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Cognitive Radio (CR) is developed to provide effective spectrum usage. CR is much significant in improving the efficiency of the global internet in applications. The evolutionary measurement technology is utilised to improve the evaluation of channel-state information. The outcome attained very few spectrums sensing in CR for complex mobility. A good optimisation method is needed to improve the accurate channel state prediction in successful channel access. Thus, this paper aims to implement a novel power and channel allocation mechanism with the help of a new Modified Levy Flight-based Artificial Ecosystem Optimisation (MLF-AEO) Optimisation Strategy. This paper achieves the optimal power control and channel allocation mechanism intending to solve the multiple objective functions based on the constraints like Interference among users, Outage Probability, and throughput. The superiority of the proposed algorithm is thoroughly verified by various simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we consider a new interference problem caused by idle small cells, which have no associated user equipment in ultra-dense small cell long-term evolution (LTE) networks. Specifically, we investigate the effect of idle small cells on the signal to interference and noise ratio (SINR) of the cell-specific reference signal (CRS) and the data signals. We confirm that CRS interference from idle small cells produces uneven interference pattern across CRS and data signals and eventually causes an SINR mismatch between CRS and data signals as well as between data signals with and without CRS symbols. In addition, these phenomena become severe with cell densification. In order to solve this mismatch problem, we propose a simple link adaptation framework, which utilizes clustered CRS assignment and hybrid SINR measurement. The numerical results show that the proposed method improves the average sum throughput compared with the conventional approaches. Overall, this paper sheds new light on investigating and coping with the interference problem coming from idle small cells in future ultra-dense small cell LTE networks. [ABSTRACT FROM PUBLISHER]

LTE-Advanced is an integral part of the 3rd Generation Partnership Project (3GPP) Release 12 onwards technologies, in which the high speed mobile services and number of smart phone users have been increased drastically. The increase in the demand of high data rate has placed huge traffic load at the base stations. In 5th generation the Device to Device (D2D) communication is the most promising technology which offload the data traffic from the conventional cellular networks. It provides an increase in system throughput, spectral efficiency and reduces the overall transmitted power. However there is an issue of interference between D2D and cellular users, as they are using the same frequency slot called resource block (RB) in sharing mode. In this paper we have proposed a scheme to maximize the sum rate and throughput of the system and minimize the co-tier interference that occurs between cellular and D2D users. In proposed scheme we have designed interference limited area (ILA) based algorithm for the resource allocation to the D2D and cellular users. The ILA provides a spatial separation of the interference aggressor and victim. The proposed algorithm has been simulated and the simulation results have been compared with other conventional scheme. The maximum achievable sum rate using proposed scheme ILA with RA is 30 bps/ Hz, and as the number of active D2D users increased to 50 the sum rate is maintained to a value of 25 bps/Hz. The maximum achievable throughput of the system is obtained near about 0.9 when the number of active D2D users is taken 50. It shows the improved sum rate and throughput. We have also analyzed the computational complexity and the degree of convergence of proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers a green cognitive radio network wherein each secondary user (SU) is solely powered by an energy harvester which extracts energy from RF signals of a primary user transmitter. Each SU operates in a harvesting–transmitting fashion periodically. In each period, all SUs first harvest energy for a fixed duration and then transmit data using the harvested energy to one access point for the rest of the period in the code-division multiple access method. Considering the intrinsic harvesting-throughput tradeoff, we jointly optimize the harvesting time and transmit powers of SUs to maximize the sum throughput of the network, subject to the primary interference constraint and the energy causality constraint. Despite of the nonconvex nature of the formulated optimization problem, we find an equivalent but convex substitution to the original problem. Then, we propose a dual-decomposition-based solution method to solve the problem. Simulations finally demonstrate the efficiency of this paper. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we propose MDSR, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, MDSR takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph, MDSR improves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluate MDSR on the testbed using commodity mmWave radios. The evaluation results demonstrate that MDSR improves network throughput by multi-folds compared with the state-of-the-art one. [ABSTRACT FROM AUTHOR]

Carrier sense multiple access with collision notification (CSMA/CN) is a typical representation of physical-layer (PHY)/medium access control (MAC) co-designs, where the MAC control frames are implemented or detected using PHY techniques. With CSMA/CN, the sender detects an unsuccessful transmission, with the aid of a collision notification (CN) from the receiver. In this paper, we first theoretically study the crucial impact of the CN attributes (namely, the detection threshold and the length) on system throughput in a wireless local area network (WLAN). We identify that the false-alarm probability of CN (more generally, the control frames in PHY/MAC co-designs) is a dominating metric that influences system performance. This paper will help developers select optimal CN attributes to balance various factors influencing CN detection performance. Extensive simulation results verify that our performance model is very accurate. [ABSTRACT FROM PUBLISHER]

Two-path relay networks can be deployed to achieve full-rate transmissions. In this paper, we introduce stable throughput analysis into decode-and-forward (DF) two-path relay networks. We establish the queueing model and take outage probabilities into consideration to characterize the packet-level cooperation and derive the closed-form stable throughput region. Based on the stable throughput region, we also propose a power-allocation scheme between the source node and relays to maximize the achievable packet rate. The maximal achievable packet rate determines the admission control of certain cooperation (i.e., when to cooperate) under a specific interrelay interference (IRI) cancellation technique. The power allocation is utilized to illustrate how to cooperate between the source node and relays. Three IRI responses are considered, and the system behaviors under various circumstances are presented through simulations. [ABSTRACT FROM AUTHOR]

