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Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. In this paper, we address challenges of edge-enabled AIGC service provisioning, which remain underexplored in the literature. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We subsequently introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics. We obtain mathematical relationships of these metrics with the computational resources required by GenAI models via experimentation. Afterward, we decompose the formulation into a model caching subproblem on a long-timescale and a resource allocation subproblem on a short-timescale. Since the variables to be solved are discrete and continuous, respectively, we leverage a double deep Q-network (DDQN) algorithm to solve the former subproblem and propose a diffusion-based deep deterministic policy gradient (D3PG) algorithm to solve the latter. The proposed D3PG algorithm makes an innovative use of diffusion models as the actor network to determine optimal resource allocation decisions. Consequently, we integrate these two learning methods within the overarching two-timescale deep reinforcement learning (T2DRL) algorithm, the performance of which is studied through comparative numerical simulations., Comment: 14 pages, 8 figures, 39 references

Recently, semantic communication (SC) has been regarded as one of the potential paradigms of 6G. Current SC frameworks require channel state information (CSI) to handle severe signal distortion induced by channel fading. Since the channel estimation overhead for obtaining CSI cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn't require CSI. In Ti-GSC, two main modules, i.e., an autoencoder-based encoder-decoder module (AEDM) and a GAN-based signal distortion suppression module (GSDSM) are included where AEDM first encodes the data at the source before transmission, and then GSDSM suppresses the distortion of the received signals in both syntactic and semantic dimensions at the destination. At last, AEDM decodes the distortion-suppressed signal at the destination. To measure signal distortion, syntactic distortion and semantic distortion terms are newly added to the total loss function. To achieve better training results, joint optimization-based training (JOT) and alternating optimization-based training (AOT) are designed for the proposed Ti-GSC. Experimental results show that JOT is more efficient for Ti-GSC. Moreover, without CSI, bilingual evaluation understudy (BLEU) score achieved by Ti-GSC is about 40% and 62% higher than that achieved by existing SC frameworks in Rician and Rayleigh fading, respectively. (*Due to the notification of arXiv "The Abstract field cannot be longer than 1,920 characters", the appeared Abstract is shortened. For the full Abstract, please download the Article.)

Radio frequency fingerprinting (RFF) is a promising device authentication technique for securing the Internet of things. It exploits the intrinsic and unique hardware impairments of the transmitters for RF device identification. In real-world communication systems, hardware impairments across transmitters are subtle, which are difficult to model explicitly. Recently, due to the superior performance of deep learning (DL)-based classification models on real-world datasets, DL networks have been explored for RFF. Most existing DL-based RFF models use a single representation of radio signals as the input. Multi-channel input model can leverage information from different representations of radio signals and improve the identification accuracy of the RF fingerprint. In this work, we propose a novel multi-channel attentive feature fusion (McAFF) method for RFF. It utilizes multi-channel neural features extracted from multiple representations of radio signals, including IQ samples, carrier frequency offset, fast Fourier transform coefficients and short-time Fourier transform coefficients, for better RF fingerprint identification. The features extracted from different channels are fused adaptively using a shared attention module, where the weights of neural features from multiple channels are learned during training the McAFF model. In addition, we design a signal identification module using a convolution-based ResNeXt block to map the fused features to device identities. To evaluate the identification performance of the proposed method, we construct a WiFi dataset, named WFDI, using commercial WiFi end-devices as the transmitters and a Universal Software Radio Peripheral (USRP) as the receiver. ...

With the rapid development of Internet-of-Things (IoT) technology and machine-type communications, various emerging applications appear in industrial productions and our daily lives. Among these, applications like industrial sensing and controlling, remote surgery, and automatic driving require an extremely low latency and a very small jitter. Delivering information deterministically has become one of the the biggest challenges for modern wire-line and wireless communications. In this paper, we present a review of currently available wire-line deterministic networks and discuss the main challenges to build wireless deterministic networks. We also discuss and propose several potential techniques enabling wireless networks to provide deterministic communications. By elaborating the coding/modulation schemes of the physical layer and managing the channel-access/packet-scheduling at the media access control (MAC) layer, it is believed that wireless deterministic communications can be realized in the near future., Comment: 9 pages, 5 figures. Accepted for publish in IEEE Internet of Things Magazine

