Ahmed Douik

Instantly decodable network coding for real-time device-to-device communications

This paper studies the delay reduction problem for instantly decodable network coding (IDNC)-based device-to-device (D2D) communication-enabled networks. Unlike conventional point-to-multipoint (PMP) systems in which the wireless base station has the sufficient computation abilities, D2D networks rely on battery-powered operations of the devices. Therefore, a particular emphasis on the computation complexity needs to be addressed in the design of delay reduction algorithms for D2D networks. While most of the existing literature on IDNC directly extend the delay reduction PMP schemes, known to be NP-hard, to the D2D setting, this paper proposes to investigate and minimize the complexity of such algorithms for battery-powered devices. With delay minimization problems in IDNC-based systems being equivalent to a maximum weight clique problems in the IDNC graph, the presented algorithms, in this paper, can be applied to different delay aspects. This paper introduces and focuses on the reduction of the maximum value of the decoding delay as it represents the most general solution. The complexity of the solution is reduced by first proposing efficient methods for the construction, the update, and the dimension reduction of the IDNC graph. The paper, further, shows that, under particular scenarios, the problem boils down to a maximum clique problem. Due to the complexity of discovering such maximum clique, the paper presents a fast selection algorithm. Simulation results illustrate the performance of the proposed schemes and suggest that the proposed fast selection algorithm provides appreciable complexity gain as compared to the optimal selection one, with a negligible degradation in performance. In addition, they indicate that the running time of the proposed solution is close to the random selection algorithm.

Partially Blind Instantly Decodable Network Codes for Lossy Feedback Environment

In this paper, we study the multicast completion and decoding delay minimization problems for instantly decodable network coding (IDNC) in the case of lossy feedback. When feedback loss events occur, the sender falls into uncertainties about packet reception at the different receivers, which forces it to perform partially blind selections of packet combinations in subsequent transmissions. To determine efficient selection policies that reduce the completion and decoding delays of IDNC in such an environment, we first extend the perfect feedback formulation in our previous works to the lossy feedback environment, by incorporating the uncertainties resulting from unheard feedback events in these formulations. For the completion delay problem, we use this formulation to identify the maximum likelihood state of the network in events of unheard feedback and employ it to design a partially blind graph update extension to the multicast IDNC algorithm in our earlier work. For the decoding delay problem, we derive an expression for the expected decoding delay increment for any arbitrary transmission. This expression is then used to find the optimal policy that reduces the decoding delay in such lossy feedback environment. Results show that our proposed solutions both outperform previously proposed approaches and achieve tolerable degradation even at relatively high feedback loss rates.

Cost-effective hybrid RF/FSO backhaul solution for next generation wireless systems

The rapid pace of demand for mobile data services and the limited supply of capacity in the current wireless access networks infrastructure are leading network operators to increase the density of base station deployments to improve network performance. This densification, made possible by small-cell deployment, also brings a novel set of challenges, specifically related to the cost of ownership, in which backhaul is of primary concern. This article proposes a cost-effective hybrid RF/free-space optical (FSO) solution to combine the advantages of RF backhauls (low cost, NLOS applications) and FSO backhauls (high-rate, low latency). To first illustrate the cost advantages of the RF backhaul solution, the first part of this article presents a business case of NLOS wireless RF backhaul, which has a low cost of ownership as compared to other backhaul candidates. RF backhaul, however, is limited by latency problems. On the other side, an FSO solution, which offers better latency and higher data rate than RF backhauls, remains sensitive to weather and nature conditions (e.g., rain, fog). To combine RF and FSO advantages, the second part of this article proposes a lowcost hybrid RF/FSO solution, wherein base stations are connected to each other using either optical fiber or hybrid RF/FSO links. This part addresses the problem of minimizing the cost of backhaul planning under reliability, connectivity, and data rate constraints, and proposes choosing the appropriate cost-effective backhaul connection between BSs (i.e., either OF or hybrid RF/FSO) using graph theory techniques.

Resource allocation in heterogeneous cloud radio access networks: advances and challenges

Base station densification is increasingly used by network operators to provide better throughput and coverage performance to mobile subscribers in dense data traffic areas. Such densification is progressively diffusing the move from traditional macrocell base stations toward heterogeneous networks with diverse cell sizes (e.g., microcell, picocell, femotcell) and diverse radio access technologies (e.g., GSM, CDMA), and LTE). The coexistence of the different network entities brings an additional set of challenges, particularly in terms of the provisioning of high-speed communications and the management of wireless interference. Resource sharing between different entities, largely incompatible in conventional systems due to the lack of interconnections, becomes a necessity. By connecting all the base stations from different tiers to a central processor (referred to as the cloud) through wire/wireline backhaul links, the heterogeneous cloud radio access network, H-CRAN, provides an open, simple, controllable, and flexible paradigm for resource allocation. This article discusses challenges and recent developments in H-CRAN design. It proposes promising resource allocation schemes in H-CRAN: coordinated scheduling, hybrid backhauling, and multicloud association. Simulations results show how the proposed strategies provide appreciable performance improvement compared to methods from recent literature.

Delay reduction in lossy intermittent feedback for generalized instantly decodable network coding

In this paper, we study the effect of lossy intermittent feedback loss events on the multicast decoding delay performance of generalized instantly decodable network coding. These feedback loss events create uncertainty at the sender about the reception statues of different receivers and thus uncertainty to accurately determine subsequent instantly decodable coded packets. To solve this problem, we first identify the different possibilities of uncertain packets at the sender and their probabilities. We then derive the expression of the mean decoding delay. We formulate the Generalized Instantly Decodable Network Coding (G-IDNC) minimum decoding delay problem as a maximum weight clique problem. Since finding the optimal solution is NP-hard, we design a variant of the algorithm employed in [1]. Our algorithm is compared to the two blind graph update proposed in [2] through extensive simulations. Results show that our algorithm outperforms the blind approaches in all the situations and achieves a tolerable degradation, against the perfect feedback, for large feedback loss period.

