence-based access and carrier sensing, this paper proposes a new protocol sequence-based scheme, called the PS-CS. We derive the theoretical average throughput, average access delay, worst-case delay, and average energy consumption of the PS-CS. It is shown that the PS-CS produces average throughput close to the optimal capacity of p-persistent carrier sense multiple access (CSMA), and enjoys smaller access delay than the optimal p-persistent CSMA. In addition, we study the energy-delay tradeoff, impact of carrier sensing fault and channel error, and how to modify the PS-CS to support real-time downlink for feedback control.

A large-scale fully digital receive antenna array can provide very high-resolution direction of arrival (DOA) estimation, but resulting in a significantly high RF-chain circuit cost. Thus, a hybrid analog and digital (HAD) structure is preferred. Two phase alignment (PA) methods, HAD PA (HADPA) and hybrid digital and analog PA (HDAPA), are proposed to estimate DOA based on the parametric method. Compared to analog PA (APA), they can significantly reduce the complexity in the PA phases. Subsequently, a fast root multiple signal classification HDAPA (root-MUSIC-HDAPA) method is proposed specially for this hybrid structure to implement an approximately analytical solution. Due to the HAD structure, there exists the effect of direction-finding ambiguity. A smart strategy of maximizing the average receive power is adopted to delete those spurious solutions and preserve the true optimal solution by linear searching over a set of limited finite candidate directions. This results in a significant reduction in computational complexity. Eventually, the Cramer-Rao lower bound (CRLB) of finding emitter direction using the HAD structure is derived. Simulation results show that our proposed methods, root-MUSIC-HDAPA and HDAPA, can achieve the hybrid CRLB with their complexities being significantly lower than those of pure linear searching-based methods, such as APA.

Protocol sequences are periodic zero-one sequences for the scheduling of packet transmissions in a time-slotted channel. A special class of protocol sequences, called shift-invariant sequences, plays a key role in achieving the information-theoretic capacity of the collision channel without feedback. This class of shift-invariant protocol sequences has the property that the pairwise Hamming crosscorrelation functions are invariant to relative delay offsets. However, the common period of shift-invariant sequences grows exponentially as a function of the number of supported users. In this paper, we consider a family of protocol sequences, whose period increases roughly as a quadratic function of the number of the users, and show that it is close to shift-invariant by establishing a bound on the pairwise Hamming crosscorrelation. The construction is based on the Chinese remainder theorem (CRT), and hence the constructed sequences are called CRT sequences. Applications to collision channel allowing successive interference cancellation at the receiver are discussed.

The collision channel without feedback (CCw/oFB) model depicts a scenario in which multiple users share a communication channel with random relative time offsets among their clocks. This paper considers an extension of this model, which allows the receiver to use successive interference cancellation (SIC) to iteratively cancel the interference caused by those collided packets that have been decoded by the receiver. We derive the zero-error capacity region of this channel in the slot-synchronous case, and present a zero-error capacity achieving scheme by joint protocol sequences and channel coding design. It is shown that the negative impact on the zero-error capacity due to a lack of time synchronization can be removed by SIC.

Data centers play an important role in today's Internet development. Research to find scalable architecture and efficient routing algorithms for data center networks has gained popularity. The fat-tree architecture, which is essentially a folded version of a Clos network, has proved to be readily implementable and is scalable. In this paper, we investigate routing on a fat-tree network by deriving its global packing number and by presenting explicit algorithms for the construction of optimal, load-balanced routing solutions. Consider an optical network that employs wavelength division multiplexing in which every user node sets up a connection with every other user node. The global packing number is basically the number of wavelengths required by the network to support such a traffic load, under the restriction that each source-to-destination connection is assigned a wavelength that remains constant in the network. In mathematical terms, consider a bidirectional, simple graph, G and let N ⊆ V(G) be a set of nodes. A path system P of G with respect to N consists of |N|(|N| -1) directed paths, one path to connect each of the source-destination node pairs in N. The global packing number of a path system P, denoted by Φ(G, N, P), is the minimum integer k to guarantee the existence of a mapping φ : P → (1, 2, ..., k), such that φ(P) ≠ φ(P̅) if P and P̅ have common arc(s). The global packing number of (G, N), denoted by Φ(G, N), is defined to be the minimum Φ(G, N, P) among all possible path systems ?. In additional to wavelength division optical networks, this number also carries significance for networks employing time division multiple access. In this paper, we compute by explicit route construction the global packing number of (Tn, N), where Tndenotes the topology of the n-ary fat-tree network, and N is considered to be the set of all edge switches or the set of all supported hosts. We show that the constructed routes are load-balanced and require minimal link capacity at all network links.

