In this paper, we provide the detailed analysis of the achievable throughput of infrastructure-based vehicular network with a finite traffic density under a cooperative communication strategy, which explores the combined use of vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications, the mobility of vehicles, and cooperations among vehicles and infrastructure to facilitate the data transmission. A closed form expression of the achievable throughput is obtained, which reveals the relationship between the achievable throughput and its major performance-impacting parameters, such as distance between adjacent infrastructure points, the radio ranges of infrastructure and vehicles, the transmission rates of V2I and V2V communications, and vehicular density. Numerical and simulation results show that the proposed cooperative communication strategy significantly increases the throughput of vehicular networks, compared with its non-cooperative counterpart, even when the traffic density is low. Our results shed insight on the optimum deployment of vehicular network infrastructure and the optimum design of cooperative communication strategies in vehicular networks to maximize the throughput.

Due to the time-varying channel conditions and dynamic topology of vehicular networks attributable to the high mobility of vehicles, data dissemination in vehicular networks, especially for content of large-size, is challenging. In this paper, we propose a cooperative communication strategy for vehicular networks suitable for dissemination of large-size content and investigate its achievable throughput. The proposed strategy exploits the cooperation of vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications and the mobility of vehicles to facilitate the transmission. Detailed analysis is provided to characterize the data dissemination process using this strategy and a closed-form result is obtained on its achievable throughput, which reveals the relationship between major performance-impacting parameters such as distance between infrastructure, radio ranges of infrastructure and vehicles, transmission rates of V2I and V2V communications and vehicular density. Simulation and numerical results show that the proposed strategy significantly increases the throughput of vehicular networks even when the traffic density is low. The result also gives insight into the optimum deployment of vehicular network infrastructure to maximize throughput.

In this paper, the long-term average achievable rate over block-fading buffer-aided relay channels is maximized using a hybrid scheme that combines three essential transmission strategies, which are decode-and-forward, compress-and-forward, and direct transmission. The proposed hybrid scheme is dynamically adapted based on the channel state information. The integration and optimization of these three strategies provide a more generic and fundamental solution and give better achievable rates than the known schemes in the literature. Despite the large number of optimization variables, the proposed hybrid scheme can be optimized using simple closed-form formulas that are easy to apply in practical relay systems. This includes adjusting the transmission rate and compression when compress-and-forward is the selected strategy based on the channel conditions. Furthermore, in this paper, the hybrid scheme is applied to three different models of the Gaussian block-fading buffer-aided relay channels, depending on whether the relay is half or full duplex and whether the source and the relay have orthogonal or non-orthogonal channel access. Several numerical examples are provided to demonstrate the achievable rate results and compare them to the upper bounds of the ergodic capacity for each one of the three channel models under consideration.

This paper proposes a low-complexity interference-free channel assignment scheme with improved desired downlink performance in coordinated multi-antenna small-coverage access points (APs) that employ the open-access control strategy. The adopted system treats the case when each user can be granted an access to one of the available channels at a time. Moreover, each receive terminal can suppress a limited number of resolvable interfering sources via its highly-correlated receive array. On the other hand, the operation of the deployed APs can be coordinated to serve active users, and the availability of multiple physical channels and the use of uncorrelated transmit antennas at each AP are exploited to improve the performance of supported users. The analysis provides new approaches to use the transmit antenna array at each AP, the multiple physical channels, the receive antenna array at each user in order to identify interference-free channels per each user, and then to select a downlink channel that provides the best possible improved performance. The event of concurrent interference-free channel identification by different users is also treated to further improve the desired link associated with the scheduled user. The analysis considers the practical scenario of imperfect identification of interference-free channel by an active user and/or the imperfectness in scheduling concurrent users requests on the same channel. The developed formulations can be used to study any performance metric and they are applicable for any statistical and geometric channel models.

This work proposes a suboptimal multi-user scheduling scheme for Gaussian broadcast channels which improves upon the classical single user selection, while considerably reducing complexity as compared to the optimal superposition coding with successful interference cancellation. The proposed scheme combines the two users with the maximum weighted instantaneous rate using superposition coding. The instantaneous rate and power allocation are derived in closed-form, while the long term rate of each user is derived in integral form for all channel distributions. Numerical results are then provided to characterize the prospected gains of the proposed scheme.

The aim of this work is to maximize the long-term average achievable rate region of a primary and a secondary source-destination pairs operating in an overlay setup over block-fading channels. To achieve this objective, we propose an opportunistic strategy to grant channel access to the primary and secondary sources based on the channel conditions in order to exploit the available multiple-link diversity gains in the system. The secondary source has causal knowledge of the primary messages and it acts as a relay of the primary source in return for getting access to the channel. To maximize the gains of relaying, the relay and destination are equipped with buffers to enable the use of channel-aware adaptive link selection. We propose and optimize different link selection policies and characterize their expected achievable rates. Also, we provide several numerical results to demonstrate the evident mutual benefits of buffer-aided cooperation and adaptive link selection to the primary and the secondary source-destination pairs.