The Third-Generation Partnership Project (3GPP) has recently started standardizing the “licensed-assisted access using LTE” for small cells, which is referred to as dual-band femtocell (DBF) in this paper, which uses the Long-Term Evolution (LTE) air interface in both the licensed and unlicensed bands based on the LTE carrier aggregation feature. Alternatively, the Small Cell Forum introduced the integrated femto-WiFi (IFW) small cell, which simultaneously accesses both the licensed band (via cellular interface) and the unlicensed band (via WiFi interface). In this paper, a practical algorithm for IFW and DBF to automatically balance their traffic in licensed and unlicensed bands, based on the real-time channel, interference, and traffic conditions of both bands, is described. The algorithm considers the fact that some “smart” devices (sDevices) have both cellular and WiFi radios, while some WiFi-only devices (wDevices) may only have WiFi radio. In addition, the algorithm considers a realistic scenario where a single small-cell user may simultaneously use multiple sDevices and wDevices via either the IFW or the DBF in conjunction with a wireless local area network. The goal is to maximize the total user satisfaction/utility of the small-cell user, while keeping the interference from small cells to macrocells below predefined thresholds. The algorithm can be implemented at the radio link control or the network layer of the IFW and DBF small-cell base stations. Results demonstrate that the proposed traffic-balancing algorithm applied to either IFW or DBF significantly increases the sum utility of all macrocell and small-cell users, compared with the current practices. Finally, various implementation issues of IFW and DBF are addressed. [ABSTRACT FROM PUBLISHER]

This paper analyzes the reliability and throughput of the telegram-splitting concept in the presence of inter-system and intra-system interference. Using telegram splitting, the data of one telegram is split into multiple sub-packets, which are then transmitted with temporal spacing. As a result, collisions with other telegrams destroy only some sub-packets that can be recovered using forward error correction. Consequently, the reliability of the data transmission in case of inter- and intra-system interference—a significant problem in license-exempt bands—is highly improved. In this paper, probabilities for the resulting telegram error rate of random media access systems using telegram splitting are derived. Furthermore, the achievable throughput is analyzed. The theoretical considerations in addition to simulation results prove the significant gain of the telegram-splitting concept for telemetry systems. [ABSTRACT FROM PUBLISHER]

With the increasing demand of spectrum and emergence of mobile devices, a cloud radio access network (C-RAN) is a promising technology to improve network capacity and coverage. The key concept of the C-RAN is to separate the radio function unit in remote radio heads (RRHs) from the digital function unit in baseband units (BBUs). This separation facilitates efficient spectrum and infrastructure sharing in the C-RAN. This paper considers the cooperative interference management among RRHs in the C-RAN. The RRHs can form coalitions to mitigate intra-interference and jointly serve their subscribers. To characterize RRH performance under the coalition, this paper develops two signal-to-interference-plus-noise-ratio (SINR)-based downlink throughput models of RRH, where one adopts tools from stochastic geometry to capture the interference from vehicular users (VUs) belonging to noncooperative RRHs, and another ignores the VU interference. In this paper, a coalition formation game is formulated to model the situation in which RRHs of C-RAN can make an individual decision to cooperatively serve their VUs if the throughput of the RRH can be improved. To obtain the solution of the proposed game, we develop a distributed coalition formation algorithm and analyze the stability of the coalition structure using a Markov chain model. Simulation results show that, as compared with the noncoalition and grand coalition, our distributed coalition formation can improve the C-RAN throughput by at least 20% and 80%, respectively. Furthermore, according to extensive simulation, we can define the conditions needed for RRHs to form a coalition and obtain higher throughput. [ABSTRACT FROM PUBLISHER]

Wireless caching networks have been extensively researched as a promising technique for supporting the massive data traffic of multimedia services. Many of the existing studies on real-data traffic have shown that users of a multimedia service consecutively request multiple contents and this sequence is strongly dependent on the related list of the first content and/or the top referrer in the category. This paper thus introduces the notion of “temporary preference”, characterizing the behavior of users who are highly likely to request the next content from a certain target category (i.e., related content list). Based on this observation, this paper proposes both probabilistic caching and dynamic delivery policies for categorized contents and consecutive user demands. The proposed caching scheme maximizes the minimum of the cache hit rates for all users. In the delivery phase, a dynamic helper association policy for receiving multiple contents in a row is designed to reduce the delivery latency. By comparing with the content placement optimized for one-shot requests, numerical results verify the effects of categorized contents and consecutive user demands on the proposed caching and delivery policies. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has been extensively studied to improve the performance of space-terrestrial satellite networks on account of the shortage of frequency band resources. In this paper, terrestrial network and satellite network synergistically provide complete coverage for ground users. A user association scheme on account of the channel gain and distance between the ground users and the BSs is proposed to identify the users to be associated by the BSs, and there is an upper limit for the number of users associated with each BS. Then calculate the channel condition ratio to select the users served by the satellite. The all BSs provide service for those unselected users, and the NOMA technology is applied to terrestrial network. Then, a user pairing scheme which maximize the minimum the ground user channel correlation coefficient is formulated to match the terrestrial users in a NOMA group. On account of multiple antennas equipped by the BSs and satellite, beamforming is performed among groups of BSs and among satellite users so as to reduce multi-user interference. In the power allocation scheme, we introduce the alternative direction method of multipliers (ADMM) algotithm so as to optimize system energy efficiency. In addition, the objective function is a non-convex function, so the Dinkelbach-style scheme is presented to convert non-convex function into the convex-form function. Eventually, the performance of the presented algorithm is simulated and compared with the existing NOMA-FTPA algorithm. The results indicate that the presented algorithm has high superiority in system energy efficiency and it can be applied to this network. [ABSTRACT FROM AUTHOR]

