Mazin Al-Shalash

Distributed Resource Allocation in Device-to-Device Enhanced Cellular Networks

Cellular network performance can significantly benefit from direct device-to-device (D2D) communication, but interference from cochannel D2D communication limits the performance gain. In hybrid networks consisting of D2D and cellular links, finding the optimal interference management is challenging. In particular, we show that the problem of maximizing network throughput while guaranteeing predefined service levels to cellular users is non-convex and hence intractable. Instead, we adopt a distributed approach that is computationally extremely efficient, and requires minimal coordination, communication and cooperation among the nodes. The key algorithmic idea is a signaling mechanism that can be seen as a fictional pricing mechanism, that the base stations optimize and transmit to the D2D users, who then play a best response (i.e., selfishly) to this signal. Numerical results show that our algorithms converge quickly, have low overhead, and achieve a significant throughput gain, while maintaining the quality of cellular links at a predefined service level.

Resource Optimization in Device-to-Device Cellular Systems Using Time-Frequency Hopping

We develop a flexible and accurate framework for device-to-device (D2D) communication in the context of a conventional cellular network, which allows for time-frequency resources to be either shared or orthogonally partitioned between the two networks. Using stochastic geometry, we provide accurate expressions for SINR distributions and average rates, under an assumption of interference randomization via time and/or frequency hopping, for both dedicated and shared spectrum approaches. We obtain analytical results in closed or semi-closed form in high SNR regime, that allow us to easily explore the impact of key parameters (e.g., the load and hopping probabilities) on the network performance. In particular, unlike other models, the expressions we obtain are tractable, i.e., they can be efficiently optimized without extensive simulation. Using these, we optimize the hopping probabilities for the D2D links, i.e., how often they should request a time or frequency slot. This can be viewed as an optimized lower bound to other more sophisticated scheduling schemes. We also investigate the optimal resource partitions between D2D and cellular networks when the dedicated spectrum approach is used.

Optimal caching for device-to-device content distribution in 5G networks

Content distribution using direct device-to-device D2D communication is a promising approach for optimizing the utilization of air-interface resources in 5G network. Research in this area has indicated, perhaps unexpectedly, that the optimal caching distribution for D2D content does not match the distribution of user demands/requests. Therefore, in order to achieve the best utilization of network resources, the network should bias the distribution of cached content. The optimal caching distribution is dependent on, but not identical to, the content demand distribution. In this paper we model transmitting and receiving devices as member of a homogeneous Poisson Point Process (PPP), and use results from stochastic geometry to derive the probability of successful content delivery in the presence of D2D interference and noise. We formulate an optimization problem, and find the best caching distribution that maximizes the total probability of content delivery, assuming user demands/requests are modeled by a Zipf distribution with exponent γr. Our results show that the optimal caching distribution follows a similar trend as the demand distribution, and can also be modeled as a Zipf distribution. The exponent γc of the optimal caching distribution is related to γr through a simple expression involving the path loss exponent.

Interference constrained soft frequency reuse for uplink ICIC in LTE networks

Careful management of inter-cell interference is important in OFDMA based systems such as LTE. In this paper, we study an uplink ICIC (Inter-cell Interference Coordination) mechanism, which fully utilizes the flexibility of frequency selective scheduling and rate adaptation, while dynamically limiting the interference experienced by the neighboring cells. This technique can be seen as an extension of soft frequency reuse SFR, which has been widely studied for the downlink. The proposed technique provides a flexible and efficient way of sharing resources between the cell-edge and cell-center users, without the need to strictly classify each UE into one of these categories. It avoids the loss of trunking efficiency inherent in static classification of users according to their geographical location within the serving cell. We analyze the proposed method, and evaluate its performance using system level simulations. The simulation results illustrate the effectiveness of our method.

Optimizing Content Caching to Maximize the Density of Successful Receptions in Device-to-Device Networking

Device-to-device (D2D) communication is a promising approach to optimize the utilization of air interface resources in 5G networks, since it allows decentralized opportunistic short-range communication. For D2D to be useful, mobile nodes must possess content that other mobiles want. Thus, intelligent caching techniques are essential for D2D. In this paper, we use results from stochastic geometry to derive the probability of successful content delivery in the presence of interference and noise. We employ a general transmission strategy, where multiple files are cached at the users and different files can be transmitted simultaneously throughout the network. We then formulate an optimization problem, and find the caching distribution that maximizes the density of successful receptions (DSR) under a simple transmission strategy, where a single file is transmitted at a time throughout the network. We model file requests by a Zipf distribution with exponent γr, which results in an optimal caching distribution that is also a Zipf distribution with exponent γc, which is related to γr through a simple expression involving the path loss exponent. We solve the optimal content placement problem for more general demand profiles under Rayleigh, Ricean, and Nakagami small-scale fading distributions. Our results suggest that it is required to flatten the request distribution to optimize the caching performance. We also develop strategies to optimize content caching for the more general case with multiple files, and bound the DSR for that scenario.