We consider an Internet-of-Things (IoT) system in which an energy harvesting powered sensor node monitors the phenomenon of interest and transmits its observations to a remote monitor over a Gaussian channel. We measure the timeliness of the signals recovered by the monitor using age of information (AoI), which could be reduced by transmitting more observations to the monitor. We evaluate the corresponding distortion with the mean-squared error (MSE) metric, which would be reduced if a larger transmit power and a larger source coding rate were used. Since the energy harvested by the sensor node is random and limited, however, the timeliness and the distortion of the received signals cannot be optimized at the same time. Thus, we shall investigate the timeliness-distortion trade-off of the system by minimizing the average weighted-sum AoI and distortion over all possible transmit powers and transmission intervals. First, we explicitly present the optimal transmit powers for the performance limit achieving save-and-transmit policy and the easy-implementing fixed power transmission policy. Second, we propose a backward water-filling based offline power allocation algorithm and a genetic based offline algorithm to jointly optimize the transmission interval and transmit power. Third, we formulate the online power control as an Markov Decision Process (MDP) and solve the problem with an iterative algorithm, which closely approach the trade-off limit of the system. Also, we show that the optimal transmit power is a monotonic and bi-valued function of current AoI and distortion. Finally, we present our results via numerical simulations and extend results on the save-and-transmit policy to fading sensing systems., Comment: 14 pages, 10 figures, submitted to IEEE Journals

We consider an Internet of Things (IoT) system in which a sensor delivers updates to a monitor with exponential service time and first-come-first-served (FCFS) discipline. We investigate the freshness of the received updates and propose a new metric termed as \textit{Age upon Decisions (AuD)}, which is defined as the time elapsed from the generation of each update to the epoch it is used to make decisions (e.g., estimations, inferences, controls). Within this framework, we aim at improving the freshness of updates at decision epochs by scheduling the update arrival process and the decision making process. Theoretical results show that 1) when the decisions are made according to a Poisson process, the average AuD is independent of decision rate and would be minimized if the arrival process is periodic (i.e., deterministic); 2) when both the decision process and the arrive process are periodic, the average AuD is larger than, but decreases with decision rate to, the average AuD of the corresponding system with Poisson decisions (i.e., random); 3) when both the decision process and the arrive process are periodic, the average AuD can be further decreased by optimally controlling the offset between the two processes. For practical IoT systems, therefore, it is suggested to employ periodic arrival processes and random decision processes. Nevertheless, making periodical updates and decisions with properly controlled offset also is a promising solution if the timing information of the two processes can be accessed by the monitor., Comment: 15 pages, 8 figs, IEEE IoTJ

This paper investigates a fog computing-assisted multi-user simultaneous wireless information and power transfer (SWIPT) network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multi-user scheduling, the time assignment, the sensors' transmit powers and the PS ratios. Since the problem is a mixed integer programming (MIP) problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions and theoretical analysis. For a scheduled sensor, the closed-form and semi-closedform solutions to achieve its minimal required energy are derived, and then an efficient multi-user scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server (FS), the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors., Comment: 18 pages, 29 figures, IEEE Internet of Things Journal

This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with time between two received updates. Due to EH, some waiting time is required at the sensor before transmitting the status update. To find the optimal transmission policy, an optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. As the problem is non-convex and with no known solution, a two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and in the inner loop, a semi-closed-form expression of the optimal waiting time policy is derived based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results shows that our proposed optimal transmission policy outperforms the the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are non-congested. It is also observed that in order to achieve the lower U-AoI, the sensor should transmit the next update without waiting when the network is congested while should wait a moment before transmitting the next update when the network is non-congested. Additionally, it also shows that the system U-AoI first decreases and then keep unchanged with the increments of EH circuit's saturation level and the energy outage probability., Comment: 8

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing network, where a multiple antenna fog function integrated hybrid access point (F-HAP) transfers information and energy to multiple heterogeneous single-antenna sensors and also helps some of them fulfill computing tasks. By jointly optimizing energy and information beamforming designs at the F-HAP, the bandwidth allocation and the computation offloading distribution, an optimization problem is formulated to minimize the required energy under communication and computation requirements, as well as energy harvesting constraints. Two optimal designs, i.e., fixed offloading time (FOT) and optimized offloading time (OOT) designs, are proposed. As both designs get involved in solving non-convex problems, there are no known solutions to them. Therefore, for the FOT design, the semidefinite relaxation (SDR) is adopted to solve it. It is theoretically proved that the rank-one constraints are always satisfied, so the global optimal solution is guaranteed. For the OOT design, since its non-convexity is hard to deal with, a penalty dual decomposition (PDD)-based algorithm is proposed, which is able to achieve a suboptimal solution. The computational complexity for two designs are analyzed. Numerical results show that the partial offloading mode is superior to binary benchmark modes. It is also shown that if the system is with strong enough computing capability, the OOT design is suggested to achieve lower required energy; Otherwise, the FOT design is preferred to achieve a relatively low computation complexity., Comment: Submitted to IEEE conference