The stochastic properties of vehicles’ arrival process and the high-speed mobility of vehicles lead to dynamic changes in the topology of vehicular networks, affecting the data download experience of users, especially for large files. In this paper, a novel elastic-segment-based vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) cooperative strategy (ESCS) is proposed to reduce the impact of the density of vehicles on achievable throughput. The achievable throughput and optimal parameters of the proposed system are analyzed. The strategy takes the impact of the density of vehicles on the data download process as the key element for consideration. The density of vehicles has a great influence on the distance between vehicles, and further affects the transmission data rate. To solve the problem of low transmission data rate induced by the density of vehicles, the sizes of the elastic-segments are optimized according to the distance between the vehicles. Owing to the stochastic process of the distance between vehicles, it is particularly challenging to derive a closed-form expression for the achievable throughput. To tackle this problem, we develop a new V2I/V2V cooperative model to adapt to the general two-way lanes, analyze the distribution and mobility of vehicles, and derive a closed-form expression for the achievable throughput, as a function of V2I effective transmission range, the V2V effective transmission range, and other system parameters. We then optimize V2V effective connection range and the vehicle density to maximize the achievable throughput. Simulation results show that the proposed strategy significantly improves the throughput compared with the existing cooperative strategy, especially in the scenario of high vehicle density. [ABSTRACT FROM AUTHOR]

In this paper, the scaling law of the network throughput of a special type of heterogeneous networks is studied, where a secondary network composed of cognitive users shares the same resources opportunistically with a primary network of licensed users. Some pioneering works in this area showed that this type of heterogeneous networks performs as well as two stand-alone networks under the dense network model. A key assumption behind this conclusion is that the density of the secondary network is higher than that of the primary network in the order sense, which essentially decouples the two overlaid networks as the secondary network dominates asymptotically. In this paper, we endeavor to investigate this problem with a weaker condition that the dimensions of the two overlaid networks are on the same order, under the extended network model. Our analysis shows that, with the ratio of the two network sizes larger than a threshold, this weaker (and arguably more practical) condition does not degrade either network throughput scaling. Our result further reveals the potential of cognitive radio (CR) technology in real applications. [ABSTRACT FROM PUBLISHER]

OFDMA femtocell is a promising technology to improve indoor cellular network coverage cost-effectively. Large-scale deployment of femtocells in the urban area is expected to be realized in the near future. However, inter-femtocell interference significantly limits the achievable throughput of an OFDMA femtocell system, which calls for interference management tailored for femtocell networks. A typical approach to mitigate inter-femtocell interference is known as resource isolation, which aims at assigning non-overlapping resources to interfering femtocells. One of the main challenges for interference mitigation in femtocell networks is that end consumers often install the femtocells. Very limited information about the femtocells is available, making it hard to decipher the inter-femtocell interference. Previous studies either take time to resolve collisions online or adopt a conservative approach to identify interferers. Although the latter approach avoids wasting time on resolving collisions, it may result in resource underutilization. In this paper, we propose an efficient method to identify inter-femtocell interference by analyzing the received patterns observed by mobile stations. We conducted experiments on GNU Radio/USRP to demonstrate that the proposed interference identification method can successfully identify real interferers while excluding non-interfering femtocells from suspect femtocells. Based on the proposed interference identification, we propose a weighted vertex-coloring based resource assignment algorithm to allocate resources with better fairness and higher throughput. [ABSTRACT FROM AUTHOR]

Radio spectrum sensing (SS) has been an active topic of research over the past years due to its importance to cognitive radio (CR) systems. However, in CR networks (CRNs) with multiple primary users (PUs), the secondary users (SUs) can often detect PUs that are located outside the sensing range, due to the level of the aggregated interference caused by the PUs. This effect, known as spatial false alarm (SFA), degrades the performance of CRNs because it decreases the SUs' medium access probability. This paper characterizes the SFA effect in a CRN, identifying possible actions to attenuate it. Adopting energy-based sensing (EBS) in each SU, this paper starts to characterize the interference caused by multiple PUs located outside a desired sensing region. The interference formulation is then used to write the probabilities of detection and false alarm, and closed-form expressions are presented and validated through simulation. The first remark to be made is that the SFA can be neglected, depending on the path-loss factor and the number of samples collected by the energy detector to decide the spectrum's occupancy state. However, it is shown that by increasing the number of samples needed to increase the sensing accuracy, the SUs may degrade their throughput, namely, if SUs are equipped with a single radio that is sequentially used for sensing and transmission (split-phase operation). Assuming this scenario, this paper ends by providing a bound for the maximum throughput achieved in a CRN with multiple active PUs and for a given level of PUs' detection inside the SUs' sensing region. The results presented in this paper show the impact of path loss and EBS parameterization on SUs' throughput and are particularly useful to guide the design and parameterization of multihop CRNs, including future ad hoc CRNs considering multiple PUs. [ABSTRACT FROM AUTHOR]

In this paper, the joint spectrum sensing and resource allocation problem is investigated in a multi-band-multi-user cognitive radio (CR) network. Assuming imperfect spectrum sensing information, our goal is to jointly optimize the sensing threshold and power allocation strategy such that the average total throughput of secondary users (SUs) is maximized. In Addition, the power of SUs is constrained to keep the interference introduced to primary users under certain limit, which gives rise to a nonconvex mixed integer non-linear programming (MINLP) optimization problem. Our contribution in this paper is threefold. First, it is illustrated that the dimension of the nonconvex MINLP problem can be significantly reduced, which helps to re-formulate the optimization problem without resorting to integer variables. Second, it is demonstrated that the simplified formulation admits the canonical form of a monotonic optimization, and an $\epsilon $ -optimal solution can be achieved using the polyblock outer approximation algorithm. Third, a practical low-complexity spectrum sensing and resource allocation algorithm is developed to reduce the computational cost. Finally, the effectiveness of proposed algorithms is verified by simulations. [ABSTRACT FROM AUTHOR]

Considering a multi-user interference network with an eavesdropper, this paper aims at the power allocation to optimize the worst secrecy throughput among the network links or the secure energy efficiency in terms of achieved secrecy throughput per Joule under link security requirements. Three scenarios for the access of channel state information are considered: the perfect channel state information; partial channel state information with channels from the transmitters to the eavesdropper exponentially distributed; and not perfectly known channels between the transmitters and the users with exponentially distributed errors. The paper develops various path-following procedures of low complexity and rapid convergence for the optimal power allocation. Their effectiveness and viability are illustrated through numerical examples. The power allocation schemes are shown to achieve both high secrecy throughput and energy efficiency. [ABSTRACT FROM AUTHOR]

