Can One Achieve Multiuser Diversity in Uplink Multi-Cell Networks?

Abstract

We introduce a distributed opportunistic scheduling (DOS) strategy, based on two pre-determined thresholds, for uplink K-cell networks with time-invariant channel coefficients. Each base station (BS) opportunistically selects a mobile station (MS) who has a large signal strength of the desired channel link among a set of MSs generating a sufficiently small interference to other BSs. Then, performance on the achievable throughput scaling law is analyzed. As our main result, it is shown that the achievable sum-rate scales as K log(SNR log N ) in a high signal-to-noise ratio (SNR) regime, if the total number of users in a cell, N , scales faster than SNR K−1 1−ǫ for a constant ǫ ∈ (0, 1). This result indicates that the proposed scheme achieves the multiuser diversity gain as well as the degrees-offreedom gain even under multi-cell environments. Simulation results show that the DOS provides a better sum-rate throughput over conventional schemes. Index TermsWireless scheduling, inter-cell interference, cellular uplink, degrees-of-freedom, multi-user diversity. Now we would like to consider realistic uplink networks with K cells, each of which has one base station (BS) and N mobile stations (MSs). IA for such uplink K-cell networks was first proposed in [7], but has practical challenges including a dimension expansion to achieve the optimal degrees-of-freedom.In the literature, there are some results on the usefulness of fading in single-cell broadcast channels, where one can exploit a multiuser diversity gain: opportunistic scheduling [13], opportunistic beamforming [14], and random beamforming [15]. In single-cell downlink systems, the impact of partial channel knowledge at the transmitter has also been studied by showing whether the multiuser diversity gain can be achieved through opportunistic scheduling schemes based on limited feedback [16], [17]. Moreover, scenarios obtaining the multiuser diversity have been studied in cooperative networks by applying an opportunistic two-hop relaying protocol [18] and an opportunistic routing [19], and in cognitive radio networks with opportunistic scheduling [20]. Such opportunism can also be utilized in downlink multicell networks through a simple extension of [15]. In a decentralized manner, it however remains open how to design a constructive algorithm that can achieve the multiuser diversity gain in uplink multi-cell networks, which are fundamentally different from downlink environments since for uplink, there exists a mismatch between the amount of generating interference at each MS and the amount of interference suffered by each BS from multiple MSs, thus yielding the difficulty of user scheduling design.In this paper, we introduce a distributed opportunistic scheduling (DOS) protocol, so as to show that full multiuser diversity gain can indeed be achieved in time-division duplexing (TDD) uplink K-cell networks with time-invariant channel coefficients. To our knowledge, such an attempt for the network model has never been conducted before. The channel reciproc...

Optimal Multiuser Diversity in Multi-Cell MIMO Uplink Networks: User Scaling Law and Beamforming Design

“…If a single antenna is adopted at both MS and BS sides, i.e., the single-input single-output (SISO) IMAC model is assumed, then the required user scaling law for achieving the optimal multiuser diversity gain becomes N = ω(SNR K−1 1− ) by setting M = L = 1, which is consistent with the result in [11]. Furthermore, if a single antenna at the MS sides but multiple antennas at the BSs are adopted, i.e., the SIMO IMAC model is assumed, then the required user scaling law for achieving the optimal multiuser diversity gain becomes N = ω(SNR KM−1 1− ) by setting L = 1, which is also consistent with the result in [12].…”

“…However, the distributed IA technique requires an iterative beamformer optimization for data transmission. The authors of [11] proved that the optimal multiuser diversity gain can be achieved by introducing a distributed user scheduling even in the presence of inter-cell interference when both MSs and BSs have a single antenna, which was later extended to the case deploying multiple antennas at each BS, i.e., the single-input multiple-output (SIMO) IMAC model [12]. When multiple antennas are deployed at both users and BSs, i.e., the multiple-input multiple-output (MIMO) IMAC model is assumed, however, how to achieve such diversity gain remains open to debate; it is a non-straightforward issue since one needs to jointly construct user scheduling as well as transmit/receive beamforming in a distributed manner while guaranteeing the optimal multiuser diversity gain.…”

A distributed scheduling with interference-aware power control for ultra-dense networks

“…A threshold-based user scheduling algorithm was proposed in multi-cell single-input single-output (SISO) uplink networks, where a base station (BS) selects the user who has a large desired signal strength among a set of users generating a sufficiently small interference to other cell BSs [14]. A similar technique was proposed for improving sum-rate of the original OIA technique in multi-cell MIMO uplink networks [15].…”

“…A similar technique was proposed for improving sum-rate of the original OIA technique in multi-cell MIMO uplink networks [15]. However, the existing schemes [14], [15] did not consider a power control at users even though the power control has been played a important role for interference management in cellular networks, and they discarded the users who generate the interference larger than a certain threshold when BSs schedule the users.…”

On the Multiuser Diversity of Relay-Aided Downlink Systems Using Reduced Feedback

“…Such opportunism has also been studied in multicell uplink networks through a distributed opportunistic scheduling [11, 12]. Moreover, the concept of opportunistic interference alignment has been introduced in [13–17] for cellular uplink and downlink networks, also known as the interfering multiple-access channel and interfering broadcast channel, respectively, where user scheduling is incorporated into the classical interference alignment framework by opportunistically selecting certain users in each cell.…”