High-mobility adaption and massive Multiple-input Multiple-output (MIMO) application are two primary evolving objectives for the next generation high speed train (HST) wireless communication system. In this paper, we consider how to design a location-aware beamforming for the massive MIMO system in the high traffic density HST network. We first analyze the tradeoff between beam directivity and beamwidth, based on which we present the sensitivity analysis of positioning accuracy. Then, in order to guarantee a high efficient transmission, we derive an optimal problem to maximize the beam directivity under the restriction of diverse positioning accuracies. After that, we present a low-complexity beamforming design by utilizing location information, which requires neither eigen-decomposing (ED) the uplink channel covariance matrix (CCM) nor ED the downlink CCM (DCCM). Finally, we study the beamforming scheme in future high traffic density HST network, where a two HSTs encountering scenario is emphasized. By utilizing the real-time location information, we propose an optimal adaptive beamforming scheme to maximize the achievable rate region under limited channel source constraint. Numerical simulation indicates that a massive MIMO system with less than a certain positioning error can guarantee a required performance with satisfying transmission efficiency in the high traffic density HST scenario and the achievable rate region when two HSTs encounter is greatly improved as well., Comment: This paper has been accepted for future publication in IEEE ACCESS. arXiv admin note: substantial text overlap with arXiv:1702.02121

This paper considers a wireless powered communication network (WPCN) with group cooperation, where two communication groups cooperate with each other via wireless power transfer and time sharing to fulfill their expected information delivering and achieve "win-win" collaboration. To explore the system performance limits, we formulate optimization problems to respectively maximize the weighted sum-rate and minimize the total consumed power. The time assignment, beamforming vector and power allocation are jointly optimized under available power and quality of service requirement constraints of both groups. For the WSR-maximization, both fixed and flexible power scenarios are investigated. As all problems are non-convex and have no known solution methods, we solve them by using proper variable substitutions and the semi-definite relaxation. We theoretically prove that our proposed solution method guarantees the global optimum for each problem. Numerical results are presented to show the system performance behaviors, which provide some useful insights for future WPCN design. It shows that in such a group cooperation-aware WPCN, optimal time assignment has the greatest effect on the system performance than other factors., Comment: 13 pages, 14 figures, to appear in IEEE Transactions on Wireless Communications Information Theory (cs.IT)

Memory caches are being aggressively used in today's data-parallel systems such as Spark, Tez, and Piccolo. However, prevalent systems employ rather simple cache management policies--notably the Least Recently Used (LRU) policy--that are oblivious to the application semantics of data dependency, expressed as a directed acyclic graph (DAG). Without this knowledge, memory caching can at best be performed by "guessing" the future data access patterns based on historical information (e.g., the access recency and/or frequency), which frequently results in inefficient, erroneous caching with low hit ratio and a long response time. In this paper, we propose a novel cache replacement policy, Least Reference Count (LRC), which exploits the application-specific DAG information to optimize the cache management. LRC evicts the cached data blocks whose reference count is the smallest. The reference count is defined, for each data block, as the number of dependent child blocks that have not been computed yet. We demonstrate the efficacy of LRC through both empirical analysis and cluster deployments against popular benchmarking workloads. Our Spark implementation shows that, compared with LRU, LRC speeds up typical applications by 60%., Comment: 9 pages

In mobile edge computing systems, mobile devices can offload compute-intensive tasks to a nearby cloudlet,so as to save energy and extend battery life. Unlike a fully-fledged cloud, a cloudlet is a small-scale datacenter deployed at a wireless access point, and thus is highly constrained by both radio and compute resources. We show in this paper that separately optimizing the allocation of either compute or radio resource, as most existing works did, is highly suboptimal: the congestion of compute resource leads to the waste of radio resource, and vice versa. To address this problem, we propose a joint scheduling algorithm that allocates both radio and compute resources coordinately. Specifically, we consider a cloudlet in an Orthogonal Frequency-Division Multiplexing Access (OFDMA) system with multiple mobile devices, where we study subcarrier allocation for task offloading and CPU time allocation for task execution in the cloudlet. Simulation results show that the proposed algorithm significantly outperforms per-resource optimization, accommodating more offloading requests while achieving salient energy saving., Comment: 6 pages, 5 figures, conference