We present a distributed stable protocol for local broadcast in multi-hop wireless networks, where packets are injected to the nodes continuously, and each node needs to quickly disseminate the injected packets to all its neighbors within a given communication range $R$. We investigate the maximum packet injection rate and the minimum packet latency that can be achieved in a stable protocol. This paper assumes the signal-to-interference-plus-noise-ratio (SINR) interference model, which reflects more accurately the physical characteristics of the wireless interference, such as fading and signal accumulation, than conventional local interference models, e.g., graph-based models. More specifically, we present a stable protocol that can handle both stochastic and adversarial injection patterns. The protocol is asymptotically optimal in terms of both injection rate and packet latency. To the best of our knowledge, this paper is the first one studying the properties of stable protocols for the basic primitive of local broadcast in a multi-hop setting under SINR. Our proposed protocol utilizes a static local broadcast algorithm as a subroutine. This static algorithm is of independent interest, and it closes the $O(\log n)$ gap between the upper and lower bounds for static local broadcast. Simulation results indicate that our proposed algorithms can perform well in realistic environments. [ABSTRACT FROM AUTHOR]

In this paper, the throughput maximization of millimeter-wave (mm-Wave) ultradense networks (UDN) using dynamic spectrum sharing (DSS) is considered. Most of the existing works only allow temporal-domain access and admit at most one user at each time slot, resulting in significant underutilization of spectrum resource, which will be less attractive to mm-wave UDN applications. A generalized temporal-spatial sharing scheme is proposed in this paper for UDN by exploiting the location information of incumbent devices, where multiple users are allowed to access each channel simultaneously via spatial separations. For distributed applications, the global information exchange among secondary users (SU) tends to be impractical, given the unaffordable signaling overhead and latency. Thus, a noncooperative game with fine-grained two-dimensional reuse is formulated, which leads to a more efficient access strategy. It is then proved to be an ordinary potential game (OPG), which guarantees the existence of the strategy Nash equilibrium (NE). Finally, an improved decentralized reinforcement learning algorithm is designed, with which SUs can learn from wireless environments and adapt toward an NE point, relying on the individual observation and the historical action reward (rather than the global information exchanging). The convergence efficiency of the new scheme is also rigorously proved. Numerical simulations are provided to validate the performances of the proposed schemes. [ABSTRACT FROM AUTHOR]

Distance-based attenuation is a critical aspect of wireless communications. As opposed to the ubiquitous power-law path loss model, this paper proposes a stretched exponential path loss model that is suitable for short-range communication. In this model, the signal power attenuates over a distance r as e^{-\alpha r^{\beta }} , where \alpha$ and $\beta$ are tunable parameters. Using experimental propagation measurements, we show that the proposed model is accurate for short to moderate distances in the range $r \in ~(5,300)$ meters and so is a suitable model for dense and ultradense networks. We integrate this path loss model into a downlink cellular network with base stations modeled by a Poisson point process, and derive expressions for the coverage probability, potential throughput, and area spectral efficiency. Although the most general result for coverage probability has a double integral, several special cases are given, where the coverage probability has a compact or even closed form. We then show that the potential throughput is maximized for a particular BS density and then collapses to zero for high densities, assuming a fixed signal-to-interference-plus-noise ratio (SINR) threshold. We next prove that the area spectral efficiency, which assumes an adaptive SINR threshold, is nondecreasing with the BS density and converges to a constant for high densities. [ABSTRACT FROM PUBLISHER]

Game-theoretic approaches have provided valuable insights into the design of robust local control rules for the individuals in multi-agent systems, e.g., Internet congestion control, road transportation networks, and so on. In this paper, we introduce a non-cooperative medium access control game for wireless networks and propose new fully distributed carrier sense multiple access (CSMA) algorithms that are provably optimal in the sense that their long-term throughputs converge to the optimal solution of a utility maximization problem over the maximum throughput region. The most significant part of our approach lies in introducing novel price functions in agents’ utilities so that the proposed game admits an ordinal potential function with no price-of-anarchy. The game formulation naturally leads to game-based dynamics finding a Nash equilibrium, but they often require global information. Toward our goal of designing fully distributed operations, we propose new game-inspired dynamics by utilizing a certain property of CSMA that enables links to estimate their temporary throughputs without message passing. They can be thought of as stochastic approximations to the standard dynamics, which is a new feature in our work, not prevalent in other traditional game-theoretic approaches. We show that they converge to a Nash equilibrium, and numerically evaluate their performance to support our theoretical findings. [ABSTRACT FROM PUBLISHER]

Most of the existing technologies for cognitive radio network (CRN) is essential for providing an effective solution for spectrum utilization problem in the wireless medium. Power allocation plays a dual and complex role in the multi-hop network, and these dual roles are known to be minimizing the total transmission power and also minimize the outage probability. Cognitive Radio technology enables a feasible way of using white spaces by incorporating diverse spectrum-sharing approaches. Interference makes efficient communication while sharing the channels among unlicensed and licensed users. In addition, Signal to Interference Noise Ratio also enhances the channel capacity. To investigate a joint channel and power allocation for CRNs, this paper aims to optimize the channel allocation and power control of secondary users to minimize interference between primary and secondary users and maximize throughput in CRN. This joint optimization is carried out with the combination of two renowned heuristic strategies that is termed Adaptive Luciferin Enhancement-based Team Work-Glowworm Optimization, which is carried out by deriving the multi-objective function regarding functions like Throughput and Interference. The analysis has demonstrated that the developed CRN framework has maximized the throughput of the CRN and also minimized the interference among the primary users over the existing power allocation strategies. Further, this model has enhanced the network lifetime and analyzed the convergence and complexity of the algorithm. [ABSTRACT FROM AUTHOR]