Consider a time-slotted communication channel that is shared by K active users transmitting to a single receiver. It is assumed that the receiver has the ability of the multiple-packet reception to correctly receive up to γ (1 ≤ γ <; K) simultaneously transmitted packets. Each user accesses the channel following a deterministic binary sequence, called the protocol sequence, and transmits a packet within a channel slot if the sequence value is equal to one. If the users are not time synchronized, the relative shifts among them can cause significant fluctuation in throughput. If the throughput of each user is independent of relative shifts, then the adopted protocol sequence set is said to be throughput-invariant (TI). If we define worst-case system throughput as the minimal system throughput that can be guaranteed for any set of relative shifts, then TI sequences maximize it and hence are of fundamental interest. This paper investigates TI sequences for γ ≥ 1. Several new results are obtained including throughput value as a function of the duty factors, a lower bound on the sequence period, a construction that achieves the lower bound on the sequence period, and theorems on the intrinsic structure that establish connections with some other families of binary sequences.

We consider the slot-synchronized collision channel without feedback, in which K active users all transmit their packets to one sink. It is assumed that the channel has the ability of the multiple-packet reception (MPR), i.e., can accommodate at most γ (1 ≤ γ1. For both design objective, we establish a lower bound on sequence period and prove the lower bound can be achieved by some construction.

In this paper we investigate the identification and synchronization problems on a multiple access collision channel. Following Massey's lead, solutions to these problems are addressed by protocol sequences. This paper considers two different levels of user synchroneity: frame-synchronous access and slot-synchronous access. For the identification problem, we study user-detectable sequences. These are sequences with the cross-correlation property that allows each active user be detected within a bounded delay basing only on the channel activity information observed. Furthermore, we investigate the synchronization problem for delay-detectable sequences under the slot-synchronous access assumption. The goal of the synchronization problem is to determine the offset relations among all the active users. Sequences that allow such determination can be viewed as a special subset of user-detectable sequences. For both of these sequence families, it is desirable that the sequence length should be as short as possible. Hence, it is important to derive the minimum sequence lengths for these respective families. This is an extremely difficult open problem. Nevertheless, lower and upper bounds on these minimum lengths are presented in this paper under different levels of synchroneity assumptions. In addition, the performance of these sequences is demonstrated via numerical simulation.

Strongly conflict-avoiding codes (SCACs) are used in the slot-asynchronous multiple-access collision channel without feedback to guarantee that each active user can send at least one packet successfully in the worst case within a fixed period of time. The number of codewords in an SCAC is the number of potential users that can be supported. In this paper, a general upper bound on the size of SCAC is derived. We further improve the upper bound if the code has some special structure, called equi-difference, and we show this bound is asymptotically tight.

The limited bandwidth and the finite battery power of mobile devices represent one of the greatest limitations of current WPANs. In this paper, an analytical model for the energy efficiency of the IEEE 802.15.4 slotted CSMA/CA which can be studied as slotted non-persistent CSMA was proposed and validated. Key to the accuracy of the model is a careful study of the idle period after last collision, which has been studied by Zhang et al. (2006). Specifically, a distributed mechanism to improve the energy efficiency for contention control in IEEE 802.15.4 was also present and evaluated and show that power saving and throughput maximization can be jointly achieved. Simulation results indicate that our mechanism is very effective and robust. This mechanism can be used to extend the standard 802.15.4 access mechanism without requiring any additional hardware.