Wireless energy transfer, namely RF-based energy harvesting, is a potential way to prolong the lifetime of energy-constrained devices, especially in wireless sensor networks. However, due to huge propagation attenuation, its energy efficiency is regarded as the biggest bottleneck to widely applications. It is critical to find appropriate transmission policies to improve the global energy efficiency in this kind of systems. To this end, this paper focuses on the sensor networks scenario, where a mobile control center powers the sensors by RF signal and also collects information from them. Two related schemes, called as harvest-and-use scheme and harvest-store-use scheme, are investigated, respectively. In harvest-and-use scheme, as a benchmark, both constant and adaptive transmission modes from sensors are discussed. To harvest-store-use scheme, we propose a new concept, the best opportunity for wireless energy transfer, and use it to derive an explicit closed-form expression of optimal transmission policy. It is shown by simulation that a considerable improvement in terms of energy efficiency can be obtained with the help of the transmission policies developed in this paper. Furthermore, the transmission policies is also discussed under the constraint of fixed information rate. The minimal required power, the performance loss from the new constraint as well as the effect of fading are then presented., Comment: 30 pages, 5 figures. The short version was presented in part at 2015 IEEE International Conference on Communication

This paper investigates the energy efficiency (EE) in multiple relay aided OFDM system, where decode-and-forward (DF) relay beamforming is employed to help the information transmission. In order to explore the EE performance with user fairness for such a system, we formulate an optimization problem to maximize the EE by jointly considering several factors, the transmission mode selection (DF relay beamforming or direct-link transmission), the helping relay set selection, the subcarrier assignment and the power allocation at the source and relays on subcarriers, under nonlinear proportional rate fairness constraints, where both transmit power consumption and linearly rate-dependent circuit power consumption are taken into account. To solve the non-convex optimization problem, we propose a low-complexity scheme to approximate it. Simulation results demonstrate its effectiveness. We also investigate the effects of the circuit power consumption on system performances and observe that with both the constant and the linearly rate-dependent circuit power consumption, system EE grows with the increment of system average channel-to noise ratio (CNR), but the growth rates show different behaviors. For the constant circuit power consumption, system EE increasing rate is an increasing function of the system average CNR, while for the linearly rate-dependent one, system EE increasing rate is a decreasing function of the system average CNR. This observation is very important which indicates that by deducing the circuit dynamic power consumption per unit data rate, system EE can be greatly enhanced. Besides, we also discuss the effects of the number of users and subcarriers on the system EE performance., Comment: 35 pages, 15 fihures, submitted to IEEE Journal

In this work, a MIMO broadcast channel under the energy harvesting (EH) constraint and the peak power constraint is investigated. The transmitter is equipped with a hybrid energy storage system consisting of a perfect super capacitor (SC) and an inefficient battery, where both elements have limited energy storage capacities. In addition, the effect of data processing circuit power consumption is also addressed. To be specific, two extreme cases are studied here, where the first assumes ideal/zero circuit power consumption and the second considers a positive constant circuit power consumption where the circuit is always operating at its highest power level. The performance of these two extreme cases hence serve as the upper bound and the lower bound of the system performance in practice, respectively. In this setting, the offline scheduling with ideal and maximum circuit power consumptions are investigated. The associated optimization problems are formulated and solved in terms of weighted throughput optimization. Further, we extend to a general circuit power consumption model. To complement this work, some intuitive online policies are presented for all cases. Interestingly, for the case with maximum circuit power consumption, a close-to-optimal online policy is presented and its performance is shown to be comparable to its offline counterpart in the numerical results., Comment: 30 pages