In dense small cell networks, dynamic time-division duplex (D-TDD) technology has emerged as a promising solution to accommodate the fast variants of volatile traffic conditions because it allows each cell to dynamically configure the uplink and downlink transmission directions. However, the flexibility of traffic configuration introduces additional inter-cell interference, which largely deteriorates network throughput. This paper proposes an interference coordination technology for D-TDD small cell networks by integrating fractional frequency reuse (FFR) with cell clustering. To evaluate the system performance, we develop a theoretical framework to analytically characterize the mean packet throughput (MPT) performance by considering the impact of spatio-temporal traffic. The analytical model can be extended to further study the FFR-based D-TDD, clustered D-TDD, and traditional D-TDD networks. We verify the accuracy of our analysis through simulations and whereby explore the effect of different network parameters. Numerical results demonstrate that the proposed scheme outperforms clustered D-TDD and traditional D-TDD for both the downlink and uplink spatially averaged MPT, and can significantly improve the performance in uplink while slightly decreasing that in downlink compared with FFR-based D-TDD. Furthermore, by jointly optimizing network parameters, the spatially averaged MPT can be maximized while enduring MPT per user. [ABSTRACT FROM AUTHOR]

Ultra-Reliable and Low Latency Communications (URLLC) is an important use case in 5G NR that targets at 1-ms level delay sensitive applications. For fast transmission of URLLC traffic, a promising mechanism is to multiplex URLLC traffic into a channel occupied by enhanced Mobile BroadBand (eMBB) service through preemptive puncturing. Although preemptive puncturing can offer transmission resource to URLLC on demand, it will adversely affect throughput and link reliability performance of eMBB service. To mitigate such an adverse impact, a possible approach is to employ link adaptation (LA) through modulation and coding scheme (MCS) selection for eMBB users. In this paper, we study the problem of maximizing eMBB throughput through MCS selection while ensuring link reliability requirement for eMBB users. We present DELUXE – the first successful design and implementation based on deep learning to address this problem. DELUXE involves a novel mapping method to compress high-dimensional eMBB transmission information into a low-dimensional representation with minimal information loss, a learning method to learn and predict the block-error rate (BLER) under each MCS, and a fast calibration method to compensate errors in BLER predictions. For proof of concept, we implemented DELUXE through a link-level 5G NR simulator with GPU and MathWorks 5G toolbox. Through extensive experiments, we show that DELUXE can successfully choose MCS for eMBB transmissions to maintain the desired link reliability while striving for spectral efficiency. In addition, our implementation can meet the real-time requirement ($< 125 \mu \text{s}$) in 5G NR. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is a new and promising paradigm to substantially improve the spectral and energy efficiency of wireless networks, by constructing favorable communication channels via tuning massive low-cost passive reflecting elements. Despite recent advances in the link-level performance optimization for various IRS-aided wireless systems, it still remains an open problem whether the large-scale deployment of IRSs in wireless networks can be a cost-effective solution to achieve their sustainable capacity growth in the future. To address this problem, we study in this paper a new hybrid wireless network comprising both active base stations (BSs) and passive IRSs, and characterize its achievable spatial throughput in the downlink as well as other pertinent key performance metrics averaged over both channel fading and random locations of the deployed BSs/IRSs therein based on stochastic geometry. Compared to prior works on characterizing the performance of wireless networks with active BSs only, our analysis needs to derive the power distributions of both the signal and interference reflected by distributed IRSs in the network under spatially correlated channels, which exhibit channel hardening effects when the number of IRS elements becomes large. Extensive numerical results are presented to validate our analysis and demonstrate the effectiveness of deploying distributed IRSs in enhancing the hybrid network throughput against the conventional network without IRS, which significantly boosts the signal power but results in only marginally increased interference in the network. Moreover, it is unveiled that there exists an optimal IRS/BS density ratio that maximizes the hybrid network throughput subject to a total deployment cost given their individual costs, while the conventional network without IRS (i.e., zero IRS/BS density ratio) is generally suboptimal in terms of throughput per unit cost. [ABSTRACT FROM AUTHOR]

In this paper, we explore the throughput–delay tradeoff in a mobile ad hoc network (MANET) operating under the practical reference point group mobility model and also a general setting of node moving speed. In particular, we consider a MANET with unit area and n nodes being divided evenly into \Theta(n^{\alpha}) groups, \alpha\in[0,1] , where the center of each group moves according to a random direction model with speed of no more than \upsilon\in[0,1] . We determine the regions of per-node throughput and average delay and their tradeoffs that can be achieved (in order sense) in such a network. For the regime of \upsilon=0, we first prove that the per-node throughput capacity is and then develop a routing scheme to achieve this capacity, resulting in an average delay of \Theta(\max\n^1/2,n^1-\alpha\) for any \alpha\in[0,1]. Regarding the regime of \upsilon>0, we prove that the per-node throughput capacity can be improved to \Theta(1) , which is achievable by adopting a new routing scheme with an average delay of \Theta(\max\n^1-\alpha,n^\alpha/2/\upsilon\) for \upsilon=o(1) and $\Theta(n)$ for $\upsilon=\Theta(1) $. The results in this paper help us to have a deep understanding on the fundamental performance scaling laws and also enable an efficient throughput–delay tradeoff to be achieved in MANETs with correlated mobility. [ABSTRACT FROM PUBLISHER]

In theory, coordinated multipoint transmission (CoMP) promises vast gains in spectral efficiency. However, industrial field trials show rather disappointing throughput gains, whereby the major limiting factor is proper sharing of channel state information. Many recent papers have considered this so-called limited feedback problem in the context of CoMP, usually taking the following assumptions, namely, infinite SNR regime, no user selection, and ideal link adaptation, rendering the analysis too optimistic. In this paper, we make a step forward toward a more realistic assessment of the limited feedback problem by introducing an improved metric for the performance evaluation, which better captures the throughput degradation. We find the relevant scaling laws (lower and upper bounds) and show that they are different from existing ones. Moreover, we provide a robust iterative interference alignment algorithm and corresponding feedback strategies achieving the obtained scaling laws. The main idea is that, instead of sending the complete channel matrix, each user fixes a receive filter and feeds back a quantized version of the effective channel. Finally, we underline our findings with simulations for the proposed system. [ABSTRACT FROM PUBLISHER]

