Adaptive Distributed Association in Time-Variant Millimeter Wave Networks

Adaptive Distributed Association in Time-Variant Millimeter Wave Networks

Abstract: The underutilized millimeter-wave (mm-wave) band is a promising candidate to enable extremely high data rate communications in future wireless networks.

However, the special characteristics of the mm-wave systems such as high vulnerability to obstacles (due to high penetration loss) and to mobility (due to directional communications) demand a careful design of the association between the clients and access points (APs). This challenge can be addressed by distributed association techniques that gracefully adapt to wireless channel variations and client mobilities. We formulated the association problem as a mixed-integer optimization aiming to maximize the network throughput with proportional fairness guarantees. This optimization problem is solved first by a distributed dual decomposition algorithm, and then by a novel distributed auction algorithm where the clients act asynchronously to achieve near-to-optimal association between the clients and APs. The latter algorithm has a faster convergence with a negligible drop in the resulting network throughput. A distinguishing novel feature of the proposed algorithms is that the resulting optimal association does not have to be recomputed every time the network changes (e.g., due to mobility). Instead, the

algorithms continuously adapt to the network variations and are thus very efficient.

We discuss the implementation of the proposed algorithms on top of existing communication standards. The numerical analysis verifies the ability of the proposed algorithms to optimize the association and to maintain optimality in the time-variant environments of the mm-wave networks.

Existing system: However, these existing studies focused on static network scenarios. In other words, the association mechanisms of are not able, by design, to adapt to the wireless channel variations and mobility effects, which are typical in mm-wave mobile networks. In particular, upon appearance of a new obstacle in the wireless link between any clients and any APs, we should re-execute the association algorithms proposed into re-associate this client with another AP. To this end, all the parameters describing the association problem (including channel gains) should be gathered at a central controller and then usually an NP-hard problem arises to find the re-association solution.

Proposed system: A class of user association schemes that achieve load balancing were proposed .The authors consider jointly cell association and resource allocation. They formulate a logarithmic utility maximization problem, where the equal resource allocation is optimal, and design a distributed algorithm via dual decomposition.

Unfortunately, these studies are optimized for conventional wireless networks and are not directly applicable (or are highly sub-optimal) when applied to mm-wave networks. For instance, the reduced multiuser interference footprint and possibility of noise-limited operation in mm-wave networks in policies.

Advantages: The work in presents self-configuring algorithms that provide improved AP association and fair resource sharing compared to the RSSI based association. Such an approach is based on Gibbs sampler and does not require explicit coordination among the wireless devices.

Moreover, the “multihoming� scenario is introduced in, where the traffic is split among the available APs. In this approach, the throughput is maximized by

constructing a fluid model of user population that is multi-homed by the available APs in the network. Another line of research considers user service requests by readjusting the load across all APs. Disadvantages: They provide a rigorous formulation of the association control problem that considers bandwidth constraints of both the wireless and backhaul links. The work in presents self-configuring algorithms that provide improved AP association and fair resource sharing compared to the RSSI based association.

Such an approach is based on Gibbs sampler and does not require explicit coordination among the wireless devices. Moreover, the “multihoming� scenario is introduced in, where the traffic is split among the available APs. In this approach, the throughput is maximized by constructing a fluid model of user population that is multi-homed by the available APs in the network. Another line of research considers user service requests by readjusting the load across all APs. Modules: Wave network: Our previous approaches were among the first studies of the AP association in 60 GHz mm-wave wireless networks aiming to maximize the network performance and to provide load balancing. However, these existing studies focused on static network scenarios. In other words, the association mechanisms of are not able, by design, to adapt to the wireless channel variations and mobility effects, which are typical in mm-wave mobile networks. In particular, upon appearance of a new obstacle in the wireless link between any clients and any APs, we should reexecute the association algorithms proposed in to re-associate this client with another AP. To this end, all the parameters describing the association problem (including channel gains) should be gathered at a central controller and then usually an NP-hard problem arises to find the re-association solution. This centralized procedure is clearly highly inefficient. Therefore, to summarize, mmwave networks demand a new way of thinking when designing association not only in the static scenarios as in, but also in the time-variant scenarios, where adaptive distributed mechanisms are essential. Rather than focusing more on the

network performance metrics as in conventional resource allocation, we must now devise efficient distributed mechanisms that are able to seamlessly adapt to the time-variant behavior of the mm-wave networks.

Long term evolution: THE impressive growth of mobile traffic has triggered the design of new communication technologies, such as, large antenna systems, advanced beam forming techniques, channel bonding, and interference cancellations. However, these improvements may not meet even the minimum requirements of important future wireless services. Long Term Evolution (LTE)-Advanced as the most prominent standard for cellular networks can support up to 3 Gbps data rate , and IEEE 802.11ac as the most prominent standard for local area networks can support up to 1 Gbps . Although these values are very high for traditional wireless applications, they are apparently substantially smaller than the minimum requirements of certain future applications such as 8K video transfer in smart buildings (_ 20 Gbps), wireless font hauling and backhauling in small cells (_ 20 Gbps), and wireless backup links in data centers (_ 40 Gbps) as presented in the ongoing project IEEE 802.11ay project. IEEE 802.11ad, released in 2012, can support up to 6.7 Gbps at 60 GHz, and its modifications, recently established as IEEE 802.11ay, are envisioned to support more than 40 Gbps, a data rate that cannot be easily achieved in near future by traditional networks working at the sub6 GHz bands.

Received Signal Strength Indicator: The compact design of mm-wave radio allows the installation of a large number of antenna elements both at the Access Points (APs) and at the user devices (clients), providing substantial gain by directional communications. Such directional communications, besides boosting link budget, can substantially reduce multiuser interference in mm-wave networks. These distinguishing properties of mm-wave networks, together with its especial propagation characteristics such as severe channel attenuation and high penetration loss (blockage), demand re-thinking of, among others, short-term and long-term resource allocation protocols. In this paper, we focus on the design of efficient long-term resource allocation through AP-client association. The association between clients and APs (or base station) in

different types of wireless access networks (e.g., cellular, local area, small-cells) has been the focus of intense research in the recent years.

Time-Variant Networks: We define a time-variant network as a network with dynamics due to the variations of the channel gains cij over the time. This variation is caused by, for example, random large-scale fading, mobility of the clients and APs, appearance and disappearance of the obstacles, and antenna misalignments. In practice, a timevariant network manifests itself in the variations of cij over the time. Note that the definition does not include traffic variations of the clients. Moreover, in this paper, the AP-client association is considered as long time resource allocation, because it is impractical to establish associations regarding the instantaneous channel gains in the networks. Let binary variable xij be 1 if client i is associated with AP j, and 0 otherwise.