We introduce an opportunistic interference mitigation (OIM) protocol, where a user scheduling strategy is utilized in K-cell uplink networks with time-invariant channel coefficients and base stations (BSs) having M antennas. Each BS opportunistically selects a set of users who generate the minimum interference to the other BSs. Two OIM protocols are shown according to the number S of simultaneously transmitting users per cell: opportunistic interference nulling (OIN) and opportunistic interference alignment (OIA). Then, their performance is analyzed in terms of degrees-of-freedom (DoFs). As our main result, it is shown that KM DoFs are achievable under the OIN protocol with M selected users per cell, if the total number N of users in a cell scales at least as SNR (K−1)M . Similarly, it turns out that the OIA scheme with S(< M ) selected users achieves KS DoFs, if N scales faster than SNR (K−1)S . These results indicate that there exists a trade-off between the achievable DoFs and the minimum required N . By deriving the corresponding upper bound on the DoFs, it is shown that the OIN scheme is DoF-optimal. Finally, numerical evaluation, a two-step scheduling method, and the extension to multi-carrier scenarios are shown. Base station (BS), channel state information, cellular network, degrees-of-freedom (DoFs), interference, opportunistic interference alignment (OIA), opportunistic interference mitigation (OIM), opportunistic interference nulling (OIN), uplink, user scheduling. Recently, as an alternative approach to show Shannon-theoretic limits, interference alignment (IA) was proposed by fundamentally solving the interference problem when there are two communication pairs [7] . It was shown in [8] that the IA scheme can achieve the optimal degrees-of-freedom (DoFs), which are equal to K/2, in the K-user interference channel with time-varying channel coefficients. The basic idea of the scheme is to confine all the undesired interference from other communication links into a pre-defined subspace, whose dimension approaches that of the desired signal space. Hence, it is possible for all users to achieve one half of the DoFs that we could achieve in the absence of interference. Since then, interference management schemes based on IA have been further developed and analyzed in various wireless network environments: multiple-input multiple-output (MIMO) interference network [9] [16] . These constraints need to be relaxed in order to apply IA to more practical systems. In [9], a distributed IA scheme was constructed for the MIMO interference channel with time-invariant coefficients. It requires only local CSI at each node that can be acquired from all received channel links via pilot signaling, and thus is more feasible to implement than the original one [8] . However, a great number of iterations should be performed until designed transmit/receive beamforming (BF) vectors converge prior to data transmission. Now we would like to consider practical wireless uplink networks with K-cells, each of which has N users. IA for K-cell uplink networks was first proposed in [13] , where the interference from other cells is aligned into a multi-dimensional subspace instead of one dimension. This scheme also has practical challenges including a dimension expansion to achieve the optimal DoFs. In the literature, there are some results on the usefulness of fading in single-cell downlink broadcast channels, where one can obtain a multi-user diversity (MUD) gain as the number of mobile users is sufficiently large: opportunistic scheduling [17], opportunistic BF [18] , and random BF [19] . More efficient opportunistic interference management strategy [20] , [21] , which requires less feedback overhead than that in [19] , has been developed in broadcast channels, where similarly as in our study, the minimum number of users needed for achieving target DoFs has been analyzed. 1 Scenarios exploiting the MUD gain have also been studied in cooperative networks by applying an opportunistic two-hop relaying protocol [22] and an opportunistic routing [23] , and in cognitive radio networks with opportunistic scheduling [24] , [25] . In addition, recent results [16], [26] have shown how to utilize the opportunistic gain when we have a large number of channel realizations. More specifically, to amplify signals and cancel interference, the idea of opportunistically pairing complementary channel instances has been studied in interference networks [16] and multi-hop relay networks [26] . In cognitive radio environments [27]- [29] , opportunistic
A user scheduling strategy for multi-cell uplink networks was investigated in REF .
Opportunistic Interference Mitigation Achieves Optimal Degrees-of-Freedom in Wireless Multi-cell Uplink Networks
Computer Science Mathematics