In this paper, we propose using group partition and dynamic rate adaptation for scalable throughput optimization of capacity-region-aware device-to-device communications. We adopt network information theory that allows a receiving device to simultaneously decode multiple packets from multiple transmitting devices, as long as the vector of transmitting rates is inside the capacity region. Based on graph theory, devices are first partitioned into subgroups. To optimize the throughput of a subgroup, instead of directly solving an integer-linear programming problem, we propose using a fast iterative algorithm to select active devices and using aggression levels for rate adaptation based on channel state information. Simulation results show that the proposed algorithm is scalable and could significantly outperform the greedy algorithm by more than 50%. [ABSTRACT FROM PUBLISHER]

This paper addresses fundamental issues in a shared channel where the users have different priority levels. In particular, we study a two-user cognitive shared channel consisting of a primary (higher-priority) and a secondary user, operating in the cognitive underlay fashion, but in a novel way where interference suffered by the primary user is compensated by requiring the secondary user to cooperatively relay some of the primary's packets. We start by analyzing the case of no node cooperation, where nodes transmit their own packets to their respective destinations. We then extend the analysis to a system in which the secondary node acts as a relay for the primary user, in addition to serving its own packets. Specifically, in the cognitive cooperation case, the secondary node forwards those packets to the primary destination that it receives successfully from the primary source. In such cognitive shared channels, a tradeoff arises in terms of activating the secondary along with the primary so that both transmissions may be successful, but with a lower probability, compared to the case of the secondary node staying idle when the primary user transmits. Results show the benefits of relaying for both the primary as well as the secondary nodes in terms of the stable-throughput region. [ABSTRACT FROM PUBLISHER]

In this paper, a hybrid radio frequency (RF) and visible light communication (VLC) system is considered. A hybrid system with multiple VLC access points (APs) and RF APs is designed and analyzed. In indoor environments, VLC APs provide very high data rates whilst proving illumination, and RF APs offer ubiquitous coverage with moderate data rates. Since VLC networks piggyback on existing lighting infrastructures, they may not always be able to provide full coverage despite supporting very high data rates in some areas. Hence in practical deployments, the standalone VLC networks should be augmented in order to improve the per user data rate coverage. In this context, RF APs can be used to improve the per user rate coverage of VLC networks as well as to provide the ubiquitous control functionalities. In this paper, a simple RF deployment is proposed in order to improve the per user outage data rate performance of standalone VLC networks. It is assumed that the VLC system resources are fixed, and this paper quantifies the minimum spectrum and power requirements for a RF system, which after introduction to the VLC system, the hybrid RF/VLC system achieves certain per user rate coverage performances. [ABSTRACT FROM AUTHOR]

This paper focuses on interference coordination between the small cell and macro cell tiers of a wireless access network. We present a self-optimization mechanism for LTE-A eICIC parameters (CRE bias and ABS ratio) following a novel approach based on a model-free learning strategy, not requiring any previous knowledge about the network (e.g., topology, interference graph, and scheduling algorithms). Our proposal is built upon a stochastic optimization algorithm known as response surface methodology (RSM), that we use to find efficient eICIC configurations during network operation (online learning), adapting to changing network conditions, such as traffic or user distribution. The objective consists of optimizing a performance metric for which, in general, mathematical expression is unavailable. In particular, we consider the fifth percentile throughput defined by the 3GPP. By means of RSM, our mechanism obtains local approximations of the objective function to perform steepest ascent iterations with an adjustable level of statistical accuracy. The algorithm can be extended to account for stochastic constraints, allowing the network to optimize one performance metric while maintaining other metrics above a desired level. [ABSTRACT FROM PUBLISHER]

In this paper, we study resource allocation for full-duplex communications in an orthogonal frequency division multiple access femtocell network. We aim to maximize the throughput of the femtocell while avoiding severe inter-tier interference to the macrocell via joint sub-channel assignment and power allocation. To be more practical, we take channel estimation error into account and use the robust optimization theory to model the uncertainty in interference channels. By using the Lagrangian dual method, we decompose the original optimization problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the non-convex primal problem into a tractable form through sequential convex approximations and then utilize the sub-gradient method to solve the dual problem. Simulation results show the effectiveness of the proposed algorithm and demonstrate the impact of channel uncertainty on the system performance. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel approach to assess the performance in terms of end-to-end throughput of an ad hoc multi-hop wireless network, where each link is affected by interference coming from other multi-hop paths nearby. The approach captures the mutual impact of each path on all others. It can be applied to both, contention-based and scheduled, medium access control (MAC) protocols. Sources have data to send to destination nodes through $n$ relays. Nodes are assumed to be uniformly and randomly distributed in the 2-D infinite plane. The model shows the impact on the end-to-end throughput of $n$ ; it also captures the influence of node density, traffic generated, number of retransmissions, and other MAC parameters. Finally, the model provides the throughput-delay tradeoff. Unlike most previous approaches, the mathematical tool proposed appears to be scalable, allowing easy extension to any number of hops. Comparison with simulation results is provided to prove that the impact of the approximations introduced in the analysis is almost negligible. [ABSTRACT FROM PUBLISHER]