Delay reduction in multi-hop device-to-device communication using network coding

This paper considers the problem of reducing the broadcast delay of wireless networks using instantly decodable network coding (IDNC) based device-to-device (D2D) communications. In D2D-enabled networks, devices help hasten the recovery of the lost packets of devices in their transmission range by sending network coded packets. To solve the problem, the different events occurring at each device are identified so as to derive an expression for the probability distribution of the decoding delay. The joint optimization problem over the set of transmitting devices and the packet combinations of each is formulated. Due to the high complexity of finding the optimal solution, this paper focuses on cooperation without interference between the transmitting users. The optimal solution, in such interference-less scenario, is expressed using a graph theory approach by introducing the cooperation graph. Extensive simulations compare the decoding delay experienced in the Point to Multi-Point (PMP), the fully connected D2D (FC-D2D) and the more practical partially connected D2D (PC-D2D) configurations and suggest that the PC-D2D outperforms the FC-D2D in all situations and provides an enormous gain for poorly connected networks.

A Lossy Graph Model for Delay Reduction in Generalized Instantly Decodable Network Coding

The problem of minimizing the decoding delay in Generalized instantly decodable network coding (G-IDNC) for both perfect and lossy feedback scenarios is formulated as a maximum weight clique problem over the G-IDNC graph in . In this letter, we introduce a new lossy G-IDNC graph (LG-IDNC) model to further minimize the decoding delay in lossy feedback scenarios. Whereas the G-IDNC graph represents only doubtless combinable packets, the LG-IDNC graph represents also uncertain packet combinations, arising from lossy feedback events, when the expected decoding delay of XORing them among themselves or with other certain packets is lower than that expected when sending these packets separately. We compare the decoding delay performance of LG-IDNC and G-IDNC graphs through extensive simulations. Numerical results show that our new LG-IDNC graph formulation outperforms the G-IDNC graph formulation in all lossy feedback situations and achieves significant improvement in the decoding delay especially when the feedback erasure probability is higher than the packet erasure probability.

Hybrid Radio/Free-Space Optical Design for Next Generation Backhaul Systems

The deluge of date rate in todays networks imposes a cost burden on the backhaul network design. Developing cost-efficient backhaul solutions becomes an exciting, yet challenging, problem. Traditional technologies for backhaul networks, including either radio-frequency (RF) backhauls or optical fibers (OF). While RF is a cost-effective solution as compared with OF, it supports the lower data rate requirements. Another promising backhaul solution is the free-space optics (FSO) as it offers both a high data rate and a relatively low cost. The FSO, however, is sensitive to nature conditions, e.g., rain, fog, and line-of-sight. This paper combines both the RF and FSO advantages and proposes a hybrid RF/FSO backhaul solution. It considers the problem of minimizing the cost of the backhaul network by choosing either OF or hybrid RF/FSO backhaul links between the base stations, so as to satisfy data rate, connectivity, and reliability constraints. It shows that under a specified realistic assumption about the cost of OF and hybrid RF/FSO links, the problem is equivalent to a maximum weight clique problem, which can be solved with moderate complexity. Simulation results show that the proposed solution shows a close-to-optimal performance, especially for reasonable prices of the hybrid RF/FSO links. They further reveal that the hybrid RF/FSO is a cost-efficient solution and a good candidate for upgrading the existing backhaul networks.

Coordinated scheduling for the downlink of cloud radio-access networks

This paper addresses the coordinated scheduling problem in cloud-enabled networks. Consider the downlink of a cloud-radio access network (CRAN), where the cloud is only responsible for the scheduling policy and the synchronization of the transmit frames across the connected base-stations (BS). The transmitted frame of every BS consists of several time/frequency blocks, called power-zones (PZ), maintained at fixed transmit power. The paper considers the problem of scheduling users to PZs and BSs in a coordinated fashion across the network, by maximizing a network-wide utility under the practical constraint that each user cannot be served by more than one base-station, but can be served by one or more power-zones within each base-station frame. The paper solves the problem using a graph theoretical approach by introducing the scheduling graph in which each vertex represents an association of users, PZs and BSs. The problem is formulated as a maximum weight clique, in which the weight of each vertex is the benefit of the association represented by that vertex. The paper further presents heuristic algorithms with low computational complexity. Simulation results show the performance of the proposed algorithms and suggest that the heuristics perform near optimal in low shadowing environments.

On Minimizing the Maximum Broadcast Decoding Delay for Instantly Decodable Network Coding

In this paper, we consider the problem of minimizing the maximum broadcast decoding delay experienced by all the receivers of generalized instantly decodable network coding (IDNC). Unlike the sum decoding delay, the maximum decoding delay as a definition of delay for IDNC allows a more equitable distribution of the delays between the different receivers and thus a better Quality of Service (QoS). In order to solve this problem, we first derive the expressions for the probability distributions of maximum decoding delay increments. Given these expressions, we formulate the problem as a maximum weight clique problem in the IDNC graph. Although this problem is known to be NP-hard, we design a greedy algorithm to perform effective packet selection. Through extensive simulations, we compare the sum decoding delay and the max decoding delay experienced when applying the policies to minimize the sum decoding delay and our policy to reduce the max decoding delay. Simulations results show that our policy gives a good agreement among all the delay aspects in all situations and outperforms the sum decoding delay policy to effectively minimize the sum decoding delay when the channel conditions become harsher. They also show that our definition of delay significantly improve the number of served receivers when they are subject to strict delay constraints.