This paper investigates the simultaneous wireless information and energy transfer for the non-regenerative multipleinput multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) relaying system. By considering two practical receiver architectures, we present two protocols, time switchingbased relaying (TSR) and power splitting-based relaying (PSR). To explore the system performance limit, we formulate two optimization problems to maximize the end-to-end achievable information rate with the full channel state information (CSI) assumption. Since both problems are non-convex and have no known solution method, we firstly derive some explicit results by theoretical analysis and then design effective algorithms for them. Numerical results show that the performances of both protocols are greatly affected by the relay position. Specifically, PSR and TSR show very different behaviors to the variation of relay position. The achievable information rate of PSR monotonically decreases when the relay moves from the source towards the destination, but for TSR, the performance is relatively worse when the relay is placed in the middle of the source and the destination. This is the first time to observe such a phenomenon. In addition, it is also shown that PSR always outperforms TSR in such a MIMO-OFDM relaying system. Moreover, the effect of the number of antennas and the number of subcarriers are also discussed., Comment: 16 pages, 12 figures, to appear in IEEE Selected Areas in Communications

This paper investigates the simultaneous wireless power and information transfer (SWPIT) for network-coded two-way re- lay transmission from an information theoretical viewpoint, where two sources exchange information via an energy har- vesting relay. By considering the time switching (TS) relay receiver architecture, we present the TS-based two-way re- laying (TS-TWR) protocol. In order to explore the system throughput limit with data rate fairness, we formulate an op- timization problem under total power constraint. To solve the problem, we first derive some explicit results and then de- sign an efficient algorithm. Numerical results show that with the same total available power, TS-TWR has a certain per- formance loss compared with conventional non-EH two-way relaying due to the path loss effect on energy transfer, where in relatively low and relatively high SNR regimes, the perfor- mance losses are relatively small., Comment: 5 pages,4 figures, submitted to ICASSP2015

Harvesting energy from ambient environment is a new promising solution to free electronic devices from electric wire or limited-lifetime battery, which may find very significant applications in sensor networks and body-area networks. This paper mainly investigate the fundamental limits of information transmission in wireless communication system with RF-based energy harvesting, in which a master node acts not only as an information source but also an energy source for child node while only information is transmitted back from child to master node. Three typical structures: optimum receiver, orthogonal receiver and power splitting receiver are considered where two way information transmission between two nodes under an unique external power supply constraint at master node are jointly investigated in the viewpoint of systemic level. We explicitly characterize the achievable capacity-rate region and also discuss the effect of signal processing power consumption at child node. The optimal transmission strategy corresponding to the most energy-efficient status, namely the point on the boundary of achievable capacity-rate region, is derived with help of conditional capacity function. Simulation confirms the substantial gains of employing optimal transmission strategy and optimum receiver structure. Besides, a typical application on minimizing required transmit power to green system is presented., Comment: 12 pages, 12 figures, a short version will appear in GLOBECOM 2014

Pilot contamination has been regarded as a main limiting factor of time division duplexing (TDD) massive multiple-input-multiple-output (Massive MIMO) systems, as it will make the signal-to-interference-plus-noise ratio (SINR) saturated. However, how pilot contamination will limit the user capacity of downlink Massive MIMO, i.e., the maximum number of admissible users, has not been addressed. This paper provides an explicit expression of the Massive MIMO user capacity in the pilot-contaminated regime where the number of users is larger than the pilot sequence length. Furthermore, the scheme for achieving the user capacity, i.e., the uplink pilot training sequence and downlink power allocation, has been identified. By using this capacity-achieving scheme, the SINR requirement of each user can be satisfied and energy-efficient transmission is feasible in the large-antenna-size (LAS) regime. Comparison with two non-capacity-achieving schemes highlights the superiority of our proposed scheme in terms of achieving higher user capacity., Comment: Accepted by IEEE GLOBECOM 2014, 6 pages, 3 figures

Energy harvesting technique is a potential way for relay node energy supply in cooperative networks in terms of deployment flexibility and maintain charge reduction. Unlike traditional power source, relay node in this case may run out of energy with certain probability, which can degrade the benefit from relay-aided cooperative transmission. In this paper, we concentrate on the outage behavior of cooperative networks aided by energy harvesting relay node in slow fading channel, and attempt to derive the closed-form expression of outage probability of proposed cooperative protocol. Compared with traditional direct transmission protocol, two conclusions are derived: 1) the diversity gain cannot be increased excepting the extreme case that energy-exhausted probability is zero; 2) a multiplicative gain for improving system performance can be obtained in terms of minimizing outage probability., Comment: 5 pages, 3 figures