Consumer electronic (CE) devices increasingly rely on wireless local area networks (WLANs). Next generation WLANs will continue to exploit multiple antenna systems to satisfy the growing need for WLAN system capacity. Multipleinput multiple-output (MIMO) antenna systems improve the spectral efficiency and single user throughput. Multi-user MIMO (MU-MIMO) systems exploit the spatial separation of users for increasing the sum-throughput. In an MU-MIMO system, efficient channel sounding is essential for achieving optimal performance. The system analysis in this paper provides insights into the rate at which to perform channel sounding. This paper shows that optimal sounding intervals exist for single user transmit beamforming (SU-TxBF) and MU-MIMO, and proposes a low-complexity dynamic sounding approach for practical MUMIMO WLAN deployments. The proposed approach adjusts the sounding interval adaptively based on the real-time learning outcomes in the given radio environment. Using real over-the-air channel measurements, significant throughput improvements (up to 31.8%) are demonstrated by adopting the proposed dynamic sounding approach, which is compliant with IEEE 802.11ac1. [ABSTRACT FROM PUBLISHER]

Cellular heterogeneous networks (HetNets), which are realized through shared carrier deployments of macrocells and small cells, are widely seen as a potential solution to serve growing data capacity demands. While these networks pose significant challenges in interference management, much of the focus in earlier efforts has been limited only to small-cell range expansion in the downlink. In this paper, we provide a comprehensive analysis of interference management techniques applicable to the uplink of cellular HetNets, with the goal of characterizing and improving edge user (e.g., 5th percentile) performance. In particular, we propose and study virtual soft-handoff (VSHO), which offers a low-complexity and low-overhead solution that exploits the link asymmetry observed in the uplink geometry of HetNets. VSHO exploits the fact that, in the uplink, an edge macro user who suffers from very low throughput may be closer to a nearby small cell than its own macrocell. The intuition is that such macro users should have better decoding opportunity when their signals are processed at the nearby small cell with better channel conditions. We also study how interference cancelation (IC) and interference avoidance through resource blanking can be incorporated into our VSHO proposal for further throughput gains. In addition to the VSHO techniques outlined in this paper, we also analyze other candidate techniques available in the Third Generation Partnership Project Long-Term Evolution (3GPP-LTE), such as cell-range expansion (CRE) and joint processing, to present a unified view of interference management options available in HetNets. [ABSTRACT FROM PUBLISHER]

There have been some rapid advances on the design of full duplex (FD) transceivers in recent years. Although the benefits of FD have been studied for single-hop wireless communications, its potential on throughput performance in a multi-hop wireless network remains unclear. As for multi-hop networks, a fundamental problem is to compute the achievable end-to-end throughput for one or multiple communication sessions. The goal of this paper is to offer some fundamental understanding on end-to-end throughput performance limits of FD in a multi-hop wireless network. We show that through a rigorous mathematical formulation, we can cast the multi-hop throughput performance problem into a formal optimization problem. Through numerical results, we show that in many cases, the end-to-end session throughput in a FD network can exceed $2 \times$ of that in a half duplex (HD) network. Our finding can be explained by the much larger design space for scheduling that is offered by removing HD constraints in throughput maximization problem. The results in this paper offer some new understandings on the potential benefits of FD for end-to-end session throughput in a multi-hop wireless network. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favorable line-of-sight (LoS) propagation. To reap the potential gains of UAV swarm, the remote control signal sent by ground control unit (GCU) is essential, whereas the control signal quality are susceptible in practice due to the effect of the adjacent channel interference (ACI) and the external interference (EI) from radiation sources distributed across the region. To tackle these challenges, this paper considers priority-aware resource coordination in a multi-UAV communication system, where multiple UAVs are controlled by a GCU to perform certain tasks with a pre-defined trajectory. Specifically, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the UAVs by jointly optimizing channel assignment and power allocation strategy under stringent resource availability constraints. According to the intensity of ACI, we consider the corresponding problem in two scenarios, i.e., Null-ACI and ACI systems. By virtue of the particular problem structure in Null-ACI case, we first recast the formulation into an equivalent yet more tractable form and obtain the global optimal solution via Hungarian algorithm. For general ACI systems, we develop an efficient iterative algorithm for its solution based on the alternating optimization methods. Extensive simulation results demonstrate that the proposed algorithms can significantly enhance the minimum SINR among all the UAVs and adapt the allocation of communication resources to diverse mission priority. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of scheduling real-time traffic in wireless networks under a conflict-graph interference model and single-hop traffic. The objective is to guarantee that at least a certain fraction of packets of each link are delivered within their deadlines, which is referred to as delivery ratio. This problem has been studied before under restrictive frame-based traffic models, or greedy maximal scheduling schemes like LDF (Largest-Deficit First) that can lead to poor delivery ratio for general traffic patterns. In this paper, we pursue a different approach through randomization over the choice of maximal links that can transmit at each time. We design randomized policies in collocated networks, multi-partite networks, and general networks, that can achieve delivery ratios much higher than what is achievable by LDF. Further, our results apply to any traffic (arrival and deadline) process that evolves as an unknown positive recurrent Markov chain. Hence, this work is an improvement with respect to both efficiency and traffic assumptions compared to the past work. We further present extensive simulation results over various traffic patterns and interference graphs to illustrate the gains of our randomized policies over LDF variants. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a buffered decode-and-forward (DF) relay-based three-node underlay cooperative cognitive relay network (CRN) with a direct path to the destination, assuming that the source and the relay use limited predefined discrete rates. Optimum rules are evolved for joint link and rate selection that maximize throughput while ensuring buffer stability. When two or all three links can provide the same maximum rate, then one of them is picked randomly based on a “coin-toss” with probability chosen to ensure buffer stability. System throughput expressions are derived, with the buffered and direct components evaluated separately. It is seen that the system throughput of the balanced buffer is independent of the coin-toss probabilities that assist in buffer balancing, and also, the buffer balancing condition can be evaluated from the expression of the system throughout itself. The analysis clearly confirms the importance of the buffer. Furthermore, it is observed that considering the direct path is important in underlay cognitive radio. Additionally, a scheme in which the direct link signal is combined with the relayed signal is also considered, and it is demonstrated that it offers improvement in performance when relay-to-destination link is weak. [ABSTRACT FROM AUTHOR]