In this paper, we obtain the optimal resource allocation scheme in order to maximize the achievable rate region in a dual-hop system that consists of two independent source-destination pairs sharing a single half-duplex relay. The relay decodes the received information and possesses buffers to enable storing the information temporarily before forwarding it to the respective destination. We consider both non-orthogonal transmission with successive interference cancellation at the receivers and orthogonal transmission. Also, we consider Gaussian block-fading channels and we assume that the channel state information is known and that no delay constraints are required. We show that, with the aid of buffering at the relay, joint user-and-hop scheduling is optimal and can enhance the achievable rate significantly. This is due to the joint exploitation of multiuser diversity and multihop diversity in the system. We provide closed-form expressions to characterize the average achievable rates in a generic form as functions of the statistical model of the channels. Furthermore, we consider sub-optimal schemes that exploit the diversity in the system partially and we provide numerical results to compare the different schemes and demonstrate the gains of the optimal one.

In this paper, we study power allocation strategies for a fixed-gain amplify-and-forward relay network employing multiple relays. We consider two optimization problems for the relay network: 1) maximizing the end-to-end signal-to-noise ratio (SNR) and 2) minimizing the total power consumption while maintaining the end-to-end SNR over a threshold value. We investigate these two problems for two relaying protocols of all-participate (AP) relaying and selective relaying and two cases of feedback to the relays, namely full and limited. We show that the SNR maximization problem is concave and that the power minimization problem is convex for all protocols and feedback cases considered. We obtain closed-form expressions for the two problems in the case of full feedback and solve the problems through convex programming for limited feedback. Numerical results show the benefit of having full feedback at the relays for both optimization problems. However, they also show that feedback overhead can be reduced by having only limited feedback to the relays with only a small degradation in performance.

This paper investigates the energy-efficiency enhancement of a variable-gain dual-hop amplify-and-forward (AF) relay network utilizing selective relaying. The objective is to minimize the total consumed power while keeping the end-to-end signal-to-noise-ratio (SNR) above a certain peak value and satisfying the peak power constraints at the source and relay nodes. To achieve this objective, an optimal relay selection and power allocation strategy is derived by solving the power minimization problem. Numerical results show that the derived optimal strategy enhances the energy-efficiency as compared to a benchmark scheme in which both the source and the selected relay transmit at peak power.

In this paper, we consider a two-way communication system in which two users communicate with each other through an intermediate relay over block-fading channels. We investigate the optimal opportunistic scheduling scheme in order to maximize the long-term average transmission rate in the system assuming symmetric information flow between the two users. Based on the channel state information, the scheduler decides that either one of the users transmits to the relay, or the relay transmits to a single user or broadcasts to both users a combined version of the two users' transmitted information by using linear network coding. We obtain the optimal scheduling scheme by using the Lagrangian dual problem. Furthermore, in order to characterize the gains of network coding and opportunistic scheduling, we compare the achievable rate of the system versus suboptimal schemes in which the gains of network coding and opportunistic scheduling are partially exploited.

In this paper, we maximize the achievable rate region of a dual-hop network with two sources serving two users independently through a single shared relay. We formulate the problem as maximizing the sum of the weighted long term average throughputs of the two users under stability constraints on the long term throughputs of the source-user pairs. In order to solve the problem, we propose a joint user-and-hop scheduling scheme, which schedules the first or second hop opportunistically based on instantaneous channel state information, in order to exploit multiuser diversity and multihop diversity gains. Numerical results show that the proposed joint scheduling scheme enhances the achievable rate region as compared to a scheme that employs multi-user scheduling on the second-hop alone.

We propose joint user-and-hop scheduling over dual-hop block-fading broadcast channels in order to exploit multi-user diversity gains and multi-hop diversity gains all together. To achieve this objective, the first and second hops are scheduled opportunistically based on the channel state information. The joint scheduling problem is formulated as maximizing the weighted sum of the long term achievable rates of the users under a stability constraint, which means that in the long term the rate received by the relay should equal the rate transmitted by it, in addition to power constraints. We show that this problem is equivalent to a single-hop broadcast channel by treating the source as a virtual user with an optimal weight that maintains the stability constraint. We show how to obtain the source weight either off-line based on channel statistics or on real-time based on channel measurements. Furthermore, we consider special cases including the maximum sum-rate scheduler and the proportional fair scheduler. We also show how to extend the scheme into one that allows multiple user scheduling via superposition coding with successive decoding. Numerical results demonstrate that our proposed joint scheduling scheme enlarges the rate region as compared to scheduling schemes that exploit the diversity gains partially.

In this paper, a new resource allocation scheme to minimize Symbol Error rate (SER) for relay assisted cognitive radio networks is studied. A cognitive relay network with amplify-and-forward (AF) fixed gain relays and opportunistic access to the licensed spectrum is considered. The SER objective function and the optimization constraints are derived first. The resource allocation algorithm is then derived using the Langrangian multiplier method. Closed form expressions are derived for the source and relay powers and, symbol time. Analytical results show that the optimal power allocation follows an extended water-filling solution.

In this paper, we present two optimal resource allocation schemes that maximize throughput and symbol correct rate (SCR). The throughput and SCR are derived. The derived throughput and SCR are optimized with respect to the sensing time, the source transmission power and the relay transmission power. Numerical results show that the optimal sensing time is dependent on the primary user's signal-to-noise-ratio (SNR). They also show that SCR increases with increase in the number of relays.