To meet the surging demand for throughput, 5G cellular networks need to be more heterogeneous and much denser, by deploying more and more small cells. In particular, the number of users in each small cell can change dramatically due to users' mobility, resulting in random and time varying uplink interference. This paper considers the uplink interference in a 5G heterogeneous network which is jointly covered by one macro cell and several small cells. Based on the L\'{e}vy flight moving model, a mobility-aware interference model is proposed to characterize the uplink interference from macro cell users to small cell users. In this model, the total uplink interference is characterized by its moment generating function, for both closed subscriber group (CSG) and open subscriber group (CSG) femto cells. In addition, the proposed interference model is a function of basic step length, which is a key velocity parameter of L\'{e}vy flights. It is shown by both theoretical analysis and simulation results that the proposed interference model provides a flexible way of evaluating the system performance in terms of success probability and average rate., Comment: 14 pages,12 figures

In the paper, we study the service process $S(t)$ of an independent and identically distributed (\textit{i.i.d.}) Nakagami-$m$ fading channel, which is defined as the amount of service provided, i.e., the integral of the instantaneous channel capacity over time $t$. By using the Characteristic Function (CF) approach and the infinitely divisible law, it is proved that, other than certain generally recognized curve form {or a stochastic process}, the channel service process $S(t)$ is a deterministic linear function of time $t$, namely, $S(t)=c_m^\ast\cdot t$ where $c_m^\ast$ is a constant determined by the fading parameter $m$. Furthermore, we extend it to general \textit{i.i.d.} fading channels and present an explicit form of the constant service rate $c_p^\ast$. The obtained work provides such a new insight on the system design of joint source/channel coding that there exists a coding scheme such that a receiver can decode with zero error probability and zero high layer queuing delay, if the transmitter maintains a constant data rate no more than $c_p^\ast$. Finally, we verify our analysis through Monte Carlo simulations., Comment: 17 pages, 5 figures

High speed railways (HSRs) have been deployed widely all over the world in recent years. Different from traditional cellular communication, its high mobility makes it essential to implement power allocation along the time. In the HSR case, the transmission rate depends greatly on the distance between the base station (BS) and the train. As a result, the train receives a time varying data rate service when passing by a BS. It is clear that the most efficient power allocation will spend all the power when the train is nearest from the BS, which will cause great unfairness along the time. On the other hand, the channel inversion allocation achieves the best fairness in terms of constant rate transmission. However, its power efficiency is much lower. Therefore, the power efficiency and the fairness along time are two incompatible objects. For the HSR cellular system considered in this paper, a trade-off between the two is achieved by proposing a temporal proportional fair power allocation scheme. Besides, near optimal closed form solution and one algorithm finding the $\epsilon$-optimal allocation are presented., Comment: 16 pages, 6 figures

Signal-time coding, which combines the traditional encoding/modulation mode in the signal domain with signal pulse phase modulation in the time domain, was proposed to improve the information flow rate in relay networks. In this paper, we mainly focus on the efficient signal-time coding design. We first derive an explicit iterative algorithm to estimate the maximum number of available codes given the code length of signal-time coding, and then present an iterative construction method of codebooks. It is shown that compared with conventional computer search, the proposed iterative construction method can reduce the complexity greatly. Numerical results will also indicate that the new constructed codebook is optimal in terms of coding rate. To minimize the buffer size needed to store the codebook while keeping a relatively high efficiency, we shall propose a combinatorial construction method. We will then consider applications in wireless Gaussian relay networks. It will be shown that in the three node network model, the mixed transmission by using two-hop and direct transmissions is not always a good option.

Diamond relay channel model, as a basic transmission model, has recently been attracting considerable attention in wireless Ad Hoc networks. Node cooperation and opportunistic scheduling scheme are two important techniques to improve the performance in wireless scenarios. In the paper we consider such a problem how to efficiently combine opportunistic scheduling and cooperative modes in the Rayleigh fading scenarios. To do so, we first compare the throughput of SRP (Spatial Reused Pattern) and AFP (Amplify Forwarding Pattern) in the half-duplex case with the assumption that channel side information is known to all and then come up with a new scheduling scheme. It will that that only switching between SRP and AFP simply does little help to obtain an expected improvement because SRP is always superior to AFP on average due to its efficient spatial reuse. To improve the throughput further, we put forward a new processing strategy in which buffers are employed at both relays in SRP mode. By efficiently utilizing the links with relatively higher gains, the throughput can be greatly improved at a cost of queuing delay. Furthermore, we shall quantitatively evaluate the queuing delay and the tradeoff between the throughput and the additional queuing delay. Finally, to realize our developed strategy and make sure it always run at stable status, we present two criteria and an algorithm on the selection and adjustment of the switching thresholds.