In this paper, we optimize the energy efficiency (bits/s/Hz/J) of device-to-multi-device (D2MD) wireless communications. While the device-to-device scenario has been extensively studied to improve the spectral efficiency in cellular networks, the use of multicast communications opens the possibility of reusing the spectrum resources also inside the groups. The optimization problem is formulated as a mixed integer non-linear joint optimization for the power control and allocation of resource blocks (RBs) to each group. Our model explicitly considers resource sharing by letting co-channel transmission over a RB (up to a maximum of $r$ transmitters) and/or transmission through $s$ different channels in each group. We use an iterative decomposition approach, using first matching theory to find a stable even if sub-optimal channel allocation, to then optimize the transmission power vectors in each group via fractional programming. In addition, within this framework, both the network energy efficiency and the max-min individual energy efficiency are investigated. We characterize numerically the energy-efficient capacity region, and our results show that the normalized energy efficiency is nearly optimal (above 90% of the network capacity) for a wide range of minimum-rate constraints. This performance is better than that of other matching-based techniques previously proposed. [ABSTRACT FROM AUTHOR]

Driven by the recent advancement in unmanned aerial vehicle (UAV) technology, this paper proposes a new wireless network architecture of coordinate multipoint (CoMP) in the sky to harness both the benefits of interference mitigation via CoMP and high mobility of UAVs. Specifically, we consider uplink communications in a multi-UAV enabled multi-user system, where each UAV forwards its received signals from all ground users to a central processor (CP) for joint decoding. Moreover, we consider the case where the users may move on the ground, thus the UAVs need to adjust their locations in accordance with the user locations over time to maximize the network throughput. Utilizing random matrix theory, we first characterize, in closed form, a set of approximated upper and lower bounds of the user’s achievable rate in each time epoch under the practical Rician fading channel model, which is shown to be very tight, both analytically and numerically. UAV placement and movement over different epochs are then optimized based on the derived bounds to maximize the minimum of user average achievable rates over all epochs for both cases of full information (of current and future epochs) and current information on the user’s movement. Interestingly, it is shown that the optimized location of each UAV at any particular epoch is the weighted average of the ground user locations at the current epoch as well as its own location at the previous and/or next epoch. Finally, simulation results are provided to validate and compare the performance of the proposed UAV placement and movement designs under different practical application scenarios. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have attracted increasing attention in wireless communications due to the high mobility. This paper investigates a fixed-wing UAV-to-UAV (U2U) communications system, with the aim of minimizing the information transmission time via proactively designing the UAV paths. First, we propose a general optimization framework for U2U communications, which covers the communication throughput requirement, interference from terrestrial transmitters, UAV maximum/minimum speeds and accelerations, and minimum U2U distance. To tackle the formulated optimization, the communication throughput constraint that contains uncertain locations of terrestrial transmitters is transformed into a deterministic expression with the aid of S-procedure, and the nonlinear equality constraints on the UAV paths are replaced by linear equality constraints with additional positive semidefinite matrix constraints. Then, we develop a path planning algorithm based on the exact penalty method and successive convex approximation. Furthermore, we design a heuristic path planning algorithm that solves the completion time minimization problem by iteratively addressing a series of throughput maximization problems. The proposed heuristic algorithm strikes a good tradeoff between the computational complexity and the achievable performance. Finally, the simulation results are presented to verify the proposed path planning algorithms under various parameter configurations. [ABSTRACT FROM AUTHOR]

This paper considers the bit loading problem for communication systems that utilize orthogonal frequency division multiplexing (OFDM) in conjunction with precoding (POFDM) or time-domain interleaving (IOFDM). In particular, we propose a new bit loading algorithm for P/I-OFDM that has substantially higher effective throughput and less computational complexity, when compared to bit loading in conventional OFDM systems. The obtained results show that the effective throughput of P/I-OFDM can be more than fourfold the conventional OFDM while the complexity is less than $1.5\%$. Moreover, the results show that the peak-to-average power ratio (PAPR) properties of the considered systems are preserved under the adaptation process. [ABSTRACT FROM AUTHOR]

Multi-User scheduling is challenging due to the channel unbalance problem, which leads to system performance degradation. In this paper, two optimal multi-user scheduling schemes maximizing the system throughput are proposed for the fixed and adaptive power transmission scenarios of the full-duplex (FD) multi-user buffer-aided relay system, respectively. Independent and non-identically distributed (i.ni.d.) model is used to characterize the unbalanced channels of different links. In particular, the optimal weight factor of each pair is designed based on the statistical channel state information in both scenarios. With the weight factors, the proposed schemes are able to balance the throughput gaps between different links. In addition, we propose an optimal power allocation scheme with closed-form expressions under average power constraint for the adaptive power transmission scenario. By combining the optimal weight factor and the power allocation scheme, novel optimal selection function is obtained to facilitate the selection process. Considering the specific i.ni.d. Rayleigh fading, the system throughput is further derived in both cases. Theoretical analysis is verified by the numerical simulations and the results demonstrate the superiority of the proposed schemes. [ABSTRACT FROM AUTHOR]

Regularized zero-forcing beamforming (RZFB) is an interesting class of linear signal processing problems, which is very attractive for use in large-scale communication networks due its simple visualization as a straightforward extension of the well-accepted zero-forcing beamforming (ZFB). However, unlike ZFB, which is multi-user interference free, RZFB must manage multi-user interference to achieve its high throughput performance. Most existing works focus on the performance analysis of particular RZBF schemes such as the equip-power allocated RZBF under a fixed regularization parameter. This paper is the first work to consider the joint design of power allocation and regularization parameter for RZFB to maximize the worst users’ throughput or the quality-of-service awarded energy efficiency under a fixed transmit power constraint. Such designs pose very computationally challenging optimization problems, for which the paper proposes two-stage optimization algorithms of low computational complexity. Their computational and performance efficiencies are substantiated through numerical examples. [ABSTRACT FROM AUTHOR]