Uplink Inter-Cell Interference Coordination through Soft Frequency Reuse

In this paper, we study an uplink ICIC (Inter-cell Interference Coordination) mechanism based on the principle of soft frequency reused SFR. The proposed approach exploits the flexibility of frequency selective scheduling and rate adaptation, while dynamically limiting the interference experienced by the neighboring cells to a predefined limit. The technique provides a flexible and efficient way of sharing UL resources between the cell-edge and cell-center users, without the need to statically classify each user into one of these two categories. It avoids the loss of trunking efficiency inherent in static classification of users according to their geographical location within the serving cell. We analyze the proposed method, and evaluate its performance using LTE as an example.

Genetic and Greedy User Scheduling for Multiuser MIMO Systems with Successive Zero-Forcing

In this paper we consider efficient and low complex- ity scheduling algorithms for multiuser multiple-input multiple- output (MIMO) systems. The optimal user scheduling involves an exhaustive search, which becomes very complex for realistic num- bers of users and transmit antennas. Among various suboptimal but low complexity algorithms, greedy algorithms with heuristic scheduling metrics have been shown to achieve performance close to an exhaustive search. Meanwhile, genetic algorithms (GAs) are a rapid, though suboptimal, option of performing a utility (in this case scheduling) metric optimization. In this paper, we propose and analyze the performance and complexity of greedy and genetic scheduling algorithms for multiuser MIMO systems with successive zero-forcing precoding. We demonstrate that at lower K, the genetic algorithm performs better than the greedy algorithm, where K denotes the total number of users requesting service. For large K, however, the greedy algorithm outperforms the genetic algorithm. The greedy algorithm also achieves similar sum-rate growth (with K) as the exhaustive search. A detailed complexity analysis shows that the order of complexity of the genetic algorithm is higher than that of the greedy algorithm by a factor equal to K 2 ,w hereK0 denotes the maximum number

A tractable model for optimizing device-to-device communications in downlink cellular networks

In this paper, we develop a tractable and accurate framework for a Device-to-Device (D2D) enabled downlink cellular network with a dedicated spectrum approach, meaning that D2D links use a frequency band orthogonal to the cellular users. Using stochastic geometry, we provide accurate expressions for SINR distributions and average rates, under an assumption of interference randomization via time and/or frequency hopping. The obtained analytical results allow us to easily explore and optimize the impact of system parameters. For example, we find the optimal frequency hopping probability for the D2D users, i.e. how often they should randomly access a subband. We also propose an optimization approach for mode selection, i.e. when should potential D2D users transmit directly, and when should they fall back to the cellular mode. This can be viewed as an optimized lower bound to other more sophisticated scheduling schemes.

Greedy and Genetic User Scheduling Algorithms for Multiuser MIMO Systems with Block Diagonalization

In this paper, we consider efficient and low complex- ity scheduling algorithms for multiuser multiple-input multiple- output (MIMO) systems. Due to the dimensionality constraint imposed by linear precoding techniques like block diagonalization (BD), user scheduling is required. Optimal user scheduling involves an exhaustive search, which becomes very complex for realistic numbers of users and transmit antennas. Hence, various suboptimal but low complexity algorithms have been considered in the literature. Among them, greedy algorithms with heuristic scheduling metrics have been shown to achieve performance close to an exhaustive search. Meanwhile, genetic algorithms (GAs) are a rapid, though suboptimal, option of performing a utility (i.e. scheduling) metric optimization. In this paper, we propose and analyze the performance and complexity of greedy and genetic scheduling algorithms for multiuser MIMO systems with BD precoding. We demonstrate that except at low SNR with a smaller number of transmit antennas, the genetic algorithm outperforms the greedy algorithm. A detailed complexity analysis shows that the order of complexity of the genetic algorithm is higher than that of the greedy algorithm by a factor equal to K0 ,w here K0 denotes the maximum number of simultaneously supported multiple-antenna users.

Location-assisted clustering and scheduling for coordinated homogeneous and heterogeneous cellular networks

The multiple-input multiple-output (MIMO) downlink with transmitter coordination in a cellular network is considered. The transmitters are assumed to be either neighbouring base stations (homogeneous) or a base station with a number of remote radio heads that form picocells in its coverage area (heterogeneous). In centralized coordinated transmission from a cluster of nodes, the channel state information (CSI) of users needs to be sent to a central processor for precoding and resource allocation. Real-time CSI feedback from the users to their home base station and from the base stations to the central processor is a serious challenge from a practical point of view. In this work, efficient location-assisted limited-feedback schemes for homogeneous and heterogeneous cellular networks are proposed. First, a hybrid mode transmission scheme with reduced feedback requirement is proposed for a homogeneous network, in which on the basis of the location of users, some are served using a single-cell multiuser MIMO approach and some using a network MIMO approach. Next, for a heterogeneous network, a location-assisted clustering and scheduling scheme is proposed for the case of joint reference signals, in which multiple transmission nodes that share the reference signals cannot be distinguished from each other. We evaluate the performance of our schemes with a series of simulations. In the homogeneous scenario, we compare with the case of full CSI, and in the heterogeneous scenario, we compare with joint transmission from all nodes in a cell. Copyright